DevOps is the ideal practice for software development businesses that want to code, build, test, and release software continuously. It’s popular because it stimulates cross-skilling and self-improvement by creating a fast-paced, results-oriented, collaborative workplace. QA in DevOps fosters agility, resulting in speedier operational support and fixes that meet stakeholder expectations. Most significantly, it ensures the timely delivery of high-quality software.
Quality Assurance and Testing (QAT) is a critical component of a successful DevOps strategy. QAT is a vital enabler that connects development and operations in a collaborative thread to assure the delivery of high-quality software and applications.
Integrating QA in DevOps
Five essentials play a crucial role in achieving flawless sync and seamlessly integrating QA into the DevOps process.
1. Concentrate on the Tenets of Testing
Testing is at the heart of QA; hence the greatest and most experienced testers with hands-on expertise must be on board. Some points to consider: the team must maintain a strong focus on risk, include critical testing thinking into the functional and non-functional parts of the application, and not lose sight of the agile testing quadrant’s needs. Working closely with the user community, pay particular attention to end-user experience tests.
2. Have relevant technical skills and a working knowledge of tools and frameworks
While studying and experimenting with the application is required, a thorough understanding of the unique development environment is also required. This guarantees that testers contribute value to the design stage talks and advise the development team on possibilities and restrictions. They must also be familiar with the operating environment and, most significantly, the application’s performance in the actual world.
The team’s skill set should also include a strong understanding of automation and the technologies required, as this adds rigor to the DevOps process and is necessary to keep it going. The QA team’s knowledge must be focused on tools, frameworks, and technologies. What should their automation strategy be? Do they advocate or utilize licensed or open-source software?
Are the tools for development, testing, deployment, and monitoring identified for the various software development life cycle stages? To avoid delays and derailing the process at any stage of development, it is critical to have comprehensive clarity on the use of tools. Teams with the competence should be able to experiment with technologies like artificial intelligence or machine learning to give the process a boost.
3. Agile Testing Methodologies
DevOps is synchronized agility that incorporates development, quality assurance, and operations. It’s a refined version of the agile technique. Agile/DevOps techniques now dominate software development and testing. Can the QA team ensure that in an Agile/DevOps environment, the proper coverage, aligned to the relevant risks, enhances velocity? Is the individual or group familiar with working in a fast-paced environment? Do they have the mechanisms to ensure continuous integration, development, testing, and deployment in cross-functional, distributed teams?
4. Industry experience that is relevant
Relevant industry knowledge ensures that the testers are aware of user experiences and their potential influence on business and can predict potential bottlenecks. Industry expertise improves efficiency and helps testers select testing that has the most impact on the company.
5. The Role of Culture
In DevOps, the QA team’s culture is a crucial factor. The DevOps methodology necessitates that QA team members be alert, quick to adapt, ethical, and work well with the development and operations teams. They serve as a link between the development and operations teams, and they are responsible for maintaining balance and ensuring that the process runs smoothly.
In a DevOps process, synchronizing the three pillars (development, quality assurance, and operations) is crucial for software products to fulfill expectations and deliver commercial value. The QA team serves as a link in this process, ensuring that software products transfer seamlessly from development to deployment. What factors do you believe QA can improve to integrate more quickly and influence the DevOps process?
There’s a buzz around cross-platform mobile app development lately. From start-ups to entrepreneurs, everybody’s looking into the potential of cross-platform frameworks. While many are enticed with the speed of development and simplicity of having to manage a single code base, wider reach is a big draw for companies.
What’s tricky is that the mobile app development landscape is constantly evolving, with new frameworks and updates constantly being added. Ionic apparently is not great for gaming apps, while React Native is just not what you would want for large products or heavy industry applications. Flutter is the youngest, yet very powerful and popular platform. So how do you determine the way forward? While the choices can be overwhelming, you will need a perfect solution for your business yet not too much for your development teams to handle.
The shift from native to cross-platform development may make good business sense, but you should be ready with hard facts to know it’s worth diving into. You will have to ask the right questions, understand things from a developer’s perspective without losing sight of your business objectives. A developer will be more inclined to choose a framework based on ease of development, programming language prerequisites, and the libraries offered. However, the organization may be keen on having something agile, effective, cost-efficient, and easy to maintain.
Cross-platform solutions for mobile app development
Cross-platform mobile app development continues to offer countless benefits to developers and users alike. User engagement with an app often depends on the ease of use associated with app navigation. Intuitive user experience increases the popularity of an app and, in turn, leads to better conversions. If you are battling with the native versus cross-platform dilemma to deliver fluid user experiences, you are not alone.
While native and cross-platform strategies are strong contenders, cross-platform are gaining popularity and mobile time with apps that promise consistent user experience across mobile platforms, smoothness, efficiency, and flexibility. What’s more, they are a huge help while integrating phone functions and ensuring shorter time-to-market. A report by Statistica1 suggests Flutter is the most popular cross-platform mobile framework used by 42 percent of software developers, followed by React Native in popularity.
Native apps promise superlative performance since they are created for a particular platform and are fast and responsive. But developers need to code in 2 different languages for Android and iOS. Maintaining feature parity and consistent user experience across the two code bases is the biggest challenge – Invariably, there are situations where some features are available on Android, but not iOS, and vice versa. Version management and feature upgrades are doubled with 2 independent code bases.
So how would you know if choosing cross-platform would be the right decision for your business?
Cross-platform solutions for mobile app development would be great for you if:
You need to target a large user base via multiple platforms.
If you wish to build it with a small team of developers using a single codebase.
You want to build an app quickly and ensure that it hits the market fast.
You want an app that can be amended easily for changes and updates.
You wish to save money with reusable codes that save development time as well as cost.
You want flawless cloud integration for better compatibility and versatility.
The app does not require high-performance access to device resources like GPS, camera, accelerometer, etc. unlike gaming and AR/VR apps.
Of course, there will be challenges, but none that cannot be fixed with proper preparation in the planning and design phase.
Some of the top challenges include:
Coding issues in cross-platform mobile app development
Also, there is the problem of slow code. It happens when inexperienced developers resort to cross-compliance while developing cross-platform apps. This results in sluggish code and a slower application.
A lengthy integration process
While using cross-platform, the integration process with local settings can take a long time. This further increases the development time. The integration issues with certain operating systems often impede performance due to the lack of compatibility between the native and non-native components of different devices.
User experience (UX) concerns
While cross-platform aids in ensuring feature parity and user experience, access to device-specific resources involves overheads. This slows the app load time and dampens the user experience. App abandonment is a major concern and the inability to deliver the perfect UX can be detrimental. As per Statistica2, 25 percent of the apps downloaded by users worldwide were accessed only once.
The operating system may not always support all the features of a framework and thus affect the overall experience. For instance, every time there is an iOS update or a new feature gets added, you can use it only after updating the iOS version of your app. But until Google releases the same update or feature, you will not be able to update the Android version.
Every app is vulnerable to cyber-attacks. While frequent updates give native apps the power to rectify loopholes, cross-platform apps struggle on the security front due to limited updates. Organizations that need to deal with colossal amounts of business data on a daily basis, therefore, are less inclined to use cross-platform apps. Cross-platform app development companies are now relying on cutting-edge tools and architecture to address this issue and enhance the security of their apps.
Understanding the unique challenges of iOS and Android
When considering native application development, companies must have the right skills. Both iOS and Android apps are expected to be identical in looks, functionality, aesthetics, and experience but the structural building blocks of both these applications are completely different. Besides, these apps need to be built for different devices and need very experienced developers to build flawless apps. Apps are being used on smartphones, laptops, and tablets which means the challenge is not limited to multiple operating systems but involves varying screen sizes too.
It is important to look into their user interface (UI) and user experience (UX) peculiarities and understand the acceptance criteria laid down by official app marketplaces. Apple is known for having very strict guidelines to ensure a certain look and feel of iOS apps. Apps that fail to adhere to these guidelines are often rejected by the App Store. On the other hand, Google Play Store despite not being so rigid does not offer much relief either. This is mainly because Android users are accustomed to a certain level of experience and performance from their apps and anything that strays from these de facto levels is likely to get low ratings.
Even UI rendering and hardware features for both these platforms differ. Each OS renders UI standards differently and calls for unique coding approaches. Having one codebase to manage all these requirements can be extremely challenging. Hardware features too are realized differently and each feature will work differently on multiple platforms. While a certain feature may be accessible on one platform, it may not be on another. The fact that both Android and iOS are constantly evolving further adds to the challenge of forcing developers to play catch-up all the time.
A robust team of developers that has adequate experience in using modern tools and frameworks can however build the perfect cross-platform app despite all these challenges. You need to determine the essential features you would like to include in your app based on customer needs and then work on other aspects such as timelines and budget.
The cross-platform mobile app development frameworks dilemma
Among the many cross-platform frameworks available today, React Native, Flutter, Xamarin, and Ionic are some of the most popular ones. Greater device compatibility and faster development make them a dependable choice for building enterprise-grade apps.
The biggest challenge for a developer however is choosing the right cross-platform framework, especially since there are many distinct pros and cons.
Alibaba and Google Ads, for instance, use Flutter for their apps. It supports Android, iOS, Windows, and Mac, and its architecture is based on reactive programming. But it comes with limited libraries and extensive application size. React Native, on the other hand, used by Facebook and Skype, comes with native functionalities and platform-specific code. However, its navigation is not as seamless as Flutter.
Companies like Pinterest and Slack rely on Xamarin which complies with native code for better performance and experience. But it has platform-specific limitations that hinder multi-touch functionality and platform-specific gestures.
Others like Node.JS, Ionic, NativeScript, PhoneGap, and Sencha Touch are distinct and useful. It takes an expert to know what’s best for your unique business objectives.
Cross platform development services – Work with experts
If the whole idea of developing an app is overwhelming for you, you may consider partnering with app development experts. There will be crucial decisions to make both before and during app development. For instance, with windows becoming passé, how equipped is your app to serve Android and iOS users? Cross-platform technologies like Flutter, React Native, and Xamarin are all very popular, which one’s right for you?
You need a development partner who not only understands app development but also your market to give you a competitive advantage. The experienced ones know exactly how to optimize to save costs. For example, the Facebook mobile app merely extends the same set of features as its web counterpart.
The development partner you choose can do the same for your company by extending your existing software’s logic and components to your mobile app using APIs. This ensures a consistent connected customer experience while keeping the development cost low.
Build high-performing Omnichannel apps
Enterprises are aware of the role apps play in augmenting their business revenue. You need a large user base to do so, and that will happen only when you are able to reach a broader audience in cost-effective ways with an app that offers excellent performance and experience. Industry experts believe cross-platform apps ensure a better reach.
The only way to build a high-performing cross-platform app is to research, analyze, and choose the proper framework. What’s easier is to reach out to experts who know the app landscape like the back of their hands.
Build your app and customer base with Trigent
Trigent has been a tech partner for several organizations in diverse sectors offering unique solutions tailor-made for them. We leverage advanced tools and technologies to keep you on top of your game.
We can assist you too in building your app to increase customer acquisition while minimizing development costs.
In a highly demanding buyers’ market, giving your clients what they need can be very challenging. Also, every client is different, and as they say – one man’s trash is another man’s treasure. A huge living room, for instance, maybe a waste of space for you but would be perfect for someone who loves to host parties.
AR/VR in real estate presents the perfect solution to the changing needs of discerning customers. The global AR VR in the real estate market ecosystem1 is expected to grow at a CAGR of 31.2%, increasing in value from USD 298.6 million in 2018 to USD 1,151.9 million in 2023.
The pandemic has compelled realtors to change the way they work, and there is no going back. Real estate companies now look to implement perfect customization to help customers flip through properties like the pages of a magazine until they find exactly what they want.
Virtual reality home tours are becoming a thing as customers visit their prospective homes through strategically placed 360° cameras. The footage acquired is put together to create a seamless, real-life, 3-D experience to give your customers the feeling of actually being there sizing up the space with exact dimensions.
The virtual experience evokes strong emotions giving potential buyers the feel of owning the place. While this looks great from a customer experience perspective, we seek to gauge the impact of these disruptive technologies on the real estate landscape. And more importantly, to help you decide if it’s for you.
Real estate needs digital transformation
The salability quotient of any property depends on its Days on Market or the DOM index. There are several factors that affect the DOM index significantly. These include the property’s condition, seasonal variability, buyer’s availability, seller’s lead time to allow in-person showing, competition, location, and price.
While you may put in a lot of hard work in each area to improve the index, AR and VR can save you considerable time and money even in times of a potential downturn. With the help of a headset and a smartphone or a tablet, you can harness the benefits of these immersive technologies to sell properties in the residential and commercial segment.
Says Maty Paule, head of product at Commercial Real Estate2, “Real estate is all about location and appearances, while two emerging themes in AR are geo-location and image detection. The ability for users to access property data in their current location is a powerful proposition. In contrast, the possibility of modifying a property’s visual appearance to understand development or renovation potential is a game-changer.”
Considering the number of tools available today, it is easier to develop content quickly. Start with AR and VR training use cases keeping the devices and tools you will require and how you are planning to source them. After the initial hiccups, you would be able to plan to scale and incorporate exciting ideas along the way to tailor the perfect experience for your customers.
2. Keep it simple
A test-and-learn approach may be ideal as you can get your team involved in the project to get a taste of how the user experience will be. Starting with augmented reality would be a good idea to get a fair idea of how your digital journey will pan out. Most importantly, start now to be ready to handle intricacies and challenges with better capabilities going forward.
3. Prepare for change
Every new technology will bring along a shift in the way you work. You need to figure out how AR and VR will change the experiences for your users and how they will impact your team and workforce. There will be a need for greater collaboration since everything will be managed virtually. You need to plan in advance to let change not impede your work.
4. Assess your needs
You must have a very realistic assessment of your business needs to choose technologies accordingly. For instance, if your people are struggling to finish tasks, the right technologies will empower them with everything they need. AR will enable augmented learning while VR will let them explore, replace, and repair parts albeit in a virtual scenario, to understand and practice adequately before implementing the skill. You must also decide which tools would be required depending on the content you need to create.
5. Choose your people and skills
Your existing workforce may require upskilling, or you may need additional staff to manage new requirements and extend your capabilities. Address the skill gaps early on so that you don’t have to suffer any delays.
Building on-demand capabilities with Virtual Tours
Those on the lookout for properties can be allowed to experience the property virtually from the comfort of their home, thanks to virtual tours. Guided visits can be shared through 360-degree videos for existing properties, while interactive visits allow users to focus on a specific area. Potential buyers can utilize VR capabilities on-demand to virtually access a property on the very same day.
Leveraging VR for Virtual staging
As per a survey, 40% of buyers’ agents have confessed home staging affects buyers’ view of the home, while 17% of respondents revealed that property staging had increased the home’s dollar value between 6-10%.
Does that mean you blatantly hide all the flaws and mislead buyers?
Rather than using virtual staging to hide ugly details, you can always be honest and give a more realistic picture. As Rick Davis, a real estate attorney from Kansas points out3, “Most sellers think it is in their best interest to disclose as little as possible. I completely disagree with this sentiment. In the vast majority of cases, disclosing the additional information, especially if it is something that was previously repaired, will not cause a buyer to back out or ask for a price reduction.”
The adoption of AR/VR in real estate has been helping realtors expand their portfolio the way they did in the case of Sotheby’s International Realty that has been growing by leaps and bounds with an ever-expanding suite of technology-driven tools. After leveraging VR to help their sales team sell homes globally without the buyer setting foot on the property, the company has partnered with Google and RoOomy for their AR offering ‘Curate’.
Visualizing full-scale models with virtual architecture
It is always difficult to get buyers interested in a property that is yet to be built. The virtual architecture allows customers to visualize the interiors and exteriors of the property with the help of full-scale models. This saves realtors time and money while generating a buzz around their property. A mere piece of land can be transformed into complete architecture to enable experiences in the early stages of design. AR comes in handy from the prototyping to the construction phase generating pop-up 3D models of projected structures.
Enhancing customer experience with virtual commerce
While the above principles give your buyers a chance to visualize and experience the property, virtual commerce goes a step further in ensuring that they get to make those tiny tweaks and experiment with the elements on their own. In other words, if they are on a virtual tour and want wooden flooring with an oak finish instead of the plain porcelain tiles that are currently being offered, they can go for an upgrade right away. This applies to all virtual staging objects such as curtains, light fixtures, and furniture by purchasing what they need from partnering hardware and upholstery providers.
They can even choose a property and then move on to other providers like IKEA to spruce up the space with everything they need. After helping customers digitally place furniture in their homes via Place App, IKEA has now come up with IKEA Studio, a much-needed overhaul of its predecessor. It lets you capture 3D room plans with accurate measurements, including ceilings, windows, and doorways, while taking into account the current arrangement of your furniture.
Houzz, a leading platform for home renovation and design, is also helping customers transform living spaces and even tile their floors virtually. The company had added visual tech to its mix not too long ago, starting with 2D stickers. The mobile team took product photos and offered them as stickers after removing the background. This enabled shoppers to view them in their rooms in the form of 2D stickers, and this straightforward strategy gave them a 3X boost in conversions.
Several product cycles later, Houzz offered AR visualization to visualize products before shopping and saw an 11X boost in conversions.
Building practical solutions with virtual apps
AR/VR apps are convenient and a practical solution to showing the world exactly how a finished property looks like. They are intended to show how it would look in real environments. An app such as RealAR gives your customers the freedom to simply stand on a piece of land and get a good representation of how a property would look like using a smartphone or a tablet. It converts floor plans into walkthroughs that can be used onsite or remotely to understand room size and layouts and get a realistic picture of the property.
AR/VR in real estate is transforming the landscape
VR and AR technologies are changing the tide for realtors worldwide, helping them make stellar first impressions. VR/AR is just taking off now, and real estate firms are getting their feet wet.
There is tremendous potential, and we are yet to experience the full benefits of these amazing technologies.
So if you are still wondering if you should invest in AR/VR for your real estate business, we’d say, “By all means, go for it!” You can save time scheduling in-person visits and unproductive viewings and create targeted, personalized experiences instead. What’s more, adopting AR/VR is fairly easy. All you need is an expert to help you transform digital engagement and experience one solution at a time.
Adopt AR/VR in your real estate firms with Trigent
Our decades of experience give us the skills to help realtors increase the effectiveness of their business in the residential as well as commercial sectors. We empower real-estate stakeholders with AR/VR solutions to connect with their customers and build trust. We can help you too.
Allow us to help you build a dynamic, detailed, and immersive experience that will not just reduce costs but give you a competitive edge in a relatively volatile market.
The adoption of systems based on Artificial Intelligence (AI) and Machine Learning (ML) has seen an exponential rise in the past few years and is expected to continue to do so. As per the forecast by Markets and Markets, the global AI market size will grow from USD 58.3 billion in 2021 to USD 309.6 billion by 2026, at a CAGR of 39.7% during the aforementioned forecast period. In a recent Algorithmia Survey, 71% of respondents mentioned an increase in budgets for AI/ML initiatives. Some organizations are even looking at doubling their investments in these areas. With the sporadic growth in these applications, the QA practices and testing strategies for AI/ML applications models also need to keep pace.
An ML model life-cycle involves multiple steps. The first is training the model based on a set of feature sets. The second involves deploying the model, assessing model performance, and modifying the model constantly to make more accurate predictions. This is different from the traditional applications, where the model’s outcome is not necessarily an accurate number but can be right depending on the feature sets used for its training. The ML engine is built on certain predictive outcomes from datasets and focuses on constant refining based on real-life data. Further, since it’s impossible to get all possible data for a model, using a small percentage of data to generalize results for the larger picture is paramount.
Since ML systems have their architecture steeped in constant change, traditional QA techniques need to be replaced with those focusing on taking the following nuances into the picture.
The QA approach in ML
Traditional QA approaches require a subject matter expert to understand possible use case scenarios and outcomes. These instances across modules and applications are documented in the real world, which makes it easier for test case creation. Here the emphasis is more on understanding the functionality and behavior of the application under test. Further, automated tools that draw from databases enable the rapid creation of test cases with synthesized data. In a Machine Learning (ML) world, the focus is mainly on the decision made by the model and understanding the various scenarios/data that could have led to that decision. This calls for an in-depth understanding of the possible outcomes that lead to a conclusion and knowledge of data science.
Secondly, the data that is available for creating a Machine Learning model is a subset of the real-world data. Hence, there is a need for the model to be re-engineered consistently through real data. A rigor of manual follow-up is necessary once the model is deployed in order to enhance the model’s prediction capabilities continuously. This also helps to overcome trust issues within the model as the decision would have been taken through human intervention in real life. QA focus needs to be more in this direction so that the model is closer to real-world accuracy.
Finally, business acceptance testing in a traditional QA approach involves the creation of an executable module and being tested in production. This traditional QA approach is more predictable as the same set of scenarios continue to be tested until a new addition is made to the application. However, the scenario is different with ML engines. Business acceptance testing, in such cases, should be seen as an integral part of refining the model to improve its accuracy, using real-world usage of the model.
The different phases of QA
Three phases characterize every machine learning model creation:
The QA focus, be it functional or non-functional, is applied to the ML engine across these 3 phases.
Data pipeline: The quality of input data sets has a significant role in the ability to predict a Machine Learning system. The success of an ML model lies in the testing data pipelines which ensure clean and accurate data availability through big data and analytics techniques.
Model building: Measuring the effectiveness of a model is very different from traditional techniques. Out of a specified number of datasets available, 70-80% is used in training the model, while the remaining is used in validating & testing the model. Therefore, the accuracy of the model is based on the accuracy shown on the smaller of datasets. Ensuring that the data sets used for validating & testing the model are representative of the real-world scenario is essential. It shouldn’t come to pass that the model, when pushed into production, will fail for a particular category that has not been represented either in the training or the testing data sets. There is a strong need to ensure equitable distribution and representation in the data sets.
Deployment: Since the all-round coverage of scenarios determines the accuracy of an ML model and the ability to do that in real life is limited, the system cannot be expected to be performance-ready in one go. A host of tests need to be done to the system like candidate testing; A/B testing to ensure that the system is working correctly and can ease into a real-life environment. The concept of a sweat drift becomes valid here whereby we arrive at a measure of time by when the model starts behaving reliably. During this time, the QA person needs to manage data samples and validate model behavior appropriately. The tool landscape that supports this phase is still in an evolving stage.
The QA approaches need to emphasize the following for ensuring the development and deployment of a robust ML engine.
The ideal ML model should be nonjudgmental and fair. Since it depends largely on learning based on data received from real-life scenarios, there is a strong chance that the model will be biased if it gets data from a particular category/feature set. For example, if a chatbot with learning ability through ML engine is made live and receives many inputs that are racist, the datasets that are being received for learning by ML engine are heavily skewed towards racism. The feedback loops that power many of these models ensure that racist bias comes into the ML engine. There have been instances of such chatbots being pulled down after noticeable differences in their behavior.
In a financial context, the same can be extended to biases being developed by the model receiving too many loan approval requests from a particular category of requestors, as an example. Adequate efforts need to be made to remove these biases while aggregating or slicing and dicing these datasets and adding them to the ML engine.
One approach that’s commonly followed to remove the bias that can creep into a model is by building another model (an adversary) that understands the potential of bias from the list of various parameters and incorporates that bias within itself. By frequently moving back and forth between these two models with the availability of real-life data, the possibility of a model that removes the bias becomes higher.
Many ML models are finding widespread adoption across industries and are already beginning to be used in critical real-life situations. The ML model development is very different from that adopted for software development. It is more error-prone on account of loopholes that can cause malleable attacks and a higher propensity to err on the wrong side on account of erroneous input data.
Many of these models do not start from scratch. They are built atop pre-existing models through transfer and learning methods. If created by a malicious actor, these transfer learning models have every possible way of corrupting the purpose of the model. Further, even after the model goes into production, malicious intent data being fed into the model can change the prediction generated by the model.
In conclusion, assuring the quality of AL/ML-based models and engines needs a fundamentally different approach from traditional testing. It needs to be continuously changing to focus on the data being fed into the system and on which predictive outcomes are made. Continuous testing, focusing on the quality of data, the ability to affect the predictive outcome, and remove biases in prediction is the answer.
Wikipedia definition: “A digital asset is anything that exists in a digital format and comes with the right to use”. For example – video, music, documents, images, presentations, digital tokens (including crypto), data, or anything an organization or individual owns or has the right to use.
As we move ahead with digital transformation more and more businesses are increasingly dependent on digital assets. Today, even existing physical assets such as documents and prints are actively digitized. Digital assets are convenient as they occupy less physical space, are easy to retrieve, and can be transported/transferred easily.
Businesses who have already made the shift to digital assets include,
Legal / Law firms
Advertising Agencies, Media houses
HR and Recruitment firms
Movie Production houses
Major industries such as retail, manufacturing, import-export houses, insurance, finance, and logistics companies are all in various stages of digital transformation.
With increasing convenience comes its own set of problems, and in this case, it is the management of the digital assets that we create and convert from the existing ones. This is especially true for Business Service companies that create, use and distribute different types of documents and related content.
How it starts
Every individual and organization starts by organizing their files and their assets in a traditional hierarchical system on their local computers, USB storage devices, and of late on the cloud ( Google Drive, email, Dropbox, etc.). Once there is a need to share these assets and use them in collaboration, they resort to shared drives and transfer these assets via email, etc.
While this kind of organization works on a small scale, the system gets easily overwhelmed with an increase in the number of users and assets.
Eventually, the challenges present themselves:
Single paradigm of classifying our assets – different users / functional-units classify assets differently. E.g. Sales dept will want contracts classified by customers or geography while the accounts teams may want them classified by chronology, billing, risk etc. In short, one size does not fit all.
Sharing assets with others – Providing access to “other teams” or third parties is initially simple and can be monitored. However over time, as the content and the teams involved increases, it can spiral into a complete chaos. The most ideal use case would be to provide access to specific assets and probably for a finite amount of time. This brings us to the next point.
Security of assets – In 2015, all the first four episodes of the Game of Thrones season surfaced online before it even got aired because the Media outlets provided the episodes for viewing as a part of the review process. This was catastrophic. Sensitive content especially of monetary value needs to be secured and there should be an audit trail to trace any leaks.
Version control – While presentation.ppt, presentation1.ppt, presentation-ver2.ppt would work for an individual or at a small team level, it would require additional tracking effort or worse cause confusion under unwanted circumstances.
Automation – Digital assets typically go through a standard workflow including (not limited to) publishing onto websites, pushing to 3rd parties, Watermarking, QA QC, Approvals etc which could be potentially automated to provide better efficiency.
Enforcement is a key challenge in a discipline-based system and things get cumbersome. There are several Sophisticated DAMs available in the market and when the time comes it is best to get one in place.
When is the right time to consider a DAM?
Adopting the right technology at the right time is significant for the growth of any business. Here are some points that will help you identify if it is the right time to adopt a DAM in your business
Are digital assets a significant part of your business?
Does your workforce spend a lot of time looking for files?
Have you had to do a work from scratch when it could have been repurposed from an existing asset?
Are you making duplicate purchases of assets because existing assets cannot be found?
Are unapproved files being used fairly regularly?
Are you losing time validating the “Final version” against the other versions?
Are you spending a significant amount of time on tasks that can be automated such as watermarking, resizing, transcoding etc?
Does sharing large files require a process which is not as easy as sending email?
Are you finding difficulty in identifying a secure store for your assets?
If you have 3 or fewer “yes”
You still have some time. Keep a sharp lookout for the most common cases mentioned.
If you have 4 – 6 “yes”
It is time to start looking for a DAM. It is also a good time to get familiar with a Digital Asset management system.
If you have more than 6 “yes”
Now might be a good time to get your DAM in place.
The losses and risks associated with the loss of Digital Asset Management systems are becoming a standard around the world. The cost of loss and efficiency is real and it has a direct impact on your business.
Hence ensure to be proactive rather than reactive. Also keep in mind that once you have identified the DAM and Vendor, there is still time left (you are the best judge of this) for Deployment, Migration, and User-acceptance. Ensure you plan it well to make this initiative successful.
Find the right DAM
Once the decision is made to go in for a Digital Asset Management system, there are several choices that need to be made. Broadly they are based on capability/features and cost model.
Features and capability
Consider the following features:
Types of assets you will store on the DAM. E.g. Audio, documents, images etc.
Attributes of indexing for search and retrieval. E.g. content keywords, Approval status, date, value, vendor etc
AI based DAMs can automatically tag features for indexing such as contents of scanned documents, image contents, video and audio content keywords which makes content ingestion a much simpler step
Any automated processes you would like to run on the assets – watermarking, transcoding, resizing
Federated Authentication – Consider a DAM that will be able to integrate with your existing authentication system so that the existing system Admin processes will take care of your access management and the users will not have to remember another set of credentials
Sharing and permissions – the access various users have to the assets or groups of assets
Compatibility with your existing platform and software
Any APIs that need to be integrated with the DAM
Buy vs Hire
There are many solutions that can be bought off the shelf, configured, and deployed onto the cloud of local infrastructure based on your requirement. If you already have IT infrastructure and personnel then this is probably a good approach.
Several DAM solution companies offer a SaaS model where you can just pay a monthly fee and everything is handled. This is typically a good option if you don’t want the upfront expenses or don’t have a dedicated infrastructure team.
Migrate to a Digital Asset Management System
By now you should have zeroed in on the Digital Asset Management system if not already purchased one or subscribed to one.
Make sure all the use-cases of all the teams involved are handled. All integrations are in place and all the automated processes are working with their respective types of assets.
Ensure you have a buy-in from all the stakeholders involved about the move and set a date.
Create the required structure and the attribute lists.
Ensure all potential users get their credentials on the new system
Provide training to all the personnelle who will access the DAM
Move / Import all existing Assets to the DAM and ensure all new assets are added to the new system.
Decommission the old system. This is a very important step as “old habits die hard” and familiarity makes users go back to the older system.
Some popular DAMs
Here are some popular DAMs as per industry leadership sites. Most of these are SAAS-based models. These are pay-as-you-go models and can be a good starting point.
A good approach here is to involve your technical team to check on technical skills compatibility and also evaluate the features and their maturity. Even better is to deploy a working copy and test out all the use cases required by all the teams. Most of the open-source projects come with APIs and defined frameworks to extend their functionality.
Get in touch with us for a quick free assessment of your requirement and suggestion for a suitable solution.
There was a time when the healthcare industry largely relied on phone calls and fax machines to establish interoperability within the system. Health records would exist in different versions in different places and often critical health data would be too scattered to give a clear picture of a patient’s health. Then came a time when the concept of value-based care began taking root and the importance of having data in one place in an easy-to-access format made tremendous sense rather than collecting it from a multitude of data silos.
The healthcare industry was now waking up to healthcare analytics, interoperability, and the importance of APIs. Across a forever-expanding healthcare landscape, application programming interfaces (APIs) gave organizations the opportunity to streamline and share data for meaningful exchanges between systems.
APIs allow systems to communicate and depending on how they are configured they can do a lot more. They can send data, retrieve data, or even update individual health records as and when required. The ability of a healthcare facility to determine the coverage a patient is entitled to for a particular procedure after feeding information about the patient into their system that is linked to insurance companies is a classic example of how empowering APIs can be.
Interoperability lies at the core of APIs and demonstrates how critical coordinated care is for the healthcare industry. Understanding a patient’s journey is important to ensure they are on the road to recovery quickly and effectively. The fractured details of a patient’s clinical story however often pose a big challenge. For instance, it is important to know if a patient after leaving a facility following a surgery signed up for a remote monitoring program or was taken care of at home with the health of a home health agency. These are the finer things that add up to create the bigger picture.
APIs are a booming market and the healthcare API market is expected to grow at a CAGR of 8.72% in the forecast period 2021 – 2028 accounting for USD 440.76 million by 20281. APIs are creating dynamic digital ecosystems to help the healthcare industry attain operational excellence and improve customer experiences. APIs are clearly setting the stage for successful treatments and recovery ensuring interoperability every step of the way.
The role of APIs in the evolving healthcare landscape
The proliferation of smart wearable devices and wellbeing apps is further iterating the role of APIs in the digitally advanced health and wellness industry of today. The global wearables market grew 27.2% year over year in the fourth quarter of 2020 and the shipments of wearable devices globally have now touched 153.5 million2.
The pandemic has further accelerated the need for a better lifestyle and wider access to healthcare. The US Centres for Medicare and Medicaid Services are largely relying on APIs to bridge the gap between patients and healthcare. In fact, both healthcare organizations and payers need to use APIs – particularly the Fast Healthcare Interoperability Resources (FIHR) standards to attain optimum interoperability.
Explains Jay Bercher, deputy program manager at Solutions By Design II, “It goes without saying that APIs have closed the gap in many ways on how information is sent, retrieved, and processed. However, some technological gaps have appeared. As there is a lack of data standards in the industry and multiple technologies, APIs must be created custom to the needs of the service it is providing for each system.”
Technologies such as Artificial Intelligence (AI) are also key drivers. AI is facilitating the conversion of patient information into crucial diagnostic information to help detect conditions early on. Today, data sharing with correlations is helping in a big way. Just to iterate this, if 500 people are buying medicines for cough and cold using their credit cards in a particular area around the same time, it indicates the possibility of an outbreak in that particular area.
Different instances and scenarios highlight the importance of data and data sharing. APIs are increasingly being used to conduct wellness programs using cloud-based solutions to promote healthy lifestyles, offer behavioral change capabilities, and set fitness goals to stay on the wellness track.
As wearables and the Internet of Things (IoT) become mainstream, APIs enable the swift transfer of data for users to review and act upon. Data from third-party accounts is also gathered to enable a more integrated approach towards healthcare.
Ensuring interoperability in the healthcare ecosystem
The implementation of FHIR (Fast Healthcare Interoperability Resources) in healthcare systems for electronic data exchange will make sharing and accessing healthcare data faster. They prepare both the healthcare payer and the provider systems to afford greater access for patients to their own healthcare information by defining a standard minimum of data that must be made available.
As Health IT system developers implement the interoperability standards, they must shift the focus to meet their immediate interface requirements to conform to interoperability standards. The FHIR specification provides a roadmap to interoperable data exchange. This ensures that the adherence to this specification means all of the supported system interactions will work with other systems claiming conformance to the same standard.
The challenges and barriers to API adoption
Despite all the attention that APIs get and with nearly 90% of healthcare stakeholders considering APIs to be mission-critical for business strategies as per a study3,
Only 24% are actually using them at scale
67% of providers, 61% of payers, and 51% of healthcare tech vendors expect to use APIs at scale in 3 years
Providers participating in the study were more concerned about security (52%) and cost (47%) while payers had other concerns such as technical infrastructure (45%), privacy (43%), and lack of industry standards (43%). The learning curve is steep and calls for specialized skill sets to create or use APIs and address the challenges in adopting them. Patients need to understand the role of APIs too and standardization methods need to be employed to ensure efficient use of APIs.
Ben Moscovitch, project director of health information technology at Pew Charitable Trusts points out, “Increased use of APIs—particularly those based on common adopted and consistently deployed standards—has the potential to make healthcare more efficient, lead to better care coordination, and give providers and patients additional tools to access information and ensure high-quality, efficient, safe, and value-based care. Yet obstacles remain, such as some hospital hesitation to grant patient access to data, lack of bidirectional data exchange, confusion around the process of implementing APIs, and potentially prohibitive fee structures.”
Some of the most common challenges include:
Data security – Providers are responsible for the security of patient data, and the absence of security measures or compliance checks can lead to vulnerabilities.
Data complexity – The healthcare system is huge and complex with patient data spread across several databases. A longitudinal health record of patients is necessary to ensure proper care delivery.
Data authority – Sometimes, a single patient may have two different medical records citing opposite or different medical conditions. This can be frustrating as physicians will be unable to determine which one is more accurate or updated.
Seamless bi-directional data interoperability is what everyone is working on. Once we figure out a way to navigate APIs in healthcare, hospitals, clinics, and facilities will discover more use cases to leverage the value of APIs. Those who have realized its potential are already leveraging tools for designing, testing, and monitoring APIs for seamless integration and interoperability across the ecosystem.
API is the backbone that is necessary to create efficient ecosystems that can support seamless data capture and exchange for an integrated value chain. If the data is clear and accurate, stakeholders will be able to connect the dots more efficiently.
Trust Trigent for a successful API implementation
Trigent with its domain knowledge and technology expertise helps stakeholders across the healthcare continuum drive innovation and scale to meet enterprise requirements. We offer tools and solutions for the effective implementation of APIs and help you monitor them throughout the API cycle.
Our integration solutions have been helping healthcare providers and healthcare-related professionals leverage patient data successfully for better health management. We can help you too.
Just about everyone is talking about the cloud. Cloud computing in finance sector is driving agility for enterprises, and everyone from business leaders to forward-thinking CIOs is using it to support their business strategies to improve performance and returns. The banking, financial services, and insurance (BFSI) industry are also leveraging the cloud to address specific requirements and unique challenges.
Cloud is a game-changer for businesses, and COVID-19 has further accelerated cloud adoption. Today, most finance enterprises have eagerly embraced cloud-based systems to enable remote working, upgrade customer-facing software, prevent frauds, and increase efficiency. It aids operational activities such as new account opening, fund transfers, loan approvals, insurance advice policy renewal, and more. All you need is a cohesive cloud strategy to realize its business value fully.
As per a Google Cloud survey1, 83 percent of respondents said they were deploying cloud technology as part of their primary computing infrastructures, with hybrid cloud being the most popular (38 percent),, followed by single cloud (28 percent) and multi-cloud (17 percent). It, therefore, becomes imperative to understand the different cloud deployment models and options and define your objectives to create a roadmap for cloud adoption.
Adoption of cloud computing in finance and banking
The financial sector is far from full adoption as far as core, back-office workloads are concerned. BFSI companies have adopted cloud technology for various reasons, including Data and IT security, regulatory reporting, fraud detection and prevention, data reconciliation, and underwriting activity.
Those who have embraced cloud adoption unanimously agree on its benefits that include:
Ø Adapting to changing customer behaviors and market trends
Ø Enhancing efficiencies and operational resilience
Ø Enabling innovation to upgrade product and service portfolio
Ø Enhancing data security capabilities
Ø Eliminating silos to ensure connectedness and transparency
Ø Scale up or scale down capacity to manage diverse workloads and activities
Ø Driving integrated decisions by integration of business units to share data and respond quickly
A cloud computing environment is just what you need in the current scenario. But you need to first understand the influencing factors and the cloud compositions and service models to decide which one’s right for you.
Factors to consider
BFSI enterprises often embark on a cloud journey with infrastructure-as-a-service (IaaS) models to later evolve to platform-as-a-service (PaaS) and software-as-a-service (SaaS) models as they mature. To choose the right model and devise a strategy, you need to be very clear about your objectives.
Your business case for adopting Cloud
Critical applications are often built on legacy technologies, and moving them to the cloud usually entails high costs. Also, migration is not easy until they undergo a bit of a revamp. A detailed cloud strategy and roadmap, therefore, becomes essential to initiate cloud adoption.
Intended customer experience & service differentiation
A differentiated service experience is a key to the success of a Cloud implementation. You can either build your custom solution for max flexibility or use an existing Off-The-Shelf (OTS) or SaaS solution with their out-of-the-box features. OTS and SaaS solutions are suitable when the time to market is key – A custom solution works better to deliver a differentiated CX
The modern regulatory framework requires enterprises to comply with stringent privacy, security, and regulatory standards in regards to the capture, storage, and sharing of customer data. Understanding these requirements is essential before choosing between on-premise, private, or public cloud to host systems.
Choosing the right cloud deployment and service models
Financial institutions vary in their business functions and technology priorities. Feasibility analysis, cloud migration assessment, and strong decision-making capabilities are necessary to choose the right deployment and service models.
A leading investment bank is using a public cloud for its regulatory reporting solutions. In contrast, a prominent Norwegian bank has adopted the PaaS model to transform its peer-to-peer mobile payment application using microservices on the public cloud. The bank has captured nearly 80% 2 of the market, ensuring high application availability and scalability to service more than 2M customers. While doing so, it was able to increase transaction processing throughput by 10x and reduce infrastructure setup time from 60 to 6 days and release time by 3x.
A major North American bank specializing in cards, wealth management, and investment services had spectacular results after migrating to the cloud. It leveraged the IaaS and PaaS models on the private cloud to modernize its channels by enabling digital identity, push notifications, e-wallets, behavioral biometrics, etc. The bank saw a 40% improvement in time-to-market, an increase in active mobile user base by 20%, and a drop in annual infrastructure cost by 15%.
Chinese banks3 such as Minsheng Bank, Ping An Bank, Industrial Bank, and Beijing Zhongguancun Bank are using cloud-native SaaS platforms to strengthen their anti-fraud systems and increase the responsiveness of the front-office and agility of the back-office.
Clearly, cloud adoption differs significantly in scope and value for every organization. While some banks have adopted PaaS and SaaS models to modernize their channel and teller applications, many adopt low code SaaS offerings for sales, customer relationship management, onboarding, and enterprise resource planning systems.
Cloud computing deployment in finance
Cloud service models
Choosing the right cloud service model is easier if you recognize its potential in furthering your business objectives. The model you select should evolve based on your needs and the business functions you want to support.
Distinctions exist among all, especially when you evaluate them with a risk-based approach. Here are the top 3 popular ones:
Infrastructure as a Service (IaaS)
It offers IT infrastructure (servers and storage) that can be managed online via a pay-as-you-use basis without getting into the complexities of purchasing and managing physical servers. It is dynamic and flexible, and the services are highly scalable. Resources are available as a service, and it allows enterprises to carry out automated administrative tasks.
Platform as a Service (PaaS)
It allows enterprises to develop, test, run, and manage applications since it is accessible to multiple users through the same development application. It can be integrated with web services and databases and supports diverse languages and frameworks. Its ability to ‘auto-scale’ gives it an edge.
Software as a Service (SaaS)
It is an on-demand software that hosts applications. Applications can be accessed easily via an Internet connection and a web browser from a central location. It is hosted on a remote server and available for use on a pay-as-per-use basis. Users do not have to oversee hardware or software updates since updates are applied automatically.
Winning with Cloud power
As the needs of modern finance enterprises evolve, mature cloud services will continue to emerge to help manage different business functions. Cloud adoption can help deliver compelling value propositions to customers and innovate along the way. Cloud power is the way forward to enhance capabilities and enable end-to-end automated processing. However, the key to successful cloud adoption lies in the cloud model you choose and the service provider you decide to work with.
Embrace cloud technology with Trigent
Trigent has been helping the BFSI sector minimize risks and maximize benefits with the right cloud transformation strategies. We help them approach cloud migration in an incremental manner to reduce transition risks and enjoy the benefits of cloud storage and compute power in a cost-efficient way.
Our cloud-first strategy enables cloud transformation at speed and scale to help enterprises drive agility and collaboration. We can help you embrace cloud technology too to operate in an agile digital environment.
Building software in the current world requires high-speed development to meet ever-changing business needs. Products and services are delivered incrementally in Agile mode.
To meet speed and quality requirements a development team will need to identify the following:
Development tools and frameworks that ensure standardization.
Ready made solutions that can be integrated directly or customized to serve their needs.
Thankfully modern development approaches have ready-to-use SDKs and APIs to meet these challenges. Instead of wasting time and resources on researching, developing, and testing, teams can use a plethora of APIs and SDKs with extensive community support.
An SDK is a full-fledged installable library, while APIs are services exposed by a third party or another service, to be communicated with. Both take away the development effort of a module or feature that you might not be ready with. Depending on the scenario a developer or team will either need an SDK or just an API. Making an informed decision on when to use one over the other is crucial to successful software development.
To understand this, let us take an example in which we want to build a native health tracking app. The app will have the following features:
Social authentication through Google or Facebook accounts.
Location tracking to figure out distance covered from point A to B as per the user’s activity. It could be cycling or walking.
The list can continue, but we do not want to digress from our main intent of understanding SDKs and APIs.
The first thing to consider while building a native mobile app is that there needs to be an Android and an iOS version to serve the majority of users. Whether one should go in for a native or a hybrid app or build the 2 variants using a Cross-Platform approach requires a separate discussion in itself. The starting point for it could be the skills available in-house.
Android app and social authentication implementation
For our scope, let’s just consider the Android app. The official language for building Android apps is Java. Kotlin also has become an official language for Android development and is heavily promoted by Google. C, C++ runs natively on the phone. Then there is LUA which is not supported natively and requires an Android SDK. You can even use C# depending on your team’s core competency. This will require either Xamarin with Visual studio or Unity.
We are going to choose Java here.
The best way to get started for a Java developer is to install Android Studio which is an IDE that automatically downloads the Android SDK and emulator. The Android SDK is a complete set of development, debugging, testing, and build tools, APIs, and documentation. Using the SDK you can generate APKs that can be deployed to different Android-supported devices. The developer just focuses on the language of his choice based on what is supported by the SDK and uses standard code and framework to get the app up and running.
The next feature to be built is single-sign-on into the app, using a social account. Both Google and Facebook provide client or server-side SDKs to hide the complexity of the actual implementation and enable the integration through popular languages. The developer just rides on the authentication provided by Facebook and Google. Additionally, the user also grants the app the permission to access information or perform operations on either platform based on our need. In our case, we will have to use the Android SDK provided by Facebook and Google.
To sum up, the Android SDK enables the following:
Enables development of the Android app using a language of our choice, Java.
Provides APIs to access location, UI, camera and other native features.
Enables localization of the app for different languages through the SDK’s framework if required.
The Java code is compiled to an Android application package along with the required libraries of the SDK
Hence for our health tracking app, we can use the Android SDK for social authentication
One of the key features of the app we are trying to build here is to figure out the distance walked or cycled by the user. We can take the route of custom implementation by spending a couple of days or weeks to come up with an algorithm, implementing and finally testing it. A better approach would be to use an out-of-the-box solution such as Google Maps and save on SDLC time and effort. Google provides both SDK and APIs related to Maps and distance. In our case, we do not really need the entire Google MAP SDK. We can use just the relevant APIs such as the Distance Matrix API. It gives you the distance and time between one or more endpoints.
Based on the above URL we can glean that an API comprises of the following –
Protocol – SOAP, REST or GraphQL. In our case it is REST. SOAP is the oldest mode of interaction with heavy schemas and data. REST is an architectural style relying on HTTPs GET, POST,PUT and DELETE operations. GraphQL is a query language promoted by Facebook which solves the problem of under-fetching or over-fetching by REST.
URL – as provided by the service provider.
Request Parameters – Either all parameters are mandatory or some are optional. Any service exposing APIs will share the parameters and their structure. In our case for instance – destinations and origins are required parameters. Mode (bicycling or walking) is an optional parameter.
API Key – We will need to pass a unique API key that points to our application using the service for authentication and authorization.
Response – The output is either JSON or XML.
An API (Application Programming Interface) enables easy and seamless data transfer between a client application and the server providing the service. There is no installation required, unlike an SDK. The API logic is completely abstracted by the service provider from the client. APIs contribute to a loosely coupled, flexible architecture. Since the API code lies on the server, it’s maintained by the provider. Because of this dependency, we need to ensure that we choose a reliable provider and also keep an eye out for newer versions.
Hence for our health tracking app, we can use the Google Map API for location tracking.
BMI calculator and diet options implementation
This would be either a custom implementation, an API, or SDK. If it’s not available readily as an API or SDK and is required in a number of different health services or products the organization wants to provide, it would be best to expose it as an API for current and future use.
Diet options clearly are a custom implementation in our scenario.
Differences between SDKs and APIs
An API is used to provide a feature by running on a third-party system in a request-response mode.
An SDK provides all the tools, libraries, APIs, and documentation necessary to build the application or feature.
APIs run on separate servers (internal or 3rd party) and hence have a continued dependency on the service for reliable operation.
SDKs typically run on the same environment and hence have no interdependencies. However, they use the processing power of the existing environment of the application being built.
This just requires a SOAP/REST/GraphQL call to the server end-point with request parameters defined as per the API documentation.
This is available in languages supported by the provider which is mostly based on what can run in the environment expected and the popularity of the language. For instance, Java, NodeJS, Python, GO, PHP are the usual languages popular with the developer community.
No installation is required.
It requires installation and is therefore bulky. Any upgrades will need to be handled at our end. Some SDKs also allow customizations as per our needs.
In a scenario where just a few APIs are required from the entire stack provided by the SDK and these APIs can be independently run, it’s better to opt for the APIs alone.
Error handling is left to the application based on what is thrown back by the server.
SDKs lean on the language’s error handling mechanism besides what the server platform returns. Therefore error handling is handled in a more effective way.
Examples – Map Apis, Payment Apis, AdMob API provided by Google.
Examples – JAVA SDK, Android SDK, Facebook’s Single Sign-on SDK.
While SDKs are a superset of APIs, used appropriately, they both have many advantages over custom development.
Advantages of using SDKs and APIs
Fast and easy adoption – A few lines of code and your feature is ready. The developer can focus on the core business functionalities of the application instead of re-inventing the wheel or working on something that is not our core area of expertise.
Saves time and effort – Ready to use and can be directly plugged into, thereby shortening development cycle.
Language – In the case of SDKs, they usually support all the popular languages that the implementation needs. For APIs you just have to ensure the communication protocol and parameters are as per the requirements.
Support -APIs and SDKs ensure best practices, provide robustness and have community support.
Documentation – APIs and SDKs have good documentation for developers to understand and use. No expertise required other than knowing the language to be implemented in.
Updated – Newer features keep getting added to the stack by way of versions which the developer if required needs to just update. Mostly backward compatibility is already handled by the service provider.
Disadvantages of using APIs and SDKs
To summarize, whether it’s an API or SDK, it’s better to follow the reviews of the community before making a selection. Things to look out for are known bugs, limitations, and cost.
Trigent provides a number of ready-to-use SDKs and APIs for many domains such as mobile app development, SCM workflows, Logistics, AR/VR development services, enabling you to focus on your core expertise and saving you a lot of time and effort in your development cycles. To know more, please contact us.
The modern workplace is seeing widespread usage of machines and automation. Enterprise digital transformation, Artificial intelligence (AI), and automation are changing the tide for businesses globally. This means a significant change in the work culture as employees will have to acquire new skills and adapt to the advanced capabilities of machines.
As per a recent study1 involving over 600 business leaders from 13 countries, more than 50 percent of respondents confessed to having already invested over $10 Million in intelligent automation projects. The AI market globally is presently growing at a CAGR of 40%, all set to touch $26.4 Billion by 2023.
AI, along with robotic process automation (RPA), voice recognition, natural language processing (NLP), and machine learning (ML), is allowing businesses to blend automation with human capabilities successfully to create intelligent working environments.
Automation is driving agility for businesses giving them the much-needed competitive edge over others with quick decision-making powers. Clearly, decision velocity powered by AI-driven insights gives you data supremacy to lead in a highly volatile market.
Making a case for Intelligent Process Automation (IPA)
When automation meets artificial intelligence, you get intelligent process automation to scale up your business. While it allows you to off-load routine, repetitive tasks, it empowers better guardrails for all your automation initiatives. It takes the uncertainty out of the picture and enables more personalized execution and processes.
Intelligent automation enhances the overall customer experience. The speed of response has often been a critical consideration while evaluating the customer experience. Intelligent automation is helping organizations meet customer expectations with personalization. Through customized offers, services, and content, businesses are acquiring and retaining customers.
What do the right IPA endeavors ensure?
Agile services due to a significant reduction in processing time
Greater flexibility and scalability for being able to operate round the clock with capabilities to scale up and down as required
Improved quality control due to greater traceability of events and instances and checks at different levels
Increased savings and productivity due to a high level of automation
Clear, actionable insights to predict and improve drivers of performance
While there is unanimous agreement on the benefits of intelligent automation, not everyone has leveraged these benefits across the organization. What you need is an enterprise-wide approach that promotes a new way of working.
Adding intelligence to the digital mix
A highly automated world does not focus on reducing the headcount but increasing its potential to do more in an agile manner to solve the business challenges of tomorrow. It relies on structured and unstructured data the company collects from the public domain and other stakeholders rather than depending on traditional methods.
Intelligent automation compels you to rethink key business processes. The sales and marketing team gets deeper segmentation to target and sell through advanced analytics. Those working to strengthen the supply chain get to improve production and distribution by leveraging technologies like cloud and analytics across the value chain. Planning and development teams, on the other hand, rely on data-driven insights to integrate them into product performance and boost innovation.
Alibaba Group2 is a classic example of what you can achieve with intelligent automation.
After making significant strides in eCommerce and retail, it has further revolutionized its business processes with its ‘Industrial Vision AI‘ solution for manufacturing and production. It allows the company to inspect raw materials thoroughly to detect minute defects, resulting in a 5X increase in production efficiency. Its automated warehouse is managed entirely by robots taking precision and efficiency to a whole new level.
Regardless of your goal, you need to create a strategic roadmap to align it with your business priorities. This is not possible unless you assess your digital maturity.
What is the role of IT in successful IPA transformation?
Intelligent process automation (IPA) is a melting pot of technologies enabling significant gains for businesses worldwide. IPA should not be confused with robotic process automation (RPA) as unlike RPA that performs repetitive, automated tasks based on predefined rules and inputs, IPA can understand the context, learn, and iterate to support informed decision-making using unstructured and structured data.
Those who have been able to get the full value of IPA have been the ones who have put IT leaders at the helm of their IPA endeavors. CIOs need to strengthen their core with IPA programs to support automation.
Here’s what we recommend:
Assess the high-level value potential
You may start with help-desk requests since that’s where a significant amount of incidents originate. While tickets with low difficulty levels are resolved immediately, those with more complexity are often escalated to specialized teams. Determine how many such requests were handled the previous year, and by multiplying them by the average handling time (AHT) required, you can evaluate the value of this whole exercise.
For instance, an organization with a significant number of requests for password reset or access can leverage RPA bots that work across multiple applications via the user interface to automate ticket resolution and free up employee capacity. Reducing resolution times and a drop in costs associated with outsourcing help-desk support will thus improve performance and profits.
The effort required for these activities often varies. Everything needs to be evaluated critically from backups and patching to security audits and upgrades to understand the effort involved and the value you can garner by planning activities for automation.
Identifythe use cases best suited for IPA
Let’s consider the same example mentioned above. In order to automate incidents, organizations need to first identify the ones ideal for automation. An organization may be effectively logging incidents in detail, but due to the large numbers and complexities, support teams may not respond quickly and effectively.
AI can make sense of the chaos and understand the reasons behind the alerts. It may be trained to make appropriate recommendations or even make better decisions to ensure suitable responses.
Elevate customer experiences with better service
AI and automation are changing the customer service landscape for every industry, from retail to aviation. Boeing has a fleet of passenger service robots that operate via sensors installed in their bodies. They are doing their best to reduce the manual work of cabin crews. Though experts argue a human perspective is required for these robots to do what humans can.
The key is to understand the power of automation and integrate it seamlessly into processes and workflows to complement human efforts and endeavors perfectly, as we did in the case of one of our clients Surge Transportation.
The company links shippers and carriers and has an automated tracking and monitoring system to assign loads. But the pricing and quotation were being done manually. This drained their resources, led to a huge turnaround time, and left a long log of emails, calls, and paper trails.
Trigent critically evaluated the complexities in its pricing mechanism to bring down the turnaround time to less than a second. Apart from 100% pricing accuracy, the company improved profits by 25%, revenue by 40%, and reduced the load processing time by 91%. With seamless carrier integration, the company now processes 4000 more loads per day.
Other use cases where AI and Automation are driving value
Amazon is popularizing the concept of cashier-less stores with Amazon Go and Just Walk Out. Robotization of stores helps save operational expenses and gives shoppers a smart shopping experience.
Automated medical appointment scheduling
No-shows have been the cause of losses of over $150 billion a year for the U.S. healthcare system with every unused time slot costing individual physicians $200 on an average. No-shows also impact the health of patients since continuity of care is interrupted. IPA challenges traditional scheduling methods by ensuring error-free appointment scheduling based on the nature of the illness, the convenience of patients, and the availability of doctors and healthcare facilities. While patients get to choose a date and time for different health issues, follow-up appointments can be scheduled automatically along with reminders.
Automated supply chains
The ideal supply chain is where there is neither wastage nor out-of-stock scenarios. In tandem with machine learning, AI predicts demand based on location, weather, trends, promotions, and other factors. Revenue losses of up to $4Trillion have been caused due to supply chain disruptions following the pandemic with 33% attributed to commodity pricing fluctuations as per a report.
The automobile giant Toyota is using AI in its manufacturing environment to address waste control with its ability to predict when excess parts, products, and practices threaten to impede work.
Intelligent Automation is clearly the Winner streak!
The potential value of AI and automation is immense for different sectors and will vary depending on the type of industry, availability of abundant and complex data, use cases, and other factors. To get the most out of your automation initiatives, it is however important to tide over organizational challenges with the right mindset and approach.
Create impact and value with Trigent
Trigent with its team of technology experts empowers you to stay relevant and competitive. It is equipped with insights and intelligent solutions to dramatically boost your bottom line and improve customer engagement.
Allow us to help you grow your business and increase revenue with strategies and solutions that are perfect for you.
Artificial intelligence is now all around us in household gadgets as well as business workflows. AI adoption is rampant across sectors; the global artificial intelligence market is expected to reach $ 266.92 billion by 20271 at a CAGR of 33.2% during 2019-2027. Nearly half of the respondents who had participated in a survey confessed to being interested in AI implementation and machine learning to improve data quality.
No doubt, artificial intelligence is the proverbial genie that does everything we want it to do without even rubbing the magic lamp. But the lack of nuance and failure to spell out caveats can result in AI systems that will make us think twice before we wish for anything.
Believe it or not, misaligned AI can be a nightmare.
A classic case is YouTube2, with its AI-based content recommendation algorithms that led to users accusing it of radicalization. Its constant upping-the-ante approach led users to extreme content in a bid to maximize viewing time. So videos on vegetarianism led to veganism, and jogging searches resulted in ultramarathons. This unintentional polarizing and radicalizing highlights one significant challenge: we have yet to define the goals accurately for our AI systems!
The sad truth is that we don’t even know what we want, at least not from our autonomous systems and gadgets and other possessions. For instance, a self-driving car may be too slow and brake too often just the way it was designed to prevent itself from colliding with nearby objects. But the object could be as insignificant as a paper bag that was blown away by the wind.
What we need is goal-oriented AI born with a solid sense of purpose with excellent human-machine coordination. But only after you have answered the question- Do I really need AI?
Here’s is your ultimate AI implementation checklist
AI has ample scope in many sectors. AI can interact on your behalf with customers, as in the case of chatbots, or help healthcare providers diagnose cancer and other ailments. If leveraged well, it can help you turn a new leaf in critical interactions with your customers. Understanding the potential of AI and applying it to enhance critical business values can make a world of difference to your business. The key is to know where you stand and whether AI can help you attain your business goals.
Identify the purpose
Organizations with successful AI implementations are usually the ones that have assessed its financial impact or conducted a thorough risk analysis for AI projects. Having the right metrics in place gives you a sneak peek into the risks and benefits of AI implementation and how it would perform in those chosen areas. While it may not guarantee a positive ROI, it gives you a fair idea about what to expect.
Accuracy, for instance, is an important metric, but it’s not enough to understand how well your AI systems are performing. You need to correlate AI metrics to business outcomes to ensure you get the most out of your investments.
The smart pricing tool created by Airbnb to eliminate pricing disparities between black hosts and white hosts presents a classic example. While the AI-based system performed the assigned tasks with precision, the business results fell short – widening the gap further by 20%.
Appoint mixed-role teams for all AI initiatives
Those who have implemented AI successfully will tell you how crucial it is to build mixed-role teams comprising project managers, strategists, application designers, AI researchers, and data scientists to ensure a diversity of thought and skillsets. As per a Garnet Research Circle survey3, skills are the first barrier to AI adoption, and 56 percent of respondents believed new skills are required for new and existing jobs.
AI needs experts for it to evolve to its best version. TayTweets, a promising chatbot by Microsoft, was nothing but fun, and people loved talking to her. Until, of course, she became the nastiest chatbot ever in less than 24 hours, responding with offensive tweets. It demonstrates how horribly things can go wrong when AI and ML go awry when left unchecked.
Diversity in technical acumen enhances the value of AI to customers since the people working with AI know-how and where it should be used to have the most significant impact. Whether you want to hire new people or train existing ones for newer roles and responsibilities is something you will have to decide based on the business initiatives you have in mind.
Make a business case for AI
Businesses need AI for different reasons ranging from data security and fraud detection to supply chain management and customer support. You need to identify the use cases and applications to determine how AI can be effectively used. Organizations depend on AI to analyze contextual interaction data in real-time and compare it with historical data to get insights and recommendations.
Data plays a pivotal role in every aspect of a business. While a lot of emphases is placed on coding, math, and algorithms, many organizations are not able to apply the data acquired effectively in a business context. You will have to understand who you are building these solutions for and what technology framework you will require to do so.
As Moutusi Sau, principal research analyst at Gartner4, points out, “Business cases for AI projects are complex to develop as the costs and benefits are harder to predict than for most other IT projects. Challenges particular to AI projects include additional layers of complexity, opaqueness, and unpredictability that just aren’t found in other standard technology.”
Assess your AI maturity
It is impossible to arrive at a strategy without evaluating where you stand against the AI maturity model. Once you know it, you can decide the next steps. Typically, the AI maturity model has five levels:
Ø Level 1 – There is awareness in the organization, and AI implementation is being considered, but no concrete steps have been taken in that direction.
Ø Level 2 – AI is actively present in proofs of concept and pilot projects.
Ø Level 3 – AI is operational, and at least one AI project has made its way to production with a dedicated team and budget.
Ø Level 4 – AI is part of new digital projects, and AI-powered applications are now an essential part of the business ecosystem.
Ø Level 5 – This should be the ultimate goal where AI is now ingrained in the organizational DNA and plays a transformative role for your business.
Look beyond the hype
AI can cause ‘cultural anxiety’ as a significant shift in thought and behavior is necessary for successful AI adoption. A compelling story to help employees understand how AI would be beneficial to all is necessary to ease the resistance they might feel towards the change. CIOs should recognize their fears and anxiety of the possibility of being replaced by machines and encourage an open dialogue with team members. This will build trust and help determine if the organization is ready for AI.
The hype around AI itself can sometimes be the biggest problem as organizations hurry to hop onto the AI bandwagon with insufficient understanding of its impact. Explains Whit Andrews, distinguished vice president analyst at Gartner, “AI projects face unique obstacles due to their scope and popularity, misperceptions about their value, the nature of the data they touch, and cultural concerns. To surmount these hurdles, CIOs should set realistic expectations, identify suitable use cases and create new organizational structures.”
AI to Impact
The biggest mistake organizations make when they invest in AI is that they have too many expectations and little understanding of AI capabilities. Rather than getting caught in the hype, you have to be realistic and evaluate its role critically in furthering your business objectives.
AI is an expensive investment that will give you good returns if you know how to use it. A lot of tools are good, but not every AI tool is suitable for your business. What you need is the right AI implementation strategy created with professional help from those who know AI like the back of their hand.
Adopt AI with Trigent
Artificial intelligence is a defining technology that can be successfully integrated into business workflows and applications. We at Trigent have been helping organizations from diverse sectors, including healthcare, retail, BFSI, and logistics, create AI operating models that are optimized for faster and effective outcomes.
We can help you, too, with everything from strategy and implementation to maintenance and support.
A look at how the use of technology in retail stores are helping them outplay the e-Commerce giants
It’s no secret that retail businesses are going through a pivotal phase; an existential crisis triggered by skyrocketing rate of digital adoption and the burgeoning presence of biggies like Amazon and others. The pandemic with its perennial need to follow social distancing and stay-at-home mandates has strengthened the demand for eCommerce.
Just about one-third of U.S. consumers were willing to enter shopping malls again in April 2021 while 25-48% of European consumers from different countries were keen on avoiding brick and mortar stores even in the beginning of 20211.
The decline in the demand and popularity of physical stores has had a crippling effect on several businesses. Some declared bankruptcy while others closed down a few units to shrink their business. The list of store closings is rather long – a record 12,200 stores2 to be precise in the U.S. alone in 2020.
At a time when profits are becoming elusive and footfall remains uncertain, the retail sector, especially boutiques and smaller businesses, are up for a major upheaval. The decline is evident but it’s definitely not the end for the traditional brick and mortar stores experience we’ve so thoroughly enjoyed all our life. As the legendary Mark Twain would have said, “the reports of the death of brick-and-mortar stores are greatly exaggerated.”
A lot can be done to shift the tide in their favor. The onus is on local and boutique retailers to ensure that the gratification continues albeit online for their customers. Luckily, it’s not so difficult if you identify the core areas that draw customers to the in-store experience and leverage the technology spectrum accordingly.
Averting the retail apocalypse
A bit of a tweak in your approach and digital adoption can put you on the road to retail recovery. See how Nordstrom revamped their business model to serve its customers. Be it a quick fix for a leather jacket or getting pants hemmed in an hour, the sprawling flagship store offers everything from style tips to personal guidance for free to its customers. As Sonia Lapinsky, managing director at Alix Partners puts it, “Nordstrom is providing a reason for the customer to walk in the door.”
Relevance is the key here and all the resources, be it time, money, or efforts, should be used to elevate the customer experience. Ultimately, it’s all about the relationships you build with your customers especially when 56% of customers stay loyal to brands that ‘get them’.
Taking a cue from its biggest competitor Amazon for the digital maturity it has achieved in such a short time, Walmart too had transitioned to eCommerce in a big way. It has witnessed a 97 percent3 surge in eCommerce sales with total revenues increasing by 5.6 percent to $ 137.7 Billion. With the help of AI, Walmart is helping buyers make smarter substitutions for out-of-stock products by suggesting them the next available items. Their choices are analyzed and fed into learning algorithms to make more accurate recommendations in the future.
There are 7 areas of retail that are of paramount importance to ensure the most satisfactory shopping experience for buyers. These include:
Swift digital payments – As consumer faith in online transactions has grown, contactless, digital payments have become the norm.
Smooth navigation – With better search algorithms and smarter devices, the shopping experience is expected to be omnichannel.
Centralized inventory – Digital businesses enjoy greater economies of scale and improved turnover due to centralized inventory with smarter technologies and robotics at the helm.
Ease & convenience – The craving for something and the convenience to have it right away can translate into greater satisfaction.
Product experience – Through touch and feel, consumers want to physically experience the things they buy.
Immersive exploration – Consumers love to be involved in brand journeys and eagerly participate in activities that involve entertainment and engagement.
Personal advice – It is always heartening to know you are understood and expert advice is always welcome.
Leveraging new technologies to excel in these areas can help you regain strategic momentum and offer a uniquely differentiating customer experience.
Here’s how you can win the digital game.
Go BOPUS (BOPIS)
A lot of retailers are going the ‘buy online, pick up in store’ way to blend the speed and convenience of eCommerce with the in-store product experience. Nordstrom Local is leveraging it well to offer pickups and returns along with express alterations and a whole lot of services to walk that extra mile to ensure customer satisfaction.
Says eMarketer’s vice president of forecasting Martín Utreras, “BOPUS provides tangible benefits to both consumers and retailers. Consumers get convenience, instant gratification, and avoid shipping costs. Retailers reduce operational costs, and it gives them the opportunity to bring customers back to physical stores for additional purchase opportunities.”
Make sure that you offer speed and process efficiency like Amazon Go that bypasses the checkout altogether with a grab-and-go model or Target’s in-app shopping lists that offers aisle to aisle assistance to customers in the physical store.
Thanks to AI, retailers now have the data and intelligence necessary to understand their customers’ shopping habits and choices. You can personalize marketing content, customize newsletters, and entice them with relevant ads on social media based on their social footprint, location, hobbies, and other factors. You can also make product recommendations via email marketing to generate leads and revenue.
The right product recommendations aligned with their tastes will not only enable the discovery of new products but also instill trust. Case in point – Hanes Australasia dramatically improved its revenue and grew across new and existing markets with AI-based personalized recommendations.
Provide 24/7 customer care with Conversational AI
Brands are increasingly leveraging chatbots to offer personalized assistance and customer service round the clock. Assistance can now be offered through speech and text in local languages with natural language interactions. The benefits of having chatbots are many – greater operational efficiency, minimized manual effort, increased customer satisfaction, and lower handling costs. Automated customer care does not take away the human connection but strengthens it by ensuring that customer concerns are heard and addressed on priority.
According to research4, 40% of shoppers don’t care whether they are assisted by a tool or human as long as they are attended to while 80% of consumers who have engaged with a chatbot claim to have had a positive experience.
Bolster the supply chain
Addressing supply chain challenges can be stressful for retailers and inefficiencies can result in loss of revenue and dissatisfied customers. Inventory optimization is crucial in this era of fast-changing demands. AI-powered inventory optimization helps businesses increase the accuracy and granularity of SKU and store-level stock planning, preparing them to handle sudden shifts in demand. Routing, end-to-end transaction visibility, and dashboards for inventory tracking are some of the many solutions you should consider to drive agility and maintain business continuity.
What’s more; it also helps analyze costs to create a pricing model that determines the right price for your products while staying on top of managing supplier costs. This kind of dynamic pricing is being used by Walmart and Amazon too due to the tons of data they have, with the latter reportedly changing its prices 2.5 million times a day.
Drive an omnichannel experience
To facilitate a seamless shopping experience both online and offline, it is important to ensure it’s omnichannel. Whether customers shop via mobile device, laptop, or a physical store, there has to be back-end integration of distribution, promotion, and communication channels for greater flexibility.
The same experience should be extended on social media too to enable a high level of customization. This also helps you provide targeted offers while creating more engaging ways to interact and connect with customers.
Starbucks struck a chord with patrons through its loyalty program that encouraged them to earn stars on their purchases. These stars could be redeemed for free products, top-ups, etc. through their app or website. The program not only served as a fine example of customer engagement but was responsible for 40% of its total sales.
Evaluate virtual fitting technology
The ‘try before you buy’ psyche is here to stay which explains why the global virtual fitting room (VFR) market is expected to touch $ 10 Billion by 2027 at a CAGR of 20.1%. Virtual try-on and fitting rooms enabled using Augmented Reality and Virtual Reality are catching on big time making both sellers and buyers very happy.
While apps like SneakerKit help you choose the right footwear, there are apps for virtually everything you wish to buy from hats and glasses to clothes and masks. Brands like Macy’s, Adidas, and many others allow users to upload a full-body photo and then try on clothes based on their body type. Getting a feel of what they are buying offers both comfort and confidence to buyers.
Business models need to be altered keeping the following objectives in mind:
Ensure convergence across channels and touchpoints as boundaries between the physical and digital worlds blur.
Customize the delivery format based on changing shopper behaviors through personalization
Collaborate instead of competing with other suppliers and retailers to enhance customer value
Improve the value proposition through interactions and communication in real-time.
Facilitate self-learning through AI-enabled data to enhance customer satisfaction.
Create a seamless customer experience with Trigent
As you gear up to deliver unique shopping journeys, we can help you with our broad range of offerings to build a technology stack that’s replete with features and functionality. With an array of pre-built as well as custom solutions for diverse retail use cases, we empower you to offer personalization and delightful digital experiences at scale.
We’d be happy to be your trusted technology partner on your digital transformation journey.
Cloud computing is now the foundation for digital transformation. Starting as a technology disruptor a few years back, it has become the de facto approach for technology transformation initiatives. However, many organizations still struggle to optimize cloud adoption. Reasons abound – ranging from lack of a cohesive cloud strategy to mindset challenges in adopting cloud platforms. Irrespective of the reason, assuring the quality of applications in cloud environments remains a prominent cause for concern.
Studies indicate a wastage of $17.6 billion in cloud spend in 2020 due to multiple factors like idle resources, overprovisioning, and orphaned volumes and snapshots (Source parkmycloud.com). Further, some studies have pegged the cost of software bugs to be 1.1 trillion dollars. Assuring the quality of any application hosted on the cloud not only addresses its functional validation but also its performance-related aspects like load testing, stress testing, capacity planning, etc invariably addressing both the issues described above, thereby exponentially reducing the quantum of loss incurred on account of poor quality.
The complication for QA in cloud-based application arises due to many deployment models ranging from private cloud, public cloud to hybrid cloud, and application service models ranging from IaaS, PaaS, to SaaS. While looking at deployment models, testers will need to address infrastructure aspects and application quality. At the same time, while paying attention to service models, QA will need to focus on the team’s responsibilities regarding what they own, manage, and delegate.
Key aspects that mandate a shift in the QA approach in cloud-based environments are –
Earlier and to some extent even now, when it comes to legacy applications, QA primarily deals with a monolithic architecture. The onus was on understanding the functionality of the application and each component that made up the application, i.e., QA was not just black-box testing. The emergence of the cloud brought with it a shift to microservices architecture, which completely changed testing rules.
Multiple scrum teams work on various application components or modules deployed in containers and connected through APIs in a microservices-based application. The containers have a communication mechanism based on contracts. QA methodology for cloud-based applications is very different from that adopted for monolith applications and therefore requires detailed understanding.
Security, compliance, and privacy
In typical multi-cloud and hybrid cloud environments, the application is hosted in a 3rd party environment or multiple 3rd party environments. Such environments can also be geographically distributed, with data centers housing the information residing in numerous countries. Regulations that restrict data movement outside countries, service models that call for multi-region deployment, and corresponding data storage and access without impinging on regulatory norms need to be understood by QA personnel.QA practitioners also need to be aware of the data privacy rules existing across regions.
The rise of the cloud has given way to a wide range of cybersecurity issues – techniques for intercepting data and hacking sensitive data. To overcome these, QA teams need to focus on vulnerabilities of the application under test, networks, integration to the ecosystem, and third-party software deployed for complete functionality. Usage of tools to simulate Man In The Middle (MITM) attacks helps QA teams identify and overcome any sources of vulnerability through countermeasures.
Building action-oriented QA dashboards need to extend beyond depicting quality aspects to addressing security, infrastructure, compliance, and privacy.
Scalability and distributed ownership
Monolithic architectures depend on vertical scaling to address increased application loads, while in a cloud setup, this is more horizontal in nature. Needless to say that in a cloud-based architecture, there is no limitation to application scaling. Performance testing in a cloud architecture need not consider aspects like breakpoint testing since they can scale indefinitely.
With SaaS-based models, the QA team needs to be mindful that the organization may own some components that require testing. Other components that require testing may be outsourced to other providers, and some of these providers may include cloud providers. The combination of on-premise components and others on the cloud by the SaaS provider makes QA complicated.
Reliability and Stability
This entirely depends on the needs of the organization. An Amazon that deploys 100,000 times a day – features and updates of its application hosted in cloud vis-a-vis an aircraft manufacturer that ensures the complete update of its application before its aircraft is in the air, have diverse requirements for reliability stability. Ideally, testing done for reliability should uncover four categories – what we are aware of and understand, what we are aware of but do not understand, what we understand but are not aware of, and what we neither understand nor are we aware of.
Initiatives like chaos testing aim to uncover these streams by randomly introducing failures through automated testing and scripting and seeing how the application reacts/sustains in this scenario.
QA needs to address the below in a hybrid cloud setup are –
What to do when one cloud provider goes down
How can the load be managed
What happens to disaster recovery sites
How does it react when downtime happens
How to ensure high availability of application
Changes in organization structure
Cloud-based architecture calls for development through pizza teams, smaller teams fed by one or two pizzas, in common parlance. These micro product teams have testing embedded in them, translating into a shift from QA to Quality Engineering (QE). The tester in the team is responsible for engineering quality by building automation scripts earlier in the cycle, managing performance testing strategies, and understanding how things get impacted in a cloud setup. Further, there is also increased adoption of collaboration through virtual teams, leading to a reduction in cross-functional QA teams.
Tool and platform landscape
A rapidly evolving tool landscape is the final hurdle that the QA practitioner must overcome to test a cloud-based application. The challenge becomes orchestrating superior testing strategies by using the right tools and the correct version of tools. Quick learning ability to keep up with this landscape is paramount. An open mindset to adopt the right toolset for the application is needed rather than an approach steeped with blinders towards toolsets prevailing in the organization.
In conclusion, the QA or QE team behaves like an extension of customer organization since it owns the mandate for ensuring the launch of quality products to market. The response times in a cloud-based environment are highly demanding since the launch time for product releases keeps shrinking on account of demands from end customers and competition. QA strategies for cloud-based environments need to keep pace with the rapid evolution and shift in the development mindset.
Further, the periodicity of application updates has also radically changed, from a 6-month upgrade in a monolith application to feature releases that happen daily, if not hourly. This shrinking periodicity translates into an exponential increase in the frequency of test cycles, leading to a shift-left strategy and testing done in earlier stages of the development lifecycle for QA optimization. Upskilling is also now a mandate given that the tester needs to know APIs, containers, and testing strategies that apply to contract-based components compared to pure functionality-based testing techniques.
A look at how the use of latest property management technology trends are helping real estate firms to thrive through the post-pandemic season
With the pandemic refusing to slow down, it is difficult to predict what the future will be like. But as Winston Churchill said, “Never let a good crisis go to waste.” Despite the economic slowdown and an achingly slow market, the pandemic has given us some serious lessons in resilience. The property sector is no different and was quick to alter its ways to match steps with others through digital adoption.
The use of technology in property management have given property managers the much-needed traction to grow their business while improving operational efficiencies and streamlining processes. With these technologies at the helm, property managers must match the latest trends to mitigate risks and create opportunities.
The real estate market globally is predicted to touch $ 4,263.7 billion1 by 2025, while the global property management market size in 2020 was $ 13.88 billion. Technology applications are currently being used for everything from rent collection and maintenance requests to accounting and sales. The tech portfolio is continuously swelling, giving them greater power and impetus to manage their business. While 86% of respondents2 in a survey saw digital & technology innovation as an opportunity, 49% expect to collaborate with an existing or new supplier to enhance their technological innovation capability.
So let’s dive in to know the latest property management tech trends or proptech trends that are currently changing the tide for the property sector.
Greater convenience with cloud-based technology
Thanks to the cloud, real estate stakeholders can access data on a particular property from wherever they are, thereby saving a significant amount of time and effort. Cloud computing with SaaS (Software-as-a-Service) integrated services that operate on a subscription-based model is emerging as a preferred option. SaaS solutions simplify tedious processes by automating workflows to manage property portfolios efficiently.
What’s more, the SaaS model is also ideal for legacy systems to ensure multi-vendor device compatibility. Property managers often leverage SaaS solutions to integrate advanced payment systems in their property management solutions to simplify and accelerate transactions.
WeWork, a leader in providing shared and private working spaces for tech startups, allows you to specify the desired parameters and locations through their online platform. With niche services, this category is called SpaaS (Space-as-a-Service) that provides tenants everything they need. Companies like Spaceos offer a fine blend of tech and tools through a cloud-based SaaS platform that allows you to manage everything in a hybrid workplace.
Energy and cost savings with smarter homes
The concept of ‘smart’ homes or smart buildings caught on pretty quickly for the sheer convenience they gave to residents. These homes were made extremely intelligent with home automation to ensure comfort, safety, and efficiency from the very beginning.
Millennials and Gen-Z residents are now used to living in such homes, and anything less will not cut ice. Right from opening and closing of doors to switching on the lights and air conditioning, everything can be managed with a remote control device.
Intelligent thermostats and HVAC help save energy and predict your bills based on usage, while sensors and integrated systems ensure security. And of course, there’s Alexa showing us more intuitive ways to control lights and devices with voice commands.
WeMaintain, for instance, has been using tech to place sensors onto elevators to enhance efficiency, drive cost savings in commercial buildings, and even help owners make greener decisions. Says Benoit Dupont, cofounder of WeMaintain, “If you know when people are moving into the building with the elevator, and at which floor they’re stopping, you can compare that to the heating systems. So if a building turns its heating on at 6 am, but really people only start using it from 9 am, that’s three hours of heat that could be saved.”
Enhanced security with automated security systems
The growing need for security is driving modern homes to have automated access systems. Access control technologies ensure that you are able to guard a property with just a few clicks. Area surveillance via drones is becoming increasingly common too. In fact, drone technology is being used extensively to record everything around high-rise commercial properties due to its ability to capture extraordinary aerial imagery.
Motorola recently acquired LA-based proptech startup Openpath that specializes in touchless, cloud-based access control and safety automation. Its solutions with remote management capabilities ensure powerful safety for every door. Instead of a key card, the automated security systems rely on smartphones and face recognition to authorize access.
It also helps in contactless visitor management to ensure deliveries are handled securely 24/7. With its two-way video intercom and video conferencing facility that enables visual verification of visitors or deliveries before granting access, the company is playing a huge role in property management. Its advanced capabilities facilitate remote monitoring and management thus preventing theft, tailgating, and unauthorized access.
A more connected, data-driven world with AR, VR, AI, Machine Learning, and Big Data
Virtual home tours are now extremely popular as social distancing becomes the norm. Even otherwise, virtual viewing of homes closes the gap between owners and tenants as Augmented Reality (AR) and Virtual Reality (VR) facilitate enhanced 3D experiences and a 360-degree view of the property. Viewers can view every nook and corner and get a sense of space through virtual visits.
Virtual tours also come in handy while selling properties since they can be viewed from any part of the world. AI in tandem with machine learning and big data is doing the necessary digital footwork for property managers. These technologies assess property demand and price trends to ensure that buyers and renters get exactly what they are looking for. This in turn allows you to showcase more relevant properties based on what they are looking for.
Big data also gives you a better picture of what’s happening with your property and allows you to have a solid grip on things in general in real-time. Then of course there are AI-powered chatbots that help property managers offer complete support, be it by handling tenant inquiries or by replying to their emails. Chatbots are also being integrated into websites to track leads and garner higher lead-to-lease conversions.
The final verdict
Proptech is just what everyone needs in the real estate business irrespective of the size of their business. It isn’t going anywhere and will in fact continue to offer better functionalities as it evolves. It is the only way to tide over all the hurdles that the ongoing pandemic has brought along. It improves your performance with better reporting, monitoring, and prediction capabilities.
All you need is a perfect technology partner who can help you get started. As Mark Rojas, CEO and Founder of Proper points out, “Property managers don’t often come from an accounting background — usually, they have a real estate license, so that lack of expertise can put them in a position where they can’t scale their portfolio, or if they try to, things break.”
Build your Proptech stack with Trigent
A cloud-based property management platform can do wonders for your real estate business. Though moving manual processes to automated platforms can be a bit overwhelming for the uninitiated. Trigent with its competent team of technology experts can help you build a robust proptech stack aligned with your business goals to help you drive growth and revenue.
Ever wondered what’s common between Apple, Google, and Facebook? Apart from being insanely popular tech giants, all of them have derived tremendous value from their ecosystems. The same holds true for many others like Amazon and Alibaba. We are now part of an economy where ecosystem integration is revolutionizing how organizations address the changing needs of their customers across the globe.
Interestingly, the ecosystem as a concept is not so difficult to understand. It serves as a one-stop shop for your customers where they get extraordinary benefits through your network of connections. The best part is that it works equally well for all sectors, including transportation and logistics.
Importance of ecosystem integration in logistics
Modern-day challenges require shippers and logistics companies to build resilience to mitigate impacts on supply chains irrespective of the circumstances. Several organizations are already pulling up their socks to protect their businesses on multiple fronts with the help of efficient crisis-management mechanisms. An efficient ecosystem is the game-changer they need to achieve all their goals and survive pandemic-like disruptions.
While it is important to create an ecosystem of collaboration and trust, ecosystem partners need to work together to address capability gaps. This can happen only when they critically evaluate the challenges they encounter on the road to building ecosystems and know the pitfalls to avoid. What they need is efficient ecosystem integration that connects critical revenue-generating business processes. The fast-paced eCommerce market also necessitates a robust ecosystem to attain supply chain interoperability and respond efficiently to market disruptions.
As Simon Bailey, senior director analyst with the Gartner Supply Chain Practice, rightly puts it, “Major disruption, such as the COVID-19 pandemic, are the ultimate test for the resiliency of a supply chain network. However, not all disruptions are unplanned. Many CEOs are planning to offer the new value proposition of their products and services, and they expect that their organizations require new capabilities to support these new products and services.”
Challenges in ecosystem integration affecting supply chains
Ecosystem integration can be a bumpy road for some unless you know the three most common mistakes to avoid. Once they are out of the way, you can attain supply chain interoperability through successful ecosystem integration. So let’s delve deeper to know how we can rectify them to be part of a thriving ecosystem.
The digital abyss and failing to adopt an API-first approach
Around 46% of shippers and logistics companies still use legacy systems with minimal digitization. Though they are fast understanding the importance of articulating their needs through technology. While some are content with Electronic Data Interchange (EDI), others have migrated to Application Programming Interfaces (APIs) to facilitate better data exchange for profitable business outcomes.
The lack of adequate digitization in supply chains hampers both EDI and API integration. We have used APIs inadvertently in our daily transactions, be it for booking a new car online or shopping for insurance products. APIs work as intermediaries between diverse systems globally to enable communication between businesses and customers in logistics parlance.
Today, organizations need advanced technologies to improve experiences for stakeholders and customers. Likewise, they need APIs to make their systems agile to respond and interact in real-time. To successfully design and adopt APIs, you must first determine the end-user experience you wish to deliver. You need to remember that APIs drive online ecosystems, and it would be impossible to connect applications and services in their absence.
Considering that the modern architecture is API-centric, it is imperative that you take cognizance and the necessary steps to adopt it. The transportation and logistics sector uses APIs to connect their physical and digital assets to create an integrated supply chain to digitize the current supply chains and create new business models. You need to successfully adopt APIs to automate business processes and ensure ecosystem integration.
A digital ecosystem so created would comprise suppliers, third-party data service providers, and logistics providers with many advanced tools and technologies at its helm. As you embark on building it, you need to adopt the right ecosystem integration approach to connect all the revenue-producing processes with mission-critical internal applications. Luckily, it’s never too late to begin from wherever you are. All you need to do is get out of the digital abyss and accelerate digital transformation to enable exceptional customer experiences with an API-first approach.
Failing to build trust and transparency with ecosystem integration
A multitier supply chain needs a lot more than operations teams and production teams to keep going. They require trust and transparency to overcome disruptions across supply chains. You need to assess risks to identify those that can stop or slow production lines and directly impact operations costs. You need to ensure that you are sourcing the right items at suitable locations and have a cohesive network to rely on. You may have to look for alternative suppliers to ensure government policies do not stand in the way.
You need to go beyond Tier 1 suppliers to know you have the right network. Car manufacturers, for instance, often have a network comprising multiple suppliers to cater to the unique requirements of all of their manufacturing regions. This helps them address sudden disruptions that may arise due to changes in foreign trade policies or tariffs. While this strategy works perfectly to mitigate risks, it also allows them to engage with multiple vendors to supply raw materials and stay competitive continuously.
Trust and transparency can be crippling factors necessitating partners to focus on collective goals. As we all know, lack of trust leads to friction that, in turn, may cause churn. BCG research iterated the examples of ride-hailing biggies like Uber and Lyft that lost $8.5 billion and $2.6 billion respectively due to a high driver-churn rate that propelled their marketing and promotion costs to stay afloat.
Trust-building instruments and initiatives must be deployed wherever necessary to build lasting relationships and robust supply chains. Questions should be asked to identify and respect each partner’s role within the ecosystem, and information-sharing agreements should be created to maintain transparency.
Says Simon Bailey, senior director analyst Gartner, “It’s crucial that supply chain leaders create a collaborative and trusting culture where ecosystem partners are willing to work together and share information across the network. This will only be the case when all members agree on mutual quantitative and qualitative standards.”
Undermining the role of visibility in improving supply chain interoperability
A throbbing logistics industry requires a high level of interoperability. The global logistics market is expected to spike at a CAGR of 6.5% from 2020 to 20271 touching $12,975.64 billion by 2027. Shippers and logistics companies are tightening their grip on costs and inventory management. While doing so, they sometimes fail to sharpen their visibility into the supply chain.
Visibility usually concerns the movement of parts, components, or products in transit as they travel to their destinations. Data related to these movements need to be accessible to all stakeholders, including your customers. Only then would you be able to attain interoperability in its true sense. There are visibility platforms to ensure multichannel integration across the ecosystem. Merely having dashboards is not enough unless you know how to use the data they send out to make smarter supply chain decisions from a transportation perspective.
There could be disruptions due to natural calamities such as floods and hurricanes or labor disputes and political events that could upset the natural rhythm of supply chains. Also, data is often spread across disparate systems, and unless you have access to it, you will never be able to increase collaboration or forecast future demands.
Tom Madrecki, CBA vice president of supply chain and logistics, while emphasizing the role of visibility, says, “The greater degree that you have to what’s happening throughout the supply chain, then you’re able to better manage your costs, you’re better able to predict where are you going to have an issue ahead of time and have that more enhanced real-time visibility to everything.”
Supply chain excellence comes from data-driven decisions. It is important to have data from suppliers, forwarders, brokers, and third-party logistics companies to ensure end-to-end visibility in real-time. Mobile device integrations are now an essential aspect of ecosystem integration to facilitate data from diverse geographical locations. They allow you to identify bottlenecks and address issues in a single environment.
The right ecosystem will strengthen your supply chain capabilities and empower you to adopt best practices to foster interoperability. Due diligence and proper planning can help you tide over the many challenges and create an ecosystem for a more sustainable future.
Enable hassle-free ecosystem integrations with Trigent
Trigent, with its highly experienced team of technology experts, has been helping enterprises with frictionless data transfer integrations through EDI/API. We help reduce costs and the complexity of logistics supply chain management while optimizing loads and routes. We offer prescriptive analytics to gain customer insights and drive revenue.
We can help you build operational efficiencies, too, with hassle-free integrations.
In today’s world, embracing inclusivity with accessibility is not only about being humane or legally correct but also makes a lot of business sense. Recent studies have shown that businesses can tap into an additional prospective user base of up to 15% to market their products. Persons with disabilities (PWD) are responsible for 25% of all healthcare spending in the U.S.
Businesses have increasingly become aware of the requirements of people who need accessible technologies to contribute to a work environment or who can also be prospective customers.
“At Barclays, accessibility is about more than just disability. It’s about helping everyone to work, bank and live their lives regardless of their age, situation, abilities, or circumstances. – Paul Smyth, Head of Digital Accessibility, Barclays
What is digital accessibility?
The Web Accessibility Initiative (WAI) states that websites, tools, and technologies should be designed and developed so that even differently-abled people can use them. More specifically, these people should be able to perceive, understand, navigate, and interact with the Web and contribute to the Web.
Web Accessibility enables people with disabilities to participate equally on the Web. Broadly speaking, Web accessibility encompasses all disabilities that affect access to the Web, including:
When an organization removes barriers by making its application accessible to its full potential, it will be an inclusive product, as millions of people with various disabilities can use it.
Mandated by Law – American with Disabilities Act (ADA)
One hears the terms “Section 508”, an amendment to the Workforce Rehabilitation Act of 1973 that requires that all Information Technology assets of the United States’ federal government be accessible by people with disabilities.
Also, ADA (American with Disabilities Act) the Title III requires that all private businesses that are open to the public be accessible to people with disabilities. There is a steady rise in the number of lawsuits filed over the years under this section, resulting from the growing awareness of the ADA Title III. Digital accessibility compliance helps organizations protect themselves against this rising trend of ADA Title III Federal lawsuits.
Foundation for accessibility
The web accessibility guidelines, technical specifications, and educational resources to help make the web accessible to people with disabilities are developed by Web Accessibility Initiative (WAI). They are an integral part of the W3C (World Wide Web Consortium), focusing on accessibility. Over time, the WAI has developed several recommendations, some of which are:
The latest edition of Web Content Accessibility Guidelines (WCAG) 2.1 has additional coverage for mobile and non-W3C technologies (non-web documents and software).
Four principles of accessibility
The WCAG guidelines lay down the four principles which are the foundation for Web accessibility: Perceivable, Operable, Understandable, and Robust (POUR for short).
Perceivable: The objective is to make content available to the senses, primarily vision, and hearing, via either the browser or through assistive technologies like screen readers, screen enlargers, etc. For the visually impaired who mainly rely on a screen reader to have the website’s content read, one needs to add an alternative text that provides a textual alternative to non-text content in web pages that makes the content perceivable. Another example is videos and live audio must have captions and a transcript. With archived audio, a transcription may be enough.
In this video example: Perceivable, the video page uses “voice recognition” and is also updated to use “speech recognition.” “Voice recognition” or “speaker recognition” is a technology that identifies who the speaker is, not the words they’re saying. “Speech recognition” is about recognizing words for speech-to-text (STT) transcription, virtual assistants, and other speech user interfaces. Together they allow a person with visual impairment to enhance their experience of the web.
Operable: The objective is to enable a user to interact with all controls and interactive elements using either the mouse, keyboard, or an assistive device. Most people will get frustrated by the inability to use a computer because of a malfunctioning mouse. Many people prefer to use only the keyboard to navigate websites. Whatever be the reason, either personal preference or circumstance like temporarily limited mobility, a permanent physical disability, or simply a broken mouse, the result is the same: Websites and apps need to be operable by a keyboard. For example, all links and controls on the web page must be accessible using the Tab key on the keyboard.
In addition, Operable principles allow users enough time to use and interact with the content. It also helps them navigate and find content. For example, if all rich, interactive features of the web page like dropdown menus, carousels, modal dialogs, etc., comply with the W3C’s WAI-ARIA 1.0 Authoring Practices recommendations, will ensure that users can easily navigate to the right content.
Understandable: The objective is to ensure that every functionality of web content is easily understandable. A user must be able to understand all navigation and other forms of interaction. To provide a user the best possible experience, every point of interaction deserves careful attention. Navigation should be consistent and predictable throughout the context of the website. Interactive elements like form controls should also be predictable and clearly labeled. For example, Instead of saying: “To postulate a conceit more irksome than being addressed in sesquipedalian syntax is adamantine,” it is better to say: “Being spoken to in unnecessarily long and complicated language is a pain.”
Despite knowing these basics, many websites lack structuring using headings, lists, and separations. Some even use overly complex language, jargon, and unexplained acronyms. It makes these websites difficult and unappealing for many people, including non-native speakers and makes them unusable for people with cognitive and learning disabilities.
Robust: People get familiar and comfortable with different technologies like operating systems, browsers, and versions of browsers with usage and time. Some people like advanced features, whereas many disable them. There are early adopters of new technologies while others are slow to adapt to the rapidly-changing currents in the flow of technological advances.
A User should have the freedom to choose their technologies to access web content. This allows the user to customize the technology to meet his needs, including accessibility needs. In some cases, it might take additional time and effort to develop web content, depending on the specifications of the technologies used. However, in the long run, it will produce more reliable results and increase the chances that the content is accessible to people with disabilities.
You might have experienced the frustration of being told your technology is out of date or no longer supported. Whilst frustrating, you’ve probably found a way around the issue – but what if you couldn’t because you rely on that technology to interact with the digital world.
Beyond the four principles
Web Content Accessibility Guidelines 2.1 are organized into three levels of conformance:
Level A – addresses the most basic features of web accessibility features. Level AA – deals with the most common and biggest barriers for disabled users and is covered in most accessibility regulations globally, including the ADA. Level AAA – the highest level of web accessibility will make the software or product accessible to the maximum number of users. However, these requirements are not entirely easy to conform to and can be focused on if your audience is primarily people with disabilities.
These levels are across all of the previously mentioned principles. Some of the key requirements include:
Google Aces Accessibility
Google’s investment in accessibility provides the company with an innovation edge in a broad array of products and services. Some of the innovations are:
Contrast minimums: a feature designed especially for people with low vision, and the feature also helps everyone see in bright light glare situations.
Auto-complete: initially designed for people with disabilities, now used widely by all users.
Voice control: although initially implemented for users with physical impairments is now widely adopted by millions of users for the convenience it provides
Artificial intelligence: originally integrated to provide visual context to users with visual impairments
Auto-captioning leveraging machine learning designed mainly for deaf users did not see many adopters in that target audience, as many feel it is still inadequate to meet their needs. However, advances in machine learning itself have found broader applications.