6 Cloud Migration Mistakes that Businesses Need to Avoid

Businesses worldwide are busy moving their legacy applications to the cloud in the wake of the pandemic. While reducing infrastructure costs and enhancing security remain important reasons for many, it is crucial to assess the individual business environment to understand why cloud migration is important for you. 

Cloud migration needs to be seamless and simple for it to be effective. Cloud adoption has tangible benefits as over 70% of companies have already moved some of their workloads to the public cloud, as confirmed by Gartner1. Yet, it predicts that 60% of infrastructure and operations leaders may experience cost overruns by 2024 that may end up hurting their on-premises budgets.

As per a Cloud Security Alliance report2, 90% of CIOs have experienced failure or disruption in data migration projects caused by complexities encountered while migrating from on-premises environments to the cloud. Only 35% of the survey respondents met their migration deadlines. The 2019 Fortinet study reveals 74% of the companies moved their applications back to on-premises on failing to attain desired returns.

This brings us to the most pertinent questions – why do these migrations fail, and what can you do to ensure a successful cloud migration? And most importantly, why is cloud migration an essential endeavor for organizations?

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Advantage Cloud – Why migration from legacy systems to cloud is important for an organization

There are several benefits of migrating to the cloud as it gives businesses much-needed flexibility and scalability. The Coca-Cola Company achieved 40% operational savings while reducing maintenance costs and improving performance by migrating to the cloud. 

Cloud-native startups are already collecting data from their customers and markets to accordingly align their offerings and implement product updates in the production stage. 

SaaS is preferred by many who wish to add or delete features based on customer feedback. Subscription-based models that allow marketing teams to have more enriching interactions with end-users and implement changes across marketing, sales & pricing, and customer support functions are proving to be extremely valuable. 

According to Gartner, end-user spending on cloud services will grow at a CAGR of 21.7% taking it from $396 billion in 2021 to $482 billion in 2022. Explains Brandon Medford, senior principal analyst at Gartner, “Organizations are advancing their timelines on digital business initiatives and racing to the cloud in an effort to modernize environments, improve system reliability, support hybrid work models and address other new realities compelled by the pandemic.”

To meet retail’s new mandates, the largest grocery chain in the U.S. Kroger, recently unveiled a privacy-compliant collaborative cloud that offers a granular view of customer behavior. 

Planning for successful cloud migrations

Cloud migration calls for a lot of planning to ensure a positive business and operation impact.

When Netflix decided to go all-in on the cloud, most were unaware of the existence of the cloud. But it had problems that needed immediate attention, and thus came its famous Simian Army that unleashed the Chaos Monkey. It is a software tool developed by Netflix engineers to simulate failures of cloud instances and test the resiliency and recoverability of their Amazon Web Services (AWS).

Proper planning is the key to successful migrations and should consider various aspects from the present and future perspectives. Remember, you need to build the cloud for the future, anticipating growth and new business models. A simple ‘lift and shift’ may work for some while others may need a complete overhaul of application architectures through re-architecting. To avoid downtime and performance degradation, you need to understand cloud migration best practices. 

But before you start anything, you need to ask the right questions. Why do you want to move to the cloud, and what do you expect? Is your workforce ready for this transition? What migration strategies will work for your business? 

Here are a few mistakes you must avoid at all costs to ensure smooth cloud migration. 

Failing to understand organization networks and infrastructure

Every solution provider will offer unique attributes making it highly overwhelming for businesses to choose solutions based on their business and data needs. Lack of proper understanding causes breaks in the systems, and some data may be left behind. This can disrupt functions and lead to additional costs and frustrations.

For instance, Sime Darby Industrial Sdn Bhd (SDISB) faced scalability and security issues due to config limitations in their Cloud infrastructure. Periodic lags and downtime arising from these misconfigurations caused major disruptions driving up costs for the company.  A personalized, end-to-end solution by a partner was needed to help SDISB improve its Cloud e-commerce site performance.

Trying to migrate everything in one go

While migrating data and workflows, simply prioritizing a ‘life and shift’ approach to move workloads without modifying or analyzing them may not be the best move. You may have to rewrite and re-release applications in a cloud-native manner or replace them based on a proper assessment. Workloads need to be assessed first before initiating a migration project. 

Besides, every storage and solution will have its own merits and limitations that need to be evaluated, and the right backup strategies would play a critical role. Organizations like OVHcloud, Europe’s largest cloud provider, had already taught us a lesson in data backup the hard way when its data center suffered a catastrophic fire. Those who had purchased the backup and disaster-recovery services offered by the company were able to resume operations while others suffered.

The right migration partner can play a crucial role here. A proof-of-concept or minimal viable cloud is recommended to get a realistic view of the migration. This will eliminate the threat of losing essential data or causing breaks or breaches arising from inefficient migration. 

Not having the right migration partner

The migration partner you choose will largely determine the success of your cloud migration. Rather than choosing them based on familiarity or low pricing, you should focus on their experience. A partner with certified experts across Cloud Vendor solutions will provide you with a design that is right for your business rather than pushing a specific technology. Handing the migration project to internal teams may be feasible only if the team has relevant prior experience and expertise.

You may ask your migration partner for a cost proposal along with the necessary recommendations following proper assessment of your data and infrastructure to commence the project on the right foot.

Failing to map dependencies

Incomplete application assessment also leads to encountering dependency bottlenecks later. You need to discover and account for the interdependencies between on-premises systems early on to ensure there are no hiccups along the way. Failure to do so would lead to incorrect grouping and order of application migrations, eventually leading to burgeoning costs and cascading delays. It will also lead to perennial performance issues and cause your migration costs to go off the rails.

Failing to factor in hidden costs

You need to pay close attention to transformation costs that may involve upskilling, increased salaries for cloud management roles, changes in organizational structure, operating procedures, new practices, etc. 

Adobe found this out the hard way in 2018 when its engineers realized that a single computing job on Microsoft Azure was racking up charges of $80,000 per day. A week later, this had accumulated to a bill of more than half a million dollars.

No matter what the cost, budgeting must include indirect project costs to ensure the organization operates optimally without any financial stress.

Added care required to move legacy applications safely onto the cloud

It’s easier to safely move legacy applications to the cloud once you analyze the complexities involved in different types of data and apps. For instance, moving your corporate email service to a public cloud SaaS service is pretty straightforward. What’s challenging to migrate is an application that was developed years ago. The fact that your entire business relies on it can only make it even more difficult. 

There could be bespoke applications developed in-house, but the new cloud offerings cannot align with these customizations. A classic case is Autodesk’s SaaS offering that runs just one version of the software and is not compatible with customized applications or third-party add-ins. What’s more, businesses that have unique ERP applications often struggle with cloud solutions that cannot accept such personalized versions. Organizations can avoid this turmoil by choosing one of these options to ensure safe and swift migrations.

  • Infrastructure-as-a-Service (IaaS) wherein applications can be moved as is or with minor tweaks to operate from the service provider’s infrastructure.
  • Platform-as-a-Service (PaaS) offers a secure database or development environment for organizations to install and manage their customized applications or code. 
  • Software-as-a-Service (SaaS) wherein all responsibilities are managed by the service provider to offer a tailored service to organizations for cost efficiency, scalability, and flexibility. 

The right migration partner will help you with suitable cloud solutions and strategies and even offer customized ones to ensure migration success. The infamous data scraping breach or the breach that cost Facebook huge losses, including a $5 billion penalty, have taught us essential lessons in cloud security.

No matter which migration partner you choose, make sure they adopt a security-first approach. After all, security cannot be an afterthought and is the only way to reduce the attack surface and the breaches arising from it. Ensure you regularly maintain the patches and updates, configure cloud-native solutions correctly, and secure the network for vulnerability management and incident response.

Simplify your Cloud migration journey with Trigent

Our domain experts at Trigent assess your portfolio and business needs to determine the pain points and ways to address them. As a cloud migration specialist, we blend unique methodologies with purpose-built assessment tools to decide the right migration strategy for your business. Accordingly, we devise a detailed transformation roadmap prioritizing apps to be migrated and treatments to migrate to the cloud using best practices seamlessly. 

Allow us to assess your cloud readiness to help you rapidly scale and succeed with the right cloud solutions. Call us today for a business consultation.

References

  1. https://www.gartner.com/smarterwithgartner/6-ways-cloud-migration-costs-go-off-the-rails
  2. https://www.ciodive.com/spons/why-do-cloud-migrations-fail/600946/

Flutter vs Xamarin: Choose the best cross-platform framework for your project in 2022

If you are considering a mobile application for your business or service in 2022 chances, are you or your software development partner, considering a cross-platform framework? Naturally, your search would entail several different frameworks and quickly bring you to a crossroads: Flutter vs Xamarin?

A bummer? Not really. Perhaps this brief analysis will help you make that call. 

Background: A quick look at the genesis of these cross-platform frameworks

Flutter is an open-source UI SDK (software development kit) from the stables of Google in 2017. It helps develop cross-platform apps for Android, iOS, Linux, Mac, Windows, Google Fuchsia, and Web platform. The first version was code-named SKY and ran on Android. By September 2021, Dart SDK – Flutter apps are written in Dart language, and Flutter version 2.5 was released. The update was targeted at improving Android, iOS Full-Screen mode, and other enhancements.

Xamarin, a Microsoft company produces open-source software that works in tandem with .NET. It is a part of the C# / Visual Studio suite, extending it with tools and libraries for building apps on the various target OS. The promise is that developers can easily reuse their C# code and port their code across platforms.

Want to identify the right cross-platform frameworks for your mobile application? Let’s talk

Flutter and Xamarin: Architecture and components

Flutter components: Flutter consists of the following components briefly summarized here with their core functions.

Dart platform: Flutter runs in Dart VM with a JIT (Just in Time) engine, allowing stateful hot reload while the app is running, thus avoiding a restart or loss of state.

Flutter engine: The C++ code is a portable runtime software for hosting Flutter applications, and implements Flutter core libraries, files, network I/O, and plugin architecture.

Foundation library: Written in Dart provides basic classes and functions used to construct Flutter Apps and design specific widgets. Three types, stateful, stateless, and inherited widgets are used widely for most Flutter applications.

Xamarin has a Mono environment for use both in iOS and Android. Internally Mono is combined with components to give a smooth response. It runs along with Android Runtime on Android, Objective-C runtime in iOS, Linux kernel in Linux. 

Architecturally, therefore, there may be no significant edge in either of the approaches.

Kickstarting the development effort in cross-platform frameworks

Kickstarting Flutter development is a breeze. Just download the file for the OS you need and you are ready to go with all documentation built into the official site. But it is not so with Xamarin. Xamarin requires multiple steps starting with downloading the correct version of Visual Studio, installing Community, Visual Studio Professional, and then reserving hours for documentation help which you will certainly need.

Economics of framework

Microsoft expects fees for commercial deployment from enterprise users that could range from $799 to $5999 per user. The fee can prove to be a clear disincentive to a developer considering a cross-platform app framework while Flutter is entirely free.

Code reuse

C# and its intrinsic .NET heritage enable easy reuse of LINQ and async programming features – a big plus for Xamarin. Together with Xamarin.Forms API, it is said code reuse is closer to 96%; which is impressive compared to Flutter. Xamarin’s Android and iOS tools to build platform-specific features also help code reuse. However, it is important to understand that code written with Xamarin is only reusable within the .NET technology stack.

Flutter’s components are all in-built, allowing cross-platform development from the get-go. Apps on Flutter are widget-based with customization allowing native-app look and feel. Code reusability in Flutter is about 80%.

Cross-platform capabilities

Flutter: A single code base allows programmers to easily adapt to a new platform avoiding detailed system study and planning, saving time and energy.

Flutter comes with high-performance widget ergonomics allowing low data exchange between the app and mobile platform, and comes with the ability to compile into native code for Android and iOS.

Development ease

Flutter serves Hot! Hot reload is a term used to describe a framework’s ability to insert code changes live on a running app without bringing it down and restarting. It is a big deal as it completely avoids restart and saves time in rapidly changing business environments such as Q-commerce (quick commerce such as food service app, retail app, and mobility world apps).

Xamarin has an equivalent Hot reload feature, also called Live Reload, allowing users to see code changes without compilation. 

Performance

Flutter architecture obviates the need to use JS bridge to communicate with native components which uses Google’s Skia rendering engine. This boosts its cross-platform performance significantly with minimal dropped frames and low lag.

Apps built on Xamarin have performance that depends on the Xamarin framework used. For example, the performance of Xamarin.Forms, especially while handling graphics, falls short of expectations. Sometimes special components need to be developed for the iOS / Android world, thus losing its appeal for UI-heavy applications. 

Widget ergonomics

Widget library of Flutter is both convenient, beautiful, easy to use, and driven by contemporary design. A large customizable library of widgets with access to navigation, multiple options in interaction models, layouts is available with support for animation. Consistency in the look and feel of the app on different devices coming from a high-widget approach is easy on the eyes.

Flutter’s inherent performance on fast-moving graphics and animation stands head and shoulders above competing cross-development platforms. Its rapid growth within the developer community and la significantly large developer community built within a short span of 4-5 years indicate its popularity.

Developer support

Google’s strong global support makes the developer community comfortable making the plunge into Flutter. 

On that front, Xamarin stands as a formidable competitor to Flutter. As an established top global software product house, Microsoft pulls all stops to provide the necessary support to the developer with its established support processes. Besides, C# and.Net already have a large developer community, making it easier for Microsoft to extend support to new converts to the Xamarin turf. 

Xamarin or Flutter: Choosing the best cross-platform frameworks for your application

Not being tied to IDE is a big plus for Flutter users, whereas, for Xamarin developers, an intimate knowledge of Visual Studio IDE is critical for the smooth work of Xamarin. The concept of Visual Studio IDE is to be understood and implemented. However, it is not considered accessible by many. Apart from this, Microsoft also requires Visual Studio IDE licensing to be procured. 

Flutter comes with the flamboyance, aggression of a young, ready-to-go, open-source, free, and almost no barriers to develop and deploy. Xamarin has a more traditional evolutionary sense of growth but well-established clientele backing it globally. With myriad opportunities exploding on multiple verticals, Microsoft is not going to let this opportunity be lost either. Expect a robust battle between these technology giants on the cross-product platform wars.

Work with a partner you can trust

Working with a development partner with a full-stack skillset covering multi-platforms iOS and Android, and with development skills across the board from Dart, Swift, Java, JS, Kotlin, Objective C, and C# is naturally an advantage. If you have a software development partner such as Trigent, for example, who, in addition to the above, excels in multiple frameworks such as Xamarin, Flutter, Angular UI, JQuery, Appium, Cordova, and React Native, you are in safe territory and in trusted hands. 

When your software partner is multi-skilled, technology crossroads are certainly less daunting. 

Build responsive and engaging cross-platform mobile apps. Contact us now

Trigent’s Clutch Year in Review for 2021

Clutch gives Trigent a NPS score of 100%

Businesses depend on reliable metrics and data to make correct decisions. This leads teams to value and invest in tools and services that provide more accurate data. This is why when Clutch informed Trigent, of a new feature that analyzed and summarized all of the activity on our profile for the past twelve months, we jumped on it.

Clutch is an independent market research and B2B review platform. It is dedicated to showcasing the top service providers across industries and regions worldwide. The platform is widely acknowledged for its large collection of data-driven content, verified client reviews, and agency rankings.

We perused the entirety of the year-in-review feature and were happy with a lot of the results that we found. However, one statistic immediately stood out to us as the most impactful to both our reputations and operations. Every one of the clients who wrote a review for us in 2021 recommended us to their friends and colleagues.

While we always did our best to provide high-quality services to all of our clients, we never expected such a perfect referral rate would result from it. We made sure to thank every one of our clients & partners who took the time to write a review on our behalf. Now we repeat those sentiments today as this new information reveals the extent to which their efforts extended.

We also appreciate this new feature that the Clutch team developed for its users. Without it, we would never have known about this important dataset. This only increases the value that our profile provides to our core operations. It also places us in a great position coming into 2022 as it boosts our team’s morals and improves our reputation in the industry.

If you want to discover why all of our clients recommended our services, contact our team to schedule an appointment today. We are confident that our track record speaks for itself and that you’ll be recommending us to your colleagues as well.

Control Tower in Logistics – Optimizing operational cost with end-to-end supply chain visibility

The competitive business landscape and ever-changing needs of customers are reshaping traditional supply chains today. With globalization and organizations looking to extend their geographical scope for lower-cost sourcing options and emerging markets, the complexity of supply chains has increased. The increase in outsourcing makes effective collaboration with partners imperative for efficient supply chain operations. 

In addition to these complexities, organizations have continuous pressure to improve their profit margins and increase revenue. Supply chain executives are often under enormous pressure to cater to the needs of their customers while optimizing their supply chain operations cost-efficiently. These critical business challenges drive the need to create solid end-to-end capabilities for supply chain visibility.

Supply chain visibility is the most vital enabler for managing businesses both within the organizational boundaries and across the boundaries. Visibility across processes, right from the receipt of an order to its delivery, provides the flexibility, speed, and reliability to gain a competitive advantage in the form of well-controlled supply chain functions.

Supply chain control towers embody the leading principles of supply chain visibility to address this need. It integrates data from multiple sources to provide end-to-end visibility across the supply chain, improve resiliency, and respond faster to unplanned events. A control tower in the supply chain helps organizations prioritize, resolve, and understand critical issues in real-time.

Current state and phases in supply chain visibility

Supply chain visibility, in short, includes the process of how organizations capture data and interconnect it to retrieve the vital supply chain execution information. It provides a comprehensive view for tracking information, material, or cost by monitoring the main dimensions in a global supply chain, such as inventory positions or shipment status or real-time order movements, to make well-informed decisions based on facts.

Many logistics organizations have implemented or are in the process of adopting solutions for supply chain visibility. However, they reflect different phases of maturity. The maturity level is identified by the associated processes, skills, and tools involved.

Leading practices for supply chain visibility

A successful solution for supply chain visibility is deployed around five main principles for a holistic view of the inbound and outbound operations.

Understanding Control tower

Control Towers are cross-divisional organizations with integrated “information hubs” to provide supply chain visibility. These hubs gather and distribute information, allowing people trained to handle these capabilities to identify and act on risks/opportunities more quickly.

It provides end-to-end visibility to all the participants involved in supply chain logistics. These may include manufacturers, distribution centers/warehouses, logistics, shippers, carriers, 3PL, 4PL, and store-end customers.

Also read: How supply chain visibility is reducing operational costs in logistics

A control tower helps capture and correlate relevant supply chain data from all entities involved in the operation, be it freight details, inventory positions, or transportation parameters. It supports real-time update capabilities with the help of the latest technologies like IoT and predictive analytics to monitor possible supply chain disruptions in goods movement or material shortages.  In short, control towers help implement a centralized decision-making system that focuses solely on fulfilling the end user’s needs. 

The importance of control tower in logistics

A control Tower provides round-the-clock visibility, enabling real-time feedback to customers through video, voice, or text. In short, a control tower in logistics operation offers greater reassurance and efficiency, irrespective of time zones, office hours, or holidays.

A control tower implementation is managed by a team of supply chain experts, who monitors the movement of goods throughout the supply chain. The freight movement data is then collected and analyzed to ensure that the essential service requirements are met. This data can be used to prevent potential disruptions or take corrective actions.

A team of highly experienced professionals then makes decisions based on the real-time information obtained to ensure that all service commitments are met and that customers remain happy and satisfied. The customer can follow up on special requirements concerning their cargo, such as temperature control, time constraints, or relevant security/customs clearances.

Achieve end-to-end supply chain visibility and optimize operational costs with control tower solutions from Trigent. Contact us now!

Key benefits of control tower in logistics

The Control Tower is pivotal for effective supply chain management. They help manage any unpredictable, potential disruptions in supply chain operations. They enable better planning, decision-making, proactive event management, improvement of the performance of supply chain partners, and sophisticated supply chain analytics.

Some of the benefits of control tower while streamlining dynamic management of the supply chain are as follows:

  • Enhancing logistics operations
    • A control tower platform can be configured to help manufacturers gain better insights into retaining supplies and raw materials.
    • Help carriers enhance their ability to fulfill orders quickly for customers. 
    • Reduce Inventory 
    • Speed up detection and reaction times
  • Achieve end-to-end visibility
    • Control tower systems provide details on freight movement to multiple stakeholders involved in the logistics world. 
    • Correlate data across siloed systems to provide actionable insights and manage exceptions
  • Improve service levels such as total cycle time and on-time delivery
    • Better insights and accurate information help companies improve their delivery rates. An optimized control tower helps them achieve this critical goal. 
  • Reducing costs
    • Every business looks to optimize its profits, and in most cases, this is achieved by reducing costs of operation and goods.

Implementation approach

The need for a quick response is more significant than ever before. Organizations need actionable recommendations derived from intelligent strategic inputs to respond quickly and effectively to mitigate risks and unforeseen circumstances. Control towers plan, monitor, measure, and control logistics in real-time to deliver compelling, essential strategic capabilities and cost efficiencies.

Here are some pointers to ensure the successful implementation of control towers in your organization:

  • Ensure standardization

To fully realize the benefits of a control tower, it is necessary to harmonize all the processes that it is mandated to control. Hence the first step of implementation should be to define integration standards among all the actors involved in the operation, i.e., manufacturers, assembly lines, warehouses, logistics, delivery stores, and customers.

  • Central oversight, local execution

Maintaining a balance between central oversight and local coordination for execution is crucial in bringing in the required business knowledge for building robust solutions for day-to-day operations. It enables the field to use the insights and act based on ground realities.

  • Multifunctional involvement

Establish a successful control tower implementation by including representatives from all relevant functions. They should also be given a clear idea of the individual benefits and working across the system. 

  • Pragmatism

Ensure a “feasibility-first” approach over the theoretical best practices to help deliver a control tower solution that fulfills all the preset objectives within a reasonable time frame. 

  • Knowing when to stop

It is always essential to keep an eye on the returns and avoid any adoption efforts that provide a low return. 

Break free from visibility challenges with control tower solutions from Trigent 

The recent pandemic and disruptions induced by the current digital transformation wave have put logistics organizations under immense pressure to perform. The highly experienced team at Trigent provides comprehensive and customized solutions to ensure end-to-end visibility while streamlining your supply chain operations.

End-to-end visibility and significant cost reduction for our customers have made Control Tower solutions a critical service in our offerings. Book a consultation with us to know more.

Uncovering Nuances in Data-led QA for AI/ML Applications

QA for AI/ML applications requires a different approach when compared to traditional applications. Unlike the latter that has set business rules with defined outputs, the continuously evolving nature of AI models makes their outcomes ambiguous and unpredictable.  QA methodologies need to adapt to this complexity and overcome issues relating to comprehensive scenario coverage, lack of security, privacy, and trust. 

How to test AI and ML applications?

The standard approach to AI model creation, also known as the cross-industry standard process for data mining (CRISP-DM), starts with data acquisition, preparation, and cleansing. The resulting data is then used on multiple model approaches iteratively before finalizing the perfect model. Testing this model starts by using a subset of data that has undergone the process outlined earlier. By inputting this data (test data) into the model, multiple combinations of hyperparameters or variations are run on the model to understand its correctness or accuracy, ably supported by appropriate metrics. 

Groups of such test data are generated randomly from the original data set and applied to the model. Very similar to the new data simulation approach, this process dictates how the AI model will scale in the future with accuracy.

Also Read: How to adopt the right testing strategies to assure the quality of AI/ML-based models

Challenges in data-led QA for AI/ML applications

The data-led testing and QA for AI/ML applications outlined above suffer from myriad issues, some of which are given below.

Explainability

The decision-making algorithms of  AI models have always been perceived to be black boxes. Of late, there is a strong move towards making them transparent by explaining how the model has arrived at a set of outcomes based on a set of inputs. It helps understand and improve model performance and helps recipients grasp the model behavior. This is even more paramount in complaint-heavy areas like insurance or health care systems. Multiple countries have also started mandating that along with the AI model, there needs to be an explanation set on the decisions made.

Post facto analysis is key to addressing explainability. By retrospectively analyzing specific instances misclassified by an AI model, data scientists understand the part of the data set that the model actively focused on to arrive at its decision. On similar lines, positively classified findings are also analyzed.

Combining both helps to understand the relative contribution made by each data set and how the model stresses specific attribute classes to create its decision. It further enables data scientists to reach out to domain experts and evaluate the need to change data quality to get more variation across sensitive variables and understand the need to re-engineer the decision-making parameter set used by the model. In short, the data science process itself is being changed to incorporate explainability.

You may also like: 5 points to evaluate before adopting AI in your organization

Bias

Decision-making ability of an AI model hinges to a large extent on the quality of data that it’s exposed to. Numerous instances show seepage of biases into the input data or how the models are streamed, like Facebook’s gender discriminatory Ads or Amazon’s AI-based automated recruiting system that showed discrimination against women.

The historical data that Amazon used for its system was heavily skewed on account of male domination across its workforce and the tech industry over a decade. Even large models like open AI or codepilot suffer from the percolation of world biases into their models since they are trained on global data sets that are themselves biased. While removing biases, it’s sufficient to understand what has gone into data selection and the feature sets that contribute to decision-making.

Detecting bias in a model mandates evaluating and identifying those attributes that excessively influence the model compared to other attributes. Attributes so unearthed are then tested to see if they represent all available data points. 

Security

According to Deloitte’s State of AI in the Enterprise survey, 62% of respondents view cyber security risks as a significant concern while adopting AI. ‘The Emergence Of Offensive AI’ report from Forrester Consulting found that 88% of decision-makers in the security industry believe offensive AI is coming.

Since AI models themselves are built on the principle of becoming smarter with each iteration of real-life data, attacks on such systems also tend to become smarter. The matter is further complicated by the rise of adversarial hackers whose goal is to target AI models by modifying a simple aspect of input data, even to the extent of a pixel in an image. Such small changes can potentially bring out more significant perturbations in the model, leading to misclassifications and erroneous outcomes.

The starting point for overcoming such security issues is to understand the type of attacks and vulnerabilities in the model that hackers can exploit. Gathering literature on such kinds of attacks and domain knowledge to create a repository that can predict such attacks in the future is critical. Adopting AI-based cyber security systems is an effective technique to thwart hacking attempts since the AI-based system can predict hacker responses very similar to how it predicts other outcomes.

Privacy

With the increased uptake of privacy concerns like GDPR, CCPA across all applications and data systems, AI models have also come under the scanner. More so because AI systems depend heavily on large volumes of real-time data for intelligent decisions – data that can reveal a tremendous amount of information about a person’s demographic, behavior and consumption attributes, at the minimum. 

The AI model in question needs to be audited to evaluate how it leaks information to address privacy concerns. A privacy-aware AI model takes adequate measures to deanonymize, pseudonymize or use cutting-edge technology for differential privacy. By analyzing how privacy attackers get access to input training data from the model and reverse engineer effectively to get access to PII (Personally Identifiable Information), the model can be evaluated for privacy leakage. A two-stage process of detecting the inferable training data by inference attacks and then identifying the presence of PII in the data can help identify privacy concerns when the model is deployed.  

Want to know more? Read: Best practices for test data management in an increasingly digital world

Ensuring accuracy in QA for AI/ML applications

Accurate testing of AI-based applications calls for extending the notion of QA beyond the confines of performance, reliability, and stability to newer dimensions of explainability, security, bias, and privacy. The international standards community has also embraced this notion by expanding the conventional ISO 25010 standard to include the aforementioned facets. As AI/ML model development progresses, focus across all these facets will lead to better performing, continuously learning, a compliant model with the ability to generate far more accurate and realistic results.

Need help? Ensure seamless performance and functionality for your intelligent application. Call us now

AI in Education – A Realistic Look at the Effectiveness of AI in the Education Sector

A realistic view of the current adoption rate of AI in education, and pointers on how to ensure that it works, amidst the digital-learning hype.

When the kids in Montour school district (PA, USA) turned up to school that day in the fall of 2018, they were in for a surprise. They were told they would begin a brand-new course on Artificial Intelligence (AI). What on earth was AI? And what could it mean to kids in classes 5 and 6?

But this was a serious matter. MIT Media Lab and Media, Arts and Science Department at MIT, had come together and proposed to ‘catch them young’. The idea was to make an early introduction to concepts and practical AI lessons for middle school kids. All students from classes 5 to 8 would go through the AI Ethics program to identify use cases of gender / racial biases, privacy, and fairness. By the end of the 3-day course, they would know if such biases were embedded into the programs they would work on.

Welcome to generation AI. This makes millennium kids look antiquated. This new breed is sensitized to the good of AI and is aware of where it could go wrong. 

That is not all. Montour School district STEM teacher has co-developed a six-week program with Carnegie Mellon Dept of Computer Science called AI in Autonomous Robotics for 7 and 8-grade students. The implementation rigor here is quality stuff as kids are asked to solve real-world problems. 

Amper Music, the world’s first AI music composer and producer, has worked with music faculty at the school to develop a 10-day AI Music program for class 7 and 8 students. This school district is certainly leading the AI drive firing on all cylinders.

A host of universities, AI software firms, educators, and AI experts are coming together like never before to create early engagement for school kids into the AI world. And unlike what most of us would have thought: It is not only about STEM. In fact, the philosophy is to move from STEM to STEAM (with a liberal dose of Art – music, media, entertainment) thrown in for good measure. And this is happening in several pockets across the US.

AI in education sector – AI is here to stay, and the US campuses are already doing it

Across the United States, AI penetration within the education sector is tangible but may not be visible to the untrained eye. While varying in level of experimentation, schools and higher education institutes have embraced the tech and decided to learn how to harness its powers. 

Pittsburg-based Carnegie Learning1 offers AI-based personalized math, applied sciences, and language programs for post-high school students to rediscover learning. The entire program is personalized and self-paced, giving a new approach to STEM learners post-K12 schooling. The results demonstrated in some school districts in Washington and Texas prove the program creates a positive impact.

Duolingo2 is an amazingly popular AI-based customized language learning tool that allows anyone to learn a language. This is based on machine-driven instructions optimized for students based on millions of similar learning sessions held earlier. And most of the learning is for free.

California-based Content Technologies3 is a pioneer in AI and has developed several advanced AI systems for education. The Cram 101 is an AI tool that converts any textbook fed to it into chapter-wise byte-sized summaries, true or false type questions, learning concepts in record time. The company has developed similar tools for different disciplines such as nursing education, high school, and so on.

Some of the interesting outcomes of the approach of starting them young came from a US scientist, Ms. Druga, who built Cognimates, an AI platform for building games and programming robots and training AI models. Cognimates was incubated in MIT Media Labs. 

In a three-year study, where kids were taught to program bots to play games such as Rock and Scissors and build gaming applications using AI. One of the most profound observations came from Druga: When the kids came out after a session and said – “the computer is smart, but I am smarter”.

This was a powerful endorsement of how a young student comes away with a high level of confidence in the programmability of the computer to do what she wants it to do. This clearly establishes the argument about why AI perhaps should be started early on in school.

Next steps in playing this right – How can AI be used in education?

In general, schools and Universities must do the following to stay abreast of the AI curve and help imbue its benefits within the communities.

1.   Create a qualified AI resource team within the institution so they can track AI developments in peer institutes, vendor implementations and research the use cases.

2. Understand own deployments, migration of data systems into the AI realm, define implementation road map and create necessary stakeholder education of the new systems that will come.

3.   Educational institutions should also work with boards, government agencies, and accreditation bodies to define a structured AI curriculum for higher courses. This may require an industry interface also. This combination will create a Special Interest Group -university-industry – regulator group that will work together in ensuring the best interests of all concerned.

4. Faculty training, student and parent education, and awareness programs in terms of how the implementation could affect them need to be made available. Privacy and security rights of all stakeholders are paramount and need to be protected. How the schools intend to ensure data protection as machines become more powerful and open to sharing, receiving data from remote tutors, servers dynamically need to be shared transparently.

The Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) launched the AI for K-12 Working Group (AI4K12) to define for artificial intelligence what students should know and be able to do.

There are several such movements developing effective programs to deploy at various levels. These can help institutes understand better where AI is headed and how to ride this new technology wave to harness its full benefits.

Start your AI journey with Trigent

AI could well be the elephant in the classroom but if it’s a friendly elephant that can help enrich your life, you wouldn’t complain, would you?

At Trigent, we provide intuitive and easy to use AI solutions that ensuring seamless adoption of the latest technology. With the AI-powered tools from Trigent, you will be able to accelerate your digital transformation initiative in your organization successfully.

Want to know more? Get in touch with us for a quick consultation.

 References

  1. https://www.carnegielearning.com/why-cl/success-stories/
  2. https://www.duolingo.com/info
  3. http://contenttechnologiesinc.com/

Modernize Your EDI System for Faster, Flexible Integration and Scale

The challenges posed by the pandemic are urging businesses to be agile and responsive. Both consumers and companies have undergone a significant evolution since the onset of the pandemic. The focus is now on digital transformation and its role in building resilience during anticipated or unforeseen events. The responsibility on the technologies and architecture that connect retailers, distributors, suppliers, manufacturers, and customers is enormous.

To deal with the disruptions caused due to the pandemic, organizations are now dependent on a highly available and scalable Electronic Data Interchange (EDI) more than ever before. Those who have already implemented it are looking for ways to optimize it and improve their supply chain operations and ensure stability and visibility. Not surprisingly then, the global EDI market1 valued at $2.46 bn in 2019 is now predicted to touch $49.21 bn by 2027 at a CAGR of 9.5% during the forecast period 2020-2027.

EDI enables organizations to move their paper-based documents such as purchase orders, invoices, and documents related to payments, inventory, shipping status, and other business-critical processes to a standard electronic format. It replaces traditional business communication with automated capabilities that allow organizations to share data in real-time. It is a boon for modern organizations in an ecosystem where goods and services are constantly exchanged as part of their supply chains.

Why modernize your EDI system?

Although EDI has been around for years as a dominant protocol in the world of B2B, the systems that enable the exchange of EDI documents have now moved to the cloud. The modern EDI setup simplifies the regular maintenance necessitating robust data backups to safeguard data at all costs. The modernized versions of EDI offer up-to-date features and security measures that are required to streamline your operations, securely exchange data, and increase business efficiency.

EDI integration facilitates data collection, visibility, reporting, and analysis. Efficient EDI transactions also ensure prompt and reliable product and service delivery, resulting in positive business outcomes and superlative customer experiences.

But as with every other technology, EDI integration also requires a bit of work and planning. There are things to remember and pitfalls to avoid to get the full benefits.

Here are our top 3 recommendations.

1. Incorporate flexibility to scale with Modern EDI system architecture

The one lesson we learned well in 2020 is the certainty of change. Things can happen when you least expect and turn your world upside down. In business scenarios, change comes in the form of unforeseen events. While the pandemic caused significant turbulence, seasonal changes along with industry and region-specific events are widespread. When the business network is available in the form of cloud or hybrid solutions, it is easier to scale up and down to accommodate these sudden fluctuations in transaction volumes. 

Cloud EDI system software comes with technological and business process improvements to offer greater elasticity and agility to your business. While traditional EDI enabled connections between external partners and internal resources, the new version connects partners, applications, services, and data with end-to-end combinations of both internal and client-facing business processes.

2. A robust B2B infrastructure

Your trading partners and vendors need seamless connectivity to fulfill your business demands and exchange information without disruption. The modern Cloud-based infrastructure with Native Apps, modular design, and APIs offer multi-enterprise connectivity and visibility that consistently demonstrates its ability to manage unprecedented growth in transaction volumes. 

Logistics companies have confessed to losing over $250,000 annually due to poor integration2 while 9 percent said they are losing $1,000,000 or more due to technology integration issues. Thirty-four percent of the research study participants admitted they depended heavily on manual integration processes and suffered from slow decision-making.

The right technology partner can help you implement EDI best practices while providing the necessary maintenance and support. EDI/B2B data typically comes from vendors and partners and a good service provider will help you manage it well to optimize performance and free up strategic resources.

3. Privacy and data protection

With the shift to the Cloud, you need to do everything possible to protect data. EDI brings you closer to your supply chain partners, which means essential details about your organization are vulnerable. Global operations bring in different legal frameworks, cultural differences, and diverse privacy and data protection rules. Encrypted transfer protocols and proper data storage are critical for end-to-end processes. 

You need to understand the sensitivity of data to incorporate best practices. For instance, invoice data is more sensitive than order data since it includes commercially sensitive information that needs to be protected from misuse. Ohio-based logistics startup Sauder Woodworking recently embraced a B2B fulfillment suite replete with EDI automation to drive business agility and security across its B2B ecosystem.

 Making a case for API

Modern application development enables companies to innovate rapidly by using cloud-native architectures with loosely coupled microservices that interact via Application Programming Interfaces (APIs). EDI architectures are transitioning from traditional monolithic models to a modular design that is enabled by APIs. 

Justin McMillan, the COO of logistics consulting company UpstartWorks, confirms, “Traditionally, EDI is a very strict, specification-driven technology or a way of transferring data. EDI uses AS2, a data transfer protocol, to ensure that the data between two parties is secure when it’s transferred. Whereas, API’s allow for flexibility in its abilities to customize, as it’s based on a programming format where you can make calls to certain sets of data to receive whenever you need to.”

The truth is API can serve as the perfect complement for EDI. Rather than pivoting from EDI to API, we need to augment EDI integration with API capabilities to optimize supply chain efficiencies. 

While EDI works effectively for batch processing mission-critical transactions such as financial documents, APIs come in handy for real-time data exchange.

For instance, freight carriers need real-time efficiency as well as secure B2B data exchange. APIs give them a competitive advantage with real-time shipment status and load-tender responses. At the same time, EDI formats support new standards for mission-critical transactions as per government mandates to ensure reliability and security. By integrating both into backend systems, freight carriers get the ideal mix. 

Explains Frank Kenney, director of market strategy for Cleo, “All along we’ve told our customers and prospects, ‘You’ll need real-time end-to-end visibility. You’ll need a way to connect on-prem to the cloud. API isn’t replacing EDI, they’re complementary, and you’ll need a single platform that can do both. You need the agility to turn on a dime. And you’ll want the choice to either do it yourself, get it as a managed service, or figure out some combination of the two. It’s all about you being in control of the customer experience. It’s all about optimizing your business ecosystem and creating value through integration.”

APIs help connect directly to applications and transactional systems like ERP for instant data transfer. API-driven transactions need lesser storage, memory, and computing effort to manage data exchange, making it easier to secure them with encryption and authentication methods. APIs also help onboard new partners quickly and self-service onboarding processes, making it easier for faster EDI data exchanges. No wonder APIs are an essential milestone in the digitalization roadmap of modern organizations. 

So if you are already using established EDI methods and practices to support mission-critical processes, you can strengthen them with API capabilities without making an additional investment to build separate infrastructure. A unified platform that supports such a blended solution will do the trick.

Winning with EDI

While there has been a lot of speculation and deliberation around EDI systems, the fact is EDI is here to stay. It offers concrete benefits to users and works exceedingly well in diverse IT systems. Giving it an additional boost with APIs can enhance your capabilities significantly. Collaborate with a service provider with ample experience in EDI and API integration to eliminate complexities from your business environment and get an edge to manage supply chain operations efficiently.

Switch to EDI with Trigent

Decades of experience and a highly competent team of technology experts allow us to help you improve data transfer and accuracy with EDI and API-enabled solutions. We can enable advanced supply chain process automation while supporting you during the entire process. 

Count on us for your modernization endeavors and unlock the true potential of a robust EDI system.

Call us today for a business consultation

References

  1. https://www.theinsightpartners.com/reports/electronic-data-interchange-edi-market/
  2. https://www.businesswire.com/news/home/20201214005149/en/One-in-Three-Logistics-Firms-Loses-250K-Annually-Due-to-Poor-Integration 

IoT Asset Management Solutions for the Media & Entertainment Industry

IoT adoption, coupled with cloud platforms and Big Data analysis, provides the Media and Entertainment industry a significant boost to utilizing their machine and human assets. IoT (Internet of things) refers to the ecosystem of connected smart devices and environmental sensors that track assets, machine or human, across locations. 

Without IoT, asset management solutions are limited by delays and errors in manual data collection, under-utilization of assets, poor maintenance and reporting. This loss translates to a lack of awareness of real-time consumer needs, poor utilization of assets, theft, and limited data to predict overall and personalized content consumption in the media and entertainment industry. 

The Media and Entertainment industry can now make better-informed decisions by harvesting the multiple facets of consumer data such as location, time of day, parallel activities tied to consumption, age group, and region. They can develop more detailed consumer profiles that enable them to target ads and personalize content accordingly, providing higher degrees of satisfaction.

IoT bridges the physical and digital world. In general, it enables Asset management through four layers-

  • Data acquisition
    1. Sensors help detect or measure parameters such as light, sound, temperature, humidity, pressure, biometrics, proximity, acceleration, and GPS.
    2. Smart devices act upon the sensor’s input or capture input by themselves –  smartphones, wearables, smart TVs, gaming consoles, and home automation devices.
  • Data consolidation – Gateways collect and consolidate data from sensors and smart devices and transfer them to cloud platforms using a higher bandwidth. They can communicate using multiple protocols such as cellular, Bluetooth, wi-fi, and Ethernet. They also serve as a security layer for the devices.
  • Data hooks  – IoT platform collects data from the gateways or devices, processes, or directly transfers it to applications on the cloud for further processing, analysis, and action. This ties it to cloud platforms and machine learning.
  • Data visibility – Dashboarding and reporting to understand and utilize the insights to predict the future needs of content.

The meteoric rise in connected devices provides a massive opportunity for the Media industry. Consumers get to control what to watch and when to watch it while the content providers gain rich insights into the consumer’s preferences. Some of the key areas where IoT has contributed to the industry in a big way along with overall asset management  –

  1. Immersive content 
  2. Personalized content 
  3. Targeted advertising 
  4. Asset Management

Unique streaming experiences with immersive content

Let’s take the example of the Entertainment industry in the gaming arena. Augmented reality with the aid of IoT devices such as smartphones, tablets, portable gaming consoles provides the highest form of immersive entertainment. AR integrates real-world elements with the virtual world by superimposing the virtual on the real. 

A classic example of the initial showcasing of the power of IoT and AR is Pokemon GO. The game incorporates the real world through maps and smartphones, with fictional characters across the globe. It caused a stir among all age groups making them run all around town trying to gather Pokemon characters. This was in 2016. 

Today a number of the big brands are building an entire ecosystem around AR, Virtual Reality (VR), and IoT for entertainment. There’s Facebook’s AR Glasses, Microsoft’s Kinect as a motion-sensing add-on for XBOX 360, Amazon AR player, AR Emojis using a phone’s camera by Snapchat, Disney, and more. 

Disney is coming up with some disruptive AR and IoT amalgamation to track and notify guests with helpful information on delays on rides or who the particular entertainment is for, depending on where they are in the park. In the future, Disney, given its resources, could well come up with smart devices for some fantastic AR gamification experiences within the park.

Back to the mainstream world of TV, Smart TVs, streaming by OTT providers, and OTT platforms have revolutionized content watching from watching on a specific day at a particular time when the show is aired to binge-watching an entire series. Chrome casting is another new feature that enables you to watch uninterruptedly across devices, from your phone to your TV, be it the latest TED talks or the latest music trend on youtube.

The future holds unique streaming experiences with immersive live events using IoT devices, VR headsets, AR glasses, and more to huge segmented crowds.

Personalized content with user persona and viewer data

With the increasing number of smart devices, content is largely digital and not limited to viewing or listening at home.  You could be on a walk, cycling with friends, exercising, driving your car back home. For instance, based on your location or activity, the music platform you listen to could provide you with upbeat, soothing, or party music. Wearable devices, mobile phones, tablets, and social media data that can be picked from a household pretty much provide a detailed map of the family’s composition, their preferences and needs, their friend circle, and more. 

OTT providers such as Netflix already create multiple user profiles to engage with a family and not just an individual. Based on what you watch, what ratings you provide, through AI, they can figure out what kind of content you would like in the future and what kind of content demographic you fall under. Content is personalized to the level of an individual in a family using the personas and viewer data. 

Taking the social angle from the Facebook gaming world, Netflix came up with Teleparty to stream movies in sync with friends, each using their account and chatting. This was a big hit since group activities were not possible during the pandemic. This social data is something that Netflix, Disney, and others can use further to investigate group dynamics concerning content and advertising.

Targeted advertising with tailored campaigns

Earlier televisions would show ads to everyone without really knowing whether they were able to reach the target audience. There was no way of filtering it out for whom it was not relevant. 

Today thanks to digitally available content and OTT, Media and Entertainment companies can track consumers across devices. 

Consuming content on devices such as smartphones, tablets, wearables, etc., also aids in providing additional information on users in terms of location, time of day, whether they are moving, exercising, or are stationary. Through the multiple connected devices in a home, we can paint a picture of the family, which helps in targeting ads based on their specific needs.

Based on the data captured through wearables and other smart devices, we can now glean metrics on how many people saw a particular ad across devices and how many converted. Further, such detailed user information helps to tailor impactful campaigns and offers for highly effective revenue generation.

Nuances of IoT asset management solutions

Asset Management, in general, comprises of:

  1. Tracking moving assets – In the case of the Media and Entertainment industry, it could be electronic bracelets used by customers in an adventure park to guide them and give them a richer experience.
  2. Monitoring – Monitoring the health of an asset such as a setup box, checking if it’s connected to wi-fi, whether it has a technical error, and racking the usage.
  3. Workflow Automation – Use a voice-activated assistant to switch on/off an asset, decrease or increase the volume of a music system or TV, cast what you are watching on the phone to a TV.
  4. Maintenance – Based on the tracking and monitoring of assets, predictive maintenance. Detect technical faults in the asset using IoT devices such as a Home assistant and then proactively notifying the customer for maintenance.
  5. Security – At the company’s end, the digital assets need to be secured with authentication and role-based authorization to access, collaborate and add content. At the end-consumer end, assets need to be secure to prevent hacking into sensitive personal information.

Using IoT, Mobile, Chatbot, and Artificial Intelligence (AI), Entertainment companies can provide the best customer service. This is very evident at the end customer level. For example, when they choose a TV provider, and a setup box is delivered to them. Earlier, the provider needed to send a person to set it up completely. Today, with the aid of a chatbot on their website or mobile app, a customer can follow the steps to do so. Besides, the setup box is intelligent enough to figure out whether there is network connectivity or not and notify the viewer. 

Similarly, when there is a technical issue or a bill is not paid, the provider can send messages to be viewed either on the home screen of the TV or the customer’s mobile app or phone. Even if the customer faces a technical issue, she can get onto the app and start the diagnostics with the chatbot guiding her. This saves valuable time for the customer support team, which can then focus on more significant problems. It can also help have a smaller, highly skilled support team as the smart devices are connected and work things out with minimal human intervention.

Digital Home Service (DHS) is a cloud-based Oracle solution for set-top-box and service-intensive pay-TV operators. It combines Oracle IoT, mobile, chatbot, AI, and Oracle cloud platform with modern digital customer management to deliver the next generation of digital home service capabilities. This helps to reduce the effort and improve the efficiency of customer service and field services teams.

Today’s world of Smart TVs, gaming consoles, music systems, lighting, Air conditioning are IoT-enabled and interact easily with voice-activated Smart Home devices such as Alexa,  Google Assistant, Roomie Remote. Switching on/off, increasing or decreasing volumes, searching for content or information, playing music, and more can be done by using just one assistant that communicates with and manages all our smart devices. 

Content security is another critical facet to be considered. Data and devices surround everyone, including children. There are many ways to bring in parental control both on devices and platforms to ensure that children see age-appropriate content. Each IoT device and asset collects data, be it your security camera, fridge, or Amazon Echo. This makes them potential threats to privacy and overall security from cybercriminals. 

They can hack into your devices, monitor your activities, steal data both digital and physical, depending on how you have addressed your home’s security. Therefore securing the IoT environment at home is essential. We are slowly moving towards biometric security instead of using not-so-secure and multiple passwords.

IoT Asset Management solutions, therefore, bring endless possibilities to take Media and Entertainment to unimaginable heights. It serves as a powerful predictive monitoring tool that helps with asset maintenance and gives deep insights into the end consumer. Every day there are newer and better IoT devices in the market. A Media and Entertainment house would do well to invest in an intelligent IoT framework early on. We at Trigent can help you reach your IoT asset management goals. 

Call us for a quick consultation.

(Originally published in ReadWrite )

DevOps Success: 7 Essentials You Need to Know

High-performing IT teams are always looking for ways to adopt and use industry best practices and solutions. This enables them to overcome obstacles and achieve consistent and reliable commercial outcomes. A DevOps strategy enables the delivery of software products and services to the market in a more reliable and timely manner. The capacity of the team to have the correct combination of human judgment, culture, procedure, tools, and automation is critical to DevOps success.

Is DevOps the Best Approach for You?

DevOps is a solid framework that aids businesses in getting the most out of their digital efforts. It fosters a productive workplace by enhancing cooperation and value generation across all teams, including development, testing, and operations.

DevOps-savvy companies can launch software solutions more quickly into production, with shorter lead times and reduced failure rates. They have higher levels of responsiveness, are more resilient to production difficulties, and restore failed services more quickly.

However, just because every other IT manager is boasting about their DevOps success stories doesn’t mean you should jump in and try your hand at it. By planning ahead for your DevOps journey, you can avoid the traps that are sure to arise.

Here are seven essentials to keep in mind when you plan your DevOps journey.

1. DevOps necessitates a shift in work culture—manage it actively.

The most important feature of DevOps is the seamless integration of various IT teams to enable efficient execution. It results in a software delivery pipeline known as Continuous Integration-Continuous Delivery (CI/CD). Across development, testing, and operations, you must abandon the traditional silo approach and adopt a collaborative and transparent paradigm. Change is difficult and often met with opposition. It is tough for people to change their working habits overnight. You play an important role in addressing such issues in order to achieve cultural transformation. Be patient, persistent, and use continuous communication to build the necessary change in the management process.

2. DevOps isn’t a fix for capability limitations— it’s a way to improve customer experiences

DevOps isn’t a panacea for all of the problems plaguing your existing software delivery. Mismatches between what upper management expects and what is actually possible must be dealt with individually. DevOps will give you a return on your investment over time. Stakeholder expectations about what it takes to deploy DevOps in their organization should be managed by IT leaders.

Obtain top-level management buy-in and agreement on the DevOps strategy, approach, and plan. Define DevOps KPIs that are both attainable and measurable, and make sure that all stakeholders are aware of them.

3. Keep an eye out for going off-track during the Continuous Deployment Run

Only until you can forecast, track, and measure the end-customer advantages of each code deployment in production can you fully implement DevOps’ continuous deployment approach. In each deployment, focus on the features that are important to the business, their importance, plans, development, testing, and release.

At every stage of DevOps, developers, testers, and operations should all contribute to quality engineering principles. This ensures that continuous deployments are stable and reliable.

4. Restructure your testing team and redefine your quality assurance processes

To match with DevOps practices and culture, you must reimagine your testing life cycle process. To adapt and incorporate QA methods into every phase of DevOps, your testing staff needs to be rebuilt and retrained into a quality assurance regimen. Efforts must be oriented toward preventing or catching bugs in the early stages of development, as well as assisting in making every release of code into production reliable, robust, and fit for the company.

DevOps testing teams must evolve from a reactive, bug-hunting team to a proactive, customer-focused, and multi-skilled workforce capable of assisting development and operations.

5. Incorporate security practices earlier in the software development life cycle (SDLC)

Security is typically considered near the end of the IT value chain. This is primarily due to the lack of security knowledge among most development and testing teams. Information security’s confidentiality, integrity, and availability must be ingrained from the start of your SDLC to ensure that the code in production is secure against penetration, vulnerabilities, and threats.

Adopt and use methods and technologies to help your system become more resilient and self-healing. Integrating DevSecOps into DevOps cycles will allow you to combine security-focused mindsets, cultures, processes, tools, and methodologies across your software development life cycle.

6. Only use tools and automation when absolutely necessary

It’s not about automating everything in your software development life cycle with DevOps. DevOps emphasizes automation and the use of tools to improve agility, productivity, and quality. However, in the hurry to automate, one should not overlook the value and significance of the human judgment. From business research to production monitoring, the team draws vital insights and collective intelligence through constant and seamless collaborative efforts that can’t be substituted by any tool or automation.

Managers, developers, testers, security experts, operations, and support teams must collaborate to choose which technologies to utilize and which automation areas to automate. Automate tasks like code walkthroughs, unit testing, integration testing, build verification, regression testing, environment builds, and code deployments that are repetitive.

7. DevOps is still maturing, and there is no standard way to implement it

DevOps is continuously changing, and there is no one-size-fits-all approach or strategy for implementing it. DevOps implementations may be defined, interpreted, and conceptualized differently by different teams within the same organization. This could cause misunderstanding in your organization regarding all of your DevOps transformation efforts. For your company’s demands, you’ll need to develop a consistent method and plan. It’s preferable if you make sure all relevant voices are heard and ideas are distilled in order to produce a consistent plan and approach for your company. Before implementing DevOps methods across the board, conduct research, experiment, and run pilot projects.

(Originally published in Stickyminds)

AI in Media: Redefining Customer Experience with Immersive Stories

Artificial intelligence has become an important milestone in the digital transformation journey of all sectors, including media and entertainment. With the buzz it has created, it is no surprise that the adoption of AI in media and entertainment is a game-changer for the pioneering and the digitally inclined. It plays an immense role in the way content and experiences are curated and delivered at scale today. 

The next era of the Media industry is defined by customers’ increased demand for immersive, live, and shareable experiences. Consumers now wish to get more engaged, better connected, and closer with the stories they love – both in the digital and physical worlds. Companies have started empowering these experiences through emerging technologies. Big data and artificial intelligence will create the most dramatic change, redefining how the industry can connect with all stakeholders and drive growth.

Modern enterprises are now deploying AI tools and technologies to ensure effective decision-making and agile responsiveness to market changes. While over-the-top players like Netflix have already adopted a data-first approach, many others are still trying to attain AI success. The road to full-fledged AI adoption is not devoid of challenges. AI can be only as good as the data you have. Every effort must be made to efficiently manage different data types, including audience, operational, and content data.

As workflows and processes continue to become AI-enabled, we analyze the media and entertainment landscape to understand the impact of AI adoption.

Customization to optimization – the role of AI in media & entertainment sector

AI plays an important role in enhancing the user experience across all the six segments of the Media and Entertainment (M&E) industry: Films & TV, social media, journalism, gaming, music, and sports.  

Customer-focused experience with content personalization 

AI powers recommendation engines to predict what content should be promoted and when based on customer viewing data, search history, ratings, and even the device customers use. A classic case in point is Netflix’s landing cards1 helping the streaming website customize what you watch through personalized targeting. Images of lead characters are seen while scrolling to understand popular choices based on the cards people click. 

Machine classification algorithms for improved search optimization

AI also plays a significant role in search optimization thanks to machine classification algorithms that help in improving the categorization of movies. Users can search based on categories instead of individual titles to enable quick searches and smooth navigation. Streaming websites have enhanced streaming quality with AI since it helps them predict future demands and position their assets strategically to help users enjoy high-quality streaming even during peak hours.

Music streaming companies like Spotify and Apple Music rely on machine learning algorithms to segment users and songs to offer personalized recommendations and playlists. Natural Processing (NLP) gives them an edge by providing information about songs and artists from the web. AI has also been helping musicians generate lyrics and compose songs.

Enhanced news reporting with robot journalists

AI has a coveted place in social media and journalism too. While social media platforms like Facebook, Instagram, and Snapchat are using it to offer personalized products and services, Forbes and Bloomberg have been using robot journalists Bertie and Cyborg respectively to create storylines based on their parameters and data.

The Washington Post, too, gave us a taste of the future of journalism with its Heliograf2 that covered the Olympics. However, the Chinese news aggregation service Toutiao took it to the next level by creating an AI-enabled reporter Xiaomingbot that churned out a whopping 450 articles during the Rio Olympics in just 15 days.  

Gaming and customer-specific advertising

As the supply of mobile games continues to exceed demand, companies are now using AI to estimate customer lifetime value (CLV) to bid efficiently in advertising for users, focusing only on those who would enthusiastically engage with their products. AI is also helping animators bring exciting characters to life for a multitude of virtual reality games and movies.

 Improved entertainment quotient in sports broadcasting

The perennial popularization of sports brings new fans, players, and subscribers into the sports and gaming fold. AI satiates them with entertaining shots and angles during live telecasts and enhances the experience by broadcasting exclusive footage captured by drones.

Laying deeper data foundations for successful adoption of AI in media

AI has forayed into virtually all functions and areas to add value in a highly competitive market. As competitive pressures intensify, it has become more critical than ever to fast-track your AI initiatives and reap their benefits. But as with every other digitalization endeavor, AI adoption too brings along unique challenges.

Here’s what you can do to overcome them and lay deeper data foundations for successful AI adoption. 

Assess AI maturity 

M&E businesses are now shifting from B2B to B2C business models due to the direct-to-consumer delivery and consumption trends and hence are currently operating on massive amounts of data. In order to make complete sense of this data and drive decisions, data silos need to be removed first. A fragmented approach is not going to work and should be replaced with a data-first approach.

Organizations often get caught up in a quandary, wondering if they should modernize the data architecture first for their AI models to rest upon or build a model and modernize only that part of the required data. However, the right approach would be to invest in a sound strategy for your target data architecture that relies on proven models to avoid pitfalls and rework. Data management should be a top concern for organizations to interpret and get actionable insights.

Focus on people and processes 

Data sources will continue to increase, causing greater challenges for data management and project management. So while building your technology stack, it is equally important to invest in people and processes that would be at the helm of things while progressing up the AI maturity curve.

AI leaders believe in including technologists and data scientists in business teams to give them the visibility to understand business challenges. It is essential that business leaders, values, people, and culture are aligned to enable successful automation and AI adoption. Only then would human employees be able to work alongside robots and AI-powered machines to build capabilities and deliver value.

Adopt a continuous improvement approach

AI is not a one-time endeavor but will continue to evolve with time. To achieve enterprise-wide AI, it needs to be perceived as a transformational initiative that must be implemented across all front-end and back-end processes.

A comprehensive picture of ROI based on revenue and costs for different functions and processes can give organizations the clarity to track value and identify areas that need to improve. M&E companies are integrating established AI processes into finance, HR, and other functions to garner cost and operational efficiencies.

The future of entertainment looks AI-centric

AI is undeniably transforming the media and entertainment sector, empowering them to make informed decisions based on critical data analysis. It will navigate disruption and drive growth in all spheres by addressing data gaps and helping M&E companies become more agile. Clearly, AI is impacting everyday entertainment in a big way, and it’s time organizations harnessed its power to fine-tune their forward-thinking strategies and explore new avenues.

Discover the power of AI with Trigent

The technology experts at Trigent have been offering robust AI-enabled solutions to M&E companies based on data from diverse sources and powerful algorithms to enable a superlative user experience while giving them insights into customer behavior. 

We help build excellent AI capabilities and advanced features to deliver content in the most effective manner. We can help you build high-quality datasets to get the best results in diverse settings and drive impact at scale. 

Call us now for a business consultation

References

  1. https://www.wired.co.uk/article/netflix-data-personalisation-watching
  2. https://futurism.com/the-future-of-writing-chinas-ai-reporter-published-450-articles-during-rio-olympics 

5 Must-Haves for QA to Fit Perfectly into DevOps

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.

DevOps QA Testing – 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 testing 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?

(Originally published in Stickyminds)

Challenges in Cross-platform Mobile App Development That I Wish I Knew Before

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.

Things are often easier to manage at the start, with few features and user requirements. As the apps scale, new features are added and user needs grow, the development complexities and operational limitations emerge.  A classic case in point is Airbnb, which decided to invest in React Native to enhance agility and experience. In its post on Medium, the company highlighted the challenges they faced and called it immature compared to native iOS and Android. They grappled with JavaScriptCore inconsistencies, maintenance issues, and other challenges they were least prepared for, ultimately deciding to go back to native.

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

Developers often use JavaScript objects for cross-platform frameworks that cause issues while using reusable codes. Finding and fixing issues in the entire code becomes an arduous task that eventually increases the development time as well as cost.

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.

 Limited updates

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.

Security

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. 

Call us today for a business consultation

5 Principles to Ensure Successful Implementation of AR/VR in Real Estate Firms

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.”

VR allows users to explore in a three-dimensional, computer-generated environment using headsets, and AR creates an enhanced version of reality. Here are our top 5 recommendations to get started.

1. Start small; start now.

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.

Benefits of AR/VR in real estate

AR and VR together give real estate solid value and benefits that make AR and VR investments worthwhile. 

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.

Call us today to book a business consultation. 

References

  1. https://www.alltheresearch.com/report/380/augmented-reality-ar-virtual-reality-vr-in-real-estate-market-ecosystem
  2. https://www.commercialrealestate.com.au/news/how-augmented-reality-could-revolutionise-the-way-we-search-for-commercial-real-estate-47597/
  3. https://www.realtor.com/advice/sell/questions-to-ask-before-selling-your-home/

Adopt the Right Testing Strategies for AI/ML Applications

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.

Fairness:

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.

Security:

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.

(This blog was originally published in EuroStar)

Digital Asset Management System – A must-have for all businesses

What are digital assets?

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
  • Broadcasting
  • HR and Recruitment firms
  • Movie Production houses
  • OTTs

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. 

What is digital asset management?

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 

  1. Are digital assets a significant part of your business?
  2. Does your workforce spend a lot of time looking for files?
  3. Have you had to do a work from scratch when it could have been repurposed from an existing asset?
  4. Are you making duplicate purchases of assets because existing assets cannot be found?
  5. Are unapproved files being used fairly regularly?
  6. Are you losing time validating the “Final version” against the other versions?
  7. Are you spending a significant amount of  time on tasks that can be automated such as watermarking, resizing, transcoding etc?
  8. Does sharing large files require a process which is not as easy as sending email?
  9. 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. 

OR

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 digital asset management software

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.

  • Bynder 
  • Canto
  • Digizuite
  • Image Relay
  • Northplains
  • Widen Collective

For the more adventurous ones who already have IT infrastructure and a team that can manage the system, here are some open source options:

  • Islandora 
  • Phraseanet
  • Pimcore
  • Daminion Standalone Basic – The basic standalone is free. They also have a managed service which is a paid model.

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.

Confused? 

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