5 ways to measure and improve your QA Effectiveness; is your vendor up to the mark?

The benefits of QA testing in software are widely accepted.  However, quantifying these benefits and optimizing the performance is tricky.  The performance of software development can be measured by the difficulty and amount of code committed in a given sprint.  Measuring the effectiveness of QA is harder when its success is measured by the lack of problems in software application deployment to production.

If you can’t measure it, you can’t improve it.

The ‘right’ metrics to evaluate QA effectiveness depend on your organization. However, it is generally a good idea to measure efficiency and performance for a well-rounded guide for performance evaluations.

Test coverage

While improving test coverage ideally means creating more tests and running them more frequently, this isn’t the actual goal, per se.  It will just mean more work if the right things are not getting tested with the right kind of test. Hence the total number of tests in your test suite by itself isn’t a good metric or reflection of your test coverage. 

Instead, a good metric to consider would be to check if your testing efforts cover 100% of all critical user paths.  The focus should be on building and maintaining tests to cover the most critical user flows of your applications.  You can check your analytics platform like Google Analytics or Amplitude to prioritize your test coverage.

Test reliability

The perfect test suite would have the correct correlation between failed tests and the number of defects identified.  A failed test will always include a real bug and the tests would only pass when the software is free of these bugs. 

The reliability of your test suite can be measured by comparing your results with these standards.  How often does your test fail due to problems with the test instead of actual bugs? Does your test suite have tests that pass sometimes and fail at other times for no identifiable reason?

Keeping track of why the tests fail over time, whether due to poorly-written tests, failures in the test environment, or something else, will help you identify the areas to improve.

Time to test

The time taken to test is a crucial indicator of how quickly your QA team creates and runs tests for the new features without affecting their quality. The tools that you use are a key factor here. This is where automated testing gains importance.

Scope of automation

Automated testing is faster than manual testing.  So one of the critical factors to measure your QA effectiveness would include the scope of automation in your test cycles.  What portion of your test cycle can be profitably automated, and how will it impact the time to run a test?  How many tests can you run in parallel, and the number of features that can be tested simultaneously to save time?

Time to fix

This includes the time taken to figure out whether a test failure represents a real bug or if the problem is with the test. It also includes the time taken to fix the bug or the test.  It is ideal to track each of these metrics separately so that you know which area takes the most time.

Escaped bugs

Tracking the number of bugs found after production release is one of the best metrics for evaluating your QA program. If customers aren’t reporting bugs, it is a good indication that your QA efforts are working.  When customers report bugs, it will help you identify ways to improve your testing.

If the bug is critical enough in the first two cases, the solution is to add a test or fix the existing test so your team can rely on it.  For the third case, you may need to look at how your test is designed—and consider using a tool that more reliably catches those bugs.

Is your Vendor up to the mark?

Outsourcing QA has become the norm on account of its ability to address the scalability of testing initiatives and bring in a sharper focus on outcome-based engagements.

Periodic evaluation of your QA vendor is one of the first steps to ensuring a rewarding long-term outsourcing engagement. Here are vital factors that you need to consider. 

Communication and people enablement

Clear and effective communication is an integral component of QA, more so when DevOps, Agile, and similar collaboration-heavy initiatives are pursued to achieve QA at scale. Ensure that there is effective communication right from the beginning of the sprint so that cross-functional teams are cognizant of the expectations from each of them and have their eye firmly fixed on the end goal of application release.

Also, your vendor’s ability to flex up/down to meet additional capacity needs is a vital factor for successful engagement. An assessment of the knowledge index of their team in terms of ability to learn your business and their ability to build fungibility (cross skill / multi-skill) into the team can help you evaluate their performance. 

Process Governance 

The right QA partner will be able to create a robust process and governing mechanism to track and manage all areas of quality and release readiness, visibility across all stages of the pipeline through reporting of essential KPIs, documentation for managing version control, resource management, and capacity planning. 

Vendor effectiveness can also be measured by their ability to manage operations and demand inflow. For example, at times, toolset disparity between various stages and multiple teams driving parallel work streams creates numerous information silos leading to fragmented visibility at the product level. The right process would focus on integration aspects as well to bridge these gaps.

Testing Quality 

The intent of a  QA process is mainly to bring down the defects between builds over the course of a project. Even though the total count of defects in a project may depend on different factors, measuring the rate of decline in the defects over time can help you understand how efficiently QA teams are addressing the defects. 

The calculation can be done by plotting the number of defects for each build and measuring the slope of the resulting line. A critical exception is when a new feature is introduced. This may increase the number of defects found in the builds. These defects should steadily decrease over time until the build becomes stable

Test Automation 

Measuring the time efficiency often boils down to the duration it takes to accomplish the task. While it takes a while to execute a test for the first time, subsequent executions will be much smoother and test times will reduce. 

You can determine the efficiency of your QA team by measuring the average time it takes to execute each test in a given cycle. These times should decrease after initial testing and eventually plateau at a base level. QA teams can improve these numbers by looking at what tests can be run concurrently or automated.

Improve your QA effectiveness with Trigent

Trigent’s experienced and versatile Quality Assurance and the Testing team is a major contributor to the successful launch, upgrade, and maintenance of quality software used by millions around the globe. Our experienced responsible testing practices put process before convenience to delight stakeholders with an impressive industry rivaled Defect Escape Ratio or DER of 0.2.

Trigent is an early pioneer in IT outsourcing and offshore software development business. We enable organizations to adopt digital processes and customer engagement models to achieve outstanding results and end-user experience. We help clients achieve this through enterprise-wide digital transformation, modernization, and optimization of their IT environment. Our decades of experience, deep domain knowledge, and technology expertise deliver transformational solutions to ISVs, enterprises, and SMBs.

Ensure the QA effectiveness and application performance. Talk to us

Quality Assurance outsourcing in the World of DevOps-Best Practices for Dispersed (Distributed) Quality Assurance Team

Why Quality Assurance (QA) outsourcing is good for business

The software testing services is expected to grow by more than USD 55 Billion between 2022-2026. With outsourced QA being expedited through teams distributed across geographies and locations, many aspects that were hitherto guaranteed through co-located teams have now come under a lot of pressure. Concerns range from constant communication and interworking to coverage across a wide range of testing types – unit testing, API testing, as well as validating experiences across a wide range of channels.

Additionally, it is essential to note that DevOps is the preferred methodology for software development and release, with collaborating teams oriented towards faster delivery cycles augmented by early feedback. And QA is regarded as a critical binding thread of DevOps practice, thereby ensuring a balanced approach in maintaining collaboration and communication between teams while ensuring that delivery cycles are up to speed and the quality of the delivered product meets customer expectations.

Best practices for ensuring the effectiveness of distributed QA teams

Focus on the right capability: 
While organizations focus to a large extent on bringing capabilities across development, support, QA, operations, and product management in a scrum team, paramount from a quality perspective would be QA skills. The challenge is to find the right skill mix. For example, a good exploratory tester; and automation skills (not necessarily in the same person). In addition, specialist skills related to performance, security, and accessibility also need to be thought through. The key is to choose an optimum mix of specialists and generalists.

Aim to achieve the right platform/tool mix: 
It is vital to maintain consistency across the tool stacks used for engagement. According to a 451 research survey, 39% of respondents juggle between 11 to 30 tools to keep an eye on their application infrastructure and cloud environment; 8% are found to use over 21 to 30 tools. Commonly referred to as tool sprawl, this makes it extremely difficult to collaborate in an often decentralized and distributed QA environment. It’s imperative to have a balanced approach toward the tool mix by influencing the team to adopt a common set of tools instead of making it mandatory.

Ensure a robust CI/CD process and environment:
A weak and insipid process may cause the development and operations team to run into problems while integrating new code.  With several geographically distributed teams committing code consistently into the CI environment, shared dev/test/deploy environments constantly run into issues if sufficient thought process has not gone into identifying environment configurations.  These can ultimately translate into failed tests and, thereby, failed delivery/deployment.  A well-defined automated process ensures continuous deployment and monitoring throughout the lifecycle of an application, from integration and testing phases through to the release and support.

A good practice would be to adopt cloud-based infrastructure, reinforced by mechanisms for managing any escalations on deployment issues effectively and quickly.  Issues like build failure or lack of infrastructure support can hamper the productivity of distributed teams.  When strengthened by remote alerts, robust reporting capabilities for teams, and resilient communication infrastructure, accelerated development to deployment becomes a reality.

Follow good development practices:
Joint backlog grooming exercises with all stakeholders, regular updates on progress, code analysis, and effective build and deployment practices, as well as establishing a workflow for defect/issue management, are paramount in ensuring the effectiveness of distributed teams. Equally important is the need to manage risk early with ongoing impact analysis, code quality reviews, risk-based testing, and real-time risk assessments. In short, the adoption of risk and impact assessment mechanisms is vital.

Another critical area of focus is the need to ascertain robust metrics that help in the early identification of quality issues and ease the process of integration with the development cycle. Research conducted in 2020 by Gatepoint and Perfecto surveyed executives from over 100 leading digital enterprises in the United States on their testing habits, tools, and challenges. The survey results showed that 63 percent start testing only after a new build and code is developed. Just 40 percent test upon each code change or at the start of new software.

Devote special attention to automation testing:
Manual (or exploratory) testing allows you to ensure that product features are well tested, while automation of tests (or, as some say, checks) helps you improve coverage for repeatable tasks. Though planning for both during your early sprint planning meetings is essential, test automation services have become an integral testing component. 

As per studies, in 2020, approximately 44 percent of IT companies have automated half of their testing. Businesses are continuously adopting test automation to fulfill the demand for quality at speed. Hence it is no surprise that according to Data Bridge Market research, the automation testing market will reach an estimated value of USD 19.9 billion by 2028 and grow at a CAGR of 14.89% in the forecast period of 2021 to 2028.

Outsourcing test automation is a sure-shot way of conducting testing and maintaining product quality. Keeping the rising demand in mind, let us look at a few benefits of outsourcing test automation services.

Early non-functional focus: 
Organizations tend to overlook the importance of bringing in occasional validations of how the product fares around performance, security vulnerabilities, or even important regulations like accessibility until late in the day. As per the 2020 DevSecOps Community Survey, 55 percent of respondents deploy at least once per week, and 11 percent claim multiple daily deployments.  But when it comes to security, 44 percent of the mature DevOps practices know it’s important but don’t have time to devote to it.

Security has a further impact on CI/CD tool stack deployment itself, as indicated by a 451 research in which more than 60% of respondents said a lack of automated, integrated security tools is a big challenge in implementing CI/CD tools effectively. 

It is essential that any issue which is non-functional in nature be exposed and dealt with before it moves down the dev pipeline. Adoption of a non-functional focus depends to a large extent on the evolution of the product and the risk to the organization.

Benefits of outsourcing your QA

To make distributed QA teams successful, an organization must have the capability to focus in a balanced and consistent way across the length and breadth of the QA spectrum, from people and processes to technology stacks. However, the ability to make these practices work hinges on the diligence with which an organization institutionalizes these best practices as well as platforms and supplements it with an organizational culture that is open to change.

Trigent’s experienced and versatile Quality Assurance and Testing team is a major contributor to the successful launch, upgrade, and maintenance of quality software used by millions around the globe. Our experienced, continuous testing practices put process before convenience to delight stakeholders with an impressive industry rivaled Defect Escape Ratio or DER of 0.2.

Ensure increased application availability and infrastructure performance. Talk to us.

5 Ways You are Missing Out on ROI in QA Automation

QA Automation is for everyone, whether you are a startup with a single product and few early adopters or a mid-sized company with a portfolio of products and multiple deployments. It assures product quality by ensuring maximum test coverage that is consistently executed prior to every release and is done in the most efficient manner possible.

Test Automation does not mean having fewer Test Engineers – It means using them efficiently in scenarios that warrant skilled testing, with routine and repetitive tests automated.

When done right, Test Automation unlocks significant value for the business. The Return on Investment (RoI) is a classical approach that attempts to quantify the impact and in turn, justify the investment decision.

However, the simplistic approach that is typically adopted to compute RoI provides a myopic view of the value derived from test automation. More importantly, it offers very little information to the Management on how to leverage additional savings and value from the initiative. Hence, it is vital that the RoI calculations take into account all the factors that contribute to its success.

Limitations of the conventional model to compute Test Automation ROI

Software leadership teams treat QA as a cost center and therefore apply a simplistic approach to computing RoI. The formula applied is

You may quickly notice the limitation in this formula. RoI should take into account the ‘Returns’ gained from ‘Investments’ made.

By only considering the Cost Savings gained from the reduction in testing, the true value of Test Automation is grossly underestimated.

In addition to the savings in terms of resources, attributes like the value of faster time to market, the opportunity cost of a bad Customer Experience due to buggy code, and being resilient to attrition, need to be factored in to fully compute the “Returns” earned. How to determine the value of these factors and incorporate them into the RoI formula is another blog in itself

Beyond Faster Testing – 5 ways to lower costs with Test Automation

For the moment, we will explore how companies can derive maximum savings while in Test Automation implementation. While calculating the ‘Cost Savings’ component of the RoI, it is important to take at least a 3-year view of the evolution of the product portfolio and its impact on the testing needs. The primary reason is that the ratio of manual tests to regression tests decreases over time and the percentage of tests that can be automated to total tests increases. With this critical factor in mind, let us look at how businesses can unlock additional savings

Test Automation Framework – Build vs. Partner

The initial instinct of software teams is to pick one of the open-source frameworks and quickly customize it for your specific need. While it’s a good strategy to get started, as the product mix grows and the scope of testing increases, considerable effort is needed to keep the Framework relevant or to fully integrate the framework into your CI/CD pipeline. This additional effort could wipe away any gains made with test automation

By using a vendor or testing partner’s Test Automation Framework, the Engineering team can be assured that it’s versatile to suit their future needs, give them the freedom to use different tools, and most importantly benefit from the industry best practices, thereby eliminating trial and error.

Create test scripts faster with ‘Accelerators’

When partnering with a QE provider with relevant domain expertise, you can take advantage of the partners’ suite of pre-built test cases to get started quickly. With little or no customization, the ‘accelerators’ allow you to create and run your initial test scripts and get results faster.

Accelerators also serve as a guide to design a test suite that maximizes coverage

Using accelerators to create the standard use cases typical for that industry ensures that your team has the bandwidth to invest in the use cases unique to your product and requires special attention.

Automate Test Design, Execution and Maintenance

When people talk of Test Automation, the term “automate” usually refers to test execution. However, execution is just 30% of the testing process. To accelerate the pace of production releases require unlocking efficiency across the testing cycle including design & maintenance.

Visual Test Design to gather functional requirements and develop the optimal number of most relevant tests, AI tools for efficient and automated test maintenance without generating any technical debt need to be leveraged. When implemented right, they deliver 30% gains in creation and 50% savings in maintenance

Shift Performance Testing left with Automation

In addition to creating capacity for the QA team to focus on tests to assure that the innovations deliver the expected value, you can set up Automated Performance Testing to rapidly check the speed, response time, reliability, resource usage, and scalability of software under an expected workload.

Shifting performance testing left allows you to identify potential performance bottleneck issues earlier in the development cycle. Performance issues are tricky to resolve, especially if issues are related to code or architecture. Test Automation enables automated performance testing and in turn, assures functional and performance quality.

Automate deployment of Test Data Sets

Creating or generating quality test data, specially Transactional Data Sets, have been known to cause delays. Based on our experience, the average time lost in waiting for the right test data is 5 days, while for innovation use cases, they take weeks. For thorough testing, often the test data needs to change during the execution of the test, which needs to be catered for

With a Test Data Automation, the test database can be refreshed on-demand. Testers access data subsets required for their suite of test cases and consistent data sets are utilized across multiple environments. Using a cogent test data set across varied use cases allows for data-driven insights for the entire product – which would be difficult with test data silos

Maximize your ROI with Trigent

The benefits, and therefore the ‘Returns’, from Test Automation, go well beyond the savings from reduced manual testing time and effort. It also serves as insurance against attrition! Losing people is inevitable, but you can ensure that the historical product knowledge is retained with your extensive suite of automated test scripts.

Partnering with a QE Service Provider having relevant domain experience will enable you to get your Quality processes right the first time – And get it done fast. Saving you valuable time and money. And it frees up your in-house team to focus on the test cases to assure the customer experiences that make your product special.

Do your QA efforts meet all your application needs? Is it yielding the desired ROI? Let’s talk!

Five Metrics to Track the Performance of Your Quality Assurance Teams and the efficiency of your Quality Assurance strategy

Why Quality Assurance and Engineering?

A product goes through different stages of a release cycle, from development and testing to deployment, use, and constant evolution. Organizations often seek to hasten their long release cycle while maintaining product quality. Additionally, ensuring a superior and connected customer experience is one of the primary objectives for organizations. According to a PWC research report published in 2020, 1 in 3 customers is willing to leave a brand after one bad experience. This is where Quality Engineering comes in.

There is a need to swiftly identify risks, be it bugs, errors, and problems, that can impact the business or ruin the customer experience. Most of the time, organizations cannot cover the entire scope of their testing needs, and this is where they decide to invest in Quality Assurance outsourcing.

Developing a sound Quality Assurance (QA) strategy

Software products are currently being developed for a unified CX. To meet the ever-evolving customer expectations, applications are created to deliver a seamless experience on multiple devices on various platforms. Continuous testing across devices and browsers, as well as apt deployment of multi-platform products, are essential. These require domain expertise, complimenting infrastructure, and a sound QA strategy. According to a report published in 2020-2021, the budget proportion allocated for QA was approximately 22%. 

Digital transformation has a massive impact on the time-to-market. Reduced cycle time for releasing multiple application versions by adopting Agile and DevOps principles has become imperative for providing a competitive edge. This has made automation an irreplaceable element in one’s QA strategy. With automation, a team can run tests for 16 additional hours (excluding the 8 hours of effort, on average, by a manual tester) a day, thus reducing the average cost of testing hours. In fact, as per studies, in 2020, approximately 44 percent of IT companies have automated half of their testing. 

A thorough strategy provides transparency on delivery timelines and strong interactions between developers and the testing team that comprehensively covers every aspect of the testing pyramid, from robust unit tests and contracts to functional end-to-end tests. 

Key performance metrics for QA

There are a lot of benefits to tracking performance metrics. QA performance metrics are essential for discarding inefficient strategies. The metrics also enable managers to track the progress of the QA team over time and make data-driven decisions. 

Here are five metrics to track the performance of your Quality Assurance team and the efficiency of your Quality Assurance strategy. 

1) Reduced risk build-on-build:

This metric is instrumental in ensuring a build’s stability over time by revealing the valid defects in builds. The goal is to decrease the number of risks impacting defects from one build compared to the next over the course of the QA project. However, this strategy, whilst keeping risk at the center of any release, aims to achieve the right levels of coverage across new and existing functionality. 

If the QA team experiences a constant increase in risk impacting defects, it may be because of the following reasons:

To measure the effectiveness further, one should also note the mean time to detect and the mean time to repair a defect.

2) Automated tests

Automation is instrumental in speeding up your release cycle while maintaining quality as it increases the depth, accuracy, and, more importantly, coverage of the test cases. According to a research report published in 2002, the earlier a defect is found, the more economical it is to fix, as it costs approximately five times more to fix a coding defect once the system is released.

With higher test coverage, an organization can find more defects before a release goes into production. Automation also significantly reduces the time to market by expediting the pace of development and testing. In fact, as per a 2020-2021 survey report, approximately 69% of the survey respondents stated reduced test cycle time to be a key benefit of automation. 

To ensure that the QA team maintains productivity and efficiency levels, measuring the number of automation test cases and delivering new automation scripts is essential. The metric monitors the speed of test case delivery and identifies the programs needing further testing. We recommend analyzing your automation coverage by monitoring total test cases. 

While measuring this metric, we recommend taking into account:

  • Requirements coverage vs. automated test coverage
  • Increased test coverage due to automation (for instance, multiple devices/browsers)
  • Total test duration savings

3) Tracking the escaped bugs and classifying the severity of bugs:

Ideally, there should be no defects deployed into the production. However, despite best efforts, most of the time, bugs make it into production. To track this would involve the team establishing checks and balances and classifying the severity of the defects. The team can measure the overall impact by analyzing the bugs of high severity levels that made into production. This is one of the best overall metrics for evaluating the effectiveness of your QA processes.  Customer-reported issues/defects may help identify specific ways to improve testing. 

4) Analyzing the execution time of test cycles:

The QA teams should keep track of the time taken to execute a test. The primary aim of this metric is to record and verify the time taken to run a test for the first time compared to subsequent executions. This metric can be a useful one to identify automation candidates, thereby reducing the overall test cycle time. The team should identify tests that can be run concurrently to increase effectiveness. 

5) Summary of active defects

This includes a team capturing information such as the names and descriptions of a defect. The team should keep a track/summary of verified, closed, and reopened defects over time. A low trajectory in the number of defects indicates a high quality of a product.

Be Agile and surge ahead in your business with Trigent’s QE services 

Quality Assurance is essential in every product development, and applying the right QA metrics enables you to track your progress over time. Trigent’s quality engineering services empower organizations to increase customer adoption and reduce maintenance costs by delivering a superior-quality product that is release-ready.

Are you looking to build a sound Quality Assurance strategy for your organization? Need Help? Talk to us. 

5 ways QA can help you accelerate and improve your DevOps CI/CD cycle

A practical and thorough testing strategy is essential to keep your evolving application up to date with industry standards.

In today’s digital world, nearly 50% of organizations have automated their software release to production. It is not surprising given that 80% of organizations prioritize their CX and cannot afford a longer wait time to add new features to their applications.  A reliable high-frequency deployment can be implemented by automating the testing and delivery process. This will reduce the total deployment time drastically. 

Over 62% of enterprises use CI/CD (continuous integration/continuous delivery) pipelines to automate their software delivery process.  Yet once the organization establishes its main pipelines to orchestrate software testing and promotion, these are often left unreviewed.  As a result, the software developed through the CI/CD toolchains evolve frequently.  While the software release processes remain stagnant. 

The importance of an optimal QA DevOps strategy

DevOps has many benefits in reducing cost, facilitating scalability, and improving productivity. However, one of its most critical goals is to make continuous code deliveries faster and more testable. This is achieved by improving the deployment frequency with judicious automation both in terms of delivery and testing. 

Most successful companies deploy their software multiple times a day. Netflix leverages automation and open source to help its engineers deploy code thousands of times daily. Within a year of its migration to AWS, Amazon engineers’ deployed code every 11.7 seconds with robust testing automation and deployment suite.  

A stringent automated testing suite is essential to ensure system stability and flawless delivery. It helps ensure that nothing is broken every time a new deployment is made. 

The incident of Knight Capital underlines this importance. For years, Knight relied on an internal application named SMARS to manage their buy orders in the stock market. This app had many outdated sections in its codebase that were not removed. While integrating a new code, Knight overlooked a bug that inadvertently called one of these obsolete features. This resulted in the company making buy orders worth billions in minutes. It ended up paying a $460M fine and going bankrupt overnight.

A good QA protects against the failed changes and ensures that it does not trickle down and affects the other components.  Implementing test automation in CI/CD will ensure that every new feature undergoes unit, integration, and functional tests. With this, we can have a highly reliable continuous integration process with greater deployment frequency, security, reliability, and ease. 

An optimal QA strategy to streamline the DevOps cycle would include a well-thought-out and judiciously implemented automation for QA and delivery. This would help in ensuring a shorter CI/CD cycle. It would also offer application stability and recover from any test failure without creating outages. Smaller deployment packages will ensure easier testing and faster deployment. 

5 QA testing strategies to accelerate CI/CD cycle

Most good DevOps implementations include strong interactions between developers and rigorous, in-built testing that comprehensively covers every level of the testing pyramid. This includes robust unit tests and contracts for API and functional end-to-end tests. 

Here are 5 best QA testing strategies you should consider to improve the quality of your software release cycles:

Validate API performance with API testing

APIs are one of the most critical components of a software application. It holds together the different systems involved in the application. The different entities that rely on the API, ranging from users, mobile devices, IoT devices, and applications, are also constantly expanding. Hence it is crucial to test and ensure its performance. 

Many popular tools such as Soap UI and Swagger can easily be plugged into any CI/CD pipeline. These tools help execute API tests directly from your pipeline. This will help you build and automate your test suites to run in parallel and reduce the test execution time.

Ensure flawless user experience with Automated GUI testing

Just like an API, the functionality and stability of GUIs are critical for a successful application rollout.  GUI issues after production rollout can be disastrous users wouldn’t be able to access the app or parts of its functionality.  Such issues would be challenging to troubleshoot as they might reside in individual browsers or environments. 

A robust and automated GUI test suite covering all supported browsers and mobile platforms can shorten testing cycles and ensure a consistent user experience. Automated GUI testing tools can simulate user behavior on the application and compare the expected results to the actual results. GUI testing tools like Appium and Selenium help testers simulate the user journey.  These testing tools can be integrated with any CI/CD pipeline. 

Incorporating these tools in your automated release cycle can validate GUI functions across various browsers and platforms.

Handle unscheduled outages with Non-functional testing

You may often encounter unexpected outages or failures once an application is in production. These may include environmental triggers like a data center network interruption or unusual traffic spikes. These are often outlying situations that may lead to a crisis, provided your application cannot handle it with grace. Here lies the importance of automated non-functional testing

Nonfunctional testing incorporates an application’s behavior under external or often uncontrollable factors, such as stress, load, volume, or unexpected environmental events. It is a broad category with several tools that can be incorporated into the CI/CD cycle. Integrating automated non-functional testing gates within your pipeline is advisable before the application gets released to production.

Improve application security with App Sec testing

Many enterprises don’t address security until later in the application release cycle. The introduction of DevSecOps has increased focus on including security checkpoints throughout the application release lifecycle. The earlier a security vulnerability is identified, the cheaper it is to resolve. Today, different automated security scanning tools are available depending on the assets tested.

The more comprehensive your approach to security scanning, your organization’s overall security posture will be better. Introducing checkpoints early is often a great way to impact the quality of the released software. 

Secure end-to-end functionality with Regression testing 

Changes to one component may sometimes have downstream effects across the complete system functionality. Since software involves many interconnected parts today, it’s essential to establish a solid regression testing strategy.

Regression testing should verify that the existing business functionality performs as expected even when changes are made to the system. Without this, bugs and vulnerabilities may appear in the system components. These problems become harder to identify and diagnose once the application is released. Teams doing troubleshooting may not know where to begin, especially if the release did not modify the failing component.

Accelerate your application rollout with Trigent’s QA services

HP LaserJet Firmware division improved its software delivery process and reduced its overall development cost by 40%. They achieved this by implementing a delivery process that focussed on test automation and continuous integration. 

Around 88% of organizations that participated in research conducted on CI/CD claim they lack the technical skill and knowledge to adopt testing and deployment automation. The right QA partner can help you devise a robust test automation strategy to reduce deployment time and cost. 

New-age applications are complex. While the DevOps CI/CD cycle may quicken its rollout, it may fail if not bolstered by a robust QA strategy. QA is integral to the DevOps process; without it, continuous development and delivery are inconceivable. 

Does your QA meet all your application needs? Need help? Let’s talk

Enhancing the Business Strategy with Data Engineering Solutions

Businesses worldwide are realizing the importance of the considerable volume of data they possess and the need to extract value from it and incorporate it into enhancing Customer Experience or simplifying operational processes for improved results. To do this, they are constantly looking to partner with experts who can guide them on what to do with that data. This is where data engineering services providers come into play.

Data engineering consulting is an inclusive term that encompasses multiple processes and business functions. Data engineering involves integrating several technology components into a seamless solution with a common goal: to extract valuable information from data to generate value.

For example, McDonald’s collaborated with data engineering firms to automate verbal order intake via machine learning and natural language processing (NLP). The business need was to shorten wait times, improve order accuracy and free up restaurant employees to focus on enhancing one-to-one service. Data engineering enabled McDonald’s to understand how orders are placed nature of questions or requests and use that insight to design the solution.

Challenges faced by the manufacturer

  • Identifying the right business areas to apply Data Engineering. Companies need to validate the availability of relevant data for the project and quantify the impact to justify the effort to be undertaken.
  • A need for end-to-end ML engineering expertise. In addition to technical skills, domain experience is vital to analyzing and interpreting the data, which is then used to model the ML solution.
  • The complexity of NLP itself. In addition to the technical dimension, understanding the user behavior and application of Human Factors Design are critical requirements for a successful outcome.

How data engineering consulting helps

McDonald deployed and implemented business intelligence and analytics systems to overcome most challenges for decision support. In addition, they also used sophisticated NLP solutions and ML to listen to customers’ verbal orders and automatically put them through to the kitchen.

Data engineering: To turn data into valuable insights

Usually, data engineers are responsible for developing data pipelines to bring together information from several source systems. Data must be consolidated, cleansed, and structured for proper use in analytics applications. This data is then stored in Data Lakes or Data Warehouses, which makes data easily accessible for processing and further use.

The amount of data an engineer gets to work with varies with the company, particularly its size. The larger businesses have complex analytics architecture and may require more data engineers. Some businesses are more data-intensive, including retail, healthcare, and financial services.

In most cases, data engineers work in tandem with data scientists to enhance or create new data models, improve the AI algorithms, and create opportunities for customer innovation that are beneficial for the business.

What can a data engineer do for a business?

Typically, a data engineer’s day-to-day work revolves around managing Data pipelines, managing Data Lakes, and DataOps activities.

Managing data pipelines

ETL (Extract, Transform, Load) processes are a critical activity. It involves developing data extraction, transformation, and loading tasks, transferring them between several environments, and purging them so that they arrive in a regular and structured way in the hands of analysts and data scientists.

Extraction
For Mcdonald’s, this would involve extracting audio data of the order being placed, transaction record of the order from the POS, images from the store CCTV and more. Data pipelines from each source to the Data Lake need to be implemented. The source data will be tagged and annotated for easy reference.

Transformation
Next, data engineers coordinate the cleaning of the data, eliminate duplicates, fix errors, tag missing records, discard unusable material, and classify them with annotations & descriptions to aid in processing.

Load
The data is loaded into its destination: a database located on a company’s server or a data warehouse in the cloud. In addition to the correct export, one of the primary concerns in this final stage is security surveillance. The data engineer has to guarantee that the information is safe from unauthorized access within the organization and external cyberattacks.

Managing data lakes/DWH

Given the large amounts of data involved, Data Engineers need to design the Data Lake or DWH such that the desired information can be located and retrieved quickly, with minimum latency. In the case of Cloud-based storage, bandwidth requirements are associated costs for data retrieval that play a vital role in the design and operations of the data lake.

DataOps
With the constant ingress of data, Data Engineers need to ensure that the latest and correct version of data is available to the Analysts and Data Scientists for analysis and modeling. Updated data models are then correctly deployed into production for use in Customer Applications. DataOps is similar to DevOps in software development, the difference being that this involves the flow and use of data between Developers, Analysts, and Production.

Data Visualization & Analytics
The processed data is either collated and analyzed for Management decisions or used in real-time applications to enhance service delivery. Visualization tools such as Grafana, PowerBI, Tableau, and Google Charts pull relevant data from the DWH and provide various decision support options to suit every business.

What are data engineering services?

Data engineering services help businesses replace their costly, in-house data infrastructure and transform their information pipelines into robust systems with the aid of data engineers.

With the growing importance of data across all business verticals, data engineering services will become a helpful resource that enables businesses to extract valuable information.

The primary reason behind the growth of these services is that they ensure data availability in the right format at the right time.

How can data engineering consulting help businesses?

Almost all businesses face data-related roadblocks that require a certain degree of creativity and technical expertise. Data engineering consulting companies can quickly help businesses resolve such issues by comprehensively understanding data pipelines. They play a crucial role in advancing a company’s data science initiative.

For example, NextGen Healthcare was searching to provide health data to its Health Information Exchange customers in a modern platform that was easy to use, scale, and can create value-driven insights. With the help of a data engineering company, they built a new analytics solution for their existing platform, which enables their customers to use health data to its full potential for analytics and reporting.

Nowadays, many businesses are undergoing a digital transformation using design-led engineering and test-driven automation. This is why partnering with a reliable data engineering consulting service provider is necessary for businesses that want to compete in today’s competitive environment.

Data engineering consultants like Trigent can ensure that a business’s data analysis process is straightforward and effective. While every company has different data analysis requirements, many of them can benefit from collaborating with one on the team. Some of the most common ways that Trigent can help businesses include: creating and maintaining or improving infrastructure, solving complicated business problems, real-time interactive analytics, enhanced business intelligence through data models, streamlining data science processes, machine learning, data pipelines, and continued focus on cutting-edge practices in data science.

Extract actionable insight from your existing data to enhance your business. Let’s talk

Continuously Engineering Application Performance

The success of an application today hinges on customer experience. To a large extent, it’s the sum of two components, one being the applicability of the software product features to the target audience and the second, the experience of the customer while using the application. In October 2021, a six-hour outage of the Facebook family of apps cost the company nearly $100 million in revenue. Instances like these underline the need to focus on application performance for a good customer experience. We are witnessing an era of zero patience, making application speed, availability, reliability, and stability more paramount to product release success. 

Modern application development cycles are agile, or DevOps led, effectively addressing application functionality through MVP and subsequent releases. However, the showstopper in many cases is application underperformance. This is an outcome of the inability of an organization to spend enough time analyzing release performance in real-life scenarios. Even in agile teams, performance testing happens one sprint behind other forms of testing. With an increase in the number of product releases, the number of times application performance checks can be done, and the window available to do full-fledged performance testing is reducing.

How do you engineer for performance?

Introducing performance checks & testing early in the application development lifecycle helps to detect issues, identify potential performance bottlenecks early on and take corrective measures before they have a chance to compound over subsequent application releases. This also brings to the fore predictive performance engineering – the ability to foresee and provide timely advice on vulnerable areas. By focusing on areas outlined in the subsequent sections, organizations can move towards continuously engineering applications for superior performance rather than a testing application for superior performance.

Adopt a performance mindset focused on risk and impact

Adopting a performance mindset the moment a release is planned can help anticipate many common performance issues. The risks applicable to these issues can be classified based on various parameters like scalability, capacity, efficiency, resilience, etc. The next step is to ascertain the impact those risks can have on the application performance, which can further be used to stack rank the performance gaps and take remedial measures.

An equally important task is the choice of tools/platforms adopted in line with the mindset. For, e.g., evaluating automation capability for high scale load testing, bringing together insights on the client as well as server-side performance & troubleshooting, or carrying out performance testing with real as well as virtual devices, all the while mapping such tools against risk impact metrics.

Design with performance metrics in mind

Studies indicate that many performance issues remain unnoticed during the early stages of application development. With each passing release, they mount up before the application finally breaks down when it encounters a peak load. When that happens, there arises a mandate to revisit all previous releases from a performance point of view, which is a cumbersome task. Addressing this issue calls for a close look at behaviors that impact performance and building them into the design process.

·         Analyzing variations or deviations in past metrics from component tests,

·         Extending static code analysis to understand performance impacts/flaws, and

·      Dynamic code profiling to understand how the code performs during execution, thereby exposing runtime vulnerabilities.

Distribute performance tests across multiple stages

Nothing could be more error-prone than scheduling performance checks towards the end of the development lifecycle. When testing each build, it makes a lot more sense to incorporate performance-related checks as well. At the unit level, you can have a service component test for analyzing at an individual service level and a product test focusing on the entire release delivered by the team. Break testing individual components continuously through fast, repeatable performance tests will help to understand their tolerances and dependencies on other modules.

For either of the tests mentioned above, mocks need to be created early to ensure that interfaces to downstream services are taken care of, without dependency on those services to be up and running. This should be followed by assessing integration performance risk whereby code developed by multiple DevOps teams is brought together. Performance data across each build can be fed back to take corrective actions along the way. Continuously repeating runs of smaller tests and providing real-time feedback to the developers help them understand the code development much better and quickly make improvements to the code.

Evaluate application performance at each stage of the CI/CD pipeline

Automating and integrating performance testing into the CI/CD process involves unit performance testing at the code & build stages, integration performance testing when individual software units are integrated, system-level performance testing and load testing, and real user monitoring when the application moves into a production environment. Prior to going live, it would be good to test the performance of the complete release to get an end-to-end view.

Organizations that automate and integrate performance tests into the CI/CD process are a common practice that runs short tests as part of the CI cycle unattended. What is needed is the ability to monitor the test closely as it runs and look for anomalies or signs of failure that point to a corrective action to be taken on the environment or on the scripts as well as application code. Metrics from these tests can be compared to performance benchmarks created as part of the design stage. The extent of deviations from benchmarks can point to code-level design factors causing performance degradation.

Assess performance in a production environment

Continuous performance monitoring happens after the application goes live. The need at this stage is to monitor application performance through dashboards, alerts, etc., and compare those with past records and benchmarks. The analysis can then decode performance reports across stages to foresee risks and provide amplified feedback into the application design stage.

Another important activity that can be undertaken at this stage is to monitor end-user activity and sentiment for performance. The learnings can further be incorporated into the feedback loop driving changes to subsequent application releases.

Continuously engineer application performance with Trigent

Continuously engineering application performance plays a critical role in improving the apps’ scalability, reliability, and robustness before they are released into the market. With years of expertise in quality engineering, Trigent can help optimize your application capacity, address availability irrespective of business spikes and dips, and ensure first-time-right product launches and superior customer satisfaction and acceptance.

Does your QA meet all your application needs? Let’s connect and discuss

TestOps – Assuring application quality at scale

The importance of TestOps

Continuous development, integration, testing, and deployment have become the norm for modern application development cycles. With the increased adoption of DevOps principles to accelerate release velocity, testing has shifted left to be embedded in the earlier stages of the development process itself. In addition, microservices-led application architecture has led to the adoption of shift right testing and testing individual services, and releases in the later stages of development, adding further complexity to the way quality is assured.

These challenges underline the need for automated testing. An increasing number of releases on one hand and an equally reducing release cycle times on the other have led to a strong need to exponentially increase the number of automated tests developed sprint after sprint. Although automation test suites reduce testing times, scaling these suites for large application development cycles mandates a different approach.

TestOps for effective DevOps – QA integration

In its most simplistic definition, TestOps brings together development, operations, and QA teams and drives them to collaborate effectively to achieve true CI/CD discipline. Leveraging four core principles across planning, control, management, and insights helps achieve test automation at scale.

  • Planning helps the team prioritize key elements of the release and analyze risks affecting QA like goals, code complexity, test coverage, and automatability. It’s an ongoing collaborative process that embeds rapid iteration for incorporating faster feedback cycles into each release.
  • Control refers to the ability to perform continuous monitoring and adjust the flow of various processes. While a smaller team might work well with the right documentation, larger teams mandate the need for established processes. Control essentially gives test ownership to the larger product team itself regardless of what aspect of testing is being looked at like functional, regression, performance, or unit testing.
  • Management outlines the division of activities among team members, establishes conventions and communication guidelines, and organizes test cases into actionable modules within test suites. This is essential in complex application development frameworks involving hundreds of developers, where continuous communication becomes a challenge.
  • Insight is a crucial element that analyses data from testing and uses it to bring about changes that enhance application quality and team effectiveness. Of late, AI/ML technologies have found their way into this phase of TestOps for better QA insights and predictions.

What differentiates TestOps

Unlike existing common notions, TestOps is not merely an integration of testing and operations. The DevOps framework already incorporates testing and collaboration right from the early stages of the development cycle. However, services-based application architecture introduces a wide range of interception points that mandate testing. These, combined with a series of newer test techniques like API testing, visual testing, and load and performance testing, slow down release cycles considerably. TestOps complements DevOps to plan, manage and automate testing across the entire spectrum, right from functional and non-functional testing to security and CI/CD pipelines. TestOps brings the ability to continuously test multiple levels with multiple automation toolsets and manage effectively to address scale.

TestOps effectively integrates software testing skillset and DevOps capability along with an ability to create an automation framework with test analytics and advanced reporting. By managing test-related DevOps initiatives, it can effectively curate the test pipeline, own it, manage effectively to incorporate business changes, and adapt faster. Having visibility across the pipeline through automated reporting capabilities also brings the ability to detect failing tests faster, driving faster business responses.

By sharply focusing on test pipelines, TestOps enables automatic and timely balancing of test loads across multiple environments, thereby driving value creation irrespective of an increase in test demand. Leveraging actionable insights on test coverage, release readiness, and real-time analysis, TestOps ups the QA game through root cause analysis of application failure points, obviating any need to crunch tons of log files for relevant failure information.

Ensure quality at scale with TestOps

Many organizations fail to consistently ensure quality across their application releases in today’s digital-first application development mode. The major reason behind this is their inability to keep up with test coverage of frequent application releases. Smaller teams ensure complete test coverage by building appropriate automation stacks and effectively collaborating with development and operations teams. For larger teams, this means laying down automation processes, frameworks, and toolsets to manage and run test pipelines with in-depth visibility into test operations. For assuring quality at scale, TestOps is mandatory. 

Does your QA approach meet your project needs at scale? Let’s talk

QE strategy to mitigate inherent risks involved in application migration to the cloud

Cloud migration strategies, be it lift & shift, rearchitect or rebuild, are fraught with inherent risks which need to be eliminated with the right QE approach

The adoption of cloud environments has been expanding for several years and is presently in an accelerated mode. A multi-cloud strategy is the defacto approach adopted by multiple organizations, as per Flexera 2022 State of the Cloud Report. The move toward cloud-native application architectures, exponential scaling needs of applications, and increased frequency and speed of product release launches have contributed to increased cloud adoption.

The success of migrating the application landscape to the cloud hinges on the ability to perform end-to-end quality assurance initiatives specific to the cloud. 

Underestimation of application performance

Availability, scalability, reliability, and high response rates are critical expectations from an application in a cloud environment. Application performance issues can come to light on account of incorrect sizing of servers or network latency issues that might not have surfaced when the application is tested in isolation. It can also be an outcome of an incorrect understanding of probable workloads that can be managed by an application while in a cloud environment. 

The right performance engineering strategy involves designing for performance in mind and fulfilling performance validations, including load testing. This ensures that the application under test remains stable in normal and peak conditions and defines and sets up application monitoring toolsets and parameters. There needs to be an understanding of workloads with the potential to be moved to the cloud and ones that need to remain on-premise. Incompatible application architectures need to be identified. Load testing should be carried out in parallel to record SLA response times across various loads for those moved to the cloud. 

Security and compliance

With the increased adoption of data privacy norms like GDPR and CCPA, there is a renewed focus on ensuring the safety of data migrated from application to cloud. Incidents like the one with Marriott hotel, where half a million sensitive customer information like credit cards and identity were compromised, have brought the need to test the security of data loaded onto cloud environments. 

A must-have element of a sound QA strategy is to ensure that both applications and data are secure and can withstand malicious attacks. With cybersecurity attacks increasing both in quantity and innovative tactics, there is a strong need for the implementation of security policies and testing techniques, including but not limited to vulnerability scanning, penetration testing, and threat and risk assessment. These are aimed at the following.

  • Identifying security gaps and weaknesses in the system
  • DDoS attack prevention
  • Provide actionable insights on ways to eliminate potential vulnerabilities

Accuracy of Data migration 

Assuring the quality of data that is being migrated to the cloud remains the top challenge, without which the convenience and performance expectation from cloud adoption falls flat. It calls for assessing quality before migrating, monitoring during migration, and verifying the integrity and quality post-migration. This is fraught with multiple challenges like migrating from old data models, duplicate record management, and resolving data ownership, to name a few. 

White-box migration testing forms a key component of a robust data migration testing initiative. It starts off by logically verifying a migration script to guarantee it’s complete and accurate. This is followed by ensuring database compliance with required preconditions, e.g., detailed script description, source, and receiver structure, and data migration mapping. Furthermore, the QA team analyzes and assures the structure of the database, data storage formats, migration requirements, the formats of fields, etc. More recently, predictive data quality measures have also been adopted to get a centralized view and better control over data quality. 

Application Interoperability

Not all apps that need to migrate to the cloud may be compatible with the cloud environment. Some applications show better performance in a private or hybrid cloud than in a public cloud. Some others require minor tweaking, while others may require extensive reengineering or recoding. Not identifying cross-application dependencies before planning the migration waves can lead to failure. Equally important is the need to integrate with third-party tools for seamless communication across applications without glitches. 

A robust QA strategy needs to identify applications that are part of the network, their functionalities, and dependencies among applications, along with each app’s SLA since dependencies between systems and applications can make integration testing potentially challenging. Integration testing for cloud-based applications brings to the fore the need to consider the following: 

  • Resources for the validation of integration testing 
  • Assuring cloud migration by using third-party tools
  • Discovering glitches in coordination within the cloud
  • Application configuration in the cloud environment
  • Seamless integration across multiple surround applications

Ensure successful cloud migration with Trigent’s QE services

Application migration to the cloud can be a painful process without a robust QE strategy. With aspects such as data quality, security, app performance, and seamless connection with a host of surrounding applications being paramount in a cloud environment, the need for testing has become more critical than ever. 

Trigent’s cloud-first strategy enables organizations to leverage a customized, risk-mitigated cloud strategy and deployment model most suitable for the business. Our proven approach, frameworks, architectures, and partner ecosystem have helped businesses realize the potential of the cloud.

We provide a secure, seamless journey from in-house IT to a modern enterprise environment powered by Cloud. Our team of experts has enabled cloud transformation at scale and speed for small, medium, and large organizations across different industries. The transformation helps customers leverage the best architecture, application performance, infrastructure, and security without disrupting business continuity. 

Ensure a seamless cloud migration for your application. Contact us now!

Cybersecurity in Manufacturing:
How can factories manage data security risks with Smart technology?

The importance of cybersecurity in manufacturing

Does this sound like you?

After intense negotiations with dozens of vendors, grueling engineering discussions with the production team, painful budget approvals, and months of redrawing the assembly lines, you moved your semi-automated production process into something contemporary. Your modern world-class manufacturing line is now a text-book case of how a connected Industry IoT plant should look: you have robotized processes, IoT asset management, automated vendor plug-ins, remote monitoring and control of most production routines, vision managed defect assessment, and a holistic view of how your other plants halfway around the world are functioning -all in a single screen, with a few clicks.

Now that you have slashed defect rate cut down human intervention, and improved production rate, you think you have got it all figured out and can take that over-due holiday on the beach? Right?

Wrong.

Sorry to be dramatic. But this is what the cyber bots are heard saying: “Thank you for creating a fertile territory for us to proliferate. We couldn’t be luckier”.

Speed is only half the battle in IIoT

The ‘Floating assembly lines’ of industrial revolution 4.0 are designed to meet demand in the shortest time possible. Approved supplier systems automatically log in and ship components to a live assembly line to meet the production targets of an OEM producer. Most of these decisions are made by systems using a variety of software (AI, IoT hub, decision algorithms), learning systems (M2M), networking (IR, 5G NR. Cloud computing), and production systems (3D printing).

Consider the possibility that a supplier’s system is infected with malware and enters this system. It could proliferate the OEM supply chain, other supplier systems, and respective corporate IT infrastructure in minutes. The potential for damage is even more significant if, by some means, it mutates and destroys safety mechanisms in the plant and endangers human lives.

According to the Deloitte and Manufacturers Alliance for Productivity and Innovation (MAPI) study, 48% of surveyed manufacturers fear that cyber attack is a real threat and the greatest danger they envisage for smart factories. And damage due to a cyber incident in manufacturing was estimated to be about $330K.

Disconnected islands in a sea of connectivity

The single biggest threat appears to come from here: Operational Tech (OT) and Information Tech (IT) systems do not talk to each other. OT refers to hardware and software used to change, monitor, or control physical devices or processes within a production facility.
Traditionally, manufacturing systems have been proprietary with few, if any, open standards for third-party plug-ins.

Tightly coupled legacy systems become a natural barrier for easy upgrades imposing change-impact study for every minor upgrade. Security controls for such systems are vendor-driven patches that are slow to come by. Also, vendors of traditional manufacturing systems do not cover OT in service agreements and maintenance contracts. The IT team simply believes that ‘all is well as they focus on the rest of corporate ERP, DB, networking, and productivity systems.

Some important cyber security considerations for the manufacturing facility are detailed below:

  • Solution Design: Restrict device and system access to authorized personnel only. Ensure cloud or network access follows rules-based access control.
  • Access & Authorisation: Ensure default passwords are changed in all IIoT devices, the new passwords conform to IT Security policy, and access control of edge devices is regulated. Default password vulnerabilities in 3rd party connected devices are a leading cause of security vulnerability.
  • Production Planning: Ensure company-wide secure remote access policy is defined, followed, and documented. Ensure cyber intelligence information exchange, record incidents, document phishing attempts, and develop thwart methods.
  • New Technologies: 3D printing and enhancements to the existing production line should be zoned separately with one-step isolation. For network 3D printers, it may be required to run separate cyber assessment tests and share reports with corporate IT security teams.
  • RPA, ML, NLP, and AI: These new technologies have clear benefits on the shop floor but will bring in their threats. Deploy rigorous application whitelisting, access control, portable memory control (USB drives moving in and out), controlled access to the internet on such systems, and accurate real-time inventory management.
  • Asset Management: Ensure security rules and policies are risk-based rather than compliance-based. Maintain a qualified, dedicated team to create surprises in addition to routine checks. This team should be aware of company-wide incidents and trained to observe seemingly unconnected events to extract real intelligence in a security scenario.

Since digital and cybersecurity elements will become all-pervasive sooner or later within corporates, it is a matter of time before they start impacting manufacturing processes.

Conduct a thorough cybersecurity assessment

This is an independent exercise and should not be downplayed in a regular corporate IT security audit. Ideally, the cyber assessment should be done every six months, including OT in the IIoT environment, recorded results, gaps plugged, shared with corporate IT and cybersecurity intelligence groups in the industry for mutual benefits.

It is also advised to build security protocols across the corporation, cover micro-assets and entry points for physical and digital products, and make sure the protocols are part of an overall security umbrella policy applicable to all branches and personnel.

In conclusion, remember that internal view often leads to fatigue derived from familiarity. It helps tap the rich experience of industry experts who have already done some of these things.

For example, at Trigent, our industrial security experts have delivered solutions in RPA (complementing human judgment with automation-led efficiency), predictive maintenance, and AR (Augmented Reality – helping find unique ways to connect humans and machines) for big and small manufacturers. Our clients across energy and oil, retail and manufacturing, healthcare, and education stand testimony to our capabilities.

Give us a call or drop us a line. We will be happy to help.

Digital Transformation in Banking – What Is Right for Your Bank?

Digital transformation in banking has been an important trend amidst economic uncertainties induced by the pandemic. Financial companies are dipping their toes in digital waters, eager to modernize their IT structure in 2022. It is no surprise that Gen-Z and millennials want their banks to be technology-driven with competitive digital solutions.

Digital and mobile channels are now critical for customer acquisition and satisfaction. The dependency on e-payments has increased all over the world. The global mobile payment market is expected to surpass US$ 590 Bn by 2032 at a CAGR of 30% for the forecast period 2022-2032. The United States alone expects a market valuation of US$ 42 Bn in 2022, with contactless payments growing by 150% in 2020.

Banks, too, are eager to modernize their IT infrastructure with technologies that would bring about a cultural, organizational, and operational change. They are now looking for improvisation in four distinct areas: process, technology, data, and organizational change. The focus is now on building an ecosystem that facilitates personal, automated, and cohesive customer journeys.

The cornerstones of successful digital transformation in banking

As banks gear up for the ‘next normal’ waiting for the pandemic to recede, they reset their digital agenda on the road to recovery. They are shifting towards digital channels to address scalability and reliability concerns while catering to customers’ growing needs.

Every project for digital transformation in banking, however, should work towards:

  • Engaging clients with tailor-made solutions and experiences
  • Empowering employees with tools and technologies to enable accessible, holistic information
  • Optimizing internal operations with automated, synchronized processes
  • Building a connected ecosystem

Top benefits include:

  • Faster time to market for product and pricing
  • Cost-effective ways to scale
  • Future readiness with agile and remote solutions
  • Digital competitiveness with capabilities like open banking and real-time payments
  • Better services to enhance product innovation and customer satisfaction
  • Lower risks with regulatory compliance and greater security
  • Greater efficiency and productivity
  • More business value with data insights and cognitive automation

Banking infrastructure modernization – Technologies and use cases

Artificial intelligence (AI), machine learning (ML), and Big Data – Financial companies are leveraging these powerful technologies to transform the customer experience with seamless services and safe transactions. They help detect and prevent payment fraud. They offer a 360-degree view of the customer and are believed to reduce delinquency rates by almost 76%.

AI can be applied for multiple banking infrastructure use cases such as risk assessment, fraud detection, asset management, credit intermediation, process automation, client onboarding and KYC (know your customer), and algorithmic trading. Global spending on these technologies is expected to double from $ 50 billion to $110 billion in 4 years from 2020 to 2024.

While AI and ML help increase the efficiency and accuracy of workflows, feeding ML models with big data helps decision-making around portfolio allocation, assessing creditworthiness, and making underwriting decisions. HSBC has been using AI for fraud detection, transaction monitoring, sanctions screening, and identifying insider trading & bribery.

Robotic Process Automation (RPA) – The operating activities in financial companies involve a multitude of standardized processes. RPA ensures optimal data processing and takes care of rule-based and repetitive tasks quickly and efficiently. It reduces human workload, minimizes errors, and enables cost reductions. Digital processing of business transactions also helps in fraud prevention in a big way.

SBI General Insurance has used RPA and AI to build a digital-first business model. It leverages technology to get a 360-degree view of customer activity across touchpoints to understand customer expectations and personalize their offerings. It uses predictive analytics to upscale its cross-sell initiatives and AI to personalize customer journeys. The company relies on RPA to keep track of total premium payments and implement tax liability confirmation.

Blockchain – Blockchain has secured a coveted place in a world of digital currencies like Bitcoin and Ethereum. It allows you to store cryptographic encryption in a block. All blocks have a unique value distributed across the network, and it is impossible to manipulate in any manner. Data integrity thus is an essential aspect of blockchain though it is popular for its speed and transparency.

Blockchain enables transactions almost in real-time and instantly saves changes, facilitating the exchange of massive amounts of data in the shortest time. Transactions are unchangeable, traceable, and protected from money laundering. Smart contracts stored on a blockchain help execute an agreement between participants without the involvement of an intermediary.

Such is the power of blockchain technology that China is kicking off an intensive blockchain trial involving 164 entities despite its checkered history with digital currencies. President Xi Jinping describes blockchain technology as “an important breakthrough for independent innovation of core technologies.”

Cloud computing – Financial companies now rely on external data centers to manage their workloads. Cloud computing technology has become an essential aspect of mobile banking and payment services. It also plays a crucial role in trading, evaluation processes, and customer relationship management.

As per a survey, 40% of banks have already deployed cloud computing, while 30% have deployed application programming interfaces (APIs). Cloud computing enables speed to market with new capabilities.

Singapore’s Asia Digital Bank Corporation (ADBC) has collaborated with Tencent to develop cloud-based banking technology to offer personalized experiences to customers. It also aims to provide small and medium-sized enterprises with digital banking services to ensure end-to-end, frictionless, and seamless processes.

First steps to creating sustainable outcomes

It is easy to navigate through the chaos despite economic uncertainties by building on core strengths and tweaking existing business models. Here’s what you can do.

Grow an ecosystem

Banks have long relied on the tried and tested method of ensuring growth. They have been introducing new and relevant products to existing customers. But those like Ideabank and ING have gone beyond their traditional core to strengthen customer engagement with a 360-view of customer data.

They now provide other services like accounts-receivable management and cash flow analysis to small and medium enterprise (SME) customers. Post Bank has gone a step further to capture a market share in nonbanking domains. It is now the largest provider of mobile phone services in Italy, using its already strong franchises to offer new services to existing customers.

Address multiple needs of customers with a financial supermarket

A mix of third-party offerings can help customers manage their financial needs via a single integrated channel.

That’s how aggregators sell 60% of the auto insurance policies in the United Kingdom. Bank Bazar in India caters to more than 23 million customers without having proprietary offerings.

Offer value throughout the customer journey

Banks and financial companies can grow if they decide to extend the scope of their services to add more importance at different stages of the customer journey.

Commonwealth Bank in Australia (CBA) created an augmented reality app to help customers use their phone’s camera to see the price and sales history of the properties they were interested in. The app with financial tools such as a mortgage calculator allowed the bank to extend its role in the home buyer’s journey.

Monetize the data with analytics

You can use customer data (location, lifestyle preferences, age, gender, etc.) to get insights and anticipate customer needs. Some of the biggest banks in Canada have collaborated with Toronto-based SecureKey to help customers access online services offered by the federal government using bank credentials. Banks rely on the data they have to verify identities before allowing access.

Credit card companies have access to the data of customers and merchants. This data helps them foster new partnerships and gain access to new potential customers.

Develop a product portfolio

Financial companies should also consider leveraging back-end assets to create value for smaller businesses. These businesses usually lack the reach or resources for core banking products and services. This makes an opportunity sweet spot for financial companies to develop and sell products through third parties.

ING has collaborated with Kabbage, a US-based startup, to provide value-added services in Europe. ING brought to the table its reservoir of capital and relationships with SMEs. At the same time, Kabbage leveraged its easy-to-use interface and risk-management algorithms to offer quick decisions on loan applications.

Modernize your banking infrastructure with Trigent

Regardless of the technologies you choose or the digital routes you wish to pursue, a good view of your capabilities is critical to ensure infrastructure modernization. We have extensive experience in helping financial companies achieve digital transformation goals. Our services and solutions are designed to help them at different junctures in their digital journeys to boost their digital capabilities.

We drive IT modernization projects for the BFSI sector to make it agile while taking care of the complex regulatory and compliance requirements.

We can partner with you to simplify and standardize your IT infrastructure. Call us today for a business consultation.

3 Ways Intelligent Analytics Can Improve Patient Outcomes in Healthcare

Hartford HealthCare, a comprehensive and integrated healthcare system serving more than 17,000 people daily across its 400 locations, recently announced its decision to launch a novel research initiative with Ibex Medical Analytics.

Ibex is the pioneer in AI-powered cancer diagnostics and will use its AI solution ‘the Galen Breast’ to help cancer diagnosis and improve patient care.

This initiative is a natural progression in Hartford HealthCare’s ongoing digital transformation. The AI assistant could provide a greater safety net with minimal effort when pathologist staffing and recruitment are becoming more challenging due to the fast-increasing number of cancer cases. The initiative underlines the growing importance of intelligent analytics in improving diagnostics and patient care.

While everyone agrees on the importance of good health care, medication errors alone cost about US$ 42 billion each year. 4 out of 10 patients suffer during primary and ambulatory healthcare, and often, these errors are related to diagnosis, prescription, and use of medicines. Healthcare experts believe patient engagement is the key to safer healthcare and can reduce the harm and the subsequent cost by up to 15% annually.

The growing concerns in the healthcare industry have put the spotlight on intelligent analytics to help clinicians and caregivers improve patient outcomes.

Intelligent diagnostics rely on Artificial Intelligence to create new pathways for healthcare

Due to its ability to analyze massive amounts of data with efficiency and accuracy, AI plays a transformative role in healthcare. It paves the path for analytics. It empowers healthcare professionals with clinically-relevant insights to diagnose diseases correctly. Artificial Intelligence is now part of the everyday workflows of top healthcare organizations. No wonder AI spending in the healthcare and pharmaceutical industries is predicted to surge.

From US$ 463 million in 2019 to US$ 2 billion in 5 years, the growth is being attributed to the vital role AI has been playing during the pandemic. Companies like Alibaba, YITU, Graphen, and Google DeepMind have been involved in building tools that could detect the virus and analyze the virus to predict its potential protein structure and track its geographical footprint.

Typically, intelligent analytics helps healthcare companies integrate insights derived from medical device data with patients’ health records to improve workflows and gather evidence of clinical outcomes. Data comes from different sources such as clinics, hospitals, medical insurance, medical equipment, and medical research.

When analytics in tandem with Artificial Intelligence, Machine Learning, and the Internet of Things is applied over big data, it provides actionable insights to make smarter decisions, optimize resources, and offer high-quality patient care. It helps caregivers with the required algorithms to create a value framework for all.

In the healthcare industry, intelligent analytics can be:

  • Descriptive – that examines and describes an event that occurred in the past
  • Diagnostic – that looks into the factors that caused the event
  • Predictive – that analyzes trends and historical data to predict such events
  • Prescriptive – that outlines the actions to be taken to prevent such events and attain future goals efficiently

Connecting the dots in healthcare with intelligent analytics

Intelligent analytics works for diverse use cases in healthcare, including early detection, identifying at-risk patients, preventing equipment downtime, and improving patient outcomes. It accelerates decision-making radically since caregivers have access to the necessary information.

Would surgery lead to any complications? If complications arise, would the patient be able to survive them? What are the odds of a patient getting readmitted to the intensive care unit after showing recovery symptoms?

These questions can be daunting, but analytics has all the answers!

While clinicians have been making quick choices in times of uncertainty, intelligent analytics allows them to make informed decisions. It provides predictive algorithms, and organizations are quick to acknowledge its benefits. As per a survey, 42 percent of those who embraced it have seen improvement in patient satisfaction, while 39 percent have seen significant cost savings.

Let’s look at the top applications to understand why intelligent analytics is an absolute must for the healthcare sector.

Early detection of anomalies in scans

The global anomaly detection market is up for massive growth, with its value predicted to touch US$ 8.6 billion by 2026. Also called outlier detection, it points out events and instances that seem suspicious compared to the rest of the data. In healthcare parlance, detecting anomalies early on can help prevent insurance fraud. It allows clinicians to differentiate the normal from the abnormal based on algorithms derived from data.

Especially when different anomalies exist, anomaly detection plays a critical role in ensuring accurate diagnosis.

AI company InformAI is highly focused on healthcare, offering products that improve radiologist productivity. One of the leading medical imaging companies developed AI-enabled image classifiers and patient outcome predictors to speed up medical diagnosis at the point of care. With access to colossal medical datasets, the company comes with key differentiators.

Services facilitating early detection of anomalies offer:

  • Direct access to the best medical experts and proprietary AI data augmentation
  • Model Optimization
  • 3D neural network toolsets

Precision medicine

The precision medicine market is growing, too, as it continues to transform the healthcare business. It involves a thorough examination of patient-specific data that plays a pivotal role in identifying and treating diseases. Even though it is a relatively new strategy, it is expected to touch US$ 278.61 billion by the end of 2030.

It is growing at a CAGR of 11.13% for the forecast period from 2020-to 2030, enabling ground-breaking research and treatments. It investigates a variety of illnesses by analyzing the genetic composition of patients and then creating a customized treatment plan for each patient.

Genoox, the company attempting to build the largest, most diverse real-world evidence dataset to provide answers to any genomic question, is making an impact with the most up-to-date genetic information. It helps healthcare professionals with precision insights to personalize treatments by translating genomic data into accurate, actionable insights at the point of care. It attempts to uncover the unknown with AI-powered technology to offer a real-world genomic evidence dataset.

Services leveraging genetics in precision medicine help:

  • Identify defects in newborns
  • Assess risks associated with inherited diseases
  • Understand drug prescriptions and dosage
  • Offer targeted therapies

Chronic disease management

The chronic disease management (CDM) market is predicted to reach US$ 14,329.15 million by 2029 as CDM continues to assist individuals impacted by a chronic condition with knowledge, resources, and medical care. The growth can be attributed to the prevalence of chronic diseases that necessitate better solutions and services, the increasing geriatric population, and the rise in chronic diseases.

Chronic disease management leverages artificial intelligence to efficiently process data and respond to it. Remedy Health, a leading AI-powered digital platform committed to empowering caregivers and healthcare professionals, offers information and insights to help deliver the best care.

It gives them access to expert opinions and tools and helps them uncover chronic illnesses via phone screening interviews. Early detection is the key to ensuring proper treatment, care, and desired patient outcomes. While competitors depend on sparse medical details provided to them only after the patient is admitted to the hospital, those using this platform capture a vast amount of clinically-relevant data for timely decision-making.

Chronic disease management services allow you to:

  • Offer care throughout the patient journey with actionable health and medical facts, insights, and applications
  • Maintain patient-specific models to determine risks and optimal treatment
  • Personalize treatments for chronic heart problems and life-threatening diseases like cancer and stroke
  • Ensure remote patient monitoring and self-management via support from wearable devices
  • Detect warning signs of illnesses and prevent disease progression with early intervention strategies

AI and analytics are now an integral aspect of health care. They aid in robotic surgeries empowering surgeons with precision and efficiency. These technologies form the core of administrative workflows, bridging the gap between patients and caregivers. AI-powered virtual nursing assistants also play a pivotal role in modern healthcare settings.

The question is – Are you leveraging AI-powered tools and applications well?

Give your business the power of intelligent analytics with Trigent

The stupendous growth in AI and analytics emphasizes the importance of these technologies in navigating through the healthcare landscape. We can help you explore the full potential of analytics and MedTech to offer value to your customers. Our technology experts can guide you to ensure you make the right AI investments, eliminate data silos, and create cutting-edge healthcare solutions.

Allow us to help you overcome data issues and simultaneously leverage advanced technologies to improve care and cure. Call us today for a business consultation.

5 Essential Technologies to get your Distributed Enterprise Future Ready

The global pandemic of 2020 permanently changed our working dynamics.  What is easily missed is how much the workplace has changed too.  Whether and how much work will return to the workplace (versus work from home) is a matter for a different blog.  Here, we want to talk about how the workplace has changed forever: especially the distributed enterprise. 

To be fair, the distributed enterprise is not a new concept at all.  From an IT/networking perspective, distributed enterprises always moved away from a centralized IT infrastructure to connected islands to maximize convenience, networking and efficiency, faster access, and localized control. 

In a borderless economy, where businesses fiercely compete for resources and market share, it is fair to expect re-architecting systems and networks to maximize customer benefits and increase employee flexibility.

How will you make the Distributed Enterprise future-ready? 

Here are our top 5 tech picks: 

Computing is moving to an Edge near you!

Powered by a significant increase in computing power and ubiquitous bandwidth, suddenly, it is possible to move the computing power to where it needs to be, rather than where it always was.  Be it in a user’s hands, PoS (Point of Sale) at a retail counter, manufacturing assembly line, or CSP (cellular service provider), the edge has moved closest to the point where it needs to be.  This comes with advantages such as faster response times, localized approvals saving a round trip to the central server, increased privacy, security, and reduced cloud costs.

Daihen, a Japanese manufacturer of industrial electronics equipment, realized that their Osaka plant could not handle data from dozens of sensors.  The data was being processed remotely by a cloud server, and response time was slow.  The solution came in the form of an Intelligent Edge solution from FogHorn that makes complex machine learning modules run on highly constrained devices.  The results were almost immediate: improved speed, higher accuracy, drop-in defect count, all of which encouraged them to increase investment in the Edge in the following year.

The Cloud is going hybrid

While the transition from an on-premises server to the cloud has been a work in progress, the cloud itself has metamorphosed entirely.  The distributed enterprise can now have a mix of premise, public and private cloud, as required based on business needs. 

The enterprise must be careful not to get locked into a hyper-scaler vendor’s vision of the future but keep options open to realize their own.  Since many of these paths are evolving, the enterprise needs to engage deeply and tread carefully while committing to future road maps. 

The CSP (cellular service provider) is also evolving and is now a major cloud provider with advanced capabilities in Service Edge and SD-WAN (software-defined Wide Area Network).  CSPs have blurred the line between enterprise and carrier cloud with their offerings.  With upcoming 5G rollouts, massive IoT networks, mmWave, and network slicing requirements, their cloud and edge capabilities will be of an entirely different scale.  Enterprises will need to understand how to best harness offerings from each vendor without compromising their requirements.

Enterprise software also has disaggregated from a monolithic form split into microservices (via containers) where code, debugger, utilities, and algorithms may be contained within the container and control routed appropriately to the parent code block as required.  Containers make decoupling of applications convenient by abstracting them from the runtime environment.  This way, they are deployed agnostic to the target environment.  These smaller services can be highly efficient and lend themselves to high scalability, but not without loading DevOps teams with the additional pressure of housekeeping. 

When Ducati, a global automotive giant, undertook a data center modernization project, they expected gains, but that was not how it turned out.  The hybrid cloud dramatically changed their perspective of what is possible.  The data awareness, speed, and minimal footprint across departments brought a new level of productivity that was not planned.

Hyper Automation

Currently, an emerging trend but likely will become mainstream as many of the required elements are already falling in place.  Hyper Automation refers to the coming together of systems, processes, software, and networking to automate most known processes with zero-touch human intervention resulting in ‘robotic process automation’ sequences. 

This advanced state of automation will require stable AI, ML modules, and integrating IT and OT (operation technologies in the IIoT world), where machines will make decisions and keep routine systems running.  Real-time monitoring and analytics are logged for a supervisor to check and intervene if necessary. 

Understanding documents through OCR (optical character recognition), emails using NLP (natural language processing), and enhancing automation using AI / ML data flows are increasingly common.  Banking and healthcare have seen successful deployments of OCR and NLP.

Data will soon be everywhere, but how about Security?

Security will become such an essential parameter for business success that the CISO (Chief Information Security Officer) might well, if not already, be the most important executive in the economy.  With billions of IoT devices from airplane tires to connected cars and heating systems at offices and production lines going online, the opportunities for a security breach just went up a notch.  And every incident will only dilute human trust delaying further progress and slowing down growth. 

With geopolitical scenarios going worse, cyber warfare being a reality, there is no telling when some of these will start impacting enterprise system security.  This is an ongoing new reality – almost as real as the pandemic. 

Quantum computing, the emerging innovation in high-speed computing, will be a threat too.  It is believed that current-day data breaches are being tapped and data being stored for analysis and targeting after quantum computing power becomes available (because current computing could take years to decipher this data).  Some cyber experts believe the advanced planning and methods of cybercriminals are years ahead of the capabilities of corporate IT security teams.  And that can be a cause for worry.

AIOps

When Gartner coined the term AIOps, it meant Artificial Intelligence for IT Operations (or Algorithmic IT operations).  They referred to a “method of combining big data and machine learning to automate IT operations and processes, including event correlation, anomaly detection, and causality determination.”

AIOps is a set of methods or practices that makes rapid data processing possible for vast volumes of data, which then feed into an ML engine to predict issues.  AIOps will be very much a requirement for the DevOps teams.  They try catching up with data and problems across hybrid environments to support agile processes in ever-changing platforms and networked silos. 

US infrastructure provider Ensono provides infrastructure support to mission-critical processes of many top enterprises.  As its volumes started growing, it became important for Ensono to invest in AIops to ensure its ability to monitor client hardware and software would not be compromised.  Investing in TrueSight AIops helped Ensono decrease its trouble ticket numbers from over 10,000 to a few hundred per month.  This is the power of AI ops.

In conclusion, remember no one has a crystal ball into the future.  But going by current technology trends in the distributed enterprise, some things are clear: growth, chaos, and churn are predicted.  It helps to have a trusted consulting team of experts on your side to learn from, seek advice from, and leverage from experience.

At Trigent, our domain experts have delivered solutions, charted digitization route maps, and provided distributed enterprise workflow design and architecture for future growth to global leaders in every sector. 

We are happy to share our learnings.  Do give us a call.  Drop us a line.  We are listening.

Trigent Software, a Pioneer in Software Development for IT Businesses: GoodFirms

There is everything with Trigent Software that a successful software development company holds to obtain remarkable market growth and mark customer footprints for long, from happy employees to happy customers. How? Let us get it in the words of Chella Palaniappan, the Senior VP of Trigent Software, Inc.

Trigent Software, Inc is a US-based software development company that provides digital engineering and IT services. Established in 1995, Trigent partners with its clients across its value chain, enabling them to design, build, deliver, and maintain their products and services that help them rank higher in the industry. The company helps its clients to achieve their desired goals through enterprise-wide digital transformation, modernization, and absolute optimization of their IT environment.  The company’s decades of experience and deep knowledge of working in different domains deliver transformational solutions to enterprises, SMBs, and ISVs.

In an interview with GoodFirms, Chella Palaniappan, the Senior VP of Marketing & Client Services in Trigent Software, Inc., discussed the story behind the company’s establishment and his role to take it to success.

The interview starts with Palaniappan sharing that he looks after the company’s client engagement matters in Enterprise Software Development, Product Development, Cloud Integration, and Quality Engineering  Services.  The honorable VP closely works with their clients in North America to ensure the company’s ODC execution and offshore initiatives are smooth and efficient. 

The company’s CEO – Bharat Khatau, and other pillar founders have started Trigent Software with a vision to provide quality IT services to global businesses.  The aim behind its establishment has always been to transform them through customized digital solutions by improving their operational productivity, efficiency, and technology, reducing complexity in business, and increasing their bottom line.  Moreover, Trigent has created over 600 software products in the market for small, medium, and large enterprises, along with developing patented products in the semantic internet space.

Chella Palaniappan advocates the in-house team model in the company.  It means everything from planning and execution to delivery and support.  All tasks are executed within Trigent’s premises.  Clients get a clear idea about the progress of their projects and other details from a responsible and talented pool of over 500 members composed of project managers, designers, developers, QAs, maintenance, and help desk – all under the same roof.  The company comes up with the right customer engagement model.  It offers an onshore model and hybrid engagement model depending upon the requirements and budget of the clients.

Throwing light on the company’s track record, he speaks of its stellar success in the development world.  Along with developing 600+ projects and having 6 US patents, Trigent owns several trade secrets that prove the credibility of its high-tech resources. 

The company works with the same level of care and diligence to make the engagement a success.  The cutting-edge solutions, strategic insights, and execution of excellence are recognized worldwide.  The recognition for enterprise software by Zinnov in 2020 and IoT service in 2021 proves the company’s excellence.  “Our mission is to help our clients in “Overcoming Limits” of competitiveness, productivity, technology complexity, time, and budget constraints,” he adds. 

The client review below is another way to know more about how the company treats its clients. 

According to Chella Palaniappan, the customer retention rate in Trigent Software, Inc is 88%.  The company has been the trusted partner to many businesses from manufacturing, banking, retail, education, healthcare, media, transportation, logistics, insurance, financial services, and hi-tech industries.  One of the lasting relationships with their client counts for more than 12 years. 

He admits that Trigent offers next-gen future-ready applications for the new normal using AI/ML, cloud-native applications, and augmented reality mobile apps.  State-of-the-art quality engineering services like school safety systems and business benefits from patented technology as “Fast Rules Selection Engine” (FRSE) combine more than 25 years of company experience to provide a unique business advantage. 

The customer satisfaction rate of the company is always higher.  Toll-free sales lines or chat services on their website and direct access to the team through phone, email or slack channels are supported to cater to the client issues and queries.  The different pricing models are facilitated to the clients that run under client time and material fees.  Managed services model is always available at a fixed annual contract fee. 

The company is flexible enough to charge its minimum budget up to the bottom line of $25,000.  However, projects for millions of dollars are also accomplished by the company with repeat businesses and referrals.  Undoubtedly, Trigent Software, Inc ranks among Massachusetts and America’s top software development companies

“We continue to invest in our people and technology to stay in tune with the pace of technology innovation and the resulting disruptions in the ecosystem.  Trigent will partner with companies as they look to navigate the uncertainty and strike a balance between investing successfully in new tech and maximizing value from current infrastructures,” he concludes the interview. 

You can view the detailed interview on the company’s page on GoodFirms.

About GoodFirms

GoodFirms is a Washington, D.C.-based maverick B2B research and reviews firm that aligns its efforts in finding web development and web design service agencies delivering unparalleled services to its clients.  GoodFirms’ extensive research process ranks the companies, boosts their online reputation, and helps service seekers pick the right technology partner that meets their business needs.

Serverless Cloud – Why you should consider going serverless?

From client-server to servers in internet data centers to cloud computing and now ….serverless.

Phew!!

Cloud computing enabled establishments to move their infrastructure from Capex to Opex, where companies could now hire their infrastructure instead of investing in expensive hardware and software.

When you hire infrastructure on the cloud, you are still committed to hiring instances, virtual or dedicated. Auto-scaling was probably the first move towards on-demand capacity where you could spin up server instances as and when your demand went up. This was effective; however, the minimum increment unit was a server instance.

In parallel, there was another movement where people were trying to figure out if they could do something more than virtualizing instances on a piece of hardware because there was still the task of managing and monitoring these instances. This paved the way for application containers where instead of creating separate instances of the OS for each application, they just made secure spaces for them to run while broadly sharing the OS resources.

Enter serverless computing

By adhering to some basic rules, services and applications can be deployed onto serverless systems. The infrastructure can create a container, execute the code, and clean up after that based on demand. Of course, this is a significantly simplified explanation, and the systems are way more complicated.

This completely eliminates dedicated servers or containers. Instead, you are billed for the time you use your computing resources, i.e., the time and resources consumed by your application to fulfill the request. Some of the top-rated serverless solutions are AWS-Lambda and Google-Cloud-functions. The new freedom to scale on-demand and the elimination of estimating load and traffic led to the massive adoption of serverless architectures. If things failed, it was NOT due to provisioning and capacity.

Having said this, one must tread cautiously when going in for serverless architecture. Some of the things to pay special attention to are

Cold start latency impacts customer experience – Code instances spin down when not used for some time, resulting in a cold start that affects app response. These are called cold instances instead of warm instances that are ready to run and handle service requests. This is fine in most normal applications, but certain measures need to be in place in specific use cases where the frequency of server calls is low or erratic, and the response time is critical.

Most providers provide a concurrency control such that you can control the number of warmed containers that can handle the request. This means these minimum containers are kept fired up and live so that they are ready to handle requests. Exercise caution while configuring this as the containers are billed irrespective of usage.

Serverless architecture appropriate for the complexity and scale – If you have a very simple app that does not require significant performance scalability, then a serverless architecture could be more expensive in terms of development and maintenance. In the scheme of the total cost of ownership, Resource costs always tower over the infrastructure costs. Man-power for server-based architecture is more readily available than serverless because of the recency of the technology.

Factor in the limited options to use open source – Using open source modules and libraries can be a considerable cost and time saver. But a lot of these are more suited to more dedicated and traditional environments, given the recency of the serverless architecture popularity. While there is no “serverless compliant” tag that you can use to choose, you have to probably try and rely on the community recommendations and experiences when selecting these libraries or modules.

Debugging and monitoring are typically more complex – Setting up local environments to simulate production can be challenging. Though monitoring is provided at a macro level, detailed monitoring is far more complicated than a dedicated app environment. However, you can achieve the same log functionality with some discipline and a little caution. Ensure you use log levels so that the logs entries are controlled. Provide a context as most of your logging will be central, and you will need to filter each function’s logs. Use a service like CloudWatch( on AWS ) or Cloud Monitoring (on GCP) to centrally log and analyze your logs and act on them.

On-Premise or Hybrid architectures are complex & expensive – Serverless architecture has a more sophisticated and complex infrastructure management system typically proprietary to the providers. If you require an on-premises deployment, especially in specific critical systems, the infrastructure setup could be much more complex to set up and manage and probably cost much more. In these cases, it is best to avoid Serverless solutions. However, if this is not really a criteria, then the most common practice is to use a development environment on the cloud itself. Use a service such as “AWS CloudFormation” or “google deployment manager” to enable your CICD process.

Need more insights on your infrastructure requirements? Let us help

Lock-in to the provider – Serverless applications need a certain level of adherence which is custom to a provider and generally requires some amount of porting to move providers, not just in the applications but also in the data and other extensions. It is rare that companies will move providers. There is definitely a cost to the move if it comes to this. Before you choose a provider, it is important to evaluate the capabilities of the platform for current and future requirements (up to a year or two) of the application. 

Additional security considerations are required – Given that your application is hosted on the cloud and accessible on common channels, security measures and systems must be carefully implemented and adhered to. With a serverless architecture, it becomes a little more complex given that additional steps need to be taken. Some of the things to watch out for are insecure configuration, Insecure 3rd party dependencies, DDoS attacks, inadequate monitoring, etc. Engaging services of security consulting companies specializing in serverless security is also an option.

Danger from runaway costs -Serverless billing can be complex to estimate for businesses. There are Max-memory size and function execution time whose product results in the metric used to compute the billing. But the former parameters are not always that simple to estimate, given the data changes as the application performs. This could be a cost-saving and also a bottomless pit. While there are some estimation techniques and methods, one additional measure is to track the billing on a weekly basis to preemptively take corrective action.

While all these potential risks exist, Serverless architecture is the future. Getting an expert opinion on the solution can give you a better understanding of the advantages and reduce the risks significantly. 

Forge your digital future with expert help. Call us now!

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