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Performance Engineering In The Digital Era

Performance Engineering In The Digital Era

The current pandemic has finally given the push to make the whole world go online. The businesses and individuals who were resistant to change over the past years also have no other choice but to go digital due to erratic lockdowns and social distancing requirements. Either reluctantly, or with open arms, people from all stages of life are coming online from various devices for not only performing office-related work, but everyday tasks like ordering products or services, payments and banking, education, collaboration, entertainment, and a lot more. In fact, the digital transformation that has happened in the past few months due to the impact of COVID-19 is more than what we have seen in the past decade. According to a study by Mojo Vision, “50% respondents said the onset of COVID-19 caused them to use or depend more on devices, apps and services. Moreover, they feel that they would continue to do so in future too.” At a time when digitization is growing by leaps and bounds, there is a demand for applications that perform well and meet the specific needs of the users.

The role of performance engineering to ensure the best customer experience in the digital era

Continuous Quality Engineering is a vital part of the product development lifecycle and IT businesses are leaving no stone unturned to excel in the field of performance engineering. Quality engineers and application developers are working hard to ensure that their solutions perform perfectly under all kinds of loads, usage scenarios, platforms, browsers, bandwidths, and much more. They have to make sure that the source code is optimized, the integrations with third-party apps and back-end connections with data centers work seamlessly, hosting and servers are load-balanced, security is maintained without compromising the speed, and the overall application is managed in the best possible way throughout its lifecycle. All these efforts lead to a superior quality application that works well under all situations and satisfies the customers. But guess what? That is not enough.

What comes next when all the systems are already optimized?

All the good software application development companies have already reached a stage where they use the best practices and standards for development and deploy all quality engineering essentials to take the performance to the highest level with the resources they have. But how to further enhance the performance when all the systems are running at their best? How to create a distinctive competitive advantage by offering users even more. This can be done by leveraging predictive analytics with performance engineering to enhance user experience and perception.

The next level: Leveraging predictive user behavior analysis for dynamic and personalized applications

As humans, we are always inclined to visit places or people who bring us a feeling of safety and comfort. We like the company of friends who have taken the time to understand us or family members who know our little quirky likes and dislikes and go the extra mile to delight us. Don’t we want to go to such friends and places again and again? Performance engineering now has to tread the path of creating such personalized user experiences that can entice similar feelings of comfort so that it becomes a habit for the user to choose your application over the others.

This can be done through personalization of the user interface based on behavior, prioritization of the valuable information that the user might need, and ensuring context sensitivity. Google, Spotify, and Netflix have used these techniques to create that sense of personalized comfort and reliability that users do not want to switch to other providers.

Performance engineers are creating a better perception of performance through:

  • Enhanced perception of speed so that users can reach the right information faster: There are times when the application code, backend, and integrations are completely optimized and not much can be done to improve the speed further. But we can still create an enhanced perception of speed by enabling users to reach the desired information faster. By analyzing past behavior data like devices used, location, pages visited, bounces, categories liked/purchased/viewed, session time and length, exits, and user flow, we can predict what the future interaction of the user might be like. This can help in creating a dynamic and personalized interface that gives users what they might be looking for in a fewer number of clicks and consequently a lesser amount of time.
    For instance, you go to your favorite food ordering site and you get the right options and buttons based on what you like to eat at that particular time and day, your dietary needs, caloric requirements, and the matching coupon codes for discounts from your favorite restaurants. You would not need to do the extra searching for the right outlets and discounts and would order faster. It will minimize the cognitive load on the users, as they will have to make fewer decisions and hence there would be a lesser chance of them exiting too soon.
  • Seamless interconnections across platforms:
    Today users are not restricted to one common device shared with others. They move from mobile to computer to a tablet. Or they might go to different locations, for instance, office or home or to a friend’s place and login to the same account from different devices. They might visit your desktop website and then shift to the mobile website or the app, or an in-store POS. As we can see, there are ample choices for the users to interact with your business and they demand a frictionless experience at all the touchpoints. The performance engineers have to ensure that the experience should remain smooth, consistent, and seamless, without any loss of quality.
    According to Forrester, “A well-designed user interface could raise the website’s conversion rate by up to a 200%, and a better UX design could yield conversion rates up to 400%.”
  • Intuitive applications that respond to customer behavior with predictive recommendations:
    Today, in the cut-throat competitive environment, companies must leverage their data banks for predicting user behavior and creating more opportunities. Predictive modeling can be used to analyze this data and recommend items.
    Predictive and intuitive designs can be used in many ways. For instance, e-commerce giants always give recommendations to you for the products you might like based on past purchases. The banking sector uses predictive analysis to detect frauds. Netflix uses analytics to give the users the perfect recommendations based on their engagement data so that they can find the right content faster from an endless pool of entertainment. An education and learning website gives out tests based on the skill level of the learner and slowly increases or reduces the difficultly to enhance the experience and learning during each visit. All these are examples of predictive analytics to improve performance and take the user experience to the next level.

Conclusion: Extreme performance engineering will define the next generation applications

Personalization is the key that will unlock the way to an enhanced user experience and technologies like predictive analytics will take them to another level. Application developers will leverage the power of extreme performance engineering to create dynamic apps that sense, respond, learn, and forecast what choices would the users make in the future and transform according to those predictions in the next interaction. The application would prescribe the next steps to the users and in doing so, will reduce their time to information and delight them with completely personalized experiences.


Self-Service Data Analytics in Financial Services

Self-Service Data Analytics in Financial Services

In today’s world, data is the central component driving commercial successes of organizations. From better customer service to faster decision making, data analytics provides the right insights to gain efficiency across business functions. In the financial sector, an added layer of security, quality, and regulatory constraints data control.

Despite the ubiquity of data, the financial services industry is plagued by two major challenges for BI – data silos and a shortage of data science talent. For a business analyst or data scientist, the legacy systems hinder data analysis with time-consuming data extraction protocols and traditional BI tools. These legacy systems are limiting in their features with a rigid architecture, complex IT infrastructure and a lack of scalability and mobility.  Analysts are able to access the system only through reports and dashboards; preventing free exploration of data and slowing down operations.

The Power of Self-Service Data Analytics

The complexity of traditional BI tools has created a dependency on data experts for data insights. In order to increase efficiency, financial services companies are moving to advanced self-service analytical tools. As per Gartner’s predictions, “self-service analytics and BI users will produce more analysis than data scientists will by 2019”.

Self-service data analytics are user-driven solutions, leveraging application of newer technologies such as artificial intelligence (AI). One of the main functions of self-service data analysis is breaking down data silos by integrating disparate data from multiple systems. These agile systems are created for business users who can access data in real-time wherein, data is prepped, analyzed and shared as reports quickly, requiring no technical expertise or coding experience. Gartner’s report also states the implementation of a formal onboarding plan to ease the scalability of BI tools in an organization.

Most companies still have the “80/20 rule” of data analytics, wherein, data scientists spend 80% of their time on cleaning and prepping of data, while only 20% of the time is left for analysis. The ease of access to data and analytics through a self-service BI tool helps in reducing the time spent on data prep, letting the user focus more on data analysis and gaining insights. For example, business users can track investment portfolio performance through effective visualization techniques. A clear dashboard highlights areas of resource allocation to optimize profits, under-performing assets and provide insights to make better re-investment decisions across diversified investments.

From C-level executives to social media managers, various self-service BI tools are available for organizations of all sizes. Self-service BI tools provide users with an interactive dashboard. The dashboard gives a real-time overview of business parameters in the form of graphical charts and information reports. Business users can create ad-hoc reports on the fly with quick search queries and visualization tools. The intuitive platform also makes advanced and predictive analytics easily accessible to small and large organizations. Tools such as SiSense, IBM i2 Analyze, Tableau Prep, IBM Watson, Domo and many more cater to the data analytics and visualization requirements of organizations. SiSense helps startups and small organizations collate, analyze and view data without heavy investments in building IT infrastructure.

Self-service BI has gained significant momentum with the introduction of AI and machine learning. While BI tools are everywhere, the successful implementation of self-service capabilities calls for the adoption of a data-driven culture. For organizations with BI tools, a flexible and robust data governance policy is essential to enable and support the growth of self-service analytics. The use of information at every level helps in driving innovation while increasing operational efficiency at an organization. With automation insights at scale and real-time, business users can gain a deeper understanding of their customers, create efficient campaigns and improve customer service and experience.



AI & DevOps – Partners in Digital Success

AI & DevOps – Partners in Digital Success

Artificial intelligence (AI) and Machine Learning (ML) technologies are rapidly transforming business functions, including software development. In its search for efficiency, the industry has slowly shifted from the traditional Software Development Life Cycle (SDLC) to an agile development environment.

Over the last decade, DevOps has become an industry standard. Its main goal has been to improve product delivery/development by encouraging communication between software developers and IT operations. Across industries, the need for developers-operations balance has led to the rise of DevOps ethos. According to Grand View Research, the DevOps market size is expected to reach $12.85 billion by 2025 with an 18.60% CAGR.

For a culture focused on efficiency and automation of tasks, it’s no surprise that AI and ML find their application in DevOps. From enhancing continuous feedback loops to software testing, AI/ML and DevOps complement each other perfectly. As IT operations become more agile and dynamic, AI can iron out the kinks in the system.

Data    Gathering Key Data Insights

A DevOps process generates significant amount of data across servers and logs. Combing through big data to find specific instances can be cumbersome and not cost-effective. In this data deluge, ML optimizes application environments stay afloat with real-time data analysis.

Using supervised learning and training data, developers would be able to identify errors that would otherwise be missed in large data clumps. Machine learning can also be employed to analyze insensible data to identify patterns and behaviors that can be used towards data analytics. As ML reduces noise-to-signal ratio data silos are broken down for teams to use across product development. Developers are no longer limited by self-defined thresholds, giving room to assess data trends.

MOnitor Correlation across Monitoring Tools

As dev teams expand, multiple monitoring tools are used to assess data and also check application health and performance. The layered algorithms of AI/ML accept multiple data streams allowing correlation of data across multiple monitoring tools. ML systems connect disparate data systems to provide real-time health assessments of applications.

DevOps   Optimizing DevOps process

Employing adaptive-ML, DevOps teams can optimize specific values or metrics towards a certain goal. Neural networks are trained to maximize a single value/ parameter; enabling the system to adapt and change during the production phase itself. This ensures the optimization of values throughout the development lifecycle.

Security   Enhancing Security and IT operations

Mining of large complex datasets helps in gaining meaningful insights in predicting product and server failures, avoid technical drawbacks and facilitate decision making in DevOps business framework. Most security protocols are implemented the end stage of the development life cycle. In case of banking sectors, DevSecOps – the culture of integrating security within the DevOps process, is gaining ground.. This philosophy emphasizes on ‘security as code’, allowing streamlining testing parallelly to security and compliance reviews. The use of AI-based digital security technology allows banks to meet market demands while continuously monitoring potential security risks.

Resource   Smarter Resource Management

By automating routine and manual tasks, AI/ML systems aide in efficient resource management. Teams have more time to concentrate on efficient development and coding practices.

Shift Left   Software Testing and Shift-Left

Software testing is another aspect wherein AI/ML applications can be leveraged to evaluate coding errors in test results. Test automation is a critical part of shift-left testing. AI helps in running multiple diagnostic tests to identify the cause of failure. In the case of a test failure, AI is in a position to rectify the error even before the product hits the market. This approach involves minimum human intervention. Whereas, the continuous feedback loop aides developers write efficient code, reducing errors thrown by the system.

The capabilities of AI/ML-driven solutions in conjunction with DevOps is ever expanding. As organizations work towards identifying bottlenecks, hiring skilled talent is the impeding factor for the AI-driven organizations. On the foundation of a strong DevOps infrastructure, AI/ML will be synergic tools to increase efficiency. AI will continue to make inroads into more business cases with vertical-specific solutions that would transform business processes.