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How is Information Quality Engineering Changing in 2022?

How is Information Quality Engineering Changing in 2022?

As leading FIs reimagine how data quality engineering can be a game-changer, essential insights for innovations surface. Achieving business impact with data is far more than a buzzword. Never before has it been easier to make an impact with data. More and more data are available today, computing power is ever-increasing, and use cases for big data are on the rise. Data is synonymous with digital as it is reflected in how banks collect data, refine it, turn it into insights, and drive digital adoption. 

All of this translates into transformation success and a company’s profitability when CDOs consider the multiple dimensions of Data Quality (DQ) 

  

The multifold QE benefits can be realized by partnering with specialists like Maveric Systems. Leading FIs can accelerate their QE missions to the next level.  

Immediate benefits  

  • Shorter testing cycle times are delivered by intelligent automation.  
  • Continuous testing with shift-left and shift-right Delivery assurance.  
  • Capacity enhancements with improved availability.  

Longer-term value QE realization 

  • Increases automation across the entire value pipeline 
  • Identify investment opportunities that leverage market advantages 
  • Rapid feedback around product reliability and combating business risks 
  • Beyond functional validation, it delivers multi-dimensional test approaches 

Three Data Quality Engineering Trends 

Begin with an information strategy in mind.

Driving alignment of information assets for a robust QE strategy that delivers value only begins when FIs commit to a strategy roadmap, including elaborate information architecture and data governance processes. After all, managing risks and making correct regulatory decisions depend on the business intelligence that comes from investments in risk assessment systems. 

Enhanced Focus on Customer Data Profiling  

Profiling client customer data and adhering to metadata and business rules defined for cleansing and weeding out duplicates go a long way in establishing a meaningful DQ baseline for required fields and preparing for future DQ issues by validating relationships between various DQ tables.   

Growing Relevance of Deep Tech in DQ Engineering

Be it digital transformation, wealth management modernization, financial regulation compliance, or even talent skilling in DQ engineering, the use case of AI, NLP, and ML, among other deep tech applications, is increasing each day. As a new era of scalable, seamless, serverless architectures and infrastructure as a code proliferate in the QE space, successful DQ teams are getting better at understanding how particular platforms will flex to meet future spikes and spotting limitations that may obstruct agility. By using different SMEs that road-test automation frameworks, leading banks are learning how collaboration rhythms (of development, platform engineering, and testing) can run efficiently with fewer errors.  

Conclusion 

Data Quality Engineering in 2022 and the years ahead relies on a solid solution delivery framework (methodology, governance, technology, and domain expertise). From this bedrock of understanding, appropriate toolsets and DQ maturity stages (requirements, assessment, and management) emerge.  

About Maveric Systems 

Starting in 2000, Maveric Systems is a niche, domain-led Banking Tech specialist partnering with global banks to solve business challenges through emerging technology. 3000+ tech experts use proven frameworks to empower our customers to navigate a rapidly changing environment, enabling sharper definitions of their goals and measures to achieve them. 

Across retail, corporate & wealth management, Maveric accelerates digital transformation through native banking domain expertise, a customer-intimacy-led delivery model, and a vibrant leadership supported by a culture of ownership.  

With centers of excellence for Data, Digital, Core Banking, and Quality Engineering, Maveric teams work in 15 countries with regional delivery capabilities in Bangalore, Chennai, Dubai, London, Poland, Riyadh, and Singapore. 

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What role does Quality Engineering Play in your Digital Transformation

What role does Quality Engineering Play in your Digital Transformation

The two main challenges that IT companies have to contend with – continuously upgrading new technologies and creating the best customer experience from their investments. The mandate for FI’s is to produce stable, high-quality apps that provide an authentic and seamless CX as users interact with the digital world. Here, Quality Engineering (QE) helps banks and companies deal with recalcitrant problems. These are more than the usual QA snags that come up, but something that impacts the entire customer value chain.

Another cause of deep concern is the increasing recurrence of cybercrimes and cyberattacks. Users must protect their assets more than ever, which requires enhanced security and compliance testing. Even so, it is often a prudent investment for FIs to build this rigor into their mobile app testing and other digital application processes.

Three reasons how QE influences the Digital Transformation Processes

Improve processes by controlling inputs

As FIs focus on high-quality standards for their processes, they bring attention in the form of QE governance. Over time, research shows that this cuts down the defect rate and reduces the efforts and money needed to carry out the testing. So, for digital transformation interventions to succeed, strict governance wards off deviations and also course corrects for any past missteps.

Data-Driven insights that inform quality improvements

As QE informs the interrelationships of metrics, processes, and end quality across diverse project types, a bank’s IT department can better its operations and products if they follow a robust QE approach. After all, with the wealth of data generated in testing and the superior data analytics tools now available, the logical next step is to draw out the insights – that elevate the product performance, accelerate Go To Market plans, and enhance the customer experience across multiple channels. Unsurprisingly, today, QE methods are used to practice objective, and repeatable measurements, across the many phases of QA, bug fixing, and cost management.

Embracing the adoption of disruptive technologies

Integrating QE into digital transformation initiatives is the crucial lynchpin that helps banks and FIs gain confidence to embrace the next-generation technologies – AI, Big Data Analytics, IoT, etc. As a QE culture proliferates, internal processes can be measured, managed, and integrated to reveal financial and time-based benefits. Additionally, this approach allows for new quality metrics and assessment models that help assess all factors (and variables) that affect digital transformation initiatives. Employees get their learning done in emerging tech, and the organization is better positioned to trace the potential improvement areas and education required.

Conclusion

As digital technologies keep getting better, the wave of adoption will accelerate. All industries, especially IT, depend on Quality Engineering (QE) and testing to ensure that the product quality and user experience meet the stringent expectations of digitally savvy customers. QE and testing used to be tedious and time-consuming. Still, testing is built into the software development process with agile project management approaches and deploying software in a DevOps environment.

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Why Quality Assurance Models are Essential for Banking Applications

Why Quality Assurance Models are Essential for Banking Applications

A look at the banking industry as it is now

Since 2011, change has been the name of the game in banking, primarily because of the rise of the digital economy as a whole.

An influential study discusses the Disruptability Index, namely the measure of disruption across industries on a scale of 0 to 1. The research reported that 20% of all players in the banking and payments industries will be younger than 15 years old in 2020. Regarding current disruption, banking went from 0.43 in 2011 to 0.52 in 2019, moving it from the Vulnerable category to the Volatile category.

Rising customer expectations, nimble new industry players, powerful new technologies, and changing regulations are all driving these changes. This reality puts incumbent banks under much pressure to bring real innovation to their established businesses. Enter the era of “Super Apps” and especially the banking apps.

What does the rise of Super Apps mean for banking?

One app for everything digital: chatting with friends, playing games, sharing photos, buying tickets, planning trips, watching movies, and paying for a taxi. It would be great to have that. In fact, going one step further, can the same app track all your finances? The user cannot only pay for the taxi but also apply for a loan, manage their investments, or get insurance.

Consider Kakao in South Korea, WeChat, AliPay in China, Gojek and Lime in Indonesia and India. These so-called “super apps” bring many digital services to smartphones simultaneously. By offering a wide range of their services and third-party services that work well together on a single platform, they meet all their users’ daily needs in one place.

What a Banking Application Looks Like

A quick roundup of banking applications will bring standard features.

  1. Multiple levels of functionality that support thousands of user sessions at the same time.
  2. Integration on a large scale that supports complex ways of doing business: Usually, a banking application integrates with many other applications, like the Bill Pay utility and Trading Accounts.
  3. Processing in real-time and in batches with a high number of deals per second
  4. Robust safety and security measures with a vital reporting section to track daily transactions.

Testing Banking Applications for Quality Assurance

A banking app usually has a complicated structure because the team that makes it has to deal with many different features and ensure the users are safe while still making the app fun and easy to use. If a company releases a product full of code errors that don’t work well, it will probably hurt its reputation. Because of this, it is essential to track, evaluate, and improve the tool’s performance based on real-world situations.

Why you need to test your mobile banking application

Because they deal with sensitive assets, banking applications are more likely to be hacked than other projects. Hackers often go after financial software, so a company that works in the banking industry has to be super vigilant for security risks. Testing a mobile banking app is a way for the team that made it to predict and take care of security and performance problems before they happen. It has other benefits as well, such as:

Supporting complex, integrated systems better. The technology and design of banking software are often very complicated. Instead of putting out a tool that doesn’t work and having to fix bugs haphazardly, a continuous testing strategy lets the development team prepare the product for release as it’s being made.

Make sure the system follows the rules, which change often. A manager in banking needs to remember that testing products are an ongoing process. One problem is that tech debt tends to grow with added features. Additionally, a tool’s performance loses stability as new security laws and rules are enforced. In today’s age, agile developers must find new ways to keep user data safe.

Improving the user experience and retaining customers. People generally have a low tolerance for bad apps, and this is even more true for banking products. Dealing with bugs and performance problems hurt your client’s work and their ability to manage their finances. By testing the product thoroughly before putting it on the market, you can gain a customer’s trust and strengthen your relationship with them.

Ensure that sensitive data is kept safe. When software isn’t working right, data can be lost or leaked. A data leak is bad for most businesses, but it usually doesn’t kill them. There is no room for error when it comes to banking. If you don’t protect your users’ data, you could put your clients’ money at risk.

Checking how well the app works in all possible situations. When developers test an internet banking app, they can understand how it will look for people with different Internet speeds or on other web browser clients and operating systems (iOS/Android). Also important is to determine the traffic limit after which the product slows down or shows performance errors. So, a business manager can predict when there will be much traffic and put in more effort before the traffic rush.

Conclusion

Quality Assurance (QA) and testing are becoming more critical to the digital transformation of most industries. The banking industry is following the digital roadmaps of other sectors to get the most money out of using technology. In the end, quality-assured digital banking services help meet the needs of tech-savvy customers.

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Top 5 Digital Quality Engineering Trends to watch in 2022

Top 5 Digital Quality Engineering Trends to watch in 2022

Post-pandemic, many leading businesses employed DevOps and agile practices as part of their digital acceleration initiatives. Supporting geographically distributed teams on their business-critical missions meant more testing applications were needed, often faster as part of their software development lifecycle.

While AI and ML-led innovations catch attention, it is the quality assurance that becomes a key enabler. Through the appropriate tools and processes, QA facilitates quality across SDLC teams. The benefits? Productivity improvements, higher software quality, and reduced quality costs.  

When it comes to Digital QE, what is the need of the hour?  

While it is one thing to state that a quality culture fosters agility and adaptability, ultimately, organizations must aspire to achieve quality at speed. While there is a growing bias toward remote access and environments, the strategy to employ SaaS in the cloud receives widespread enterprise acceptance. Leveraging AI and ML tools with continuous testing and quality management tools is a powerful proposition.

Here are the digital QE trends for 2022

Before delving into individual QE trends, it is worthwhile to point out that the future will be anchored in achieving QE at scale by automating continuous testing and consistently exploiting actual test data.

Quality orchestration is more critical than ever. 

Intelligent automation will intensify the focus on orchestrating QE journeys. While it will free up precious human time to pursue higher strategic goals, the role of the quality architect is poised to generate more acknowledgment.

So, on the one hand, management-level expectations will guide teams. Conversely, the top software organizations will consolidate competency as part of their centers of excellence for delivering on specialized performance, security, and usability testing.

New Normals in Agile, DevOps, and CI/CD

Gartner predicts that 3 out of every four organizations will customize Agile practices for product development by 2023. Spoken like one of the top investments in the next few years, Agile and DevOps together will lead shift left approaches and continuous automated testing.

Moreover, CI/CD today symbolizes more than developing and delivering software. These practices will add to robust development as an indispensable element for deploying cloud-native applications. Consider, for instance, the CI/CD process automation in companies like Amazon and Netflix, which deploy code thousands of times daily.

Cloud adoption’s radical impact on the QE environment. 

It is common knowledge that cloud deployment boosts automation coverage, security, and scalability. So, on the one hand, the business roadmaps benefit from cloud-based testing, and on the other, there are reduced overheads vis-à-vis infrastructure costs. In the cloud-native QE climate, companies can leverage digital testing independently alongside human testers to increase testing accuracy and speed.

Innovative approaches for TDM and TEM 

Test Data Management is a functional discipline that reduces time spent on identifying and creating test data. By correctly identifying and managing test data, enterprises can drastically accelerate time to market, reduce infrastructure costs, and reduce defect rates.

Effective Test environment management (TEM), on the other hand, aids organizations in forecasting and planning for diverse environmental needs. TEM can design processes that collaborate with vendors to provision requests on time.

In 2022, both TEM and TDM will emphasize the increased use of Agile practices. Every software release will require more test environments across testing types and phases (integration, UAT, and performance).

Increase in data-driven decisions

In 2022, the consideration and use of quality indicators will play a significant role, as will the continuous QA monitoring practices. Additionally, using AI in the QE space will increase the number and quality of data-driven decisions. One instance of serious AI usage can be seen in the strategic selection of critical areas (instead of try-and-miss experimental point solutions), as also the preponderance of self-adaptive test solutions and auto-generation of scripts and data. 

Conclusion

In today’s unprecedented times, as companies scramble to capture the competitive edge and rush their competencies into the market, the one focal area that stays unchanged is their emphasis on quality and risk management.

Even as macro technological forces emerge (core and digital transformation, big data analytics, blockchain, 5G, AI, ML, IoT), top-notch organizations must enhance their QA outlook to shift from the ‘cost of quality to the ‘value of quality.

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An Outline on Quality Engineering Services

An Outline on Quality Engineering Services

Finding and fixing bugs after the fact doesn’t work in a world where experience engineering drives fundamental digital transformation, and the cost of poor-quality software is enormous.

Quality is an umbrella term. Depending on roles inside organizations, different minds have disparate associations with quality – analysis, testing, data, planning, improvement, management, customer’s voice, or even lawsuits, product recalls, or disasters.

Fundamentally though Quality is about exceeding customer expectations – every batch, every season, every product. After all, the actual quality value is measured in higher revenues from more significant customer satisfaction and higher operational efficiency and effectiveness from increased productivity and innovation.

 How does the Quality variable inform today’s competitive landscape? 

Especially when gaining new customers or increasing market share, companies must continually develop and improve products. So, then, in a marketplace with fewer barriers or boundaries, customers demand higher quality at competitive prices. This premise creates stiff challenges for product testing and quality assurance. How exactly?

Today’s faster release cycle times, cost pressures, and best-in-class user experiences don’t happen in the Quality Assurance (QA) regime, which ‘assures’ quality. Instead, Voice of Customer (VoC) needs to continually factor in the product design and waste identification and elimination that brings down product costs.

 QA and QE. 

Whereas QA is the overall process of ensuring manufacturers make things properly, Quality Engineering (QE) defines (or ‘engineers’) the system that does it. Quality engineers maintain, improve, and monitor the system.

What distinguishes QE methods and tools is the cross-functional approach involving multiple business and engineering disciplines (like Quality management system, Advanced product quality planning, Quality Function Deployment, failure modes and effects analysis, statistical process control, and root cause analysis).

 Overall QE benefits in the 2020s

Saving money: Both – bug fixing and development times – are costly. Quality Engineers have a certain mastery in identifying issues inside a complex system. The outcome? Developers spend less time tracking down bugs. Additionally, delivering on quality before the build reaches production limits costly hotfixes.

Saving time: When the rubber hits the road, teams often sacrifice testing time to meet delivery deadlines. Quality Engineers save precious time by optimizing testing approaches. How? By automating time-consuming tests, identifying efficiency-enhancing tools, and building shared infrastructure across multiple projects.

Enhancing standards: As apps get complex by the day, their architecture needs testing integration between the various layers. When traditional testing doesn’t cut it, Quality Engineers combine their enhanced architecture understanding with a grey-box testing approach. The result is more thorough testing of the whole system.

Improved Planning: Quality Analysts are primarily code-gatekeepers. However, when QA’s identify issues that require re-architecting the application (time-consuming and costly), Quality Engineers facilitate planning discussions, bring insights to highlight limitations, and coordinate with developers to strategize the best ‘build-test’ approaches. Arresting early pitfalls helps in a faster go-to-market without compromising on quality.

Effective communication: Quality Engineers test systems like end-users and understand traceability in the underlying workflows. Doing this informs their ‘bridge’ conversations with technical and non-technical roles. Their valuable insights highlight gaps from both a user and system perspective and build trust across teams. The outcome? Highest product quality.

 Quality Engineering in the age of Agile and DevOps.

As more Agile and DevOps philosophies proliferate across the software development lifecycle (SDLC), Quality Engineering (QE) governance platforms have grown in prominence.

More Quality Assurance, Less Control

In the new world of mobile and IoT, enterprises embrace shift-left or apply more QA efforts earlier in the development lifecycle. Potential problems are easier to catch and less costly to fix.

Test Early, Test Often

In Agile (and Scrum) environments, the idea is to ship products (or code faster) in more iterative loops. So, when the emphasis on debugging begins before development, the QE governance function is working with desired optimality.

Finally, QE Governance benefits the organization’s digital development initiatives. 

The entire product team (developers, designers, managers, QE’s) is trained and tuned on the development process. This radically increases the quality standards and permeates a quality culture across digital transformation programs.

As processes take root, the nascent QE mindsets have to be guarded in ways that slip back to older methods are alerted and corrected. This step is accounted for in the QE governance mechanisms.

QE actively investigates the relationships between in-process metrics, project characteristics, and end-product quality. This is where QE governance measures metrics at the product (cost and quality), process (efficiency and effectiveness), and organizational levels (employee satisfaction and economics). Based on the learnings, organizations engineer quality improvements in both the process and the product.

Conclusion 

QE makes the proposed benefits of Agile and DevOps more real. Continuously validating product attributes across SDLC, a holistic QE approach critically reduces delivery gaps and boosts production expectations.

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