Market evidences and critics have clearly shown the rise of digital transformation and the pace at which it is mounting in the banking industry. Not to forget, how COVID-19 has boosted the digitization wave. Eliminating manual, cumbersome, complex and inefficient processes using emerging technologies is the number one priority in banking today. This is the way, most banks are planning to uplift the customer experience and be competitive in their growth journeys.
Quality Assurance (QA) teams have to rethink on the traditional models and approaches used in engineering digital quality. They need to wear the hat of a Quality Engineer and instigate early age testing strategies which is in close alignment with the development efforts. This has to happen with speed and the scale has to be much wider considering both current and future scenarios. We have listed down 5-prominent steps for planning and executing digital testing as mentioned below
The 5-Prominent steps of digital testing strategy
Building an effective strategy
The paramount significance of a well-planned digital testing strategy has an enormous role to play in the overall success of digital transformation. While there’s no standard reference testing model which can fit in every digital transformation. However, it is essential to address two key factors:
- For every digital testing, it is highly imperative to include the key stakeholders of the organization. This will help in:
- Aligning the test strategy to organization goals and objectives
- Test effort prioritization in-line to targeted business outcomes (For e.g accelerate time-to-market)
- Once we have a clear understanding on the expected business outcome, it is now time to brainstorm on the testing execution plan which would include
- Types of testing, Automation engines, Testing tools, Domain V/s Tech led skillsets, In-house V/s Outsourced capacities
These factors should be considered well in advance while outlining your digital testing strategy.
Defining the test coverage
Evolving digital realities forces the test coverage to be looked beyond the complexity levels of web, mobile, OS’s, devices etc. Estimating the test coverage can be easily arrived if we start thinking on the some of the below listed questions
- Have you decided on the types of tests you are planning to run (performance, usability, regression etc.)?
- What are the hardware requirements (Memory, CPU)?
- What types of digital screen properties you are planning to test (screen resolution, screen size etc.)?
- What are the top user conditions you have kept in mind (location, network etc.)?
- Have you considered testing IoT device communication channels?
Test scenario building with the power of data
Digital testing teams can plan and execute test cases across vast scenarios which could be attained after an in-depth analysis of internal and external data. The insights drawn from internal data (such as customer analytics, historical data) and external data (such as market share data, competitor insights/benchmarking and more) can help in sharpening the digital testing process. A 3-step simple data analysis is outlined below
- In-depth analysis and validation of internal data
- Learnings from the market and making refinements as required
- Gaining visibility into what applications and its version are coming in the future.
With data-driven test scenarios, QA teams can exactly plan out each function that should be tested against the business outcome which has been planned.
Dev-QA Integration for faster-time-to-market
Paul Trotter, CTO at Atom bank emphasized the possibility of achieving quality and speed at the same time with the fusion of agile, cloud, DevOps and QE. This emphasizes the importance of strong alignment between dev and digital testing teams by embracing the best of agile/devops principles. The entire focus of this collaborative effort has to be in the lines of continuous quality. Dev teams should be responsible for developing the code and making sure they write the relevant unit tests while the digital testing team should be responsible for validating and introspecting the entire digital portfolio. Both teams have to connect on a daily basis to ensure faster product releases through continuous sprints in every stage of the software development life-cycle (SDLC).
Predictive insights led test execution
Using predictive analytics, real time insights can be drawn out to continuously evolve and improve the digital testing performance. Maveric’s IQe Lighthouse platform is a great example for aiding customers with an unique real-time visualization dashboard to monitor the end-to-end testing progress. Customers can easily detect pitfalls much earlier in the SDLC and make course corrections for superior code quality.
Conclusion
At Maveric, we advocate continuous quality measures to engineer digital quality. Using our Intelligent Quality Engineering (IQe) platform, we raise the bar of human experience through our contextual digital testing approaches. We bring our Agile/DevOps engineering practices, domain and tech-led mastery and our promise of 95% automation to deliver outcomes that accelerate transformation journeys.
