Almost all departments are experiencing their own mini-revolutions these days, fueled in large part by the surge of emerging tech adoption, heightened pressure to differentiate their product and service offerings, and data-driven imperatives from their leaders, as well as the usual pressures of time to market and high availability.

This is especially true in the QE discipline, which is deeply entrenched in its own paradigm shift. We as a community have moved well beyond the organizational maturity of our initial eras of what we at SQA Group call QE 0.0 and 1.0 (or should be!), and now leaders need to focus on ensuring that their QE departments have advanced completely to QE 2.0 — defined by Agile development, devops-driven continuous release and delivery of code, business resiliency, and near-real time feedback loops and analytics. After all, if we are not yet at 2.0, we are going to be wildly unprepared for 3.0, which is right around the corner (more on that here).

As a result of these rapid shifts, QE leaders face newfound pressure to think about how they assemble, strengthen, and invest in their teams. For starters, the skill sets that we prioritized in the earlier 0.0 and 1.0 eras are quickly being replaced in these modern paradigms. Today, our teams need to be well-versed on futuristic technologies, advanced analytics, and intelligent automation. In fact, if you are not already focused on cultivating these skill sets within your team, this is where you should start:

• Automation: We have “danced” with test automation for 20 years; now it’s time to get serious about it. We are quickly moving away from all things manual and, therefore, automation skill sets are going to become non-negotiable to build truly effective self-organizing teams. One place this is particularly evident is with the surge of SDET roles displacing more traditional testing roles.  Without test automation injected as early in the SDLC as possible, Agility inevitably bogs down.
• Internet of Things: The definition is “What is IT” has changed almost overnight, and is continuing to change to include all manner of devices previously untouched by code. With the shift to IoT-enabled devices, we need to find team members who understand how to architect, develop and test systems that have components that are IoT. Just consider that 83% of organizations have improved their efficiency by introducing IoT technology: if we are not doing what is needed to ensure the quality of IoT systems within our departments, we will fall dangerously behind.
• Data Science: There is little doubt that we are well along the pathway of data science as it relates to the SDLC, with advanced analytics supercharged through machine learning algorithms and adaptive AI. There is an exploding amount of information that can be collected, but without being able to make sense of it, it just becomes increased noise. As such, we need to round out our QE teams with professionals who bring fresh data science skill sets built on a thorough understanding of the traditional fundamentals of data governance and data quality.

Though these hard skill sets — and their prioritization — may feel new, the truth is that we have been heading in this direction for the last four decades. Over the years, as QE leaders have balanced the need to get quality code out the door faster, it has become essential that we get serious about building quality in, rather than testing defects out. To do this means we need to re-invent ourselves, elevating different skill sets to prepare our teams to keep up.

Related Reading: Are You Ready For QE 3.0?

When preparing your QE team for the future, here are a few quick philosophies to consider:

• Organize for success by making sure you have the right people in the right seats to be successful
• Anticipate how you will scale up when the business requires, by being able to inject new skill sets and hyper-specializations when needs arise. This may require the introduction of outside expertise at certain points to avoid a “trial and error” learning curve.
• Think standardization between teams. As we shift to agility at scale models with large Scrum teams, we need to build communities of practice so that good ideas filter across to all teams.
• Ensure that DevOps becomes the standard way we approach all development and testing and not just a buzzword or a “science project”

With our eyes on the future, and our teams well prepared for what’s next, we can remain a few steps ahead of new pressures and position our teams for maximum impact.

Interested in chatting through your QE team approach? Curious what roles and sill sets you should be elevating? Click here and our Engineering Leadership team will hop on a quick call to give you a complimentary Talent Assessment Run-Down.