The rise of the experimentation team
2017 was the year of DevOps with companies focusing on delivering better customer experience and improving operational efficiency. According to a recent Forrester report, over 60 percent of organizations surveyed said they have either already implemented or are expanding their DevOps efforts in the future. With the rise of DevOps, greater investments in software development and a faster release cadence comes a need to measure the impact of these efforts and ensure they are delivering valuable software faster. To do this, organizations are taking a more modern approach to code deployment, release management, and measurement of outcomes.
This approach known as experimentation is a new product development workflow, combining DevOps and Product Analytics, helping organizations both speed up product delivery while simultaneously providing critical insights on the performance of their features and products. Every feature release is measured against business and technical KPIs defined by the organization. Experimentation teams, historically only formed at leading technology companies, are now emerging in most enterprises who are starting to look for and build out engineering and product experimentation roles.
In the same study, Forrester stresses the importance of a culture to help develop, drive, and sustain DevOps processes. Teams that would have traditionally operated in silos often run into a bottleneck of innovation. This requires a focus on removing existing silos, and putting more focus on collaboration, initiating a shift in the processes, culture, and infrastructure of their development teams. Integrating experimentation into software development processes, companies will accelerate the rate of feature delivery, minimize the risk of shipping bad software, and ultimately make smarter product decisions.
Enabling a Cultural Shift
More than ever, enterprises are focusing on adopting software development practices that improve customer experience and operational efficiency. Whether its cloud migration, or automation tools, these changes call for roles within development teams to change as well. There's a growing need for someone to focus on experimenting with feature and product releases, someone to measure real-time feedback on rollouts, and even more teammates to continue to accelerate the innovation based on these results. These roles become key players on the experimentation team.
Experimentation teams are often cross-functional, meaning they bring together several roles from varying teams including product managers, software engineers, data scientists, and even software architects. With a broader perspective of the operation as a whole, teams are better equipped to look across the entire platform, evaluating what can be done to increase product adoption, decrease support tickets and drive business and technical metrics.
With this change in roles and team structures comes a call for a cultural shift. At the executive level, experimentation teams have to be empowered and given appropriate resources to develop innovative solutions. Decision making among executives will lean on the value seen in experimentation. That said, will teams get rewarded for failing an experiment that overall led to insights? The experiments being run and the results that these teams are driving must be valued across all levels of the organization in order to be fully optimized.
Mitigating Risk and Accelerating Innovation
As development teams increasingly adopt continuous delivery best practices, many find that there is a gap in how to manage the customer experience. Techniques and best practices like feature flags and feature toggles help teams to deploy a piece of code in production while restricting access to only a limited number of users. They offer a powerful way to turn ideas into outcomes, without breaking anything in the meantime. Once in production, enterprises need access to real-time insights on how customers are interacting with these features. And, these insights need to relate back to the full set of product KPIs.
This is where opportunity arises. Real-time analytics can both halt the release of bad software while also accelerate the momentum of successful releases. These insights further guide the correction and progress of product innovation and success.
Driving Business Value and Return-on-Investment (ROI)
Organizations are putting more resources than ever into building digital products and building software. With increased budgets, comes the pressure of assuring that money is being spent wisely and put towards accomplishing overall goals. This is where the value of experimentation and the insights it provides steps in. Teams equipped with the tools to deliver valuable data and analytics quickly become the backbone of an organization and its decision making, enabling businesses to critically monitor and evaluate code while adding an additional layer of visibility to inform the organization, all the way up to the C-Suite.
With the implementation of experimentation teams, decision makers in enterprises have the ability to focus on the core business and the product, as opposed to the infrastructure that enables them to achieve set business goals. Given the proper tools to release, monitor, and measure the impact of every release, experimentation teams can provide valuable insights while continuously driving innovation.
Kavitha Mariappan is Split’s Chief Marketing Officer. She brings over 20 years of experience in high technology marketing to Split, with domain expertise ranging from big data analytics, enterprise software, cloud storage, to application performance and delivery. Kavitha previously served as vice president of marketing at Databricks (the creators of the Apache Spark project), where she was responsible for developing the company’s brand and go-to-market strategy from the ground up, executing a demand generation program that grew customer count from zero to nearly 500 in two years.