Have Maturity Models Become Irrelevant?
Read how modern software power-houses will face challenges when assessing, strategizing, and future-proofing their tech roadmap using known maturity models.
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Maturity models are based on consistent, systematic, linear-scale assessments and representations of existing software delivery processes that are applied through standardized methods of evaluation. This enables maturity quantification of methods, ways of working, and applications of technology in the software delivery processes.
What Is It Good For?
Maturity models are arguably good for organizations that seek consistent, measurable improvement. In those organizations, different areas of software development can be assessed and rated (e.g., defect management process maturity, test data management process maturity, etc.), and subsequently benchmarked against a standard or industry average.
A dilemma arises when we assess a modern organization that adopts an Agile way of working in delivering its software products where teams are working together towards larger goals, but each team, even within the same project, may operate differently and deliver differently.
Relevancy and Relativity
The criticism expressed here is not about the depth, scale, or substance, of current maturity models. Rather, it is about their suitability to effectively assess a fragmented, hybrid, and sometimes global delivery model through a single-process lens. A question is becoming more apparent around whether current models and their application effectively provide a single bottom line that articulates the best solution or roadmap for an assessed organization that is using a combination of current industry-leading delivery approaches. If we cannot effectively assess using current models, any improvement plan will lack contextualization, relevancy, and ability to enable the organization to succeed.
Why Is It Important? Why Now?
In the past, maturity models have served as the guiding compass for many organizations' improvement roadmaps, giving measurable milestones in maturity that can be targeted in iterations of process refinement across various areas.
As we’ve gained an understanding that it is not practical or cost-efficient for every organization to hit the top of every maturity model, and we need context on what an organization and its teams need to hit their own ideal maturity, it becomes clear we need to change our approach. As we’ve seen changes in the software delivery approach with the rise of the Agile and DevOps processes, the increased re-use of components and hyper-connected systems whose quality isn’t always ours to govern, it is becoming clearer that incremental, contextualized, continuous improvement and refinement is a more powerful tool in modern businesses than just trying to progress globally on a one-size-fits-all framework.
Fostering a “culture of innovation” and having the right roadmap (not biting off more than one can chew) that stakeholders will buy into is the new key to keep quality and customer experience increasing in the modern software delivery landscape. This is possible with a more collaborative and compelling trigger for improvement rather than a “that’s the next level of maturity” trigger.
What Can We Do? Adopt an Excellence Continuum
Maturity should transform into practicality. Transformation and futureproofing should be executed with the assumption of an unleveled starting point for teams within any organization, for the very same topic. To effectively identify and execute on improvement, we should apply new thinking into solving what the new maturity process should look like, adopting a constant transition between Innovation Management and Execution Excellence on the excellence continuum:
The Continuum is explained in the chart below:
Edge 1: Adopt innovation management, accept fail-fast and learning culture Innovation management is a well-recognized practice by industry analysts and leading firms. It helps organizations and leaders to streamline all levels of innovation, from incremental to disruptive, and helps various levels of employees to develop and implement the latest ideas that concern internal improvement or new product features. We should implement innovation management based on relevant data and facts that can provide better accuracy in strategic direction selection. Useful: celebrate and reward lessons learned and knowledge share, not just successes. This will enable the business to embrace optimism and will get “success” delivered sooner. |
Edge 2: Execution excellency measurement and progress observability Problems become bigger when imperfect planning (a given) is imperfectly executed. Discipline, managerial involvement, knowledge share, and retrospection are existential when it comes to continuous improvement, determining priorities, and defining what is the right sequence of areas to improve to take when it comes to having the right roadmap for the right team. Measure the business outcome of innovation (e.g. UX score), apps rating, and NPS, not only technical progress (e.g. level of automation). Useful: Adoption of “tribes” or “community of practices” setups can help drive execution and uptake of continuous improvement and promote a culture of knowledge share and healthy competition. |
Structure and Strategy Are Still Very Much Needed
By no means should we think that the Agile way of working is reducing the need for QA/QE structure and strategy. It only means they shifted: in construct, in technologies, and ways of working. As quality engineers are now part of project teams/pods/Scrum teams which operate on various levels of efficiency, we need to build contextualized roadmaps and strategies in an effective and collaborative manner.
It Really Is a Never-Ending Story
We believe that as the software delivery continuously modernizes itself, we will constantly look for leaner, less cumbersome ways to solve organizational and operational efficiency challenges, in an accurate, contextualized, relevant, and communicative manner. In some cases, it will take more than an incremental change to keep up and stay relevant, just like in the case of maturity models.
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