The two mindsets of product development

This article responds to a challenge posed by Bjørn Broum regarding the necessity of incorporating both long-term strategic planning and agile responsiveness into data governance practices. His inquiry prompts reflection on how experience and learning can be integrated across both sustained efforts and rapidly adaptive phases within a functioning framework.

The concept of duality or bimodality is fundamental in the realm of product development companies like Telenor, Apple, Google, or Amazon. Understanding and navigating this duality is crucial for career success within such enterprises.

Drawing from research on creativity, I propose labeling the two modes as divergent and convergent thinking. At Telenor, we referred to the divergent mode as «product development» and the convergent mode as «productification». In the product development phase, creativity and experimentation flourished as we explored customer perspectives and innovated with available technology. Upon identifying successful strategies, we transitioned into the more structured convergent mode, focusing on quality, reliability, automation, and efficiency.

This oscillation between divergent and convergent modes is akin to operational teams shifting gears when encountering faults. The key lies in embracing both modes, with convergent thinking providing a foundation of reliability and divergent thinking fostering adaptability and learning.

For seasoned product development professionals, transitioning between modes becomes second nature. Recognizing when to employ each mode is crucial, ensuring that solutions align with responsibilities and objectives. The advantage of a framework of guidelines and procedures is that they are working for the organization no matter what the team is doing.

The pivotal question to guide mode selection is: «Are we effectively addressing the problems within our responsibility?» For a data governance team, this translates to ensuring corporate data usability, accessibility, and security. Identifying areas of poor data quality prompts a shift to divergent thinking.

In dynamic environments, a strategic approach is to initiate improvements within smaller business segments grappling with data quality issues. By equipping leaders in these areas with robust guidelines and procedures, we address their challenges and establish a precedent for broader organizational enhancement.

One effective approach to cultivating this duality is the Tight Loose Tight framework, wherein teams regularly cycle through structured reminders of organizational purpose, creative problem-solving, and critical self-assessment. By integrating Tight Loose Tight practices, teams learn to leverage both modes of thinking to their advantage.