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DIBB Framework DIBB Framework

DIBB Framework

How do companies and individuals support data-driven cultures to innovate and deliver greater value? How do you encourage alignment across diverse teams at scale? And how do you efficiently capture an argument for action, complete with its assumptions, to encourage debate and understanding? One option to help achieve all of this is to use an approach from Spotify.  The DIBB Framework involves starting with Data (D) and the observable facts; distilling an Insight (I) from that data; stating a Belief (B) or hypothesis based on the insight; and finally defining a Bet (B) to pursue and test the belief.  FROM BELIEF TO BETS. The DIBB Framework was developed as part of the broader ‘Spotify Rhythm’ and their evolving version of scaled Agile Methodology. It involves establishing cascading points of alignment, similar to traditional notions of moving from strategy to tactics, and for Spotify consists of:   Company Beliefs: The strategic understanding of the world and the resultant company focus over the next 3 to 5 years.   North Star and 2-year Goals: The handful of ambitious and measurable targets aligned to the beliefs.  Bets: at three levels:  Company Bets: large cross-organisational projects often lasting 6 to 12 months. Functional Bets: generally large function-based projects aligning to Company Bets.  Marketing Bets: smaller more rapid initiatives and investments by teams.  Spotify displays Company Bets via a ‘Bets Board’, an online Kanban board showing the respective priorities and progress of each large bet – this was all about making the hard decisions of identifying the most important focus for resources and energy. The Bet Board is a key point of alignment for functions and teams alike. Each bet was linked to a two-page document: the front page had an overview including the primary sponsor, stakeholders, success metrics, and related bets; while the second page was a DIBB summary outlining the ‘why’ and ‘what’ of the bet. See the In Practice drop down for an example.  IN REALITY. The DIBB Framework was surfaced by Henrik Kniberg who outlined it in a number of presentations around 2016 (see Origins for more). He pointed to the importance of updating and challenging bets with new information from the feedback loops that action provides, and that such bets need to be used as a guide for high-value action rather than a ‘stick’ to limit autonomy. In addition, Kniberg discussed how applying DIBBs often tended to be non-linear and messy rather than working through progressive stages. Importantly, the intention behind the process is to provide greater transparency and direction, and that includes the ability for individuals to challenge a belief, or even insights, based on new or alternative data, thus arguing for an alternative bet.    IN YOUR LATTICEWORK.  The DIBB Framework joins a range of approaches that draws much from the Scientific Method, and is particularly reminiscent of the OODA Loop, though the latter is typically about rapid action and fast iterations. The DIBB Framework uses Kanban for its Bets Boards, and is a strong partner for the application of Agile Methodology. The bets themselves might leverage Minimum Viable Products, Riskiest Assumption Tests, or even Prototypes and, in terms of making the most of each bet, you might also want to consider applying Return on Failure.

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