DIBBs (Data, Insight, Belief, Bets)

The DIBBs (Data, Insight, Belief, Bets) artifact is a structured framework used to capture and communicate key decision-making points during the software development process. It helps stakeholders understand the rationale behind product decisions, fostering transparency and alignment.

Purpose

By explicitly outlining the thought process, DIBBs shares both the initiatives that teams are focussing on as well as the context behind the decisions. This encourages critical thinking and reduces the risk of biases impacting decisions. It also provides a clear framework for evaluating the effectiveness of decisions and strategies, enabling teams to learn and adapt based on outcomes.

Format

DIBBs are best represented as a structured document or presentation, capturing the following elements:

• Data: Summarise the relevant data points considered, including quantitative data (e.g., user research findings, market analysis) and qualitative data (e.g., user feedback, expert opinions).
• Insight: Analyse the data to identify key patterns, trends, or relationships that inform the decision.
• Belief: Based on the insights, state the resulting belief or assumption about the situation or user behaviour.
• Bets: Outline the specific actions or features planned based on the belief, along with potential risks and mitigation strategies.

Worked Example

Scenario: Deciding whether to implement a new chat feature in a social media app.

• Data:

  • User research indicates high demand for real-time communication features.
  • Competitor analysis shows successful adoption of chat functionalities.
  • Internal usage data reveals high engagement with direct messaging features.

• Insight: Users value real-time communication options for increased interaction and connection within the platform.

• Belief: Implementing a chat feature will enhance user engagement and potentially increase user retention.

• Bets:

  • Develop and launch a basic chat functionality with initial features like direct messaging and group chats.
  • Monitor user adoption and engagement metrics after launch.
  • Be prepared to iterate and expand the chat feature based on user feedback and usage data.

Anti-patterns

• Missing data: Relying solely on intuition or assumptions without supporting data can lead to flawed decisions.
• Incomplete analysis: Failing to thoroughly analyse the data and identify key insights can result in overlooking crucial information.
• Unfounded beliefs: Forming beliefs based on weak evidence or personal biases can lead to misguided bets.
• Unclear bets: Not clearly outlining the specific actions planned and associated risks hinders effective execution and evaluation.
• Static document: Treating DIBBs as a static document without revisiting and updating it as new information emerges can lead to outdated decision-making.