12-step Data-informed Decision Making Methodology
There is a common misconception about data-informed decision making. Once the right analytic tools are implemented and individuals are trained on analytics, the data will turn to knowledge and will translate to better decisions. It sounds great in theory.
But in practice, there is a spectrum of critical capabilities that need to be in place at an organizational level, including a data strategy, an analytics framework, a data literate workforce, diversity and inclusion, a culture of collaboration, creativity, and communication. At an individual level, making data-informed decisions requires systemic thinking, the ability to be aware of your biases, the ability to challenge the data, and the ability to accept failures and learn quickly from them.
There are many models out there for decision making. Our model blends together the need to ask the right questions, source the right data in the right format, critically appraise and analyze the data using an analytic framework, apply your personal expertise and that of others while being aware of any unconscious bias, communicating your decision to all stakeholders, and building a review framework and mechanism to monitor the decision and iterate through the process again based off the findings.
|1||Ask||Turn business questions into analytical questions.|
|2||Ask||Classify the decision needed.|
|3||Acquire||Find and source all relevant data. Remember to think about the analytical questions systemically and to include any interrelated data that could be relevant. This means not only internal data but external data and information as well.|
|4||Acquire||Ensure the sourced data is trusted.|
|5||Analyze||Create a measurement framework to describe your data with key performance indicators (KPIs) and descriptive analytics.|
|6||Analyze||Use diagnostic analytics to find patterns, trends, and relationships that may exist but not be obvious to start to drill into root cause. If applicable, leverage inferential statistics to take a sample of data and make generalizations about the entire population, predictive analytics to run simulations or to test potential decisions/solutions, and prescriptive analytics to act on situations as they happen.|
|7||Apply||Review and orientate yourself to the data and information so far, apply your personal experiences to it, and create a hypothesis.|
|8||Apply||Challenge the data, and actively look for information to see if you can disprove your hypothesis.|
|9||Apply||Leverage strategies to become aware of and to mitigate bias, and then make a decision.|
|10||Announce||Announce your decision at the right level to ALL stakeholders (direct, indirect, upstream, and downstream) by leveraging tools like reframing, the Pyramid principle, and the Rule of Three in your storytelling.|
|11||Announce||Provide adequate time for stakeholders to unlearn any outdated mental models and to learn new ones.|
|12||Assess||Set up a review mechanism to monitor the impacts of the decision after it is made and acted upon. Leverage that review mechanism, and fail/fix/learn fast, making improvements to data, measurement frameworks, accountability, decisions, and anything else relevant.|