Data-driven decision-making continues to be the holy grail of management and continuous improvement gurus. Yet despite its popularity, effective data-driven decision-making eludes many organizations. The breakdown between theory and reality may occur for a variety of reasons: lack of quality data, inability to acquire timely data, and disconnect between key public policy questions and data, to name a few. Let me add another possibility to this list—an inability to engage stakeholders in thoughtful interpretation of THEIR data.
Evaluators are well versed in the “goal-clarification game.” We work with clients to help define logic models and go to great lengths to help them understand the basic “if then” components of this process. We pose common sense questions, such as, “If you implement X, what changes can you expect in certain outcomes?” Most clients love this game and will jump in with both feet. However, when many clients are asked how one might determine if the target outcomes have been achieved, or they are asked how one can know if the outcome’s change can be attributed to the implementation of “innovation X,” evaluators are often told, “That’s your job.”
Evaluators can crank numbers and run models to explore “if then” questions, but if stakeholders are not engaged in this exploratory process, the goal of data-driven decision-making will likely be missed. Veena Pankaj (of Innovation Network) suggests a three-phase Data Placemat process to help address this issue.
Our recent experience with the use of Data Placemats suggests that the following practice may hold promise for effectively engaging stakeholders in the interpretation and use of their data. The process works like this:
Step 1: Work with stakeholders to identify key questions and design your Data Placemats. In short: what do they want to know?
Step 2: Conduct a data interpretation meeting with selected stakeholders. During this meeting, allow stakeholders to examine a few data points associated with each key question. Then, for each question and related data points, discuss the following matters with stakeholders:
- Have we focused on the right question?
- What positives and negatives do you see in the data points?
- What surprises you about the data points?
- As a result of this conversation, what additional pieces of data and analysis do you need?
Step 3: Rinse and repeat before preparing the final analysis. Basing their actions on the results of step two, the evaluator now has specific input to focus additional analysis. The evaluator is no longer working alone, but instead has a “thought partner” group to help drive analysis, support interpretation, and ultimately promote the greater use of data for decision-making and continuous improvement.
To learn more about Veena Pankaj’s Data Placemat concept, please click here.