When you get in the morning, how do you decide what the weather is likely to be? Often, we look up the weather forecast on our phones. But we also look out the window. After all, sometimes the weather report tells us there is a 10% chance of rain at the same time as the rain is falling down all around us. You’re making the decision off a mix of someone else’s data and analysis (thanks weather.com!) and our own experiential knowledge as we rush to close the windows before the rain gets in.

I have a family member who is a Ph.D. and loves fantasy baseball. I think if you asked him, he’d tell you he crunches the numbers, looks at the data, and assembles the best possible team. But fantasy baseball is a predictive game – you can’t really know what will happen – which means you rely on a mix of the numbers and your “gut” about what is likely to happen, for example, what you believe about each individual player, the game itself, or the teams playing. He’s making his decisions off a mix of someone else’s data that he analyzes along with his intuition about what is possible.

Fantasy Baseball Perception v Reality

 

I’m a thrifty person. When I go to the grocery store, I compare brands, taking time to look at the price per unit and assess sales, before putting something in my cart. But I do not always pick the lowest price because I also consider things like brand, flavor, and how I might want to cook with the item. Then I make my choice. That garlic hummus may be less expensive, but if I’m planning to eat a hummus wrap in close quarters, I may decide to go with the more expensive roasted red pepper version. I’m making my decisions off an informal return on investment analysis – I’m paying attention to the quantitative (the price), while considering the quality of the experience I’m going to have and its impact (in this case, on those around me!).

What’s the point of all these examples? We are all very good at using data for decision-making. We do it constantly. We also know how to combine data with intuition and experiential knowledge. Most of the change agents we work with have the core skills already in place to leverage data for decision-making. But, often, we lack two critical things in our jobs to make this happen:

  • The right data at the right time.
  • The right process for applying the data to the decision we’re making.

Our new toolkit on using Data as a Tool for Change is designed to take what we are all already good at and bring it into our work as agents for change. It gives concrete advice about how to find and collect the right data given the decision you are making and provides some specific processes to incorporate that data into your decision-making process.

Next month we are going quite a bit deeper, exploring how to engage in real-time strategic learning as an ongoing, comprehensive approach to integrating data into the DNA of your program, project, organization, or collaborative. Sometimes, however, you just need data for a specific decision. Do you have one of those decisions coming up soon?

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