Yin and Yang: Intuition + Data-Driven Mindset
Data for Good Decisions Adriann Gin Data for Good Decisions Adriann Gin

Yin and Yang: Intuition + Data-Driven Mindset

In our Data for Good Decisions series, we conceptualize a ‘data-driven mindset’ as a systematic process for integrating metrics and other forms of data to test assumptions formed through our intuition. Approaching challenges with a data-driven mindset is considered a competitive advantage for leaders because it enables swift identification of business challenges and action before it’s too late. By adopting this mindset, we are actively working to minimize the influence of biases (although, it is worth mentioning that we can never be free of them). Now more than ever with unlimited data at our fingertips, adopting a data-driven mindset can help us become knowledgeable data consumers able to draw meaningful conclusions and make important decisions.

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It’s the Journey, and the Destination: The Never-Ending Data Story
Data for Good Decisions Adriann Gin Data for Good Decisions Adriann Gin

It’s the Journey, and the Destination: The Never-Ending Data Story

In the not-so-ideal scenario where your data doesn’t support your hunch, do you stop there? You might, but there is a lot of learning that comes from results you do not expect. You may challenge yourself and ask: are my assumptions about this problem flawed? What else have I learned along the way? What factors might I have overlooked?

We recognize that taking time to examine your results in the context of your research question, and broader research problem, is not always possible for leaders who need to make urgent decisions. However, the key is to lean into the ambiguity and understand that regardless of the result of an analysis, leveraging data to make decisions is an ongoing process. At each critical point—including the moment from examining assumptions, evaluating data, and interpreting results—there are opportunities to pause and get ‘feedback’ about how we understand the problem and the factors we perceive to be relevant (e.g. the data source, the data itself).

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