Yin and Yang: Intuition + Data-Driven Mindset
Article Highlights:
Intuition alone can be problematic; however, bringing a data-driven mindset alongside our intuition can lead to better decision quality.
Adopting a data-driven mindset does not simply mean finding data to answer questions. Instead, a data-driven mindset offers a structured, systematic process for testing intuition.
Underlying the data-driven mindset, it is critical to understand the overarching purpose of the question you are seeking to answer.
In the previous post from this series, we explored the ways that relying solely on intuition can be problematic, and we emphasized adopting a data-driven mindset as a means to test intuitive hunches and strengthen the quality of decisions.
In this 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.
It’s also important to note that a data-driven mindset does not mean simply engaging with data. In other words, it is tempting to think of a question and quickly search for data that supports our research question[1], or worse, run some analyses and then try to piece together a solution to a problem. As we’ll discuss throughout this series, adopting a data-driven mindset is about adhering to a structured process, incorporating data, and interpreting data while remaining flexible to the fact that as we learn more about a topic, we will be left with more questions.
The first step in adopting a data-driven mindset is simple and yet often skipped—understanding the overarching purpose or mission underlying our research questions. From there, we critically examine our research questions and assumptions about the answer or solution. Next, we assess the data we have (or what data we can easily access) to understand the extent to which key questions can be answered. A fundamental aspect of adopting this mindset is sensemaking and iteration. We’ll cover this in more depth in a forthcoming post but for now, it is important to note that engaging in a data-driven approach truly means that you’re committing to a process rather than relying solely on a result. In other words, the nature of the data-driven mindset means we are constantly evolving our understanding of our research problem as we collect data, such that analyses may take us several steps back before we are able to move forward. But rest assured, this process is all in service of making better decisions.
Adopting a data-driven mindset is a strategic and systematic process that uses data to provide insights and ultimately improve decision quality. In the next post from this series, we’ll discuss how to think critically about our assumptions and challenge our intuition before collecting data, conducting analyses, and drawing conclusions.
[1] In this blog series, we use two similar but differentiated terms: 'research problem' and 'research question'. The research problem is the broader challenge. For example, homelessness. The research question, on the other hand, carves out a specific part of the research problem and frames it for exploration: Does an underperforming economy lead to homelessness?
This is one of several forthcoming pieces in Hawaii Data Collaborative’s Data for Good Decisions Series. The purpose of this series is to showcase how to elevate data into important policy and social change decisions to solve challenging problems.
