Avoid the Data Avalanche
We’ve all been there. The spark of strategic planning hits, and suddenly everyone’s excited about becoming a more “data-driven” organization. We dive into collecting anything and everything until we’re buried in spreadsheets, dashboards, and reports from every corner of the organization. Before long, we’re sitting on a data avalanche, and none of it makes sense.
When the avalanche starts to fall, complexity takes over. Decision making becomes unclear, action slows, and the people behind the numbers are often left out of the process. If organizations want to avoid the data-driven avalanche, we have to be more intentional about how and why we use data in planning. When thinking of how to use data more intentionally, consider the following:
“What gets counted, counts*.” The choices we make about what data is measured, what gets excluded, and how information is collected can either challenge or reinforce systemic inequities. Collect data that aligns with your mission and goals but do it in a manner that ensures everyone’s stories are being told.
Complex charts are not always smarter charts. Simple, clear visuals allow people to grasp insights faster, ask intentional questions, and take action.
Data is a currency, and it has a cost. People pay a price when it comes to organizations having data, whether giving up some of their privacy or the time it takes to collect and analyze data. Recognizing and minimizing these costs is essential to building trust and ethical data practices.
Data can build trust and break it. How data is collected, shared, and used determines whether it strengthens relationships or erodes trust. Collect as little personally identifiable information as possible and use the best data security practices for storing data.
Question the data and prioritize lived experiences. Numbers alone do not tell the full story. Data should be examined critically, and insights should be informed by the experiences of those most impacted. Use focus groups and interviews to provide context and examples for explaining the numerical data.
Feedback loops should be the norm: Instead of simply presenting findings, feedback sessions should be a space to design next steps together. Feedback sessions validate and enrich your findings.
At SWIM we recognize the importance of being intentional with our use of data, we avoid the avalanche and instead, make meaning out of data.
Interested in our approach? Schedule a call today!
*This concept is from the book Data Feminism (2020) by Catherine D’Ignazio and Lauren F. Klein.