What Does It Mean To Be Data-Driven?

Based on episode 18 of The Business Thinking Podcast with our CEO, Neil Strange, and AutomateDV Product Manager, Alex Higgs.

Data literacy is a critical factor in becoming a truly data-driven organization. In a recent podcast episode, Neil Strange and Alex Higgs dive deep into the importance of data literacy, how it impacts decision-making, and how to cultivate it across your entire organization. Here's a breakdown of their insights on fostering data literacy in today’s fast-paced, data-driven world.

What Is Data Literacy?

As Neil explains, “To be data-driven, you need to have data literacy in your organization.” This means that all employees, not just those within the BI team, should understand basic data concepts to make effective use of data. From interpreting performance metrics to understanding basic statistics, data literacy ensures that everyone in the organization can handle and act on data correctly.

Without foundational data literacy, organizations struggle to leverage the tools at their disposal, even when AI and other advanced technologies are involved. It's essential for employees to understand concepts like averages, means, and distributions to make sense of the data that comes their way. As Alex points out, “If it comes back and says something about something being a mean or an average, you still got to know what that means in order to act on it.”

Raising Data Literacy Across the Organization

Data literacy goes beyond the BI team. Neil and Alex emphasize the need to raise data literacy across all departments in the organization. It starts with a basic understanding of data types, like dates, strings, and numbers, and extends to more complex concepts such as statistical analysis and data visualization.

Key Concepts to Cover:

• Basic Data Types: Employees should understand the difference between columns and rows of data and how to use appropriate data types for the right data.

• Statistical Concepts: Concepts like mean, mode, standard deviation, and how these relate to the distribution of the data are crucial for accurate decision-making.

• Observations and Data Interpretation: An observation is essentially a data point, and understanding its meaning is fundamental to using data effectively.

The Role of Tools in Data Literacy

As organizations grow, so does the need for effective tools. Neil and Alex discuss how tools like Excel can sometimes be overused, especially when they are not the most efficient solution. While Excel has its place, it's important to understand when to use more appropriate tools like SQL Server or dashboarding tools for better performance and insights.

Neil cautions, “Excel is a bit of a hammer, isn't it? Everyone turns up with Excel and uses it for every problem.” The right tools for the right job—whether it's database software, BI platforms, or even advanced statistical tools—make it easier to handle and interpret complex data.

Statistics in Decision-Making

As data literacy evolves, organizations can begin using statistical methods to support business decisions. Whether it's analyzing correlations or applying regression analysis, these statistical techniques help teams predict outcomes and make data-backed decisions.

Understanding the difference between causation and correlation is a key lesson. As Alex puts it, “Some people see what they want to see and often infer things from data.” Critical thinking is crucial to avoid jumping to conclusions based on incorrect assumptions.

Visualizing Data Effectively

Once data is analyzed, the next step is visualization. Neil and Alex highlight the importance of presenting data in a way that tells a clear story. Dashboards should avoid unnecessary clutter and use the right visualizations to communicate the key insights.

“Dashboards are always all about storytelling,” says Neil. Choosing the right visualization—such as using bar charts for comparisons instead of pie charts—helps make data more understandable and actionable.

Critical Thinking in Data Analysis

Finally, critical thinking is the foundation of effective data analysis. As Neil notes, it’s essential to challenge data when it doesn’t align with what’s expected or doesn’t make sense. For example, if sales data shows zero sales in an area where you know activity is happening, it’s important to investigate potential data issues rather than just acting on the numbers.

Neil stresses, “If I'm selling out there and my sales add up to a billion dollars, but I'm a billion-dollar company, there's something wrong in the data flows.”

Key Takeaways for Raising Data Literacy:

• Focus on Basic Concepts: Start with fundamental data concepts, like data types and basic statistics, to build a strong foundation.

• Select the Right Tools: Equip your team with the tools that best match the task at hand, avoiding over-reliance on tools like Excel.

• Leverage Statistics: Encourage the use of statistical methods like correlations and regression to support decision-making.

• Visualize Effectively: Use clear and meaningful visualizations that tell a story and drive action.

• Promote Critical Thinking: Teach your team to critically evaluate data to avoid acting on incorrect or misleading information.

Final Thoughts

Raising data literacy across your organization helps make the transition to being a truly data-driven company smoother. By focusing on basic concepts, using the right tools, applying statistical analysis, and promoting critical thinking, your team can unlock the full potential of the data at your disposal.

For more insights on improving data literacy and other data-related best practices, check out our Data Vault User Group website for past podcasts, forums, and additional learning resources.