Step 1: Shift Your Perspective

The first step is to recognize when data-driven thinking is necessary. This can be the most challenging yet transformative aspect, as it establishes the reasons behind your desire for a data-driven approach.

Consider this common scenario:

Manager: Our sales numbers aren't meeting expectations.

Team Member: We ask for sales on every call and are unsure of what else to do.

Manager: You all need additional training. I'll coordinate with HR.

Team Member: Sure, can we break for lunch now?

Relying solely on training is rarely the solution. When you hear vague terms like "enough," "better," or "decrease," it's time to pivot into data mode. Such ambiguous language can create confusion and hinder genuine problem-solving efforts.

Using the sales example, instead of asking for "more" sales, clarify what that means. How many are you currently selling? By analyzing specific metrics at each stage of the sales process, you can identify areas for improvement.

Initially, you may discover that you lack the necessary data. However, if the figure you seek results from an action, there's a good chance the data is available somewhere. If you are unable to locate it, you can approach your manager for assistance—after all, that's part of fostering a data-driven culture.

Takeaway: Whenever you express a desire for improvement, try to attach a numerical target to it.

Step 2: Simplify the Math

Hold on! Before you dismiss math altogether, know that you only need to grasp three fundamental concepts: counting, percentages, and comparisons.

For those familiar with spreadsheets, they serve as excellent tools for data analysis, allowing for efficient collection and summarization of information. Familiarize yourself with filters, basic formulas, and pivot tables, especially as many platforms now integrate AI capabilities.

However, if spreadsheets feel daunting, consider leveraging AI tools like ChatGPT or Google’s Gemini. These applications can perform basic analyses and are user-friendly.

Counting: The foundation of data analysis lies in counting occurrences. For instance, track how many times a particular event happens over time to gauge improvements.

Consider these key counts:

• Incoming work: The volume of inquiries or requests your team receives.
• Production: The completed tasks, such as sales or reports.
• Aging items: Count how many tasks have exceeded their expected completion time.

Percentages: While percentages can seem intimidating, they're essentially a way to compare two quantities. Use this formula: (occurrences) divided by (total opportunities) multiplied by 100 to get the percentage.

For example, if a team sells 20 products after 50 calls, that translates to a 40% close rate. This percentage can help predict future sales based on increased inquiries.

Takeaway: Familiarize yourself with these basic mathematical concepts. Counting and percentages are vital for assessing data trends.

Step 3: Communicate Your Insights

Finally, to establish your reputation as a data-savvy manager, you must articulate your findings effectively. The steps you've taken to analyze the data should mirror how you present your results.

Outline the problem you faced and the data illustrating its magnitude. Share the outcome and detail the changes made, supporting your claims with relevant metrics.

Utilize visuals, like graphs, to showcase how data has evolved over time. This helps to convey your story clearly and effectively.

Caution: Avoid overwhelming your audience with raw data. Instead, synthesize your findings to improve comprehension and credibility.

Conclusion: Embracing these three steps will empower you to become a data-driven manager. Always remember to articulate your story using data and visuals to support your insights.

The first video titled "How to be More Effective Data-Driven PM by fmr Groupon Sr PM" provides valuable insights on becoming a more effective data-driven product manager, emphasizing practical strategies to enhance decision-making through data.

The second video, "First steps on the journey to become more data driven: what we've learnt along the way," discusses the initial steps and lessons learned in the transition to a more data-driven approach in organizations.