At every meeting of The Presidents Forum, conversations about data analytics inevitably focus on technology—AI models, dashboards, predictive tools, and automation. But the real key to effective analytics isn’t the tools themselves—it’s the data.

The foundation of good analytics is clarity: understanding the problem you’re solving, preparing your data thoughtfully, and asking the right question. But none of that matters without accurate, complete, and well-aligned data.

Just as accountants rely on the accrual basis to match income and expenses to the correct time period, data analysts must ensure that all relevant information is properly aligned to support the decision at hand. If your data is incomplete, outdated, or inconsistent, the risk of reaching a flawed conclusion increases significantly—even with the most advanced AI tools.

It’s easy to underestimate this. A polished dashboard or a convincing AI-generated summary may give the appearance of insight, but if the underlying data is missing or misaligned, the output can be misleading. That’s why data validation is essential. Before you analyze, model, or visualize, you must confirm that the data is accurate, consistent, and complete. Garbage in still means garbage out—even in the age of artificial intelligence.

This is especially critical with AI. These tools can process massive amounts of data quickly, but they don’t know what’s missing. They can’t resolve inconsistencies in definitions or business rules across systems. They’ll confidently analyze whatever you give them—right or wrong. That means your role as the human in the loop is more important than ever.

The best outcomes occur when smart tools are paired with sound data. Technology may offer speed and scale, but quality data brings insight, integrity, and trust.

In the end, it’s not about how advanced your tools are—it’s about how reliable your data is. Because meaningful analytics doesn’t start with machines. It starts with the right information.

A future meeting of The Presidents Forum will certainly turn to the topics of data accumulation, governance, and strategic use.

Steve McCarthy

“A problem defined is a problem half solved.”

That saying holds true now more than ever—especially in a data-driven world. At our recent meeting of The Presidents Forum, we explored how organizations can make better use of their data by first asking better questions.

Let’s walk through a simple example that illustrates the entire analytics process and helps clarify when to use self-service tools like Tableau or Power BI—and when to turn to AI tools like ChatGPT.

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Step 1: Start with a Clear Problem

Imagine your organization is facing a sales slowdown. The first and most important step isn’t to open a dashboard or run an AI prompt—it’s to clearly define the problem.

Why are our product sales down this past quarter?

This simple but powerful question sets the stage for the rest of your analysis. Without a clear question, no tool—AI or otherwise—can deliver useful answers.

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Step 2: Identify the Right Data

With the problem defined, you can now ask:
“What data do we need to answer this?”

Relevant data might include:

• Sales figures by product line, region, or customer segment
• Marketing campaign activity
• Customer support feedback or product returns
• Pricing and inventory data

The goal is to build a 360-degree view that gives context to the sales dip.

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Step 3: Prepare the Data (ETL Process)

Before any analysis happens, the data needs to be:

• Extracted from source systems (like ERP, CRM, or Excel files)
• Transformed into a clean, consistent format
• Loaded into a usable platform, ideally a relational database rather than siloed spreadsheets

This ETL (Extract, Transform, Load) process is the foundation of trustworthy analysis. Without it, insights may be incomplete—or worse, incorrect.

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Step 4: Select the Right Tool for the Job

Now that the data is accessible and reliable, it’s time to decide how to analyze it.

• Self-service tools like Tableau and Power BI are excellent for structured questions where you know what you’re looking for. They help visualize trends, track KPIs, and share results through dashboards and scorecards.
• AI tools like ChatGPT are better suited for unstructured exploration. They’re useful for identifying potential causes, brainstorming questions you might not have considered, or summarizing open-ended data like customer reviews or sales call transcripts.

In other words, if you know the question, use Tableau or Power BI. If you’re still figuring out the question, try ChatGPT.

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Step 5: Deliver Actionable Insights

Once you’ve done the analysis, it’s critical to communicate findings in a way that supports decision-making.

Depending on the audience, that might mean:

• A dashboard showing trends and comparisons
• A brief executive summary
• A set of bullet points with your top three recommendations
• A presentation deck with supporting visuals

Effective storytelling turns data into decisions.

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Final Thought

“A problem defined is a problem half solved.”

By leading with the right question, you can map the rest of your analytics journey with confidence—identifying the right data, choosing the right tools, and delivering meaningful insights to the right people.

Analytics isn’t just about technology. It’s about clarity, preparation, and smart communication. And that starts with defining the problem.