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 If your data is incomplete, outdated, or inconsistent, the risk of reaching a flawed conclusion increases significantly—even with the most advanced AI tools.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

After our last meeting of The Presidents Forum, members expressed interest in exploring the use of an AI Agent. What is an AI Agent: “AI agents are autonomous intelligent systems powered by artificial intelligence (AI) and designed to perform specific tasks independently without human intervention.” Here is a summary of the steps members heard at our follow-up meeting about moving forward with this task:

1. Define objectives: Develop a comprehensive plan before implementing an AI agent to maximize efficiency. Without a clear strategy, an AI agent may not deliver the expected benefits or could require frequent adjustments. For instance, is the objective automation, decision support, fraud detection, or enhancement of efficiency?
2. Identify use cases: Break down tasks where AI Agents can add the most value, such as repetitive, data-driven, or decision-heavy processes.
3. Choose the right AI tool: Determine whether you need a chatbot (like ChatGPT), a process automation tool (like RPA), or an analytics platform (like Tableau). Ensure this tool integrates with your existing software.
4. Gather and prepare data: Ensure the AI Agent has access to quality data, structured for analysis.
5. Develop and evaluate the AI Agent: Start with a prototype for a small-scale use case, then validate for accuracy, efficiency, and user experience.
6. Monitor and optimize: Set performance metrics and continuously improve.

With numerous emerging AI agent platforms, it can be challenging for buyers to determine which agents will perform specific tasks and which platform to use for effective deployment. The key question to answer is: what business outcomes can AI Agents specifically deliver to me?

Additionally, here are some key benefits of using an AI Agent to be discussed in a future meeting:

1. Cost Savings: Implementing AI agents can lead to significant cost reductions by automating routine tasks and reducing the need for manual labor.
2. Scalability: AI agents can manage increasing workloads without a proportional increase in costs, making them highly scalable solutions for growing businesses.
3. 24/7 Availability: Unlike human workers, AI agents can operate around the clock, providing continuous support and services.
4. Enhanced Customer Experience: AI agents can improve customer interactions by providing quick and accurate responses, leading to higher customer satisfaction.
5. Data-Driven Insights: AI agents can analyze large volumes of data to uncover patterns and insights that can inform strategic decisions.