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 If your data is incomplete, outdated, or inconsistent, the risk of reaching a flawed conclusion increases significantly—even with the most advanced AI tools.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

At a past meeting of The Presidents Forum, Angela Liu, President of Crispy Green, Inc., talked with members on Data Governance. Angela said this topic’s importance is driven in part by privacy, legal and ethical concerns, presenting unique risks that need to be better managed. This discussion has now come up again due to its importance.

At our meeting, we began talking about Data Governance goals, which are different depending on the industry, organization structure, type of data captured, and overall risk management practices.

However, some common goals and initiatives include improving, data quality, compliance and security, and data sharing.

Data security has become more critical for many companies after the California Privacy Rights Act (CPRA) passage with its new requirements for data retention and the individuals’ request to access, delete, correct, or opt-out.

The key takeaways from this meeting are first to acknowledge that an organization’s data has significant value that needs to be protected. And second, there is a disciplined, formal, centralized enterprise-wide process to manage data, it is not intended for one department only.

Good governance benefits include enhanced security, improved data quality, and operational efficiency by reducing data management and storage costs.

These are good reasons to start working on a data governance plan today.

Steve McCarthy

The Presidents Forum