The Consequences of Being Just One Degree Off

We’ve all heard the analogy of an airplane being just one degree off course. Leave Los Angeles with a one-degree error and, instead of landing in New York, you could end up in Washington D.C.?

Yes, the analogy is a bit tired, but it still works in that it helps us understand how critical precision is in aviation.

Precision is just as critical in data quality–if you want to arrive at a “data-driven culture.” And it’s exponentially more critical with GenAI. As Eric Avidon argues in his recent article titled “GenAI demands greater emphasis on data quality,” GenAI exacerbates the need for accurate data.

Which means here’s the real problem.?

Working with aggregated or flattened data isn’t working with data that’s just one degree off. It’s like being multiple degrees off from the very beginning. Every time data is summarized, grouped, or restructured, more degrees are added. When your AI is built on flattened or outdated data, you’re not only starting off-course but missing the opportunity to course-correct before it’s too late.

Never mind your efforts to get close to trusted data You won’t even end up on the same continent.?

You're compensating–and you know it.

We could make a list of all the consequences that follow poor data quality: misinformed decisions, reinforced biases, etc. But what’s often overlooked is the real impact on decision-making behavior. CFOs and data analysts know when their data is unreliable—and they perform all kinds of data dances to compensate.

See if any of these decision-making behaviors sound familiar.

Double-Checking and Verifying Manually:?

Rather than trusting the data, teams spend time manually verifying reports, cross-referencing with other sources, and essentially redoing work. What should be an automated process becomes manual, costing valuable time and resources.

Creating Multiple Scenarios:?

Without confidence in a single data source, your team often relies on creating numerous models or scenarios to hedge against potential inaccuracies. This adds layers of complexity and dilutes clarity, causing further delays in making critical decisions.

Avoiding Risky or Strategic Decisions:?

When you don’t trust your data, it’s hard to be bold. Data analysts and executives often choose safer, incremental decisions over innovative, strategic ones. The fear of making mistakes outweighs the desire to drive the business forward.

Relying on Intuition Over Data:?

Without reliable data, intuition takes over. Decisions that should be driven by insights end up being based on gut feelings and past experience. While intuition has its place, it shouldn’t replace the objective clarity that real-time data provides.

Frequent Data Updates Requests:?

Knowing the limitations of stale or aggregated data, executives request more frequent data updates, leading to reactive decision-making. This turns the data team into firefighters, constantly scrambling to keep reports up to date rather than building future-focused insights.

Short-Term Focus:?

Instead of using long-term trends, data analysts often fall back on short-term metrics that are easier to verify but don’t tell the whole story. This short-sightedness can prevent businesses from making forward-thinking decisions that drive sustained growth.

Team Discussions to ‘Gut Check’ Decisions:?

Without trusted data, executives often default to consensus-driven decision-making. This means more meetings and more back-and-forth discussions–viz., wasted time—time that could be spent innovating is instead spent trying to align on half-trusted data.

These compensatory behaviors represent a massive drain on organizational efficiency, but more importantly, they erode confidence in decision-making. Businesses that cannot trust their data are stuck in a reactive cycle—forever compensating, never strategizing.

Feels like you’re trying to out-shine the sun with a flashlight, doesn’t it?

Precision-Driven Data Quality: The Key to Trusting AI Systems

For businesses today, saying you have AI systems in place is like saying you have plastic in the office. You need AI systems that can make decisions with the granularity and precision your business demands. This requires access to live, operational data.

Why is live data so critical?

Granular Decision-Making:?

AI models built on live data aren’t just precise, they’re adaptable. With live data, your systems can make decisions based on real-time information, reducing the risk of decisions being out of sync with current business conditions.

Contextual Insights:?

Aggregated or flattened data often strips out the context needed for deep insights. Live data maintains the integrity of context, allowing AI systems to provide recommendations that are not only accurate but also relevant to the specific situation at hand.

Proactive Strategy:?

When AI systems work with real-time data, they aren’t just reacting—they’re anticipating. The granularity provided by live data allows for forward-looking insights that help organizations move from a reactive to a proactive stance.

Elimination of Redundancy:?

Access to real-time data reduces the need for frequent updates and scenario-building. Decision-makers no longer need to hedge their bets or waste time cross-referencing outdated reports. Instead, they can focus on strategic growth with confidence that the data is telling the whole story.

True Optimization:?

When AI models work with live, detailed operational data, you unlock the ability to truly optimize processes and workflows. Decisions are made based on what’s happening right now, not on what happened yesterday, ensuring that every decision is as timely as it is accurate.

The ability to act on live, precise data is a strategic advantage that separates industry leaders from those constantly trying to catch up.?

Real-time, operational data empowers CFOs and data analysts to trust their AI systems and, most importantly, to arrive at the destinations they’re aiming for.

Where's your data taking you?