Editorial
Every organization is sitting on mountains of data. Performance dashboards, engagement surveys, customer feedback, financial metrics, retention reports — the list is endless. The irony? Most leaders aren’t using even a fraction of the value that’s available in that data.
Instead of seeing patterns, possibilities and opportunities, they’re drowning in noise. The biggest problem isn’t too little data, it’s the quality of the data and the way organizations use it.
Table of Contents
- The Problem With a Narrow Data Lens
- Common Barriers to Unlocking Data Value
- How to Unlock the Real Value of Your Data
- The Competitive Edge of Smarter Data Use
The Problem With a Narrow Data Lens
Data is often used to answer an already framed question, rather than being applied through a broader business intelligence lens. Someone asks, “What’s our engagement score this quarter?” or “How's the sales pipeline trending?” and the data team dutifully digs into the numbers to provide an answer.
The result is a narrow view. The organization gets the piece of the puzzle they were looking for, but they risk missing the broader context, the hidden patterns and the deeper story inside the data.
This question-driven approach can be efficient, but on its own leaves a lot of value on the table.
Common Barriers to Unlocking Data Value
Here are some of the most common barriers keeping organizations from realizing the full power of their data:
1. Lack of a Clear Data Ecosystem Map
Most organizations don’t have a clear map of their inputs, outputs and flows. Where does the data come from? What systems feed into it? What data points exist? Without visibility into the full ecosystem, leaders can rely on an isolated set of points instead of seeing how the system connects.
2. Inaccessible or Siloed Data
Even when organizations have the data, it’s often locked in silos or owned by specific departments. That means only a handful of people can actually use it. Data that isn’t accessible, usable and shared is wasted potential. While there are legitimate business reasons to limit data access, in many cases the bureaucracy and processes in place make data harder to use than is necessary, which limits its value.
3. Superficial Measurements
Leaders love trending scores: engagement ratings, satisfaction scores, productivity indexes. But these vanity metrics can be highly influenced by how someone feels in the moment or gamed by employees who know how the system works.
Too few organizations measure elemental metrics — the underlying drivers that actually cause the outcomes they care about. Without those, you’re forever managing symptoms, not root causes. A powerful data ecosystem integrates different types of measurements, weaving them together to create clear insights and guide the best possible decision making.
4. The Watermelon Effect
On the outside, metrics can look “green” (healthy) but inside they’re “red” (problems hiding under the surface). For example, a team may show strong project delivery scores while quietly burning out, disengaging or cutting corners. If you only look at surface-level dashboards, you’ll miss the full truth.
5. Biased Data
Data can be biased, shaped by how it’s collected, who collects it and how it’s interpreted. Surveys overrepresent those who are willing to respond. Performance reviews reflect the manager’s perspective as much as the employee’s reality.
And when you feed biased data into AI systems? The bias scales — fast and invisibly, creating significant AI bias risks for your organization. You don’t just have biased outcomes, you have amplified bias influencing decisions across the organization.
6. Analysis Paralysis
With so much data available, teams get stuck overanalyzing. They spend months perfecting reports instead of driving action. Valuable insights can sit unused while leaders debate methodology or make assumptions about causes.
7. Misplaced Trust in Technology
AI, dashboards and analytics tools are powerful, but they’re not magic. They don’t replace critical thinking. Just because a metric is automated, it doesn’t mean it is accurate. If you don’t ask the right questions or examine the assumptions, the outputs can mislead.
How to Unlock the Real Value of Your Data
The key to unlock data's value isn’t always more data. It’s often about making your existing data more meaningful, usable and actionable, while being clear on your gaps:
1. Map Your Data Ecosystem
Audit your landscape: what data do you collect, where does it comes from, who owns it, how does it flow and what systems feed it? A data map surfaces redundancies, blind spots and connectors you may not have realized existed — and it’s the foundation for all the steps that follow.
Tip: Create a one-page data ecosystem map for one area of business can be a helpful way to get started.
2. Identify Gaps (and Eliminate Bad Data)
Be explicit about what’s missing or unreliable. Ask: Do we lack the inputs we need? Is this dataset stale, biased or noisy? Are people clinging to a metric because of prior investment or habit, not because it drives outcomes? Call out sunk-cost attachments and decide objectively whether to improve, replace or retire the data source.
Tip: For each major dataset, record important details such as freshness, coverage, owner, known biases and a one to five “actionability” rating. You can then prioritize what needs immediate review.
3. Improve Data Accessibility, With Guardrails
Where possible, create better access through governed platforms, clear metadata and easy query tools. The goal isn’t to overwhelm everyone with all the data available — it’s to help people quickly find and understand the datasets they need.
Tip: You may need to consider what’s required to support broader data access. Do you have clear role-based mappings? Is there a need to strengthen overall data literacy? Are support structures in place to guide people as they engage with the data?
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4. Focus on Elemental, Actionable Metrics
Take stock of your current measurements: which lagging or trending scores can be influenced by momentary sentiment, and which capture the elemental inputs that produce results? Ask yourself: do you know the drivers of the outcomes you care about, or are you relying on metrics that only tell part of the story?
Tip: For one key outcome, map all the measurements you currently track, identify the gaps and select two to three elemental metrics to start monitoring this month to directly inform action.
5. Review 'Green' Numbers
Don’t let a healthy-looking metric lull you into complacency. Ask: What could this number be hiding? You may need to combine quantitative trends with qualitative data such as interviews, observations, open survey questions or other data points to surface contradictions.
Tip: Identify the “supporting data” points for your metrics. For example, if you track Time to Answer on a help desk, also consider measures like Time to Resolve and Average Hold Time to understand the true customer experience.
6. Check for Bias and Manage AI Risks
Treat bias-checking as routine. Who is represented? Who’s missing? How was the data collected? If you put biased inputs into AI, you scale biased decision making. Build bias tests, sampling checks and a simple risk register for any model-driven use.
Tip: Add a “bias checklist” to any analytic brief. For example, ask who’s included, who’s not and what assumptions were made.
7. Balance Tech With Human Judgment
Tools can surface signals, but context and judgment must remain human-led. Use analytics to inform decisions; use humans to interpret nuance, trade-offs and ethics.
Tip: You could add a one-paragraph “context” field to dashboard reports summarizing assumptions and caveats.
8. Retire What Doesn’t Work, Invest in What Will
If a metric or dataset consistently misleads or has limited use, consider sunsetting it. Reallocate effort to collecting a high-value input you’re missing. You want to focus on the metrics that truly drive decisions.
Tip: Create a short “sunset proposal” template to evaluate whether to keep, fix or remove legacy datasets.
The Competitive Edge of Smarter Data Use
Organizations that harness their data ecosystem effectively don’t just make better decisions — they gain a competitive edge. They spot risks earlier, act on opportunities faster, and unlock human potential that others overlook.
The organizations that thrive in the next decade won’t be the ones with the most data. They’ll be the ones that know how to see through the noise, uncover what truly matters and act on it.
Editor's Note: What other ways can you improve your decision-making?
- Balance Intuition and Data to Master the Art of Decision-Making — Rather than viewing intuition and data as rivals, we should view them as allies. Here's why.
- 3 Data-Driven Strategies to Boost and Measure Employee Experience — As we all try to make hybrid work more effective, it’s critical to understand how your organization can connect employee experience to business goals.
- AI Isn't Ready to Make Unsupervised Business Decisions — AI isn't ready to fly solo on business decision making. Keep humans in the loop and set up these guardrails before setting it loose.
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