Doing nothing about data doesn’t keep you safe; it keeps you stuck and quietly burns cash. Keep in mind, there’s no AI without IA (information architecture).
Data is the silent ROI killer, and it’s drowning IT.
Most organizations don’t ignore their data issues. They simply decide they’re not urgent.
Systems run. Dashboards load. Reports go out. It’s not that IT and Data teams don’t believe there’s a problem – it’s that it isn’t a priority.
And it’s crushing.
The Data About Your Data Problem
- Gartner reports that poor data quality alone costs the average enterprise $12.9 million annually.
- McKinsey found that roughly 70% of IT capacity is consumed by maintaining legacy systems.
- BCG says software expenses are climbing 15% per year, now consuming 21% of total IT budgets, up from 13% in 2019.
Every year, organizations lose millions maintaining data, processes, and platforms that no longer serve the business. Waiting isn’t neutral – it compounds losses.
Those losses never appear as a single line on the balance sheet, yet they absolutely hit the bottom line. If your business operates 10% less efficiently, what does that mean for retention, sales, or cash flow?
Quick math: If your org is 5% slower on a $200M revenue base, that is ~$10M of drag. At 2% net margin, you need ~$500M of extra sales to cover it.
I see it all the time: data scattered across systems, dashboards contradicting one another, leadership meetings spent arguing which number to believe instead of what decision to make.
This isn’t neglect. It’s inertia…thinking the status quo is safer when it’s costlier.
See also: Why Data, Not Tech, Drives Digital Transformation
The Question Leaders Should Be Asking
For leaders managing multimillion-dollar budgets, the question isn’t “What will this tool cost?” It’s “What is indecision already costing us?”
That question rarely makes it to the table.
We’ve all been doing this for decades, myself included. We price tools one by one instead of the ecosystem that speeds or blocks knowledge.
It’s not a tech problem. It’s a mindset problem. AI with bad data makes loud garbage. New tools make the mess louder.
Your AI Conundrum
Boards and shareholders are demanding proof of “AI readiness.” The pressure to deliver quick wins is real. But much of what passes for AI strategy today is, frankly, nonsense!
Technology adoption is not transformation.
AI won’t clean up data, fix processes, or make decisions smarter on its own. It can’t.
It’s literally incapable!
AI builds on the foundations you give it. Nothing more. So if your data foundations are weak, AI doesn’t solve the problem; it amplifies it. And AI with bad data makes really loud garbage.
Yet most will also make another critical mistake: defining data by platform rather than by purpose.
Data is platform-agnostic. Define by purpose, not by where it sits. Treating Salesforce, NetSuite, or any other system as the definition of data immediately constrains what’s possible. It boxes information into what that tool can or can’t do instead of focusing on what the business needs it to reveal.
Related: stop this data mindset
And that leads to data bloat. Every redundant field, every ungoverned integration, every “temporary” report becomes part of a growing pile of digital noise that slows decisions and erodes trust. Executives feel the pain when they ask a simple question and ten different answers appear.
The headlines tell the rest of the story: inflated AI budgets, pilots that stall, and projects abandoned because the data wasn’t ready. Harvard Business Review estimates leaders waste 30% of their time dealing with inaccessible or unreliable data.
That’s not an IT issue. It’s a business risk.
Doing nothing, or chasing AI without data discipline, is the same decision by a different name: both guarantee that automation will scale your inefficiencies faster than your insights.
Flipping the Equation: From Tech-First to Data-First
A data-first mindset reverses the usual order.
- Start with the business outcome.
- Align the data that supports it.
- Only after that should technology enter the conversation.
Data-first reverses the order. Outcome. Data. Then tech.
I use what I call the Four Rs test: is the data relevant, reliable, revealing, and reusable? If it fails at least three, it’s clutter. Remove it and watch how little anyone misses it – you may be surprised!
Related: more on the Four Rs test and how to categorize your data
Forrester reports that high-performance IT teams experience growth 1.8× faster than their peers.
Yet IT is too often relegated to keeping the lights on instead of innovating, consolidating, or connecting the enterprise through better data.
Modernization starts with alignment, not a platform.
Pick one outcome: faster quotes, cleaner forecasts, shorter support resolution, unified revenue reporting. Start with one (I like quotes or forecasts as it’s often a fast win, think less than 90 days)
Then, design the data to enable it.
Do Something About It
Doing nothing doesn’t keep you safe; it keeps you stuck – and quietly burns cash.
Your competitors aren’t winning because they spend more on technology. They’re winning because they trust their data and act with confidence, while others hesitate.
There’s no AI without IA. And there’s no ROI without action.
What will you do about it?
