Stop This Wrong Approach To Your Data.

Every few years, a new wave of technology promises to “finally fix” work. GenAI, data automation, Data Cloud, intent data, data lakes … you name it, the promise is the same.

And yet, for all the hype, most organizations end up in a familiar spot: too many tools, too much noise, and too little clarity about what’s truly driving performance.

That’s not a technology problem. That’s a data problem...

...or more accurately, I’d argue it’s a data mindset problem.

The Habit We Can’t Kick: Tech-First Thinking

For a long time, tech-first worked. When systems were siloed and integrations were new, buying another tool often solved real issues. A new CRM centralized sales data. A shiny BI dashboard surfaced “real-time insights.”

But that playbook no longer holds.

Today, companies are drowning in data. They have no shortage of systems. What they lack is understanding: what data actually matters, how it connects, and what it’s supposed to drive.

I see this all the time. A team has Salesforce, a marketing automation platform, a data warehouse, and spreadsheets under every rock. None of it tells the same story.

So they keep adding more tech, hoping the next platform will clean it all up. It never does.

The result is “analysis paralysis.” Meetings full of dashboards, but nobody sure which number to believe. When that happens, people stop trusting the data entirely and go back to gut-based decisions.

That’s the cost of leading with technology instead of truth.

What It Means to Go Data First

Data-first flips the order.

Data is your most strategic asset (after your people). It is not the byproduct of your systems. It is the reason those systems exist.

Data first means asking, “What business outcome are we trying to achieve?” before asking, “What platform should we buy?”

I said this during our Data Chaos to Business Insights webinar, and I’ll repeat it here:

Solutioning too soon is a detriment for you right now. Solutioning must come later.

When you take a data first approach, you start by understanding what decisions the business needs to make. Then you make sure the right data exists, is clean, and is reliable enough to support those decisions.

Only then do you bring in technology to accelerate or automate that process.

That mindset shift sounds simple, but it changes everything.

Why Tech-First Fails Today

The explosion of AI and automation has made tech first thinking even more tempting and dangerous.

When companies jump straight into tools, three things almost always happen:

1. You amplify garbage. AI doesn’t fix bad data. It multiplies it. If your data is inconsistent or incomplete, AI just makes louder bad decisions. AI with bad data makes really loud garbage.

2. You lose the why behind your systems. When tech drives the conversation, every platform defines “customer,” “revenue,” or “forecast” a little differently. Reports don’t match. Dashboards conflict. The conversation moves away from outcomes and becomes about reconciling systems.

3. You lose trust internally. When sales doesn’t trust the CRM, finance doesn’t trust the forecast, and ops doesn’t trust the dashboards, data becomes political. People stop using the systems you’ve invested in.

By then, it doesn’t matter how much AI or automation you have.

Nobody believes the results anyway.

Why Data-First Wins

Data-first forces alignment before investment.

You stop buying tools to “fix” data, and instead fix the way you think about data itself.

Here’s how that plays out:

1. Start with outcomes. Get everyone around the same table and agree on the goal. Faster quote cycles. Cleaner forecasting. Lower churn. Once that’s clear, technology decisions get easy.

2. Understand your current state data. Not systems. Your data! What exists? Where is it? Who owns it? How reliable is it? If you can’t answer those questions, you’re not ready for more technology.

3. Design for insight, not storage. Data shouldn’t just live in systems. Data should tell you something that changes behavior. Climb the data pyramid: data → information → insight → knowledge.

4. Remember this rule: No AI without IA. You can’t expect AI to work without strong information architecture. You need to know how your data is structured, how it flows, and what it means before layering AI on top. Without that, you’re just automating chaos.

What Data-First Looks Like in Practice

During our webinar, we asked attendees how many fields in their CRM opportunities were truly useful. The majority said fewer than 10.

That’s out of dozens or even hundreds of fields ... just in one place!

All those extra fields create friction. They slow down sales reps, complicate reporting, and flood the system with unhelpful noise.

When we run our Four Rs test (Relevancy, Reliability, Revealability, Reusability), we often find that half of what’s being captured doesn’t pass.

And that’s fine! It’s okay to kill a field. Just because it’s there doesn’t mean you have to use it, and you can always put a field back in later if you discover it's truly needed (and you'll understand why).

That’s data first in action!

Focus on what matters. Kill what doesn’t. Measure the decisions you can now make with confidence.

Leadership’s Role In Adopting A Data-First Mentality

Data first isn’t an IT initiative. It’s a business initiative.

Every department produces and consumes data. Sales, Finance, Marketing, Operations, Customer Success … they all depend on data to run effectively.

When leadership treats data as a shared asset, alignment follows. When they treat it as an IT issue, silos multiply.

The job of a leader is to unify around outcomes, not platforms:

• Ask harder questions before signing another software contract.

• Hold vendors accountable to business impact, not technical specs.

Data is capital - in fact, it’s your second-most valuable asset (behind your people, of course).

It carries value, risk, and return. And like any form of capital, it requires governance, stewardship, and investment.

Where to Begin

If you want to make the shift toward data first thinking, here’s where I tell most of my clients to start.

1. Pick one outcome for your first 90 days. Two I like to lead with: improving forecast accuracy or speeding up quote approvals.

2. Identify the data behind that outcome. What data drives it? Who owns it? How is it used today? What’s missing?

3. Run the Four Rs test. Determine if each data point is relevant, reliable, revealing, and reusable. If it fails three of the four, fix it or remove it.

4. Simplify your stack. Look hard at your technology. If a tool doesn’t move the outcome, park it.

5. Capture the win. When you start making better decisions because your data is cleaner, document that. Quantify the impact and share it to build trust.

The Real Payoff

A data first mindset doesn’t just make systems more efficient. It makes the organization smarter!

Leaders trust their numbers. Teams trust their tools. And the technology finally works the way it was supposed to because it’s fed by the right data serving the right purpose.

The next time someone pitches you the next great AI or automation platform, ask one simple question first.

Do we trust the data we already have? If the answer is no, that’s where your work begins.

Want to put data first? Start with a data strategy roadmap that aligns your outcomes, people, and systems. We’ll co-build it with your data.

But The Change Has To Stick, Too

That mindset shift is the hard part. But once you’ve made it, a new challenge shows up.

How do you keep it alive?

How do you stop the organization from slowly slipping back into old habits: more tools, more silos, and more noise disguised as progress?

Because the truth is, most data initiatives don’t fail in the beginning. They fail in the middle … after they’ve already started!

The Plateau Problem

You’ve cleaned up your CRM fields. You’ve applied the Four Rs test. You’ve started making better decisions because your data finally makes sense.

Then what happens?

Momentum fades. People move on to new priorities. Systems evolve, processes shift, and the same cracks start to reappear.

What was once “trusted data” becomes “data we’ll fix later.”

Every organization hits this wall. The difference between those who break through and those who stall out comes down to one thing: how they operationalize what they’ve learned.

Insight is not the finish line. It’s the starting point for building real knowledge.

Insight vs. Knowledge

• Data is what you collect.

• Information is what you organize.

• Insight is what you interpret.

• Knowledge is what you apply consistently.

Most companies stop at insight. They find a trend, make a decision, and move on. But they never embed that learning back into their processes, teams, and culture.

Knowledge means you take what you’ve learned and make it repeatable.

If you learned that deals close 15 percent faster when quotes are standardized, knowledge means you build that standardization into your quoting process, your Salesforce configuration(s), and your training. You understand how to activate this into something tangible that drives your ideal outcomes.

It becomes muscle memory, not a one-time discovery.

Turning Short-Term Wins into Long-Term Habits

To move from insight to knowledge, you need structure. Not bureaucracy, but structure. The kind that keeps good habits from slipping away once the initial excitement fades.

Here’s what that looks like in practice:

1. Create visible ownership of data. Every department should know who’s accountable for their data quality and activation. That person doesn’t have to be technical. They just need to understand how data connects to outcomes.

When ownership is clear, accountability follows. When it isn’t, you end up with everyone assuming someone else is taking care of it.

2. Build data literacy across teams. The goal isn’t to turn everyone into a data scientist. It’s to make data everyone’s responsibility.

Your sales leader should understand what “reliable” data means. Your marketing manager should know which data drives forecasting. Your finance partner should be able to read the same dashboard without needing a translator.

The more your teams speak a shared language around data, the longer your strategy lasts.

3. Establish a governance rhythm. Governance doesn’t have to be heavy. It just needs to be consistent.

Set up a quarterly “data health review.” Look at key objects, reports, and integrations. Ask what’s working, what’s breaking, and what’s becoming redundant.

Think of it like preventative maintenance for your car. You don’t wait until the engine fails to check the oil.

4. Keep metrics tied to outcomes, not activity. Avoid the trap of measuring data by how much of it you have or how fast it moves. Measure by the business results it supports.

How much has forecasting accuracy improved? How much faster are we quoting and closing? How often are insights actually used in decision-making?

Tie those metrics back to the outcomes your leadership team cares about. That’s what keeps the strategy relevant.

5. Plan for the next evolution of technology. AI isn’t slowing down. Neither is your business.

You can’t predict every new tool that will emerge, but you can build your data foundation so it’s flexible.

Architect your data to be portable, not dependent on one platform. Make sure your governance process can adapt when you swap tools or change business models.

That’s how you protect your investment over time.

Culture Eats (Data) Strategy

Technology changes fast, but people change slowly.

If your data-first effort stays locked in a project plan, it will fade the moment something shinier comes along. And ohhhhh boy, do I have stories where Shiny Object Syndrome (S.O.S. for a reason!) has cost companies millions.

To make the right mindset stick, you need the right culture.

That doesn’t mean posters about “data-driven decisions.” It means people actually believing the data helps them do their jobs better.

• When sales reps see how accurate data shortens their cycles, they start caring about entering clean information.

• When finance trusts the pipeline, they start defending the process instead of questioning it.

• When marketing has clarity into revenue impact, they lean into the data further.

• When IT sees simplification of what’s complex, they want more.

That’s when the shift becomes permanent and when the behavior reinforces the belief. If data helps people win today, the culture keeps it tomorrow.

The Long Game

A good data strategy isn’t a one-and-done deliverable. It’s a living system.

I tell clients all the time: you can get from chaos to clarity in 90 days. Staying there is a two-year effort.

That’s not because it’s hard. It’s because the business keeps moving. Teams change. Systems evolve. Markets shift.

So the data-first mindset has to evolve with it.

Companies who succeed aren’t the ones who did a “data cleanup project.” They’re the ones who keep applying the same thinking every time a new initiative starts:

1. Before launching a new product, they ask: what data do we need to measure success?

2. Before buying a new tool, they ask: what data will this improve or expose?

3. Before changing a process, they ask: what insights are we trying to generate?

It’s a culture change that impacts everyone.

The Real Marker of Maturity

You’ll know your organization has made the leap from insight to knowledge when you stop needing to remind people to think data first.

It becomes automatic.

Conversations shift from from ‘What tool?’ to ‘What outcome?’ - from ‘We think’ to ‘We know, and here’s the data.'

This is when the investment starts paying off not just in ROI, but in confidence.

Keeping It Going

Here’s the simple formula I share with clients when we wrap up an engagement:

• Keep governance light but regular.

• Keep metrics outcome-focused.

• Keep culture accountable.

• Keep learning visible.

If you do those four things, data-first mindset becomes how you operate.

And that’s the real goal. Not just having data that works, but having a business that works because of data.

We’re ready when you want to move.