Insights From Across Business Functions

Apurva Wadodkar, Sr. Director Data & AI at TI Automotive, is a data science leader driving strategic, people-first innovation at scale.

Over the last two decades, I have had the chance to roll up my sleeves and lead data strategy efforts across eight very different business functions, from automotive and manufacturing to supply chain and technology.

The tools changed, the key performance indicators (KPIs) were adjusted to the domain, but one truth stayed constant: Successful organizations don’t win with data because they bought the right technology or hired the right talent. They win because they succeed at weaving data into the fabric of their business strategy, culture and decision making.

This work hasn’t been theoretical. It’s been built in the real world, crafted in workshops, shaped in late-night war rooms and tested through messy, high-stakes transformations. Along the way, I have started to see repeatable patterns and moves that separate companies that just say they want to be data-driven from those who are actually doing it.

Here are a few of the most powerful lessons I have learned:

1. Make data a business strategy, not an add-on.

The top-performing companies don’t treat data as just an IT project or a reporting tool. They see it as central to how they compete and grow.

I have seen this play out in wildly different ways: predictive maintenance on industrial machines, hyper-personalized digital experiences for customers, you name it. What ties them together is that data is treated as a product—complete with roadmaps, governance and accountability.

This type of transformation requires executive ownership, clear outcomes and serious investment. But it’s not a one-way street. To gain executive ownership, IT leaders must show business leaders what’s possible with data. The best way is to educate them with use cases in areas they know best. This sparks ideas, builds curiosity and ultimately makes business leaders want to leverage data in their own strategies.

2. Use the FIND framework to uncover real value.

One of the tools I lean on is a framework I call FIND, which I’ve written about elsewhere, and which helps spot untapped opportunities:

• F: Fragmented decisions

• I: Invisible patterns

• N: Neglected signals

• D: Duplicated effort

This approach has helped uncover everything from hidden compliance inefficiencies to early signs of customer churn. It works across industries because it is focused on finding the friction—the places where data can quietly but powerfully change the game.

3. Build to evolve, not to be perfect.

A common trap I have seen is trying to build the “perfect” data system before showing any results. That rarely works. By the time perfection is reached, business priorities have shifted, budgets have tightened or stakeholders have lost interest. The result: a polished system that delivers too little, too late.

The best results come from shipping value early, starting small with data products that solve a real problem. This builds trust, creates momentum and provides feedback you can act on. I have seen a simple predictive model cut downtime and pay for itself in months, which opened the door to larger investments.

Growing your architecture means staying adaptable with modular design, reusable components and good governance. Track adoption, impact and data quality, then refine. That’s how you get ROI fast and keep pace with the business.

4. Culture is an architecture challenge, too.

Some of the biggest blockers to effective data aren’t technical; they’re cultural. Many organizations fall into the trap of standing up data teams and pipelines before aligning on why the data matters. The result? A flurry of activity that leads to bloated data lakes and disconnected efforts—what often becomes a “data swamp.”

The real shift isn’t just technical; it’s cultural. We need to move from a “data push” mentality (collect everything, just in case) to a “data pull” approach, where business use cases drive data priorities. That means embedding data strategists within business teams, designing architecture in response to decision needs and co-owning outcomes.

When the business pulls data because it’s solving real problems, the culture shifts—and architecture finally starts to deliver real value.

5. Orchestrate talent—don’t just hire it.

Delivering strong data outcomes isn’t just about hiring smart people—it’s about how you bring them together, align them with the business and evolve how they think. Great teams don’t simply execute tasks—they challenge assumptions, ask the right questions and build solutions that matter.

That starts with coaching your data team to go beyond technical execution. Encourage them to ask deeper “why” questions: Why is this data being collected? How does it serve the customer? What business outcome does it impact? The goal is to shift from being passive order-takers to proactive, consultative partners.

One powerful approach is embedding your data professionals with business teams. Have them shadow key stakeholders to truly understand pain points, customer journeys and decision bottlenecks.

When data teams are part of the business rhythm, not adjacent to it, they build more relevant solutions, gain trust and unlock transformative impact.

Looking Ahead: Data As A Multiplier, Not Just A Metric

Each industry I have worked in has its own pressures and pace. But the ones that win with data all have one thing in common: They use it to amplify human judgment, accelerate innovation and reveal what others can’t see.

Because in the end, the art of data strategy isn’t about dashboards or pipelines. It’s about mobilizing intelligence to create lasting value—at scale, across silos and beyond the obvious. That’s the work worth doing.

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