The Myth of the 100% Data-Driven Company with Shiv Malhotra (Tanium)

Tanium's Shiv Malhotra reveals why no company is truly 100% data-driven, how to build problem-solving teams, and why trust beats technology.

Sarah Lubeck

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Most companies claim they're data-driven. Shiv Malhotra isn't buying it.

On this episode of Crunching Data, Shiv Malhotra, Manager of Data Engineering at Endpoint Management Company Tanium (number 17 on the Forbes 100 list), shares why he believes no company is truly 100% data-driven...and why that might actually be okay. He also opens up about building problem-solving teams, the real bottlenecks in data organizations, and why observability tools are criminally underrated.

If you're building data teams or trying to drive real change in your organization, this conversation will challenge how you think about data strategy.

Here are some areas we dive into:

The controversial truth: No company is truly data-driven

Shiv drops his most controversial opinion early in the conversation, and it's a wake-up call for the industry.

"I don't think I have ever worked at a company that is 100% data driven. I have worked at companies that are decision driven, but not data driven."

He explains that if metrics show hiring 100 sales agents in LatAM would drive revenue, a truly data-driven company would hire all 100. But they won't. They'll hire five and test. That's decision-driven, not data-driven. And according to Shiv, that's where most organizations actually live..and it's still a massive improvement over pure gut instinct.

The real bottlenecks are never technical

When Shiv inherits a new data team, he doesn't start with the backlog. He starts somewhere most leaders overlook.

"More often than not, real bottlenecks are almost never purely technical. They are either cultural or they are trust related."

You can have the best data stack in the world, but if culture is broken or stakeholders don't trust your data, nothing else matters. Shiv emphasizes that solving these human problems unlocks far more value than any tool upgrade ever could.

Trust must be earned before transformation happens

Shiv has a clear philosophy about what it means to be data-driven, and it starts with relationships, not dashboards.

"You need to earn the trust before you drive transformation. That's what I think data-driven means."

He's worked at companies where teams built perfect pipelines that reduced processing time from 20 minutes to 20 seconds, only to discover nobody was using the data anymore. Without trust and stakeholder buy-in, even the most technically impressive work becomes shelf-ware.

Build teams of problem solvers, not just coders

When hiring for his data teams, Shiv looks for something most job descriptions miss entirely.

"The foundation to have a great data team is to build a great team of problem solving individuals that just coincidentally happen to know a little bit of data."

His reasoning is simple: you can teach someone SQL or Python. But then there's the stuff you can't teach in school (or work): critical thinking, curiosity, or the courage to challenge stakeholders. Shiv actively looks for people who bring solutions alongside problems, not just technical chops.

Observability is criminally underrated

When asked about underrated tools in the modern data stack, Shiv doesn't hesitate.

"I feel like observability is definitely [underrated]… if you already have a data stack that's mature, but that's not well accounted for, or that's not well utilized, or well managed, or well organized, then I feel like putting an observability tool… can do wonders for you."

Observability tools create visibility into what's actually being used, where problems are happening, and who should be notified. But Shiv also warns about error fatigue, when too many alerts from too many tools create noise instead of signal.

Co-pilots are overrated (for now)

While observability deserves more attention, Shiv thinks the industry has gone overboard on another technology.

At Tanium alone, he gets requests for four to five different co-pilots across sales, marketing, and operations. The problem? Each one produces different scores, leading to internal conflicts about which AI to trust. His advice is simple: humans make decisions, co-pilots don't. Don't base critical choices on a score without looking at underlying data, trends, and forecasts.

The three Cs: Clarity, curiosity, and courage

Shiv shares wisdom from one of his mentors about building effective data teams around three core principles:

Clarity means understanding the actual business question you're solving. Curiosity means daring to find better ways to solve problems, not just copying what's always been done. Courage means having the confidence to tell stakeholders when their proposed approach isn't optimal, and presenting better alternatives.

These qualities separate data teams that drive impact from those that just fulfill tickets.

Start with the money, work backwards

For new data leaders inheriting teams, Shiv's advice is practical and immediately actionable. First, understand exactly how the company generates revenue. Then trace backward to see if all the relevant data, clicks, customer information, user analytics, marketing data, is being captured properly. From there, the gaps become obvious, and you can architect solutions that actually matter to the business.

The days of building data pipelines in isolation are over. The future belongs to teams that understand business context, earn stakeholder trust, and have the courage to challenge assumptions — even if that means admitting that being decision-driven, not data-driven, is where most companies actually succeed.

Dance your heart out, Shiv

This deserves its own section. Of course, we couldn't end an episode without diving into the person behind the data. Shiv loves his job in data and it pays the bills, but if he wasn't in data, he would be in architecture or teaching dance full-time. He already has a head start on the dance portion. Shiv currently runs a North Indian Folk Dance Academy out of Kansas City. You can check them out at @kcbhangra.

Watch the full episode to here.