Data Management in the Enterprise: Why the Basics Still Win

4 MINS

# Data Management in the Enterprise: Why the Basics Still Win

Every few years, a new technology promises to solve enterprise data problems. Data lakes. Data mesh. Vector databases. Lakehouse architectures. The tools evolve, but after a decade building data products, I've learned that most enterprise data problems aren't technology problems. They're fundamentals problems.

The Unglamorous Reality

Walk into most large enterprises and you'll find:

Data spread across dozens of systems that don't talk to each other

No clear ownership of which data is authoritative

Quality issues that compound as data moves downstream

Documentation that's either missing or outdated These aren't problems that new architecture patterns solve. They're governance, process, and organizational problems wearing technology costumes.

Documentation That Lives

Garbage in, garbage out. No amount of sophisticated transformation or ML can fix fundamentally bad data. The organizations that succeed invest in quality where data originates—not in cleaning it up downstream.

Every critical data element needs an owner—someone accountable for its accuracy, completeness, and availability. Without ownership, data degrades. With it, someone cares enough to maintain standards.

When a number in a report looks wrong, how do you trace it back to its source? Data lineage isn't exciting, but it's essential. Understanding where data comes from, how it's transformed, and where it goes enables troubleshooting, compliance, and trust.

Static documentation dies the moment it's written. What works is documentation embedded in the systems themselves—metadata, data catalogs, automated quality checks—that stays current because it's part of the workflow.

The Technology Role

None of this means technology doesn't matter. Modern data platforms enable capabilities that were impossible a decade ago. Real-time processing. Petabyte-scale analytics. ML integration.

But technology is an enabler, not a solution. Organizations that adopt modern data platforms without addressing fundamentals just create modern messes faster.

The Product Challenge

Building data products means building for this reality:

Assume imperfect data. Your product needs to handle missing fields, inconsistent formats, and unexpected values gracefully.

Make quality visible. Help users understand data provenance and quality so they can make informed decisions about what to trust.

Enable governance. Features that support ownership, lineage, and quality monitoring are as important as analytics capabilities.

The Takeaway

The enterprise data landscape will keep evolving. New architectures will emerge. New tools will promise transformation. But the organizations that master fundamentals—quality, ownership, lineage, documentation—will be ready for whatever comes next.

Technology advances. Fundamentals endure.

Security as a Product Feature, Not an Afterthought

Raunak skipped presentations and built real AI products.

Raunak Pandey was part of the August 2025 cohort at Curious PM, alongside 15 other talented participants.