Data Governance: 12 Brutal Truths Behind Common Excuses

Data Teams Must Understand Business — But Not the Way You Think

The author mocks the vague demand that data teams "understand business." If business stakeholders cannot define metric definitions clearly and only blame data when reports break, the data team's only realistic "understanding" is knowing which button exports Excel. Cognitive bias cannot be fixed by technical means alone.

Quantifying Governance Value Is Like Charging for Air

Asking for direct ROI on data governance is compared to charging tolls on a highway or metering breaths of air. The value lies in enabling data to flow without crashes or delays. If you must quantify, calculate the cost of decision errors from bad data, duplicate development waste, and meeting hours wasted on data disputes — but few dare to face that bill.

Standards Based on "Current Reality" Cement Chaos

Accepting hundreds of conflicting definitions for a single field (e.g., "sales amount" meaning cash received, contract value, or order placed) is not "pragmatism" — it is preserving a garbage heap. The purpose of standards is to unify eight hundred ways to die into one way to live.

Building a Business-Facing Asset Catalog in One Day Is Fantasy

Creating a catalog requires lineage tracing, definition alignment, and tagging — archaeological work, not magic. A one-day delivery implies either a trivial business or a useless search engine with only a search box.

Governance Is Not a Burden; It Is a Bulletproof Vest

Prioritizing revenue over governance is like running a marathon with blood clots. Governance protects the business from regulatory fines and competitive defeat. Complaining that armor slows you down ignores the alternative: extracting bullets yourself when shot.

Completing Metadata in Three Days Is a Biohazard

Metadata is the system's DNA. Expecting developers to reverse-engineer five-year-old requirements or recalling departed staff in three days is not governance — it is a "Resident Evil" scenario turning legacy dinosaurs into humans overnight.

Using Tags (Derived Data) as Source Data Is Structural Violation

Treating cooked labels as raw ingredients is like using braised pork as a cooking base. When the label is deprecated, downstream models break. This is not dependency; it is illegal construction. Tear it down and expose the rotten foundation.

"Standards Are Too Hard to Enforce" Is Debt Avoidance

Enforcing standards is hard because repaying technical debt is harder than borrowing. Teams enjoy "buy now, pay later" development until the CEO asks why three reports show different numbers — then they must "power by love" to reconcile.

Full-Coverage Quality Monitoring Causes Alert Fatigue

Monitoring every field is like installing cameras on every water tap. Quality management must focus on critical paths, not carpet bombing. Over-alerting numbs teams so real catastrophes are dismissed as false alarms.

Tracing Lineage to the "Bottom Layer" Is Archaeology, Not Engineering

Demanding lineage down to the original database table or the intern who imported Excel three years ago is like tracking which air molecules carried your Wi‑Fi signal. Theoretical but practically self‑flagellation.

Breaking Department Walls Requires Shared Incentives, Not Just Tech

Data sharing is not one‑sided extraction. Sales will not share customer data with Marketing unless Marketing's performance counts toward Sales' quota. Silos are human nature, not technical barriers.

A Data Platform Is Not a Magic Wand

Building a central platform without unified standards merely replaces many small chimneys with one giant chimney. A platform enables reuse; it cannot clean dirty data or make business smart on its own.

Upstream Pollutes, Downstream Blamed for Dirty Water

Source systems lack basic validation (gender="unknown", amount=0, expiry=1900). Downstream teams drown in cleanup while upstream keeps polluting. Quality rules run only post‑facto — a perpetual "mending the fence after the sheep are lost" cycle.

Fake Data Lakes Built on Manual Excel Imports

Organizations claim "enterprise data lake" with PB‑scale assets but cannot list their systems, tables, or owners. Data ingestion relies on manual Excel uploads — producing a "PB‑scale recycling bin," not a data foundation.

Metadata Management Reduced to an Excel Dictionary

Copying table and column names into a spreadsheet is not metadata management. When a field's logic changes eight times in five years and breaks hundreds of reports, an intern's dictionary cannot save you.