Why Data Projects Take Longer Than You Think

Here's a statistic that should terrify anyone planning a data initiative:

Why? And how do you avoid becoming another statistic?

The Underestimation Problem

Nearly half of technology executives report that more than 30% of their projects suffer from delays or budget overruns. And BCG's 2024 research shows this is getting worse, not better.

The problem isn't technical incompetence. It's systematic underestimation of what data projects actually require.

Reason 1: The Data Is Never What You Expected

Every data project starts with assumptions about what data exists and what condition it's in. Those assumptions are almost always wrong.

The reality: - Fields that "should" be populated are null 40% of the time - Date formats are inconsistent across systems - The "unique ID" isn't actually unique - Historical data has a different schema than current data - Documentation is incomplete or outdated

According to SAS analysis, poor data quality is one of the most common reasons for project failure. Cleaning and validating data often consumes 60-80% of project time.

Reason 2: Requirements Change (Because They Should)

Data projects are discovery processes. You don't fully understand what you need until you start working with the data.

What happens: - Initial analysis reveals the original question was wrong - Stakeholders see early results and want something different - New data sources become necessary - Business priorities shift mid-project

This isn't scope creep - it's learning. But it does take time.

Reason 3: Integration Is Harder Than It Looks

Connecting systems sounds straightforward. It rarely is.

Common surprises: - APIs have rate limits that weren't documented - Authentication is more complex than expected - Data formats don't match what the documentation says - Systems have undocumented dependencies

When you're connecting five systems, you're managing n*(n-1)/2 potential integration points. Complexity grows faster than linearly.

Reason 4: Politics and Organizational Friction

According to Salesforce Ben's research, competing interests and internal politics derail project success. Limited or non-existent data sharing creates roadblocks.

Real-world friction: - Data owners won't provide access - Departments disagree on metric definitions - IT and business can't align on priorities - Security review takes months - Procurement delays tool acquisition

The technical work often waits on organizational work.

Reason 5: Testing Takes Longer Than Building

In my experience, the ratio of building to validating is often 1:2 or worse. For every hour spent building a data pipeline, expect two hours verifying it works correctly.

What testing involves: - Validating row counts and data completeness - Checking data quality and business rule compliance - Comparing outputs to legacy systems - Getting stakeholder sign-off - Handling edge cases that emerge in testing

For critical migrations, parallel testing - running old and new systems simultaneously and comparing results - can extend timelines by months.

Reason 6: The Last 20% Takes 80% of the Time

Data projects follow a deceptive progress curve. You see rapid early progress, then things slow down dramatically.

Why: - Easy cases are handled first - Edge cases emerge late - Production requirements are stricter than dev - Documentation and training take time - Cutover and rollback planning add complexity

How to Plan Realistically

Double your estimate. Whatever timeline you think is reasonable, double it. Then add buffer for unknowns.

Start with data discovery. Before committing to timelines, invest in understanding what you're actually working with.

Plan for iteration. Build in checkpoints where scope can be adjusted based on learnings.

Identify dependencies early. What decisions, approvals, or resources do you need? These often drive timeline more than technical work.

Budget for testing. Explicitly allocate time for validation. Don't treat it as something that happens "if we have time."

Staff appropriately. Data projects need dedicated attention. Part-time staffing extends timelines dramatically.

Setting Expectations

The goal isn't to eliminate delays - it's to plan honestly. Projects that acknowledge complexity upfront are more likely to succeed than those that promise unrealistic timelines and then scramble.

Stakeholders can handle honest estimates. What they can't handle is being surprised by delays. Set expectations early, communicate proactively, and treat timeline risks as seriously as technical risks.

Ready to plan your data initiative? Book a call to discuss realistic approaches.