Why Ongoing Data Maintenance Is Crucial for an Outcome‑Driven Enterprise Data Strategy

As part of the Enterprise Data Strategy series, this piece discusses the importance of leadership and responsibility in guiding a data strategy that aligns with business outcomes.

Data inevitably decays; 94% of companies suspect their customer and prospect data are inaccurate. Ongoing data maintenance is the most overlooked aspect of a results‑driven enterprise data strategy.

This fourth installment dives into continuous, proactive maintenance: why it matters, what it entails, and how to get started.

Why Continuous Data Maintenance Matters

Poor‑quality data provides no value, can be costly, and according to Gartner, bad data quality costs enterprises an average of $15 million per year, a problem that will worsen as information environments become more complex.

When building analytics platforms or migrating data from legacy systems, companies pour resources into analysis, cleaning, and enrichment, yet they often neglect building a always‑on data‑maintenance capability despite the reality of constant change.

Crucially, an outcome‑driven enterprise data strategy defines how you will continuously manage the most critical data, specifically:

Data‑quality business rules and data operations

Shared data‑maintenance services

Service‑level agreements (SLAs)

Required maintenance processes and key performance indicators (KPIs)

Responsible owners

Key to Success

The first key is ensuring your maintenance plan is proactive, coordinated, and always effective. Automation is recommended, but you still need accountable business and IT owners who are responsible for:

Creating and updating business rules

Reviewing current data operations and quality reports to identify issues

Establishing remediation for discovered problems

What Is a “Problem”?

The biggest problem is assuming that providing tools will keep data clean. In reality, people only maintain data when they have a clear incentive, such as payroll or invoice payments that depend on accurate information. Even then, some critical fields may be ignored because they are not personally important.

For example, employees often keep their bank details up‑to‑date but may neglect their business unit information; sales managers may maintain billing contacts but not shipping addresses. This is why responsible business and IT owners must oversee the effort.

How to Get Started

When establishing business rules in the data‑strategy governance phase, you have already done much of the work—reuse those rules. Many rules created for large‑scale project data are the same as those needed for ongoing maintenance.

The next step is to transform your workflow‑based system into a proactive, always‑on process. Unlike a workflow that only runs when an event occurs, an always‑on process runs on a schedule (e.g., monthly or yearly email verification).

This shift requires a mindset change: tools and workflows alone cannot prevent data decay. As part of the overall data strategy, you need a continuous, proactive data‑maintenance plan.

The article concludes with links to community resources, discussion groups, and channels where readers can engage further.