Why Ongoing Data Maintenance Is Critical for Outcome‑Driven Enterprise Data Strategy

As part of the enterprise data strategy series, this article discusses the importance of leadership and responsibility in guiding a data strategy that delivers business outcomes.

People relocate, change jobs, or switch careers; companies merge or shut down, and email addresses change. Data inevitably decays, and 94% of enterprises suspect their customer and prospect data are inaccurate (Zoomdata). Ongoing data maintenance is the most overlooked aspect of a results‑driven data strategy.

Why Ongoing Data Maintenance Is Important?

Poor‑quality data provides no value, can be costly, and according to Gartner, bad data quality causes an average annual loss of $15 million for enterprises, a problem that may worsen as information environments become more complex.

When building analytics platforms or migrating data from legacy systems, companies invest heavily in analysis, cleansing, and enrichment, yet they often neglect building a continuously online data‑maintenance capability, despite the reality of constant change.

A results‑driven enterprise data strategy defines how to continuously manage the most critical data, especially:

Data quality business rules and data operations

Data‑maintenance shared services

Service‑level agreements (SLAs)

Required data‑maintenance processes and key performance indicators (KPIs)

Ownership responsibilities

What Are the Keys to Success?

The first key is ensuring the maintenance plan is proactive, coordinated, and always effective. Automation is recommended, but responsible business and IT owners must still oversee:

Creating and updating business rules

Reviewing current data operations and quality reports for issues

Establishing remediation for identified problems

What Is a “Problem”?

The biggest problem is assuming that providing tools will keep data clean; this rarely happens unless users have a direct incentive, such as payroll or invoice payments tied to accurate information. Even when users maintain required fields, other critical fields may be ignored because they seem less important to individuals.

For example, employees often keep bank details up‑to‑date but may neglect their business unit information; sales managers may maintain billing contacts but not shipping addresses. Hence, responsible business and IT owners are needed to supervise the effort.

How Do You Get Started?

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

The next step is to transform your workflow‑based system into a proactive, always‑on process. The difference is that a workflow system requires an event to trigger a flow, whereas an always‑on process runs on a schedule (e.g., monthly or yearly email verification).

This approach requires a mindset shift. We tend to assume people keep all their account information clean everywhere, but they may not update phone numbers, addresses, titles, etc., consistently. Therefore, tools and workflows alone are insufficient to prevent data decay; a continuous, proactive data‑maintenance plan is essential as part of the overall data strategy.