An Outcome-Driven Enterprise Data Strategy: Ongoing Data Maintenance

Maria Villar

Part of the Enterprise Data Strategy series, which explores the importance of leadership and accountability in directing an overall data strategy tied to business outcomes.

People relocate, transfer jobs, and switch companies. Businesses fail and merge. Email addresses change. If you have data, it’s going to decay – that’s a given. In fact, 94% of businesses suspect that their customer and prospect data is inaccurate (Zoomdata). Yet ongoing data maintenance is the most overlooked aspect of an outcome-driven enterprise data strategy.

That’s why in this fourth installment of our data strategy series, I’m going to dive into ongoing, proactive maintenance: why it matters, what it encompasses, and how to get started.

Why is ongoing data maintenance important?

What good is data if the quality is poor? It’s not worth much and could quite possibly be costly. According to Gartner, “organizations believe poor data quality to be responsible for an average of $15 million per year in losses… and this is likely to worsen as information environments become increasingly complex – a challenge faced by organizations of all sizes.”

When building an analytic platform or moving data from legacy systems to a new solution, companies tend to put a lot of effort into profiling, cleansing, and enriching data. However, building an always-on data-maintenance capability is often neglected – and given that change is a fact of life, this is a risky proposition.

It’s essential that your outcome-driven enterprise data strategy defines how you will manage your company’s most critical data on a continuing basis, specifically:

  • Data-quality business rules and data operations
  • Data-maintenance shared services
  • Service-level agreements (SLAs)
  • Required data-maintenance processes and KPIs
  • Accountable owners

What are the keys to success?

The number-one key to success is ensuring that your maintenance program is proactive, coordinated, and always on. Automation is recommended, but you must still have accountable owners in business and IT who are responsible for:

  • Creating and updating the business rules
  • Reviewing the ongoing data operations and quality reports for issues that require resolution
  • Establishing remediation efforts for any discovered issues

What are the “gotchas”?

The biggest gotcha is assuming that people will keep their data clean if you give them the tools. This rarely happens unless they’re motivated to do so, such as their paycheck or invoice payment being dependent upon accurate information. However, even if they maintain these needed data fields, there may be other critical fields that are ignored simply because they aren’t as important to the person.

Employees, for example, will typically keep their banking information up-to-date but maybe not their business unit. Similarly, a sales executive is incentivized to maintain bill-to-contact information but not ship-to-contact. That’s why you need accountable business and IT owners to oversee the program.

How do you get started?

You’ve done a lot of the work already when establishing the business rules in the organization and governance section of your data strategy. Reuse them – again and again. Many of the rules you created to get your data ready for a big project will be the same ones you should use to maintain the fields.

The second thing you should do is transform your workflow-based systems into proactive, always-on processes. What is the difference? Workflow-only systems require a workflow before anything happens. With an always-on process, on the other hand, there’s always a program running an email validation every month or twice a year, for example.

Setting up this type of approach requires a shift in mindset. We tend to think people keep all their accounts clean everywhere. But do you update your phone number, address, title, etc. everywhere? Probably not. That’s why tools and workflows alone aren’t enough to keep your company’s data from decaying. You need an ongoing, proactive data maintenance program as part of your overall data strategy.

For more information

  • Reach out to us (Maria for North America and Tina for everywhere else) to inquire about a 1:1 enterprise data strategy discussion

And please listen to the replay of our “Pathways to the Intelligent Enterprise” Webinar, featuring Phil Carter, chief analyst at IDC, and SAP’s Dan Kearnan and Ginger Gatling.


Maria Villar

About Maria Villar

As head of Enterprise Data Strategy & Transformation at SAP, Maria Villar advises SAP customers on the crucial role of data management in their digital transformation, leveraging over 20 years of practical, operational experience as the chief data officer of three companies, including SAP from 2009-2017. In 2017, Maria was honored with the “Transformation of Collaboration from Inwards to Outwards” Award from the Massachusetts Institute of Technology. This award from MIT recognizes outstanding CDO leadership in driving business outcomes and business collaboration. In addition to her SAP experience, she has authored a book, “Managing Your Business Data from Chaos to Confidence.” She has also published two online classes and numerous articles, most recently “Time to Level Up: The Evolving Role of the Chief Data Officer.” As a trusted advisor to SAP’s most senior customers, the customer engagement approach is typically at the CXX level: i.e., CDO, CIO, COO, and CFO.