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Data Management: An Executive Briefing:
We're Not Doing What?

  Column published in DM Review Magazine
September 2004 Issue
  By George Marinos

The Top 10 Corporate Oversights in Data Governance

Perhaps it is your job to stay on top of how well your company is managing data. This would mean that, arguably, the top item on your "short list" of strategic concerns is very likely associated in some critical respect with governance - data governance.

Maybe your team is working to build a capability in this area in order to meet increasingly stringent regulatory requirements. Or, maybe it is putting out brush fires because data governance wasn't built early enough into the fabric of your IT or business infrastructure - if it was introduced at all. Either way, it might be useful to learn about some of the common errors and oversights that companies are making as they attempt to build or maintain capabilities in data governance.

10. Accountability and Strategic Participation: Those who didn't make the meeting, raise your hand. Common roadblocks include the failure to allocate specific responsibility to and secure explicit buy-in from key stakeholders. Data governance processes require complex decisions - including, for example, difficult trade-offs between security, utility and cost - as well as a broad understanding across the organization of how important data quality is for performance. The bottom line? Executive leadership is critical to keeping a data governance-building process on track.

9. Standards: Yes, we're standardized ... but not for these systems. One of the most frequent drags on the effectiveness and efficiency of a data governance program is a lack of consistency and standardization across enterprise IT infrastructure, critical systems and data fields that are most likely to impact risks and the achievement of corporate performance targets.

8. Managerial Blind Spots: ... And our sales people still can't access the data? A number of companies count themselves ahead of the curve on data quality when they start creating enterprise data stewards - even if they don't fill the positions. However, there's often another problem. If stewards are steeped in technical training, they may not necessarily be attuned to the business value of data. That's a critical deficiency, because the most important responsibility a data steward has is to optimize the alignment of data-specific technology, process and organizational components with the company's most important business objectives.

7. Embracing Complexity: Don't tell me we're going back to square one. Many boards and executives simply fail to appreciate the extent of the complexities inherent in managing data quality early enough in the process of building a data governance capability - especially given that data is often produced by one set of stakeholders, enriched by a second, distributed by a third, consumed by a fourth and maintained by a fifth (IT).

6. Cross-Divisional Issues: Well, at least we're doing what they asked ... The failure to understand and plan for complexity often first demonstrates itself internally - through a breakdown in process wherever data governance issues cross the boundaries that separate managerial responsibilities for different business units. What's the antidote? Make sure that your governance protocols include procedures - across divisions and departments - that explicitly reconcile priorities, expedite conflict resolution and build cooperation in support of data quality as a common objective shared at every level of the enterprise.

5. Metrics: Aren't we watching the right signposts? There are still far too many companies that do not engage any data quality metrics or key performance indicators (KPIs) at all. Even among the companies that do, many data governance programs still depend upon process-specific as opposed to outcome-specific metrics.

4. Partnership: What? ... We can't work with this company anymore? Often corporate planners of a data management initiative underestimate the need to ensure that external suppliers also accept explicit accountability for their contribution to the quality of shared data. Under these circumstances, the organization sooner or later finds itself faced with a difficult choice: either it continues to engage in business with partners whose inferior data management practices undermine data quality for both companies, or the organization must weather operational disruptions as established partners struggle to come up to speed or new partners are sought as replacements.

3. Choosing Strategic Points of Control: Where, precisely, should we be embedding these data quality controls? Companies often fail to develop data governance policies and procedures that explicitly acknowledge a critical challenge. As Gartner has pointed out, "quality problems frequently become apparent only when data is moved beyond the originating application." Yet, "although these problems are best fixed at the source, the application 'owners' are often reluctant to do so." Among Gartner's suggestions? "Identify the business beneficiaries of improved data quality and look to them for leadership ... [also, seek to] extend data quality practices and controls throughout the network of applications and databases in the enterprise."1

2. Compliance Monitoring: Trust me, it's working ... Many companies put together components of a well-designed data management program, but neglect to engage in regular monitoring and testing of compliance with their own data management policies and procedures. A solid governance program isn't worth much if it doesn't prescribe the steps necessary to ensure that the best intentions are actually carried out on the ground.

1. Training and Awareness: Which memo was that? Good ideas don't necessarily sell themselves - and this is particularly true of data governance. A program for training and awareness is one of the single most important strategies supporting the sustainability of any data management program. Educational and awareness programs are one of the most important (and one of the most frequently under-supported) components of an effective data governance architecture.

Why do executives continue to underestimate the value of data governance? Because many are still working to appreciate the strategic contribution that data governance makes to corporate performance.

If ROI is one of the primary drivers of business decision making, then among the issues most frequently underestimated by executives today is this: building the appropriate data governance architecture is far less about an up-front cost than it is about setting up the controls and responsibilities necessary to drive ROI today and down the road - not just for every investment in data management that follows, but also for the consistent growth in long-term, risk-adjusted returns enabled by a sustained enterprise-wide commitment to data quality.


1. Gartner, Inc., Research Note. "The Challenges of Improving Downstream Data Quality." March 2, 2004.


For more information on related topics visit the following related portals...
Data Management and ROI.

George Marinos is PricewaterhouseCoopers' National Data Quality Partner. With more than nineteen years of experience in the information systems industry, he has spent the past six years helping client organizations implement risk management solutions and data management programs that balance the need for a disciplined approach to people, process and technology challenges with appropriate action along a full spectrum of risks. Marinos can be reached at george.marinos@us.pwc.com or through http://www.pwc.com/dataquality/.

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