Many authors have highlighted the crucial importance of data governance and data quality initiatives for ensuring the success of master data management implementations. However, there is little information on the approaches being adopted by organizations that have implemented or plan to implement MDM, or of those who have chosen not to implement data governance.

The Information Difference was, therefore, interested in exploring the linkage between data governance, master data and data quality. In particular, we wanted to discover how organizations are tackling this area in practice. Additionally, we wanted to understand the scale, scope and success rates of data governance in relation to MDM and data quality initiatives in business.

To achieve this, we conducted a survey to examine the link between data governance, MDM and data quality. The aim was to find the current state of practice in data governance, the levels of effort involved and its success rate, along with how well-integrated it is with data quality and MDM initiatives.

A total of 257 respondents from across the world completed the survey. Fifty-six percent came from North America, 26 percent from Europe and the remainder from the rest of the world. Two-thirds of the respondents were from large organizations, having annual revenues greater than $1 billion. The respondents were drawn from a wide spectrum of industries including banking, finance and manufacturing.

Who has Implemented Data Governance?

Almost one-third of respondents had already implemented data governance (31 percent), with an additional 40 percent planning to do so in the coming year. The full results are shown in Figure 1.


Less than one-third had no plans or were unsure. It is interesting to compare this with our previous research on the adoption of data governance, conducted in summer of 2008. At that time, 28 percent of respondents had implemented data governance in some areas of the business while only 13 percent had plans to implement data governance in the coming year.

This clearly supports the view that there has not only been a significant increase in interest in data governance, but also organizations are more committed to implementation in the coming year. This indicates that the messages in the media concerning the importance of implementing data governance to deliver a successful MDM initiative are starting to be understood.

What is the Main Driver?

One-third of organizations reported that "supporting business intelligence and data warehousing initiatives" was the key driver for the implementation of data governance.

What is immediately striking from the results shown in Figure 2 is that almost no organization has implemented data governance to reduce costs.

Intuitively, one might expect that cost savings would be high on this list, given these difficult times and the potentially high cost of errors directly attributable to poor data, such as incorrect invoicing and deliveries. The low importance of this may be linked to the fact that few organizations measure the monetary cost of poor data.

Clearly there is still a significant need across most organizations to improve the quality of BI initiatives, as evidenced by the fact that almost one-third named this the key driver for implementing data governance.

Implementation of MDM and Data Governance

Are organizations implementing data governance in parallel with their MDM implementations, prior to implementing MDM or independent of MDM? The respondents told us about the position in their organization. Thirty-nine percent had implemented data governance at the same time as MDM while 35 percent implemented data governance prior to MDM. Twenty-six percent have not yet embarked on implementing MDM.

It is very encouraging that most organizations are electing to implement data governance either alongside or prior to MDM. This is very much in line with best practices we've experienced and a very positive sign. Clearly messages in the media to the effect that establishing data governance is crucial to a successful MDM implementation are hitting home.

The Cost of Poor Data Quality

Disappointingly, 68 percent of respondents reported that they had no form of monetary measurement revealing the cost of poor data to the organization. This is comparable to the results from our 2008 study, where 63 percent of respondents said they did not calculate the cost of poor data. Against this background, it is unsurprising that many organizations struggle to develop a formal business case for their data governance programs.

In fact, only half of the organizations in production with data governance had prepared a formal business case. We believe it is possible to create a business case, and that this is a key enabler to securing buy-in from the business.

Benefits of Data Governance

We asked respondents to share what main benefits data governance has already delivered or is expected to deliver in their organizations and to rank them in order of importance. Here is how they replied:

  1.  Better quality and faster decision-making.
  2.  Ability to respond faster to business change.
  3.  Improved business intelligence reporting.
  4.  Cost reduction.
  5.  Effective compliance with government regulation.
  6.  Improved customer satisfaction.
  7.  Improved market position.
  8.  Don't know.

The top two responses are all about the quality and speed of decision-making. It is evident from this analysis that most organizations are looking to data governance in the first instance to deliver improved quality and faster decision-making. This is perhaps unsurprising, given the current business climate where speed is of increasing importance.
What is perhaps more surprising is the relatively low ranking given to cost reduction - a favorite topic in the current financial climate, one would think. It is also noteworthy that benefits such as customer satisfaction and market position are ranked relatively low. Only a few short years ago, these two topics were hot.

Lack of Tools to Support Data Governance

Are organizations using some form of technology to support their data governance implementations? Thirty-nine percent were relying on spreadsheets to support their production data governance processes. Some 32 percent had tools that were built in house.

For Figure 3, the respondents were asked to select all the options that applied, so the percentages shown are not additive.

Around one-third actually built some form of tool to support data governance possibly alongside or in combination with other options. The high percentage of respondents who indicated that they relied at least to some extent on spreadsheets suggests a considerable untapped demand for data governance products, either as standalone or as a component of an MDM and data quality platform - and an area for vendors to consider taking action.

Success of Data Governance Implementations

Almost two-thirds of respondents expressed the view that their data governance implementation had been moderately successful, with 19 percent considering it to be very successful. As shown in Figure 4, some 79 percent in total viewed the implementation as a success.

This is perhaps, at first glance, surprising but certainly very encouraging for enterprises and vendors alike. It suggests that these implementations are indeed able to deliver benefits.

This summary shows some key results of our research. The full survey, which has more than 50 pages of detailed analysis, can found at The Information Difference site.

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