Greg would like to thank Nick Millman, senior director in Accenture Information Management Services, for his contribution to this month’s column. Research by Accenture confirms, as intuition would tell us, that high-performing organizations make far better use of information than their peers. The characteristics of underperformance on information usage are well documented: multiple versions of the truth, information silos, poor data quality, too much data and not enough insight. Conversely, effective use of information can drive significant benefits, including increasing top-line sales, identifying cost savings, improving customer satisfaction and monitoring regulatory compliance.

Data governance is fundamental in the effective use of information, and its role is becoming more crucial but at the same time more challenging as the quantity of data and number of information types increases across the average organization. Adding to the challenge is the shift from departmental and functional solutions to enterprise solutions for information management. This requires organizations to establish more sophisticated data governance across process and organizational boundaries.

The cost of poor data governance is hidden in inefficient business processes, excessive data management activities and the inability to use information for strategic business advantage. As an example, organizations with poor data governance tend to struggle to get clean data, and even if data is cleansed through a one-off activity, the data quality tends to deteriorate rapidly over time.

The impacts on business performance are real and significant. This column has previously discussed Accenture’s survey of more than 1,000 large companies in the U.S. and UK, which reveals that today’s middle manager is wasting a great deal of time searching for the right information. Key findings include:

  • Managers spend more than one quarter of their work week searching for information.
  • More than half of what they obtain is of no value.
  • Managers accidentally use the wrong data more than once a week.
  • It is challenging to get different parts of the company to share needed information.

The positive news is that most organizations, or at least their CIOs, seem to realize the cost of poor data governance and are planning to take action. One key finding from Accenture’s 2007 CIO Survey is that data governance is a key area for improvement in the current situation:

  • Thirty-six percent of organizations have little to no focus on data governance,
  • Forty-five percent have pockets of data governance for critical data,
  • Nineteen percent have established enterprise-wide data governance, and
  • Seventy-two percent of all CIOs surveyed are targeting enterprise-wide data governance within the next three years.

So, how do organizations bridge the data governance gap?
Consider the case of the lubricants division of a major oil producer, which found it difficult to share information across national boundaries due to different methods of classifying the complex product catalog. The management team of the lubricants division recognized the business benefits that would come from a common set of product data and established a program under a cross-functional steering group. The program team worked with the business to establish a set of policies and standards, and even more importantly, clear ownership and accountability was assigned to individuals to adhere to the new product data standards. Consequently, this organization has been able to derive significant benefit from the implementation of an enterprise-wide master data management (MDM) tool.

I have observed that the early adopters who have made the most progress with enterprise-wide data governance tend to share five important characteristics:

  1. The business case for data governance is established early on and is used to guide the prioritization of data governance implementation. Metrics are identified that enable measurement of the business benefits delivered.
  2. The approach to data governance accounts for the people, process and technology aspects. This shows that data governance is as much about leadership, communication and good management as it is about technical integration.
  3. The implementation of data governance is planned as a journey, with distinct phases reflecting an organization’s evolution along the spectrum of information management maturity.
  4. Realistic expectations are set about the benefits, timelines and capabilities associated with data governance. For example, the data governance program typically establishes the standards for data quality but does not perform the actual data cleansing.
  5. Data governance is tackled within the context of a comprehensive data management approach that also addresses data architecture, metadata and data structure, MDM, data quality and data security.

Data governance enables high performance because it is a key component in effective information management. This is recognized by many organizations, and the majority of CIOs seem ready to act. As these CIOs consider how to address enterprise-wide data governance, lessons learned from the experiences of early adopters provide powerful insights into how to implement effectively.
Figure 1: Data Anatomy

Figure 2: Data Lifecycle

 

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