As the need for effective data governance becomes more and more critical, we are seeing many organisations battling to get traction on their data governance initiatives. This is not uncommon as, depending on an organisation’s overall data management maturity, implementing data governance can be fraught with challenges, not least some fundamental misunderstandings, difficult politics and in particular a lack of connection to clear business value. The subject of data governance is vast, and InfoBluePrint has developed a framework containing a set of clear guidelines for anyone contemplating establishing data governance in their organisations, the most critical of which is to ensure the development of a business case to clarify the value proposition which must be aligned to corporate goals.
Coupled with this in almost every case is the issue of data quality, or rather non-quality, as a common concern, and one of the most valuable insights that we have gained over the years is to leverage data quality as an effective driver of a data governance initiative, and by extension of overall data management maturity. The reason for this is simple: data quality is a data management functional area where tangible improvement can be measured and shown, supported by meaningful, business aligned reporting. As a yardstick of data governance success, data quality monitoring lends itself as a natural KPI. Furthermore, as data quality processes evolve, they uncover real and practical data management shortcomings that can and should be addressed by data governance functions, roles and responsibilities.
If this is not done, many data governance councils eventually flounder when, quite simply, its members cannot make the connection between time and money spent on formal data governance processes and actual business value. Data quality is an effective means of helping to close that gap. This is a practical approach to data management that targets the sustained improvement of the quality of the underlying data used across the organisation, driven by the bottom-up requirements of the programme whilst triggering the necessary top-down elements of governance and control as and when required. By adopting this approach, data governance roles and responsibilities will have meaningful issues to monitor, manage and address, thus helping to deliver sustainable and value adding data governance to your organisation.
Need to discover how we can help your organisation attain business success through well-managed data? Please email us at firstname.lastname@example.org.
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