Is MDM maturity driving data governance?

A new Forrester report,  “Master Data Management: Customer Maturity Takes a Great Leap Forward” shows that the focus for Master Data Management internationally is shifting from applications to strategy and data governance.

Is this the case in South Africa?

In my experience, most local MDM projects are very firmly driven by the desire to implement one or the other of the various MDM applications. When strategy or governance is brought into play this is very often as an afterthought – the application has already been selected and functional delivery is well under way.

MDM remains a complex problem with the different uses of data by different business owners creating serious political complexities that can only be resolved by an enterprise view of data – precisely the role data governance organisations are intended to play.

Data integration and data quality hurdles are also typically seriously underestimated – for example, legacy integration issues may rule out common application architectures such as the Master / Consumer approach favoured by many MDM vendors. At the very least data inconsistencies and mapping issues need to be completely resolved before consumer systems can be overwritten – a frequently underestimated challenge.

How much attention are you giving to the strategy and governance of your MDM initiative? Are we following the international trend?



One thought on “Is MDM maturity driving data governance?

  1. I agree with your summation of the South African situation Gary. It has been my experience that the MDM technology solution is purchased and one of the following or a combination applies:
    1) data governance has never even been considered or
    2) data governance may have been established with great fanfare only to fizzle out rapidly due to inappropriate leadership, unrealistic expectations and poorly thought out “enforcement” methods or
    3) because data is still not treated as an enterprise asset there is no Enterprise Data Management Strategy and there are no measurements of data management functions, such as data quality, included in job descriptions, roles and responsibilities or KPI’s. The result is a general lack of accountability for the poor data that directly impacts the results of MDM.

    Without data governance to manage specifically the data quality issues (and politics thereof) MDM becomes an ineffective data dump. MDM is therefore forcing organisations to consider the importance of the responsibilities and accountabilities for managing their data and the quality thereof across the enterprise. This often requires taking a step back to assess the organisation’s maturity in all data management functions, building an Enterprise Data Management Strategy and implementing the logical steps – people (governance, change management, quality culture), processes and technology to improve its capability to create and maintain high quality data to achieve an effective and sustainable MDM result and support key business decisions.

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