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Transforming Your Business Through The Power of Data Management. A Data Governance View.

The challenges of implementing a business transformation initiative in an enterprise are diverse, as anyone consigned to manage the task can narrate. One of the most efficient methods is through Master Data Management (MDM). And, at its foundation is Data Governance.

MDM enables an organisation to effectively manage its core business entities, through centralised processes. These business entities include customer data, supplier data, and location data. MDM broken down to its constituent part comprises of

  • Data Governance,
  • Data Profiling,
  • Data Quality, and
  • Data Integration.

To successfully implement an MDM solution, these four steps or procedures need to be in place, functioning iteratively as well. In recent times, however, consolidation of business entities has been met by applying only application-enabled data integration. This approach has its shortcomings, which has been laid bare as organizations periodically re-engage to clean up the ‘mess’ from previous exercises.

At the last Europe Gartner MDM summit, which was sponsored by InfoData Consulting, we were met with a flurry of attendees interested in kick-starting an MDM initiative. Many posed the same underlying question – ‘How do we start?.’It didn’t take long to realize that IT and business practitioners had a very good understanding of MDM and its benefits. However, they had little knowledge of the foundation of MDM, data governance, and how to start one.

Despite the pivotal role data governance plays in MDM, it attracts the least attention. Data governance is a practice focused on standardizing policies and procedures that govern the creation, usage and archiving of enterprise-wide data within an organization. This practice is a proven way of ensuring better data quality.

A cursory look at the key points of data governance, listed below, reveal practices prevalent in organizations.

  • Develop a Strategy.
  • Identify Value.
  • Leverage Industry Sources.
  • Perform a Data Assessment.
  • Identify Technology Requirements.
  • Build a Concept Definition.
  • Establish Funding Requirements.
  • Determine Requirements for Success.
  • Document.
  • Present.

Our experience, drawn from working on various projects, exposed us to a practice where ‘data governance’ was in place, albeit divided along business unit lines. These practices were not carried out as part of a coordinated and centralized enterprise-wide initiative. Even in the face of large amount of shared data across the enterprise. Not only is this approach inefficient, it increases operational costs and erodes the confidence of the business in the transformation process.

A holistic approach is what is needed. Data governance at the fore, serving as a precursor to data integration.

The pitfall of not having a uniform approach could be illustrated in a case where the Sales department make calls and sends letters to existing customers informing them of an already subscribed service. One can deduce, and quite easily, that there exists a dis-joint between the sales department and the billing department. It would seem that the Sales department sees the name as a prospect, while the billing department has the name as a paying customer. What a difference it would make if both departments sourced data from the same repository. Cost and time readily springs to mind.

Just like building a house on a solid foundation, made of concrete and reinforced steel, MDM should be built on data governance.

Get your data to work for you, now!!

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