Ultimately, the success or failure of a Master Data Management project is not solely determined by the chosen MDM architecture.

Data governance is critical

In fact, similar to any large-scale endeavour that involves multiple stakeholders, one crucial factor is the ability to ensure consistency across different teams, effectively prioritize tasks, and navigate the intricate politics inherent in enterprise projects. Both IT and business commitment, as well as active involvement, play vital roles in achieving desired outcomes.

In our experience, these objectives hold significant importance in any competent data governance program. Successful Master Data Management (MDM) projects often have a robust and pragmatic data governance program at their core. Such a program should operate from the bottom up, maintaining a sharp focus, and empowering decision-making processes.

Bottom-up

But why is a bottom-up approach necessary?

The master data utilized across the enterprise is frequently shared and reused among various teams. To effectively manage consistency across different core teams, the data governance organization (DGO) must comprehend how each business area utilizes the data.

It’s common for data to serve different purposes within different areas. If one team makes assumptions and adapts data elements to suit their specific needs, it can create disruptions elsewhere. Only operational staff actively engaged with the data can truly grasp the impact of changes within their domains.

The DGO requires this knowledge to establish data quality rules, assess the effects of data changes (such as cleansing or enrichment), and develop metadata, including a comprehensive business glossary of data definitions.

Focus

Similarly, the DGO must maintain focus.

It is essential to address the needs of all intended users of the MDM system, taking on an enterprise-wide role rather than a departmental one. Focus, in this context, does not imply catering to just a single need.

Our MDM implementation methodology facilitates the identification and prioritization of core attributes across multiple areas. Consequently, it makes sense to concentrate on these critical data elements rather than attempting to encompass all data.

Initially, the DGO should concentrate on the data necessary to fulfil the business requirements of the MDM project, although this role may expand over time.

Decision-making

Data governance should enable effective decision-making.

This is where top-down support becomes critical. The MDM/DGO Steering Committee should be presented with key findings to facilitate decision-making. Key decisions may involve budget approvals, establishing priorities, or endorsing significant changes to existing systems.

Business-driven

Lastly, and perhaps self-evidently, the DGO must be driven by business needs and involve both business and IT stakeholders.

Achieving this balance will create a pivotal forum for identifying business requirements, managing conflicting priorities, and mitigating risks. Additionally, it will serve as the primary platform for driving data quality, another crucial success factor for MDM. I will delve further into this topic in my upcoming post.