Master data is at the center of every critical business activity today. Operations rely on master data for a consistent flow of information. Analytics relies on master data for accurate dimensions and hierarchies to find trends and make predictions. And compliance efforts rely on master data to ensure they are driven by trustworthy data. Despite this importance, there are several key facets that many organizations overlook in their master data management (MDM) programs. In this blog and the following series, we will examine these facets to help your organization improve its overall MDM strategy and therefore improve its business performance.
Let’s start off with a few key definitions. Master data is defined as data shared across business teams and IT applications, that defines key information of a company such as products, organization, customers, suppliers, financial hierarchies, assets, locations, or reference codes.
Master data is a complex, multifaceted object that:
- Has associated definitions, attributes, connections, metadata, and taxonomies
- Has relationships with other master data (and itself)
- Can live in the past, present, and future
- Needs to be adapted to multiple contexts
- Has various owners across the organization
- Is based on a specific life cycle
- And integrates with applications using many patterns
A Master Data Management platform will help define, manage, share, and govern all those shared data in a central solution where business users can collaborate and consume it in a secure, consistent way.
Not only is master data complex, but all your master data have different characteristics
So, before you launch a project or shop for MDM software, we think it is important to understand those various facets. It will have an impact not only on your vendor selection but also in the way you will conduct the initiative and ultimately get adoption—and success—of MDM across the enterprise.
Discussions around MDM cover some but not all of the characteristics that one should look for in an MDM platform. In this series, we’ll focus on the gaps in those discussions, the areas that can be the tipping point between project success or failure.
Keep a lookout for the rest of this blog series where we will examine the following factors that we believe are overlooked by many when evaluating an MDM platform:
- Modeling: Documenting and defining your master data
- Relationships: Managing the intra- and inter-domain relationships in your data
- Time: Going beyond simple versioning
- Points of entry: How does you master data get in?
- Business context: Sharing master data across groups, divisions, and functional areas
- Governance/Lifecycle: Managing different scopes
- Integration: How does your master data get out?
And for more on why your organization needs master data management to thrive, check out this infographic.