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The data used by corporations can be broken down into five different types: Unstructured (emails, marketing materials, white papers), Transactional (invoices, receipts, sales), Metadata (log files, report definitions, XML documents), Hierarchical (the relationships between all the other data), and Master (a company’s critical nouns–people, places, and things. So customers, employees, products, office locations, etc).
In most organizational setups, all of these different data types are managed by different systems or software applications. Master Data Management (MDM) provides a way to link all of that information into one big master file. If done right, this file not only provides all users with a common reference point, but it streamlines the process of sharing data among all departments and personnel.
To put it another way, MDM is a methodology put in place by a company that not only identifies their critical information, but creates tools to maintain that information. Its goal is to identify, validate and resolve data issues while creating a “Gold Copy” master dataset for downstream systems and services to consume.
It is important to note that it must clean and maintain–any time or money spent in cleaning information will be wasted if the MDM does not also keep that information clean as it grows and updates. Also important–MDM does not and cannot represent a full Data Governance or EDM program. Quality is only part of the data equation. Organizations require a broader view and transparency into the data they use for critical decision-making, and this is something that MDM systems are not well positioned to provide.
Mastering data is not the only function of a good MDM program. It should also be able to address:
- Data Classification and Retention Policies
- Original Data Sources
- Searching Information
- Enterprise Data Quality
- Operating Models and Policies
- Responsibilities/Expectations for Users
It is integral for any MDM to have effective data governance integrated into it early and through all stages of the project itself. When a data project rejects data governance, it could potentially deliver a product that is impractical for its employees or becomes too complex for the business to utilize it. Gartner says that “Through 2016, only 33 percent of organizations that initiate an MDM program will succeed in demonstrating the value of information governance.”
This only proves that businesses continue to fail to understand, embrace or value investing in critical MDM systems. The need for information governance will only increase, and businesses continuing to show a lack of effective data management will begin to fall behind their competitors, both due to productivity drops of their employees who cannot find the information they need to perform their jobs, and due to reputational damage or legal penalties if the company fails to protect the privacy of the data they manage. In fact, a Sybase study found that the median Fortune 1000 company could boost annual revenue by more than $2 billion by increasing their data usability by only 10%.