However, as information becomes your corporate asset and you wish to control and utilize it as much as possible, these forms of MDM are seldom sufficient. Likewise, the ERP promise of keeping everything in one system leads companies to think master data can be managed there. But in reality, ERP manages just the master data it needs to function and lacks governance rules to change and syndicate master data effectively.
While data warehouses focus on all data (the vast majority of which is transactional data), emerging MDM programs focus on high-quality shared data that the organization needs in numerous systems. This data is grouped into subject areas, and consequently, MDM is a culture of subject areas. Master data will ultimately comprise only 10 to 15 percent of the volume of organizational data.
One of the main subject areas companies want to conquer is "customer." It is, by a margin, the subject area with the largest gap between its current state of management and its potential to the organization. "Product" is another subject area in common need. In the early days of MDM software, these two subject areas were spawning their own software categories, and consequently, there are quite a few constructs in MDM specific to them. You should determine if this type of intellectual property around specific subject areas is interesting to your shop. To many it is not because they are not looking for new data, but instead, to harmonize master data throughout the enterprise.
Other common subject areas mastered by MDM are parts, vendors, suppliers, partners, policies, stores/locations and sales hierarchy. In reality, the list is unbounded, and you should let your business needs guide your program's definition of subject areas and rollout schedule.
MDM is an iterative project, rolled out across the organization over time. I recommend mastering one subject area at a time, although it can also be effective to support one system across subject domains before moving across systems. Often, a combination is best. Regardless of the rollout strategy, you will want to choose subject areas for MDM that have the following characteristics: high interest to the company, potential for reuse in many systems, high controversy as to ownership, diverse input to its build, and scattered pieces of data throughout the enterprise.
These may sound complicated, but that is the point. Without these kinds of data problems, MDM would not be needed. However, it is usually not hard to determine numerous subject areas that can benefit from the MDM value proposition.
Every system in the company uses data, and many are using the popular subject areas cited. Most systems need a customer list to function. Many need a product list. Where and how are they going to get what they need? Existing systems can employ point solutions to deliver the data, but these kluged solutions can clutter the network and are not compatible with corporate standards or suitable for maintenance. These are not efficient enterprise solutions.
Conversely, MDM is designed around a more efficient, "build once, use often" approach. This idea of sharing and reusing also comes with challenges and obstacles. Cultures that are decentralized, lack documentation, lack cross-departmental working relationships and lack metadata and a consistent lexicon will not function without more substance than a simple architectural construct. These conditions are not deal killers, but are precisely the obstacles to be conscious of and overcome.
The benefits of MDM are evident and well-worth pursuing for most organizations. That is because many organizations find themselves in a perfect storm of wanting to recognize information as a corporate asset, needing to manage that data operationally and continued cultural struggles achieving the desired result. MDM addresses these factors and delivers benefits that include:
- A data model for the subject areas appropriate for enterprise needs;
- A master data publish and subscribe (sources and targets) mechanism and strategy;
- Workflow processes for some master data that may support the origination of that data; and
- Improved data quality.
These benefits are possible because of disciplines that have emerged over the years in information management, including data modeling, data integration, data quality and workflow. However, the intangible byproducts of these deliverables may be more impressive. They include the fact that many players in the enterprise will be working from the same lexicon and the same set of data. That is enormously beneficial in improving an enterprise's processes. The efficient routing of this data means it is brought to bear at the right moment, without delay, for efficient, consistent decision-making and to the benefit of operational efficiency.
This column is an excerpt from William's white paper: "Master Data Management: Making Information Management the Foundation of the Future."
William McKnight brings process, organizational and architectural focus to building strategies and implementing master data management and data warehousing programs that have consistently, for many years, improved the productivity and performance for his clients including several global corporate giants. McKnight, award-winning consultant and author, can be reached at McKnight Consulting Group.
