Management of master data is not new. People have used some form of this concept in their own way from the time they started managing their data. So then, what is a precise definition of MDM? MDM is a combination of processes and technologies that will help enterprise manage their data flow, data integrity and data synchronization in a better way. This definition emphasizes enforcing policies and standards to the core data at an enterprise level.
Because of the various interpretations of MDM, some common misconceptions have developed.
Misconception 1: MDM is like data warehousing
MDM is all about using information as a service. MDM is a set of disciplines and methods which are used to ensure the accuracy, completeness, meaning, timeliness, consistencies and quality of a company’s reference data across enterprise and various subject areas.
The architecture of MDM by itself is divided into two major classes. Depending on the goal being addressed, it is categorized mainly as operation or analytical.
As the name suggests, operational MDM handles the master data from a front office perspective, whereas the analytical MDM is more for the predictive analytics, historical information analysis and forecasting. The complex data hierarchies and their relationships can be maintained more efficiently in MDM rather than a data warehouse.
An MDM architecture often has the nature of a centralized hub,and because of this, people make comparisons to the data warehouses. At the highest level, both MDM and data warehousing aim to offer clean and meaningful information to the enterprise. But other than this, these two are built for different purposes and for different audiences. MDM is more like a service and operates more on the operational data integration aspects, whereas data warehouses are built to support business intelligence applications by mining patterns and trends more with the available historic information.
Misconception 2: MDM is a technology/infrastructure initiative
MDM is more like a “discipline” than a typical IT initiative. The key to success for an MDM implementation is to have all the stakeholders on the same page; it seldom depends on the tool being offered in market or a homegrown approach. If we break down the MDM initiative into six categories as defined below, we would see interestingly that technology gets the last position. There are many other aspects to be taken care of before even getting to the technology piece of assessment and/or work. The building blocks of MDM initiatives adopted in industry are in the below order:
1. Vision: Why is MDM needed for the given enterprise, and what does MDM look like for the enterprise? Who owns it? These why, what and who questions are very important from a business case perspective, and will help stakeholders keep a close watch on whether or not the initiative is meeting the goals.
2. Strategy: This is the “how” aspect of the initiative. The strategy should address two key things:
a.The MDM vision of the enterprise is mapped correctly with the factors like commercials, profit-revenue and regulatory compliance, etc.
b.What is in scope and what is not in scope for different verticals and future phases. What is not in scope is as important to define as what is in scope because each vertical and business area will have different needs to be catered to. The expectations and priorities will be different. Hence this activity will put the prioritization in place.
3. Metrics: The only measurable link between the MDM initiative and the business value is metrics. These measurable objectives come from the MDM strategy. If you have defined, for example, profit-revenue in strategy, you can measure it with key performance indicators using relevant metrics. These metrics should be driven and owned by business and not IT because only business can fully identify and measure the value of the initiative.
4. Governance: This is the most important aspect for the success of an MDM initiative. The formal process to implement governance would be to have stewardship, which would force the structure to put map roles and responsibilities with the accountability and authority in the MDM initiative. The ideal approach would be the leadership coming from the business side, and IT should be facilitating the initiative. This should include multiple subject areas like security, change management, training and communication. The basic factors to be addressed include who creates, who manages, who owns and who consumes the data.
5. Processes: Because the whole initiative revolves around the management of the master data, it becomes even more important that processes are defined and followed for all aspects of governance as well. All the “who” questions in the governance plan should have a “how” and a process flow to achieve the goals. This would involve publishing the data including a process to identify the data quality issues.
6. Technology: This is a broad world in itself. The technology assessment should start with the big picture in mind. This includes planning an architecture and infrastructure for data integration, data storage, middleware, publishing, user interface, and integration with future applications and systems. This also includes the selection and methodology to deliver this solution, which requires making key decisions such as whether to build the capability or to invest in a market-ready tool. This also includes getting on board with the right skills and resource matrix that will be making the delivery happen on the technology front.
