What is Master Data Management
The objective of Data Management is to provide and maintain a consistent view of an organisation core business entities, which may involve data that is scattered across a range of application systems. The type of data varies across industry type. Examples include, Customers, Suppliers, Products, Employees and Finances. Presently many MDM applications concentrate on the handling of customer records and data because this aids sales and marketing process. Customer MDM solutions is called Customer Data Integration (CDI).
MDM and CDI are presented as technology but in reality they are business applications. The objective of both MDM and CDI is to provide a consistent view of dispersed referenced data. This is created using data integration techniques and technologies, and may be used by business transaction applications and analytic application. Data integration include:
- Data Consolidation – captures data from multiple sources systems.
- Data Federation – Single virtual view of one or more source data files.
- Data Propagation – Copies data from one location to another.
Benefits of Implementing Master Data Management
- Operational Efficiency.
- Improved decision making.
- Regulatory and Compliance.
- Strategic M&A.
Steps To Managing Master Data Management
Step 1 assesses the current mastering capabilities. During this step you should assess the MDM maturity of the records in scope. In order to quantify the impact of the MDM technology, it is important to have a relative point of comparison.
Step 2 involves envisioning the future data mastering capabilities and the solution plan to support them. In addition, it is important to define the implementation plan and understand the cost of implementation.
Step 3 is where we truly understand the benefits of MDM technology to the business. It is during this stage that we quantify the business value of the technology. Using the investments from step 2 and the quantified value in step 3 we are ready to calculate ROI of MDM.
Master Data Management and Data Governance
Effective Data Governance serves an important function within the enterprise, setting the parameters for Data Management and usage, creating processes for resolving data issues and enabling business users to make decisions based on high quality data and well managed information assets. Implementing a data Governance framework is not easy. Factors that come into play are Data ownership, Data inconsistencies across different departments and the expanding collection and use of big data in companies.
Businesses cannot do Master Data Management without Governance. MDM unites multiple users and Data sources. Governance creates an agreement on the rules of interaction among systems. Governance enables MDM’s success by providing business context and frameworks and ensures that MDM is not treated as a simple IT project. It brings users together to discuss business rules for data usage. MDM in turn make Data Governance more relevant because those Governance policies become tangible. Both Master Data Management and Governance need to merge to become Master Data Governance (MDG).
Enterprise level Governance that spans both data and process is increasingly a key requirement put forth by IT Executive Management.
Think of Governance as a component model, where several inter-related yet distinct components seamlessly interact to provide a connected environment that fosters ownership and accountability.