Why Do Customers Need MDM?

Master data management refers to the generic problem of managing enterprise master data property and empowering downstream people, systems, and partners with the in sequence they need. Different organizations may choose to focus on a different area or domain of master data. Nearly every company can advantage from more accurate information on their products, customers, and vendors across industries. Common challenges that MDM addresses include:

• A large number of products, parts, or assets, each with multiple attribute that need to be managed and enriched by different departments

• Need to gain more insights into customers for both new income opportunities as well as better customer service across multiple customer communication points and channels

• Need to harmonize inconsistencies across systems, business units, and Geographies

• Need to execute business processes such as new client introduction that cut across information silos and organizational boundaries – geographic and functional

• Need to receive client and security information from external feeds such as Reuters and Bloomberg for trade settlement

• Need to collaborate with other businesses in the value chain to achieve efficiency

• Partner and regulatory compliance mandates requiring the management of new types of information and the maintenance of data ancestry trails

• Need to measure metrics that require aggregation across multiple systems such as counterparty risk across all trading assets

• Need to gain efficiencies in procurement across multiple products and vendors

Most companies will choose to tackle one data domain first and then use that experience to expand into other domains. Many industries will have data domains that are individually valuable to them. For example retail chains may want to first focus on store information. An auto manufacturer may want to manage dealer information. Healthcare companies will have unique challenges around patient information. While these different data domains may appear very different to a business analyst, from a technology standpoint they pose similar challenges.

 •Comprehensive Information Management: Managing the data model and attribute information, validation and change rules, versioning and diff analysis, roles-based access and ownership control, complex data relationships including management across data domains, classifications, contextual validation rules, etc.

• Process Management: Managing the processes and procedures around introducing new data or editing existing data such as introduction a new product or updating a customer address.

• Integration: Synchronizing in real-time or batch the relevant subset of master information with transactional systems such as ERP and trading partners either directly or through interactions and data pools such as 1Synch.

MDM DRIVES ROI FOR SOA INVESTMENTS

An MDM solution provides the necessary alignment of master data across multiple back-end systems so that business services and composite applications within an SOA have accurate, consistent, and timely information. All the hype and attention in SOA deployments has gone into web service creation, deployment, and management standards and technologies. However, if data is inconsistent across applications it will be increasingly difficult if not prohibitive to build composite applications that cut across multiple systems and departments. For example a composite application in a large multi-channel financial service institution that calculates a customer’s global credit risk will only work if that customer is described in a consistent manner across retail banking, brokerage, mortgage, and credit card systems. In a retail environment, a composite application that gets a customer’s order history from a data warehouse and recommends a related product will require consistent product and customer information across all the relevant systems. On a smaller scale, even business services to update an address or provision a service require semantic consistency of master data across CRM, billing, and product systems. Creating a semantic integration layer to match and reconcile master data across the enterprise yields accurate and consistent information that helps SOA investments realize their full ROI.