Importance of Metrics in Data Governance
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A critical component of any Data Governance program is the tracking of data quality metrics over the life cycle of the data. When a new record enters a Master Data Management system, it does not stay static; it undergoes updates until the last transaction (and beyond).
After the last transaction, at some point, it should be purged to maintain the freshness of the data. At all these stages, the information’s quality, security and compliance can be prone to compromise. A good data governance program should address measurement at these various stages of the data life cycle. Efforts must be made to build suitable metrics, as the organization progresses through the maturity levels of its data governance program.
Here’s an example. As part of the Data Governance program, a company identified one key metric as the “number of validated Ship To addresses”. Why? Because for a significant number of deliveries, FedEx would return the package and charge the company for giving an undeliverable address. And FedEx, as part of its business process, would not let the company know what was wrong with the address or where the correction was needed.
If a company does a large volume of shipments, even a small percentage of returns amounts to a substantial cost. When a data governance program was instituted, the company ensured that for all new customers’ Ship To addresses, the Customer Hub validated the new addresses via FedEx’s web services. FedEx has an elaborate address validation and other shipment-related web services available on its web site.
The company also ensured that any other projects that touched the customer master were aware of this integration. This was published as an official data governance policy. If any other program or user attempted to update the validated address, an approval workflow was initiated. Periodic system refreshes were also developed that would end-date the validated address and create a new validated Ship To address, using U.S. Postal Service’s National Change of Address service.
For historical customer addresses, the company started doing validations of the “defective” FedEx addresses first and after that set was processed, the remaining addresses were cleansed and validated.
The most important thing to remember is that unless visibility is provided thru a data governance metric, it’s easy for management to lose sight of your accomplishments. Therefore, it’s critical to build the data governance metrics first, even before embarking on an MDM project.
Building Integration using SOA
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Many companies are still deploying Enterprise Application Integration (EAI), with proprietary adapters and integration servers. However, for a Master Data Management (MDM) solution, we recommend a Service-Oriented Architecture (SOA) approach for integration between the hub and source systems using web services.
A typical web server provides Hypertext Transfer Protocol (HTTP), so Web browsers can receive pages from a web site.
Application servers provide the Simple Object Access Protocol (SOAP) interface and host the web services. The web services also provide object components, which provide the business service layer above the applications.
The development time for SOA-based MDM integration will depend on the number of business entities to be exchanged, the availability of a vendor-supplied Software Development Kit for the Web Services Definition Language (WSDL), the complexity of the applications to be integrated and the number of Web services to ultimately be deployed.
Some guidelines for developing an SOA integration for an MDM hub are:
- Use XML (eXtensible Markup Language) for all data exchange (XML is a language that provides a standard way of representing data and information).
- Use UDDI (Universal Description, Discovery, and Integration) for listing and locating applications. UDDI is a directory standard that is provided by some application tools as a built-in service to use during integration.
- The WSDL (Web Services Description Language) file should be obtained from the source system to which data needs to be sent or retrieved. WSDL is a “descriptor standard” that an application uses to describe its interface and interaction rules to other applications. WSDL is a document written in XML which describes a Web service. It specifies the location of the service and the operations (or methods) the service provides.
- WSDL should be leveraged with the help of proprietary tools provided for each application to generate the XML message required to meet that data structure.
Currently, some of the Master Data Management platforms (such as Siperian, Initiate Systems, Oracle and IBM) provide excellent SOA libraries of web services.
With some work by the end customer, these products can provide a standard set of data services at the application level. We believe this approach ultimately will give you more flexibility and adaptability than EAI-based integration.
