Understand the Business Context (semantics) prior to picking a solution
As per my earlier blog on EA, BPM, SOA and MDM it is very important to understand the business context, including the semantics of what each of business units, departments and entities mean when they refer to MDM entities such as Customers or Products. For example marketing deals with leads, sales with opportunities/accounts and services with paid customers. Should all the entity be referred to as customers throughout the business process? or should the master entity be referred to as Organization? Common vocabulary goes hand-in-hand with master data.
For example what does this sign on a building mean? Is the building fully occupied and available for purchase? or does it mean that the entire building is unoccupied and available for rent? The business context needs to be clearly defined in terms of business units, departments and business processes.Develop a comprehensive entity model.
Not only should one define the common attributes for the master data, one should map the entire set of attributes and where it is used. For example, in customer services the customer is used as a reference whereas in order management it is transactional data.
A set of models and documents that can be used both by Business and IT.Establish data governance process right up front.
It is important to establish data governance process right upfront, especially as this may required dedicated resources from all the business units as well as from IT to manage the master data. The approach that worked for us is as follows:
- Executive Leadership Team Data Leadership Team that meets on a periodic basis (once a month) to establish data policies, standards, establish priorities for the data quality team
- Data Stewardship (Quality) Team are the business operations people who manage the data quality on a day to day basis. The business team enforces and ensures data quality across the enterprise.
- Enterprise Data Program team is responsible for developing and managing the programs and business rules in the various technology tools.
It is important for enterprises to consider bringing in 3rd party data providers such as D&B and Factiva for better understanding of the market. For example on a typical morning between 9:00am and 11:00am
- 706 businesses will move
- 578 businesses will change their phone numbers
- 60 businesses will change their name
- 49 businesses will shut down
- 10 businesses will file bankruptcy
- 1 business with change ownership
3rd party data from D&B could be used for leveraging legal name as the name of the organization, providing knowledge management systems that also provide news feeds as well as market statistics by industry, geography and demography and also mapping it to existing sales.
Picking the right data standardization and matching engine:This is a key technology that will either make or break the quality of your data. I agree that developing the business rules and configuring the matching tool is more important - however from a technology point of view - I would dedicate substantial amount of time evaluating and testing the tools with existing data before picking a technology. My preference would be to use one of the following matching engines:
Trillium, First Logic (now SAP), IBM or SAS.
In cases where a MDM product is packed with a different matching engine, I would be tempted to externalize the data quality to one of the above mentioned data matching engines. Just my preference - maybe the other quality engines may have got better. Do you own evaluation.
Expose all MDM functionality as services
Expose all functionality such as data (address) standardization, data matching, update master and propagate master key.
As usual please do feel free to drop me line with your comments and/or feedback.
- Yogish
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