In Part I of this two-part (plus this update) series, I talked about the importance of building a solid data foundation, informed by an overarching data strategy. A key component of that strategy is to establish the data management architecture that will most effectively support your objectives. There are two closely related disciplines that best serve this purpose:
- Customer Data Integration (CDI)
- Master Data Management (MDM)
While these terms are sometimes used interchangeably, for the purposes of this blog, we will define CDI as the collection of technologies and processes that consolidate information from multiple sources to create a centralized view of the customer. This includes ETL, cleansing, standardization, matching, identity management and creation of “master records.”
With that as a baseline, let’s take it one step further and define Master Data Management (MDM). With MDM, we expand the scope of data to include sources that might not be commonly viewed as customer data (e.g., product catalog, suppliers, and distribution channels). Further, we go beyond data ingestion and include provisions for feedback loops to source systems – and any other systems for that matter – that are able to accept the master data.
The biggest impact of MDM is the ability to create a centralized view of customer, including all activities that relate to other functional areas of your organization. With a sound MDM approach, marketing, sales, delivery, and accounting can all refer to the customer in a consistent way and have a comprehensive view.
As example, if marketing program metadata is mastered and labeled consistently across both the marketing database and the ERP database, an organization more easily can understand the “value” of the customer and the effect a marketing campaign has on purchase behavior. In theory, it can help marketing better determine its return on investment.
I understand the implications of MDM on marketing; it means working with other parts of the organization – including IT – to develop corporate-wide standards and define common data management processes. This level of process consistency requires executive level sponsorship and investment, but ultimately, it will be well worth it.
