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The Link Between Data Warehousing and Master Data Management
The Information Difference Company, April 2010, Pages: 38
Two approaches to master data management (MDM) have been described extensively: MDM for operational purposes (“Operational MDM”) and MDM for analytical purposes (“Analytic MDM”). Operational MDM focuses on ensuring that data in multiple operational systems that should be the same actually is the same. Analytic MDM is generally associated with data warehousing (DW) and seeks to leave the operational world alone, instead focusing on compiling a view of data that can be used for analytic business intelligence and management information purposes.
- In recent years, “Analytic MDM” has become established as one of the styles of MDM implementation adopted by businesses needing to effect a significant improvement in the speed and quality of their business reporting, often centered around one or more national, regional or enterprise data warehouses.
- This is unsurprising since the “dimensions” of a data warehouse are essentially master data (e.g., hierarchies of products, customers, locations, etc.).
- Despite the close relationship between MDM and data warehousing, a glance at even the recent literature on these topics reveals that these two important areas tend to be treated as entirely separate.
- Against this background, we were interested in exploring the linkage between master data and data warehouses and to understand the scale, scope and success rates of MDM and data warehousing initiatives in business. We have therefore conducted a survey into the link between data warehousing and master data management.
- In total, 208 respondents completed the survey from all around the world; the majority from North America (57%) and Europe (27%). Over half the respondents (53%) came from companies having annual revenues greater than US $ 1 billion. The respondents represented a wide spectrum of industries.
- The key findings from the survey are summarized below: ? Almost half (46%) the organizations surveyed have one or more data warehouse and MDM implementations.
- A substantial proportion have “live” MDM implementations (48% for those with both DW and MDM, only 14% for those with just MDM).
- The majority (57%) of implementations of MDM are “enterprise-wide” with a further quarter covering more than one department or location.
- Generally, MDM implementations manage a mean of 109 million records (but with a median of just 4 million records).
- Although the common master data domains of “Customer” and “Product” still unsurprisingly received the highest rankings, the mean number of domains is 4 with a median of 3.
- A mean of 52 FTEs (full time equivalents) with a median of 5 FTEs is required for ongoing maintenance of the MDM implementations (showing a wide range in the scale of the implementations of the surveyed companies).
- Fully two-thirds have data governance in place showing an improvement over our earlier studies. ? Almost two-thirds of organizations are feeding their master data directly to the BI applications. ? There is a strongly held view (81%) that master data should be sourced directly from the MDM system; some 36% were already doing so!
- Fully two-thirds consider their MDM implementation to be at least moderately successful with only 1% rating it as unsuccessful.
- Only 27% of organizations have a single data warehouse. Organizations have a mean of 24 separate data warehouses with a median of 3.
- The number of source systems is also revealing, with a mean of 71 sources and a median of 10.
- Of those who have already implemented MDM, the dimensional data in the warehouse (essentially the master data) is overwhelmingly maintained in their MDM system.
- The biggest data warehouses are around 500-800 terabytes with a mean of 34 and median of 4 terabytes. They have a mean of 25 different dimensions (e.g., customer hierarchy, product hierarchy, etc.) with a median of 10. ? The scope of the data warehouse implementations for three-quarters of organizations is “enterprise-wide” with some 23% being “departmental”.
- Organizations in general support a mean of 3,500 users (median 200) with some 28 FTEs for maintenance (median 10 FTEs).
- More than half of the organizations surveyed update their data warehouses on a daily basis with only 10% using a trickle feed approach to achieve near real-time data.
- There is an overwhelming (93%) belief by respondents that in an ideal world master data should be sourced from a central hub.
- Despite the importance of ensuring only high quality data is uploaded to the data warehouse, 43% still rely on Extract, Transform and Load (ETL) tools for this process.
- Most organizations have had live data warehousing for a mean of 8 years (median 7 years).
- Surprisingly, most organizations still maintain their business rules in Extract, Transform and Load (ETL) scripts.
- Organizations are positive about the success of their data warehousing implementations with 82% considering their implementations to be at least moderately successful. Only 2% regard them as fairly unsuccessful.
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