We've heard a lot of questions since the publication of our book this past summer about customer data integration (CDI) and master data management (MDM). Actually, that's an understatement. Lately, companies with mature data warehouses have been pounding the pavement researching MDM. They know enough about it to understand that their companies should be doing it. They're just not sure exactly what it is, who should own it or where to begin.
MDM, Begin Again?
Show us any emerging trend worth buzzing about, and we'll show you someone with a stake in the status quo ready to dismiss it. It is de rigueur to proclaim that MDM and CDI are nothing new. Many naysayers said the same thing in the early days of customer relationship management (CRM). "We've been managing our customer relationships for years," said an enterprise resource planning (ERP) vendor flummoxed by all the hoopla. By the time the market reached $20 billion, the ERP vendor had launched its own CRM offering.
True, MDM incorporates some well-known practices. For instance, current CDI and product information management (PIM) hubs integrate error detection, correction and management, and can support analytical systems. Master data is integrated from disparate silos, with rules applied to make it meaningful. But that doesn't equate MDM with data warehousing or ETL (extract, transform and load) or mandate a database system in order to work.
Product Item Master
A retailer client has a product item master, the de facto list of products sold in all of its stores. The item master identifies basics of every product the retailer sells. The item master contains product details leveraged by different organizations within the company - the disparate identifiers used by different groups, including SKU, UPC and inventory IDs. (In retailing, having multiple identifiers for a single product is commonplace.)
However, the item master doesn't contain all product data. Details about the retailer's 400,000-odd products change daily. Propagating and synchronizing these details between the retailer's many operational and analytic systems within the business is one hard job. This is why the retailer maintains a robust enterprise data warehouse (EDW) to analyze individual product status and historical sell-through data and trends.
The item master serves one purpose for the retailer: to provide the de facto product facts independent of inventory. It is the list of products the retailer can sell. The retailer's data warehouse team decided that this was easy enough for the EDW to manage. Synchronizing item master data with EDW data let the company capitalize on open systems, and a SQL interface simplified ongoing access by other tools and systems. The EDW would remain the single version of the truth for all product data.
The problem was, as the business users turned their attentions to historical analysis, data warehouse queries were often complex. Historical pricing trends, product changes and inventory fluctuations were fundamental business metrics, but not native to the item master. The operational loads necessary to support the time-sensitive needs of the product item master were imperiled by the constant ad hoc access by business users. Merchandisers, inventory and warehousing staff manipulated the item master - modifying approved products, revising suppliers and adding products - throughout the day.
At end-of-week or end-of-month periods, heavy data warehouse usage prevented operational staff from getting information from the item master. Transactional updates to the item master slowed down. In the meantime, businesspeople accustomed to quick sales and inventory reports were increasingly vexed.
Three upgrades and several million dollars later, the IT group realized that their EDW was not designed nor configured to support the operational needs of the item master. The previous item master had been part of a transactional system. As such, transaction monitoring and up-time were paramount. Real-time response time was mandatory. The constant availability of the item master to a range of business systems was imperative to the running of the company. As important to business strategy as the EDW was, the processing of the item master was ultimately much more critical to business operations. "We liked the idea of applications sharing common data," the retailer's applications architect explained. "We were just too optimistic that our EDW could evolve to support OLTP-style processing."
The IT organization migrated the product item master off of the EDW and onto a dedicated PIM hub. The hub optimized the availability of the item master and ensured that other systems in the company - including inventory, distribution and sell-through systems as well as the EDW - had item master details on demand.
Jill Dyche is a partner and co-founder of Baseline Consulting ( www.baseline-consulting.com ), a data integration and business analytics delivery firm. You can reach her at mailto:firstname.lastname@example.org.
Evan Levy is a partner and co-founder of Baseline Consulting Group, a multivendor systems integration and consulting firm. You can contact him at mailto:email@example.com.
This article originally appeared in DM Review.
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