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Suggestions for Wisely Spending Your IT Dollars in 2004

Published
  • December 01 2003, 1:00am EST

Here we are at the end of yet another year. Cliché, I know. Sadly, the year has also been a cliché - ­one of decreasing corporate budgets and gloomy outlooks for hiring and spending in general, but especially in information technology (IT). Despite the still-sputtering economy, all the chief information officers (CIOs) and IT managers I've talked with recently tell me that they have money to spend ­- as long as they can be sure that they will gain quick returns on their investments. I actually see an emerging trend in IT that will give CIOs exactly what they want. That trend is the move toward enterprise master data management (EMDM).

Data quality analysis and remediation is not a new concept.1 Most organizations have a data quality planning and control function in place. However, this function tends to reside at the business unit level. It doesn't translate to the enterprise level. For years, that approach worked pretty well.

However, over the past decade, in efforts to parcel out management tasks and put more power in the hands of their employees, many companies have adopted a decentralized management culture. As a result, data marts, executive dashboards and desktop online analytical processing (OLAP) systems have exploded onto the scene. Now, employees have information at their fingertips so that they can make on-the-spot decisions that are critical to the success of the organization's customer relationship management (CRM) goals.

However, what's good from a CRM standpoint is often bad from a data management standpoint. As the number of data marts and OLAP-based applications has increased in many organizations, organizational master data ­- data about vendors, customers, products and raw materials ­- has become fragmented (e.g., the definition of "customer" in marketing may not be the same in procurement or finance). This is why it's important to take data quality analysis to the next level and analyze and manage data across the enterprise.

EMDM does just that. Rather than leaving the data quality and remediation function at the business unit level, the EMDM process retrieves the results of individual data quality analyses and coalesces them ­- usually in a relational database -­ for enterprise-level reporting and analysis. EMDM doesn't stop there. The real advantage of EMDM is that in addition to being analyzed, the data is also cleansed, defined and standardized at the enterprise level. In practical terms, that can be enormously beneficial to any organization.

Let's take the example of a wireless network provider. The wireless network business is about to undergo a sea of change. With the advent of one-number portability, customer churn ­- or the rate of customer turnover -­ is expected by many to spike.

To combat churn, wireless providers need information. They need information on marketing initiative results, customer preferences and needs and customer/call center interactions. The common link is the "customer" entity. With common data quality assessment methodologies, it can be difficult to tell whether or not "customer" means the same thing and has the same attributes from business unit to business unit throughout the enterprise.

However, with an EMDM strategy in place, the wireless provider can standardize the definition of "customer" and its attributes across the enterprise. What will that do?

First, it will give every business unit and every management and staff-level position in the organization a common definition and picture of the customer. They will be speaking the same language. When marketing speaks of customer preferences, the folks in the call center will know which entity (as well as what that entity looks like) the marketing people are talking about -­ so will the finance people and the salespeople.

I know this sounds absurdly simple. On the surface, it may be. However, the ramifications are enormous. If the company is speaking the same language internally, all the business units can work in concert to ensure that the organization develops products and services that draw in new customers and keep existing customers satisfied. Satisfied customers mean less churn.

There's obviously more to EMDM than I can cover in this column. It involves a complex strategy of developing enterprise analysis standards and remediation methodologies. It also takes a dedication on the part of the executive level to commit valuable resources to such a far- reaching project. However, the returns will be worth it. EMDM effectively addresses data quality at the enterprise level. It enables the organization to understand its data holistically and it enables the disparate business units to speak the same language -­ success.

Reference:
1. For an excellent, thorough discussion of data quality analysis, see Larry English's book: Improving Data Warehouse and Business Information Quality. New York: John Wiley and Sons, 1999.

All information provided is of a general nature and is not intended to address the circumstances of any particular individual or entity. Although we endeavor to provide accurate and timely information, there can be no guarantee that such information is accurate as of the date it is received or that it will continue to be accurate in the future. No one should act upon such information without appropriate professional advice after a thorough examination of the particular situation. The views and opinions are those of the author and do not necessarily represent the views and opinions of BearingPoint, Inc.

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