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Can Enterprise Data Warehousing and Master Data Management Projects Survive the Recession?

Originally published March 18, 2009

“The economy has fallen off a cliff,” commented Warren Buffett, chairman of Berkshire Hathaway, in a recent interview on CNBC’s Squawk Box. This is not an understatement as theeffects of the rapid economic downturn can be seen already in decreased software revenues in many vendor organizations. Ambitious IT projects in areas such as enterprise data warehousing (EDW)and master data management (MDM) are likely to suffer as CIOs focus more on reducing IT costs, than leveraging IT to help the business fight the recession.

As I noted in a recent blog,  the solutions that will have the most impact in 2009 willbe those that offer quick and low-cost approaches that help organizations reduce costs and enable business users to become more productive and self-sufficient.

The Impact on Data Warehousing and Business Intelligence

In the past, business intelligence (BI) projects have always been largely insulated from the ravages of economic downturns because they offer a way for organizations to monitor and optimizebusiness performance to reduce costs and increase revenues in difficult times. This is especially the case for customer-facing BI solutions.

A recent study1 by Accenture of 250 executives showed that 57% of them don’t have a beneficial, consistently updated, enterprise-wide analytical capability and that 72% were workingto increase business analytics usage. The problem is, of course, that to achieve a “consistently updated enterprise-wide analytical capability,” the organization needs an enterprise datawarehouse.

Although many organizations have an enterprise data warehouse, there is still a growing trend to build standalone BI solutions each with their own data stores. Evolving technologies such as hardwareand software appliances, software-as-a-service solutions, open source software, data and presentation mashups, and web analytics applications are driving this trend.

The increasing use of these standalone so-called situational BI solutions is particularly prevalent when creating operational BI analytics that are used todrive daily and intra-day business operations. Interviews with business users who need these operational analytics indicate that business groups are building their own solutions because central ITdoesn’t have the budget or the resources to modify the enterprise data warehouse to support the required analytics. Even in situations where IT does modify the data warehouse, by the time thework has been done, the business requirements have changed.

I believe we are entering an era where situational applications will become a way of life even when the economy does recover. The role of IT in this environment will be to support core operationalapplications and strategic and tactical BI applications built on top of an enterprise data warehouse. In addition, IT will need to monitor situational operational and BI applications to capture bestbusiness practices for use in other parts of the organization and for potentially incorporating into core IT systems. This approach enables business users to rapidly prototype and deploy theapplications they need, while allowing IT to cherry pick and integrate the situational applications that will bring the most benefit to the organization as a whole.

The Impact on Master Data Management

Master data management (MDM) was a hot topic in 2008, and every vendor suddenly seemed to be offering an MDM solution. There are different types of MDM applications and also several differentways of embarking on an MDM project.

Although people talk about operational, collaborative and analytical MDM, in reality, MDM is an operational solution. The objective of an enterprise MDM system is to manage and support consistentmaster data across all operational applications. If this can be achieved, then master data flowing into a data warehousing system will also be consistent.

The problem is that building an enterprise MDM system is a major and long-term project. To shortcut this process, many organizations try to fix master data problems in the data warehouse. Whereasthis approach can provide useful benefits, such as creating a single view of the customer for analytical processing, it doesn’t solve master data problems in the source operational systems thatare responsible for creating and managing the master data.

There are three potential major starting points for an MDM project:
  1. An operational MDM project as an organization replaces legacy applications with custom-built or packaged services-based solutions.

  2. An analytical MDM data warehouse approach to solve customer, product and financial master data analytical processing issues.

  3. A data governance and data quality initiative. Many projects in category 1 and 2 often become data quality projects because the organization realizes they cannot implement operational or analytical MDM until they solve data governance and data quality issues.
A recent article on SearchDataManagement.com2 contained some interesting quotes from industry analysts about the likely direction of MDM projects in 2009. Here are two of them:

“The data quality market will grow as customers recognize it as a cheaper precursor to MDM. Data quality management is core to delivering effective master data, and customers who balk at the extremely high start-up costs of MDM software and services will recognize that the more mature data quality market may effectively meet the 80/20 rule of their trusted data requirements – at a fraction of the cost. Operational MDM that bi-directionally synchronizes master data across the enterprise will follow once the value of data quality investments is realized.” Rob Karel, Forrester Research

“As companies struggle through the recession in 2009, few large MDM projects will get started, and there will be a shift to data quality. Companies forced to manage their business with smaller IT and business operations staff will realize they can reduce the workload by eliminating inconsistencies and error in the master data for their business applications. They will come out of the recession with a better understanding of the master data needs of their company and implement more comprehensive data governance processes and tools.” Bill Swanton, AMR Research
This is good news because data quality is one of the biggest hurdles to overcome in building both enterprise MDM and enterprise data warehouse systems. If organizations really do focus more on dataquality during this recession, then enterprise projects down the road will benefit from this. The problem is that many organizations still don’t put sufficient resources into data qualityprojects because there isn’t always a quick and obvious business payback.

One problem with the way organizations approach data quality is that they view data as being either of good quality or bad quality. For certain types of projects, okaydata may be sufficient. This is particularly the case for situational applications where the governance and quality control of the data may not need to be so rigid given the constantlychanging nature of the business. I believe the role of the data governance group and data stewards in the future will be to manage the quality of critical core business data, while at the same timetagging other data with a quality indicator to show the level of confidence in the data. Business users should also be able to tag this grey data with a valuescore.

We can see then that although the economic downturn is likely to have an impact on IT budgets, there are a number of new directions IT can take to benefit the business. These new directions wereinevitable anyway, and all the recession has done is to accelerate them.

End Notes:
  1. “Most US Companies Say Business Analytics Still Future Goal, Not Present Reality,” Accenture press release, December 11, 2008.

  2. “Master Data Management Market Trends and Expert Forecasts for 2009,” Jeff Kelly, SearchDataManagement.com.

SOURCE: Can Enterprise Data Warehousing and Master Data Management Projects Survive the Recession?

  • Colin WhiteColin White

    Colin White is the founder of BI Research and president of DataBase Associates Inc. As an analyst, educator and writer, he is well known for his in-depth knowledge of data management, information integration, and business intelligence technologies and how they can be used for building the smart and agile business. With many years of IT experience, he has consulted for dozens of companies throughout the world and is a frequent speaker at leading IT events. Colin has written numerous articles and papers on deploying new and evolving information technologies for business benefit and is a regular contributor to several leading print- and web-based industry journals. For ten years he was the conference chair of the Shared Insights Portals, Content Management, and Collaboration conference. He was also the conference director of the DB/EXPO trade show and conference.

    Editor's Note: More articles and resources are available in Colin's BeyeNETWORK Expert Channel. Be sure to visit today!

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