Building the Business Case for Governance

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In business today, it is impossible to get executive sponsorship or funding for any initiative without a clear and compelling business justification. How is spending this money going to help us increase revenue? How can this program improve the business? Can we afford to fund this initiative at the current time?

To make an investment in your data -- and to ensure that it becomes a strategic corporate asset -- you must first build a solid business case.

Remember that better data management ultimately improves your business. When asked to provide substantive benefits for improving your company's data, I name these three major benefits that I guarantee are top-of-mind with the C-Suite: risk mitigation, cost control and revenue optimization.

Risk mitigation is the most likely reason a company focuses on data quality, according to an Information Age survey of 279 companies. Thirty-two percent of companies said legislation and compliance pressures were a key driver of data governance (Information Age Research Report, "Data Governance: Protecting and Exploiting a Key Business Asset," 2008).

A few years ago, I worked with a company that had just completed a difficult and time-consuming acquisition. On the surface, the acquisition looked great. The two companies had some complementary products, but there were also a fair amount of competitive products. The idea was to streamline the product offerings and reduce costs by combining redundant functions.

Since the company that was acquired generated 60 percent as much revenue as the acquiring company, the logical thinking behind the merger was that it would create a company with substantially more income. In reality, though, the results weren't so satisfactory.

The reason for this underperformance was a lack of knowledge about the new company. One of the things the new parent company never discovered during the due diligence process was that almost half of the acquired company's customers were already the parent company's customers before the acquisition. The amount of revenue that the merged company generated was substantially less than anticipated. This was a huge risk that could have been mitigated with better data management.

The second most likely reason companies will look at data quality or data governance is cost control, according to 30 percent of the respondents in the Information Age survey. Properly managed data can help companies unearth a million tiny areas where money is leaking out of the organization -- ways that could never be tracked manually. And maintaining a diligent data quality approach can yield significant gains for any organization.

One example of this is global life and science company DSM. The company wanted to control costs when purchasing items. More than 1,000 people worldwide at the company had purchasing authority at that point and they were tasked with selecting and assigning a product code to each item at the time of purchase. These codes were intended to provide a way to aggregate and sort purchased products, giving DSM a better view of its spending habits.

Unfortunately, manual entry of this product code did little to help with spend analysis. DSM neither knew nor understood what it was buying from its different suppliers. This prevented the company from attaining any sort of bulk purchasing discounts. There wasn't enough consistency within classification and the codes were of no more value than the product's name or description.

By automating the process by which codes were appended to purchases, DSM was able to analyze and eventually redesign the manner in which it procured items. The company now estimates that it will save between 5 and 7 percent on its annual indirect spend of $2B. That's a number that any executive would get excited about.

The third reason -- and one that I think companies have yet to sufficiently leverage -- is revenue optimization. Only 14 percent of respondents in the Information Age survey gave enhanced competitiveness as the reason to improve data quality. Using information wisely to become more agile and responsive is so simple it's both amazing and inconceivable how many companies dismiss it.

Wal-Mart might be one of the best in the world at revenue optimization based on consumer demand. Wal-Mart's supply chain technologies allow them to replenish inventory on the shelf in less than three days. The three-day restocking isn't just from the warehouse to the shelf, it's from the manufacturer to the shelf.

While other retailers struggle with getting adequate product on their shelves, Wal-Mart has reached back to the assembly line itself to find the answers to its supply chain programs. That's why Wal-Mart shoppers are almost always able to find the right item when they need it.

The key to Wal-Mart's success is in the quality and management of its supply chain data. Within fourteen seconds of the purchase of a product at Wal-Mart, the Wal-Mart central warehouse is notified of the change in inventory.

In addition, manufacturers of the product are made aware of the sale so that as inventory is moved from the warehouse to the store, the manufacturer replenishes the products in the warehouse. Even the raw material suppliers that the manufacturer needs are alerted so that they can supply the manufacturer with the necessary resources. And this process is repeated throughout the supply chain.

Can every company replicate the Wal-Mart model? A fourteen-second inventory change notification might be difficult without other utilities in place. But with high-quality data flowing throughout your systems, you can more accurately model your supply chain to react to market changes, customer requests and supplier dynamics.

In today's competitive environments, with the customer, employee and regulatory demands, it is essential to run your business as efficiently as possible. The key to better business is better data, managing and funding your data infrastructure like you would any other corporate asset.

This is only achievable if you develop data management and data governance processes based on business requirements. To do this, concentrate on risk mitigation, cost control and revenue optimization as you build your business case for better data.

Editor's note: This article was excerpted from Chapter 1 of The Data Asset: How Smart Companies Govern Their Data for Business Success by Tony Fisher.

About the Author

Tony Fisher is the president and CEO of DataFlux Corporation, the leading provider of end-to-end data management solutions that help companies analyze, improve and control business-critical information. Tony can be reached at For more information on DataFlux, visit

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About DataFlux

DataFlux, a SAS Company, provides companies the ability to build consistent, accurate and reliable data that can help them make better business decisions. As a leader in the data management market, DataFlux provides a full range of solutions and services and has over 750 customer installations worldwide.

DataFlux solutions utilize the building blocks of data management: data profiling, data quality, data integration, data enrichment and data monitoring. Together, these components help companies to inspect, correct, integrate, enhance and control any data used by an organization. The data management process helps you turn data into a strategic information asset that can enhance the effectiveness of data-driven applications, including customer relationship management (CRM), enterprise resource planning (ERP), data warehousing and database marketing.