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The Financial Impact of Business Analytics: Build vs. Buy

Published
  • January 01 2003, 1:00am EST
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The Financial Impact of Business Analytics, IDC's recently completed study, examined the financial impact of analytic applications on the core business processes that contribute to an organization's success. Our study was based on the experience and impact of business analytics projects undertaken by 43 organizations in North America and Europe.

The study was sponsored by 12 technology companies: Adaytum, AlphaBlox, Business Objects, HNC Software (now Fair, Isaac), IBM Business Consulting Services, Informatica, Oracle, PeopleSoft, Sagent, SAP, SPSS and Teradata, a division of NCR. IDC also received the cooperation of The Data Warehousing Institute which encouraged their members to be interviewed for the study.

Analytic applications ranged from marketing campaign analysis to fraud detection to portfolio management. Companies ranged in size from 11 to 860,000 employees and spanned a variety of industries including manufacturing, financial services, healthcare, transportation, utilities and government.

The information on costs and returns was collected via on-site interviews with end users, information technology (IT) managers and financial executives at the 43 sites. IDC has used this technique in studies of a similar design on the return on investment (ROI) of data warehousing and the ROI of ASPs.

The study found that analytics implementations generate a median five-year ROI of 112 percent with a median five-year investment of just more than $2 million. Returns ranged from 17 percent to more than 2,000 percent. While 46 percent of the organizations generated an ROI of less than 100 percent; 34 percent generated an ROI between 101 percent and 1,000 percent; and 20 percent reported ROI of more than 1,000 percent.

The study shows that although a business analytics implementation is a substantial investment for an organization, it can also deliver substantial benefits. For the study participants, value accrued through quantitative and qualitative benefits that range from increased business performance to reduced operations costs and improved customer relations. Organizations interviewed for this study consider their particular business analytics implementation either a necessary cost of business or a critical factor in their plan for success and survival in a highly competitive market.

Build vs. Buy

One of the frequently asked questions about analytics is whether it is more effective to build or to buy a solution. IDC segmented the business analytics projects into two categories, build and buy.

  • Build: The development of a custom analytic application with the supporting data infrastructure by an internal team or external services team, using business intelligence tools, templates and technologies.
  • Buy: The implementation of a packaged analytic application with the supporting data infrastructure, usually customized to meet a company's needs and requirements.

Any project involving a combination of build and buy strategies was classified in the build category for this study. An organization that takes a packaged analytic application, builds a custom application and integrates the two of them is using this combination approach to implementation.
Representative build analytics projects were broad in their scope and complex in the data integration required. Isolated silos of information were brought together in order to present a global view of information or to closely track key performance indicators across physical and organizational boundaries. For example:

  • An automobile manufacturer used analytics to tie inventory and transportation information together to get a holistic view of logistics that will make them more efficient and cost-effective.
  • A healthcare manufacturer used analytics to optimize its value chain through a comprehensive performance measurement system that helps the company to better monitor its production process and take immediate corrective actions.

Representative buy analytics projects were focused on improving process efficiency. For example:

  • A healthcare distributor, which ships critical medical equipment just in time for surgery to hospitals, deployed an analytic application to forecast customer demand based on sales data in order to maintain appropriate inventory levels.
  • A brokerage company used an analytic application to identify and retain its most profitable customers, while improving its customer interaction processes – from sales and marketing initiatives to customer fulfillment.

The Bottom Line

Figure 1 shows the overall differences in total investment and on ROI between the build analytics projects and the buy analytics projects.

  Median Total 5-Year Investment Median Return
Build $2,088,860 104%
Buy $1,807,656 140%

Figure 1: Build vs. Buy – The Bottom Line

Build analytics projects showed a higher total investment than those categorized as buy. The build projects showed a lower median ROI than buy (104 percent vs. 140 percent), although the average ROI for build was slightly higher. Building an analytic application may cost more, but there is no indication that the approach itself materially affects potential return. Organizations that chose to build an analytic application were generally not able to achieve their objective using a packaged application alone.

The total investment for build was only 15 percent higher than for buy. However, the distribution of the costs were significantly different, with build analytics projects showing higher percentages for internal services, reflecting a reliance on in-house IT resources for custom development. On the other hand, buy analytics projects had a higher percentage of costs for external services, reflecting a reliance on consultants to customize a packaged application.

Should a company build or buy an analytic application? A company is well advised to first tackle a problem with a high impact on business performance. The approach adopted (build versus buy) depends on the problem addressed, the level of skill within the organization, and the availability of packaged solutions.

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