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Decision Support: A Classic, Back-to-Basics Data Warehousing Business Driver

Big data and real-time analytics offer important and exciting benefits, don't lose sight of your organization's classic decision-making needs.

Long before it was called business intelligence, the term "decision support systems" was used to describe an organization's use of data to facilitate its decision making processes. Much of the current focus involves using data warehousing and business intelligence to address near real-time business issues such as website cross-selling, fraud detection, and sentiment analysis through the use of big data and BI analytics.

However, I believe that it is important that we also continue to address more classic problems. These frequently involve comparing current values against past values to determine trends or spot problems and opportunities such as (to name just a few) those relating to product and store sales analysis, employee productivity, inventory levels, spending trends, period-over-period budget comparisons, and healthcare issues.

I recommend that as a BI practitioner you frequently ask your user communities, "What decisions need support?" as well as whether these decisions require immediate or longer-term resolution. Do these decisions address ongoing or one-time needs? This will help you set priorities and determine if a "quick-and-dirty" approximate solution might suffice. It will also help avoid making a significant investment in gathering (and cleansing!) data not readily available only to find that opportunities have passed before the analyses were complete.

When allocating resources and setting priorities consider the concept of "expected value of perfect information" -- that is, the incremental value of having perfect knowledge of the outcome versus making a decision based only on currently available information. Consequently, you should not allocate resources to evaluating a decision that exceeds the benefits from making the correct choice. Furthermore, the value of most decisions is often based on how quickly the decision must be made. For example, if you could develop a system that optimized up-selling and cross-selling recommendations to website purchasers, it would be of little value if these recommendations were not available until after the purchaser left the website.

I certainly don't intend to downplay the importance of real-time decision making and technologies such as operational data warehouse, real-time analytics, and other enabling factors. However, many important decisions are not overly time sensitive. For example, a decision about where to locate a new physical warehouse or a manufacturing plant may take months. One potential guideline is that strategic decisions (e.g., "Should our company enter a new market?" or "Should our company try to acquire a given competitor?") are likely to be less time sensitive than operational decisions (e.g., "Should we extend credit to a particular customer?").

I have often stated that there is no single solution to an organization's data warehouse needs and that organizations should establish an overall data warehousing architecture to meet their many, diverse requirements. Although big data and real-time analytics present exciting (and resume-enhancing!) challenges, we must not lose sight of our organization's classic decision making needs and ensure that our data warehousing architectures and project priorities continue to address these as well.

About the Author

Michael A. Schiff is founder and principal analyst of MAS Strategies, which specializes in formulating effective data warehousing strategies. With more than four decades of industry experience as a developer, user, consultant, vendor, and industry analyst, Mike is an expert in developing, marketing, and implementing solutions that transform operational data into useful decision-enabling information.

His prior experience as an IT director and systems and programming manager provide him with a thorough understanding of the technical, business, and political issues that must be addressed for any successful implementation. With Bachelor and Master of Science degrees from MIT's Sloan School of Management and as a certified financial planner, Mike can address both the technical and financial aspects of data warehousing and business intelligence.

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