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Making Sense of Analytic Applications

Originally published June 17, 2004

After you’ve built your data warehouse, what next? Most vendors are betting that the next big thing is something called “analytic applications”. Analytic applications are those applications that sit on top of the data warehouse and churn the data warehouse data in order to produce business-meaningful results. Analytic applications address many facets of the business that are relevant to the business person, which include:

  • customer retention,
  • elasticity analysis,
  • profitability analysis,
  • supply chain efficiency, etc.

One of the really appealing aspects of analytic applications is that the applications are immediately obvious in their appeal for the business person. Unlike data warehousing, where ROI has always been a difficult subject, the ROI for analytic applications is obvious to, even the most uninitiated.

Not surprisingly, there are many vendors with many different approaches to analytic applications. Walk into a trade show and see how many booths there are with vendors advising that they do analytic applications. The result is bewilderment. So how do you start to make sense of the analytic applications marketplace?

The approach that I use is to put analytic application vendors into a matrix framework. Figure 1 shows the framework.


In figure 1, there are two kinds of parameters – micro/macro and static/dynamic.

The macro parameter refers to looking at information at the macro-level. In the macro-level we look at data in a summary fashion. We look at total cash on hand, total sales for the quarter, total number of customers, and so forth. Every business has a need to look at information at the summary level.

The micro-level refers to the ability to look at information at the detailed level. In the detailed micro perspective, we look at individual customers. We discover and create the 360 degree view for every customer. We look at products at a detailed level. We look at the length of time to manufacture, shelf life, reject rate, and so forth.

The micro-view and the macro-view are needed by every corporation in order to make good decisions.

The static view of information is that view that is always needed. In the static view, we can examine the corporation and discover what information is needed. This is the classical JAD session approach to beating the information out of the end user. We discover we need information about cash on hand, quarterly revenue, quarterly expenses, pipeline size, production, and so forth. This information is needed in both good and bad times. Regardless of the state of the company and the state of the economy, we need the same static information.

Dynamic information is needed information that is unpredictable. Suddenly, a law is passed that affects our information needs. The next minute, there is a revolution in South America that affects the availability of oil. Tomorrow there is a new product that enters the marketplace that competes with our product line. Next week there is a new technological breakthrough that we need to remain competitive.

Short of having a crystal ball, we don’t know what is going to happen next at any point on the globe. Change is both constant and unpredictable. To be responsive and proactive, our corporation needs to be able to quickly respond to dynamic information as it develops.

No JAD session in the world is ever going to address the dynamic need for information.

However they are put together, the different parameters form a matrix that can be used to stack up the analytic application marketplace.

Where do the different players and products in the analytic application marketplace fit? Figure 2 shows where some of the “classic” players in the analytic application marketplace fit.


Fig 2 shows that established players such as Business Objects, Cognos, MicroStrategy and SAS fit well in the static macro quadrant. They fit in the macro-dynamic quadrant as well.

But there are other players making a strong bid for this marketplace. Consider the ERP players. In particular consider the players SAP and PeopleSoft. Figure 3 shows where these ERP players fit.


There is a legitimate fit for the ERP players in all four quadrants. The ERP players have the advantage because they include of having transaction processing systems as part of their portfolio. As a consequence, they have detailed data that is easy and natural to access. But they have another advantage as well, because they also have a wealth of data at the detailed level. This positions them well for the dynamic micro-marketplace.

About the only disadvantage the ERP vendors have is that they only have software and don’t own their own hardware platform.

In order to support the ERP vendors (and other analytic vendors as well), it is necessary to have hardware. In particular, in order to support dynamic micro-processing, which is the most challenging processing of all; it is necessary to support large volumes of data at a detailed level. Figure 4 shows this requirement.


The need for support of large volumes of data is open-ended. For all theoretical purposes, there must be the ability to support an unlimited amount of detailed data.

Figure 5 shows where the different hardware vendors are positioned when it comes to supporting analytic applications.

  • Bill InmonBill Inmon

    Bill is universally recognized as the father of the data warehouse. He has more than 36 years of database technology management experience and data warehouse design expertise. He has published more than 40 books and 1,000 articles on data warehousing and data management, and his books have been translated into nine languages. He is known globally for his data warehouse development seminars and has been a keynote speaker for many major computing associations.

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