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Closed-Loop Business Intelligence: Reality or Simply Another Buzzword?

Originally published April 22, 2009

I often hear business intelligence (BI) vendors and specialists talk about closed-loop business intelligence without really explaining what this term means. In this article, I want to explore this term, explain what I believe closed-loop business intelligence involves and discuss why it is more than simply a buzzword or convenient vendor marketing term. 

Organizations use business intelligence to provide business users with information that helps them make more informed, and hopefully better, business decisions. To support this decision-making process, BI applications work in conjunction with operational and collaborative applications to provide what I think of as a decision-making system.

A system is said to perform closed-loop processing if the system feeds information back into itself. A closed-loop decision-making system therefore not only monitors business performance to provide business users with the information they need to make decisions, but also with information that allows them to see the positive or negative effects of those decisions. Such a closed-loop system is shown in the figure below.

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Figure 1: A Closed-Loop Decision-Making System

In the Figure 1, data flows from operational applications that run day-to-day business processes to BI applications that monitor and analyze the data to provide insight about the actual business performance of those processes. Business users then employ collaborative applications to share and evaluate the results produced by the BI applications. This evaluation may result in users making changes to a business process (e.g., a modified marketing campaign) or a business plan (e.g., an updated sales forecast). The positive or negative effects of those changes are then measured by the BI applications to close the decision-making loop.

The speed at which business users need to do this closed-loop processing determines over what period of time data is analyzed and how responsive the BI environment needs to be. This speed can beexpressed in terms of the elapsed or action time between a business event occurring that requires action and the user taking that action. In the case of fraud detection, for example, the required action time maybe a matter of seconds, whereas in a marketing campaign, the action time could be a few hours, days or weeks.

A more detailed picture of this closed-loop processing is shown in Figure 2.

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Figure 2: The Main Activities in Closed-Loop Decision Making 

As the figure shows, there are six main activities involved in closed-loop decision making.

Discover the operational data and other business content that can aid business decision making.

Access the operational data and business content required to make business decisions.

Integrate the retrieved operational data and business content into a shared data store, such as a data warehouse, as required.

Analyze operational data, business content and data warehouse information and produce analytics, alerts and recommendations that aid decision making.

Deliver the results of analytical processing to business users and applications.

Share the results with interested users and allow these users to collaborate to determine what decisions and actions (if any) need to be taken to resolve business issues identified by the analytical results.

Most BI vendors have done a good job of supporting the access, integrate and analyze activities. Products still need to be improved, however, in the discover and deliver areas. This is especially the case for line-of-business business users such as call center representatives and store managers who do not have a detailed understanding of the data and technologies involved in IT business systems. These types of users not only needeasy-to-use products, but also easy-to-consume information.

Information can be made more consumable by putting the information into a business context and relating it to business plans and goals, and also through the use of alerts, recommendations, guidedanalysis, expertise sharing and documented best practices. 

Although the topic of this article is closed-loop business intelligence, we can see from the discussion that business intelligence is not the only participant in the decision-making process, and perhaps a better term to use instead would be closed-loop decision making.

Figure 3 shows a more detailed version of Figures 1 and 2. This figure shows how information flows through a closed-loop decision-making system.


Figure 3:(mouse over image to enlarge)


Information Flow in Closed-Loop Decision Making

At the bottom of the figure are the services that support the management and integration of data. These services provide IT applications with the data they need to support business operations, business analysis and business collaboration.

The block in the middle of the figure contains applications that produce traditional BI data analytics that support tactical and strategic decision making. The applications in the block on the right produce content analytics that supplement data analytics. In some cases, business content may be restructured into a structured format instead and loaded into a data warehouse for analysis.

The block on the left of the figure shows the operational environment. To create strategic and tactical data analytics, operational data is first extracted and integrated into a data warehouse. For more timely intra-day decision making, data from operational systems is streamed at frequent intervals from operational systems to a low-latency datawarehouse.

For close to real-time decision making, it may become impractical to capture data into a data warehouse before it can be analyzed. In this situation, BI analytical processing is embedded into operational processes and the operational data analyzed dynamically at it flows through the operational systems. The event analytics produced by the embedded BI can then be delivered to the collaborative environment in the same manner as that for data and content analytics. The combination of data, content and event analytics can be thought of as decision analytics.

For certain types of operational applications, the decision-making process may be automated by embedding a decision engine or service into the operational workflow. In this situation, the closed-loop decision making is self-contained within the operational workflow and the workflow becomes self-optimizing. This approach is useful for fraud detection, risk management, algorithmic trading and web storefronts.

In this article, I have tried to provide a quick overview of my thoughts on closed-loop business intelligence or, more accurately, closed-loop decision making. My objective has been to show that effective decision making requires more than simply giving business users more and more information. To be useful, the information has to be easy to consume and must be integrated into a closed-loopdecision-making system.

Note that this article provides an update to some of the key concepts of an August 2008 article that Claudia Imhoff and I wrote, entitled Full Circle: Decision Intelligence (DSS 2.0). Instead of using the term decision intelligence, we now use the terms decision analytics and decision framework to explain how organizations should go about designing and building a closed-loop decision-making system. Claudia and I are currently working on additional material on this topic for publication on the BeyeNETWORK.
  • 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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Comments

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Posted April 30, 2009 by Robert Eve reve@compositesw.com

Colin -

This framework is clever in its ability to clarify a set of disparate ideas and approaches.

Your readers will gain significant value from your synthesis.

Well done.

Bob Eve

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