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The Role of Business Intelligence in Knowledge Management

Originally published March 21, 2005

Some industry buzzwords gain market acceptance, while others end up on the scrapheap. Business intelligence (BI) has gained acceptance (even though there are many definitions for it), but knowledge management (KM) has had a mixed reception. Knowledge management has struggled because organizations have often tried to implement large enterprise-wide knowledge management projects and failed, and also because of the complexity bringing together the many components and technologies involved.

I believe that knowledge management is at last viable and that business intelligence has an important role to play in knowledge management projects. The objective of this article is to explain why. Before proceeding further, I should point out that most of us, including myself, tend to be somewhat careless when using the terms data, information and knowledge. I will try and be as consistent as I can when using these terms in this article. Every time I try and define these terms in articles and presentations, however, I always get comments and e-mails disagreeing with my definitions. It is okay to disagree, but hopefully my definitions will at least ease the discussion in this article.

If you have read my previous newsletters, you will know that I view an IT system as supporting three types of applications and applications processing: business transaction (BTx) applications, business intelligence applications, and collaborative applications.

BTx applications are responsible for running day-to-day business operations and store data, or facts, about those operations in a data repository that is typically managed by a database system. The database system allows applications and users to store, access and manipulate the BTx data.

Business intelligence applications analyze business operations and produce information to help business users understand, improve and optimize business operations. This information may be produced by analyzing and reporting directly against BTx data, but is more commonly done by processing the data stored in a data warehouse. A data warehouse provides the ability to gather data from various BTx data sources and integrate it into a single data repository. Like a BTx data repository, the data warehouse data repository is managed by a database system, which uses languages such as SQL and XQuery for data access and manipulation.

Business intelligence applications in the past have simply analyzed detailed data warehouse data and produced high-level summarized data, or measurements, about business performance. The recent trend, however, is toward the use of business performance management (BPM) applications that put these measurements into a business context, i.e., they relate the data measurements to business goals and objectives (See Figure 1). Putting performance measurements into a business context improves the business decision-making and action-taking processing because the results become actionable. If you know that today’s sales figures are 10 percent below target, then you can decide how to fix this problem and take the appropriate action.

Figure 1. The Knowledge Cycle.

Putting the measurements into a business context creates business information. This information may be embedded in enterprise portal web pages, documents, spreadsheets, presentations, audio, video, e-mail and so forth. It may be stored and managed in a data repository, but it is more commonly stored in a content repository. A content repository supports additional business semantics (i.e., business metadata) like author, date produced, etc., compared with a data repository. It also adds facilities like versioning, workflow, templates and search tools. Like a data repository, a content repository is managed by a database system.

When business users receive information from a business intelligence system they use their expertise or knowledge to make decisions and take actions. The decision-making and action-taking process may involve interaction with other users. This interaction is supported by collaborative applications and processing. This approach to decision making can be considered to be a non-programmed approach. If, however, the knowledge of the business user can be captured as a set of best business practices in the form of a set of business rules, then the decision-making and action-taking process can be programmed or automated.

When business users make decisions and take actions they use their business knowledge to tie the actionable information to the business processes and activities they are responsible for in their role in the organization. The ability to relate information to business processes is very important. Unfortunately, this aspect of the decision-making process is poorly supported by BI applications and BI vendors because the developers of these applications and software have a data-centric viewpoint of business operations, rather than a process-centric perspective. This deficiency, however, is starting to be addressed.

The ability to relate actionable information to business processes also provides the foundation for other ways of automating decision making and action taking. Less experienced business users (support representatives in a support center, for example) could be given a guided-analysis workflow (developed by business experts and based on best business practices) that helps them interpret actionable information, discover additional information, and make the right decision to fix business problems, optimize business processes and satisfy customer needs.

The diagram in Figure 1 demonstrates how knowledge management can help business users improve business processes. You can see from the diagram that business intelligence plays a central role in knowledge management. For a traditional BI system to fully support a knowledge management environment, however, it must provide, or work in conjunction with, capabilities like business process management, business planning software, collaborative software, portals, content management systems and be able to support more timely data feeds (See Figure 2). The result is what I call a smart BI framework (See Figure 3). The term smart knowledge management framework could have been used, but as I have already mentioned, the knowledge management term has some baggage associated with it. 

Figure 2. Knowledge Management Technologies.

Figure 3. The Smart BI Framework.

To help people understand how to integrate BI into a knowledge management environment for smarter decision making, I have teamed up with Claudia Imhoff to create a set of deliverables that explain how to extend the Corporate Information factory (CIF) to support the smart BI framework. The Business Intelligence Network will be one of the main channels used to distribute these deliverables, so monitor their website for more information.       

  • 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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