Knowledge management (KM) comprises a range of strategies and practices used in an organization to identify, create, represent, distribute, and enable adoption of insights and experiences. Such insights and experiences comprise knowledge, either embodied in individuals or embedded in organizations as processes or practices.
Many large companies and non-profit organizations have resources dedicated to internal KM efforts, often as a part of their business strategy, information technology, or human resource management departments (Addicott, McGivern & Ferlie 2006). Several consulting companies also exist that provide strategy and advice regarding KM to these organizations.
Knowledge management efforts typically focus on organizational objectives such as improved performance, competitive advantage, innovation, the sharing of lessons learned, integration and continuous improvement of the organization. KM efforts overlap with organizational learning, and may be distinguished from that by a greater focus on the management of knowledge as a strategic asset and a focus on encouraging the sharing of knowledge. It is seen as an enabler of organisational learning and a more concrete mechanism than the previous abstract research.
Business intelligence has an important role to play in knowledge management projects.
In normal business intelligence practice, when business users receive information from a business intelligence system, they use their expertise or knowledge to make decisions and take actions- this process may involve interaction with other users, and is supported by collaborative applications and processing.
When business users are making decisions, and taking actions they are using their business intelligence knowledge to combine the information to business processes and the processes they are responsible for in their role in the organization. Their ability to relate their information to business processes is very important. Unfortunately, older business intelligence systems don’t support this activity very well although newer, more rapid-fire business intelligence does address these issues.
Having the ability to relate this information to your business processes allows you to make advances towards automating decision making in your organization. So using business intelligence for knowledge management, can even give less experienced business users a hand in making the right business decisions, fix business problems, and satisfy customer needs.
Business intelligence plays a central role in knowledge management. However, only modern business intelligence systems are currently equipped with the appropriate tools to work in conjunction with knowledge management software.
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. 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.
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.