Introduction

A crucial task that all businesses undertake is that of decision making.  Multi-million, or even multi-billion, dollar decisions are common in today’s marketplace.  When making these decisions it is imperative that businesses understand, or gain as much understanding of, the decision in question to maximize the firm’s profitability. 

To gain this understanding, businesses are increasingly relying upon information technology to help them make better informed decisions. This trend has spawned several areas of study that are specifically designed to help support business decisions.  Two such areas are business intelligence and knowledge management. 

But what do these terms mean? A cursory examination of both business intelligence and knowledge management reveals that they both employ many of the same technologies including databases, networks, data, and metadata.  Both areas also use similar techniques that manipulate data by cleaning it, transforming it and manipulating it to learn.  Are these two concepts different, or are they merely different terms for the same idea? The purpose of this paper is to clarify the differences between business intelligence and knowledge management.  This will be accomplished by defining both of these concepts, and determine the processes and activities associated with each idea.

 

Definitions

Business intelligence (BI) has many definitions.  Some of these are:

BI is a broad category of technologies that allows for gathering, storing, accessing and analyzing data to help business users make better decisions.
www.oranz.co.uk/glossary_text.htm

 

BI is a system or systems that provide directed background data and reporting tools to support and improve the decision-making process.
www.bridgefieldgroup.com/glos1.htm

 

BI is an interactive process of analyzing and exploring structured, domain-specific information (often stored in a data warehouse) to discern trends or patterns, thereby deriving insights and drawing conclusions. The BI process includes communicating findings and effecting change. BI domains include customers, products, services or competitors.
www.dep.state.pa.us/dep/deputate/oit/ec/information/plans/def/ecgl.htm

 

From these definitions, business intelligence appears to focus on processing data. By processing data business intelligence can then be used for either supporting decisions, forecasting future events or discovering trends within a set of information. 

 

Knowledge management (KM) also has several definitions.  These definitions include:

 

KM is a deliberate, systematic business optimization strategy that selects, distills, stores, organizes, packages, and communicates information essential to the business of a company in a manner that improves employee performance and corporate competitiveness.

 Bryan Bergeron – Essentials of Knowledge Management

 

KM is a system or framework for managing the organizational processes that create, store and distribute knowledge, as defined by its collective data, information and body of experience.
www.bridgefieldgroup.com/glos5.htm

 

KM is a business process that formalizes management and leverage of a firm's intellectual assets. KM is an enterprise discipline that promotes a collaborative and integrative approach to the creation, capture, organization, access and use of information assets, including the tacit, uncaptured knowledge of people.
www.dep.state.pa.us/dep/deputate/oit/ec/information/plans/def/ecgl.htm

 

These definitions characterize knowledge management as a process that organizes, captures and disseminates information and knowledge throughout an organization. 

 

Based on the definitions of business intelligence and knowledge management it is apparent that both areas focus on processes.  Business intelligence focuses on processing data and analyzing the results of the data, while knowledge management focuses on the processes within an organization and sharing this information.

 

 

 

 

Processes

In their book, Seven Methods for Transforming Corporate Data into Business Intelligence, Vasant Dhar and Roger Stein focus on the different techniques that can implement business intelligence.  They discuss how data-driven support, genetic algorithms, neural networks, rule-based systems, fuzzy logic, case-based reasoning, and machine learning can all be used to derive information from a data set.  For each technique they outline the rules associated with the technique, the intelligence density (how quickly the essence of the underlying data can be understood to provide a solution) of the technique and types of problems that are best solved by the technique. By using these different algorithms business intelligence hopes to capture the best solution to a given problem or to forecast or identify trends within an existing data set. 

However to enable the use of these different algorithms, successful business intelligence requires data collection, data organization, data scrubbing (resolving inconsistencies in the data), and data transformation (grouping information into categories).  After the data has gone through this process the data can be analyzed and then applied to a problem. 

Knowledge management is also involved with the same data processes.  Data collection, data organization, data scrubbing, and data transformation are also key activities in knowledge management.  If both business intelligence and knowledge management deal with data processes then what is the difference between the two areas?

In his book, Essentials of Knowledge Management, Bryan Bergeron stresses that knowledge management is more than just analyzing data or arriving at a “yes” or “no” conclusion.  He focuses on many different aspects of capturing and disseminating knowledge within an organization.  This includes the people and their attitudes, organizational structure, technology, and economics of knowledge management.  All with the intention of generating either new knowledge or dispersing existing knowledge within an organization.  Bergeron states that knowledge management, in its most ideal form, is the process to selectively capture, archive, and access the best practices of work-related knowledge and decision making from employees and managers for both individual and group behaviors. To this Dr. Karma Sherif of Texas Tech University would add that knowledge management is interested in the creation of new knowledge.

Therefore the major difference between business intelligence and knowledge management is the scope of activities involved in each area.  Business intelligence focuses solely on capturing data, manipulating the data and analyzing the data.  Whereas knowledge management would perform business intelligence activities while also pursuing the creation of new knowledge. 

Table 1Activities of knowledge management and business intelligence

Knowledge Management

Business Intelligence

1. Capture data

1. Capture data

2. Organize data

2. Organize data

3. Analyze data

3. Analyze data

4. Aggregate data

4. Aggregate data

5. Apply data

5. Apply data

6. Create new knowledge

6. No equivalent action!!!!!!

7. Knowledge dispersion

7. No equivalent action!!!!!!

 

 

Conclusion

            The differences between business intelligence and knowledge management are subtle, they are not readily apparent because both areas of study contain similar processes.  Both business intelligence and knowledge management perform similar activities in collecting data, organizing the data, analyzing data, aggregating data, and applying data to generate solutions to help make business decisions.  However knowledge management includes two other activities that business intelligence lacks.  These activities are the creation of new knowledge and the dispersion of knowledge throughout an organization.  This is where knowledge management encompasses the activities of business intelligence.

            The future of these areas is still uncertain; however there are several companies emerging to provide services for both business intelligence and knowledge management. Business intelligence firms, such as The Center for Business Intelligence, Microstrategy, and SAP; sell their services as decision support for executive decision makers.  These businesses sell and implement software that captures data, manipulates it into useful information and applies the information to answer specific questions, show trends, create reports or forecast future events.

Industry offers little in the way of knowledge management services.  Perhaps this is because knowledge management encompasses many activities that are classified as business intelligence.  Bergeron states that “many database companies and reengineering consultants became KM companies overnight by simply modifying copy in their sales brochures.” Despite this, there are a few dedicated knowledge management consulting firms, such as Sveiby Knowledge Associates, that are willing to provide advice, tools (including business intelligence tools) and a certification program to further knowledge management. 

Whatever industry offers now and in the future,  it is apparent that both business intelligence, and as an extension knowledge management are both areas that will continue to grow.  As long as businesses need to make important decisions that are based upon information that can be captured with information technologies these two areas of study will remain a popular topic in the future.