Business intelligence and the development of an intelligent learning organization represent a popular trend in many public and private sector organizations. Ideally, any manager or knowledge worker should be able to compose information requests without programmer assistance and achieve answers at the speed of thought. Follow-up questions should be immediately asked and answered in order to maintain continuity of thought on a particular topic of importance. Intelligence is the ability to learn, to understand or to deal with new or trying situations; the skilled use of reason; the ability to apply knowledge to manipulate one's environment or to think abstractly. Business intelligence is a set of concepts, methods and processes to improve business decisions using information from multiple sources and applying experience and assumptions to develop an accurate understanding of business dynamics. It is the gathering, management and analysis of data to produce information that is distributed to people throughout the organization to improve strategic and tactical decisions.

Business intelligence involves the integration of core information with relevant contextual information to detect significant events and illuminate cloudy issues. It includes the ability to monitor business trends, to evolve and adapt quickly as situations change and to make intelligent business decisions on uncertain judgements and contradictory information. It relies on exploration and analysis of unrelated information to provide relevant insights, identify trends and discover opportunities.

Business intelligence requires high-quality information which can only be derived from a high-quality data resource. If organizations are moving into business intelligence scenarios as a major initiative, they must understand the need for and value of a high-quality data resource. There is little doubt that the technology will support business intelligence. The real issue is how to clean up disparate data and produce a high-quality data resource that truly supports business intelligence.

Data resource quality is not the same as information quality, though the two are often confused and used interchangeably in this lexically challenged discipline. Information quality is how well the demand for business information is met. It includes the data used to produce the information and the information engineering process. The information engineering process includes everything from properly determining the information need to presenting the information.

Data resource quality is how well the data resource supports information engineering in meeting the current and future demand for business information. A high-quality data resource consistently meets the expectations of information engineering. Since the expectations for information constantly change, the expectations for data also change. A high-quality data resource consistently meets those changing expectations.

The foundation of a house is critical to the quality of the entire house. If the foundation is not level and square, the entire house will not be level or square and carpenters will fight it to the last shingle on the peak of the roof. Thus the quality of the house will be lower. The data resource, like the foundation of a house, is the foundation of an information engineering/knowledge environment/business intelligence value chain that supports business strategies, as shown in Figure 1. If the data resource is not "level and square," an organization will fight it clear to the business strategies and the quality of support will be lower. If the data resource is high-quality, that quality will enhance the entire value chain to the benefit of the business.

The value chain begins with the data resource. Information is developed from the data resource to support the knowledge environment of an intelligent learning organization. Data is the raw material for information which is the raw material for the knowledge environment. Knowledge is the raw material for business intelligence that supports business strategies.

Data is the individual raw facts that are out of context, have no meaning and are difficult to understand. Facts are numbers, characters, character strings, text, images, voice, video and any other form in which a fact may be presented. Data in context is facts that have meaning and can be readily understood. It is the raw facts in context with meaning and understanding, but is not yet information because it has no relevance or time frame.

Information is a set of data in context that is relevant to one or more people at a point in time or for a period of time. It is data in context with respect to understanding what the facts mean. Information is data imbued with meaning, relevance and purpose. A set of data in context is a message that only becomes information when one or more people are ready to accept that message as relevant to their needs. A message without meaning, relevance or purpose is simply noise.

Knowledge is cognizance, cognition, the fact or condition of knowing something with familiarity gained through experience or association. It is the acquaintance with or the understanding of something, the fact or condition of being aware of something, of apprehending truth or fact. Tacit knowledge is all the knowledge that is in people's heads or the heads of a community of people, such as an organization. It is what makes people smart and act intelligently. Explicit knowledge is knowledge that has been rendered explicitly to a community of people, such as an organization, and is what they deem to know.

Organizational knowledge is information that is of significance to the organization, is combined with experience and understanding and is retained. It is information in context with respect to understanding what is relevant and significant to business issues. It is analysis, reflection and synthesis about what information means to the business and how it can be used to advantage. It is the ability to learn, understand and deal with new and trying situations; to apply knowledge and think abstractly. It is the core of an intelligent learning organization that must be accumulated, cultivated and managed. Organizational knowledge is a rational interpretation of information that leads to business intelligence.

An organization has three primary resources: capital, human resource and data resource. The data resource and information engineering, collectively, are the technology resource that supports the human resource in an intelligent learning organization. The knowledge environment and business intelligence, collectively, are the human resource that uses information to support the business strategies. It is the human resource that possesses the business intelligence, the intelligence and the wisdom to support business strategies. Information is the link between the data resource and the human resource.

Knowledge cannot be managed in the sense that data and information are managed. Only an environment that promotes the exchange of information to create knowledge can be managed. Cognition is the application of knowledge, and no technologies today can automate cognition. Knowledge management, as often promoted today, is just another silver bullet that, like so many other silver bullets, will become tarnished with time.

Knowledge management is really the management of an environment where people generate tacit knowledge, render it into explicit knowledge and feed it back to the organization. This forms the base for more tacit knowledge which keeps the cycle going in an intelligent, learning organization. It is the process of creating, institutionalizing and distributing information between people. It is the process of finding bodies of knowledge specific to a need and presenting them in a suitable manner for specific business purposes. Knowledge management matches a knowledge seeker with the best source of knowledge through profiles to share their knowledge. It is an integrated approach to identifying, sharing and evaluating an organization's information. It is a culture for learning where people are encouraged to share information and best practices to solve business problems rather than continually reinventing the wheel.

Knowledge management promotes and relies on information sharing. Information sharing is the active sharing and utilization of information in a knowledge environment for specific business advantage. It is the sharing of memories about past situations and solutions, communicating learning experiences and exchanging a deeper understanding of problems and solutions. It is a way of tapping the tremendous hidden knowledge that resides in the human resource for the benefit of the organization.

Data and data in context can be stored in databases. Most databases are just files or tables of facts. Information, as defined above, cannot be stored in databases because relevancy to people at a point in time cannot be stored. If information is stored, it is really stored as data in context. If a report is stored with labels and headings, it is really data in context that is being stored. When that report is retrieved and has relevance and purpose, it becomes information. Data that instructs machines to perform a task or directs the actions of that machine or data that moves between applications is still data, not information. Information pertains to people and must have relevance at a point in time.

Knowledge, as previously defined, cannot be stored any more than information can be stored. Knowledge resides in the human resource of an organization. Knowledge storage is often described as subject matter experts diligently researching and capturing relevant information and storing it for sharing among knowledge workers. Really, it is data in context that is being stored to support information sharing. The data may be hard facts and soft opinions, history of past events, situational evaluations, situations to avoid, alternatives to pursue, and so on, but it is still stored as data in context.

Knowledge is intellectual capital that is retained by the human resource. The knowledge base is in the human resource of an organization. Institutional memory, business knowledge and business experience reside in the human resource of an organization. Knowledge is only a resource with respect to the human resource, not with respect to computer storage and retrieval. Knowledge quality is the both the quality of the environment for sharing information and the quality of the human resource that discovers, develops and retains the knowledge. It is the quality with which information is shared for the discovery and accumulation of knowledge.

An organization's data resource is the total of all data within and without the organization that is available to the organization. It includes primitive and derived data; tabular and non-tabular data (spatial, image, textual, voice and so on, referred to as data megatypes); elemental and combined data; automated and non-automated data; persistent and non-persistent data; and historical, current and projection data. It includes data in databases as well as data on reports, screens and documents; hard and soft data; internal and external data; global and local data; and enterprise-wide and application-specific data. It includes data used by traditional information systems, expert systems, executive information systems, geographic information systems, data warehouses and object oriented systems. It crosses all business activities, all projects and all information systems regardless of where they reside, who uses them or how they are used. It includes all data regardless of location, origin, form or method of storage.

The quality of the total data resource in most public and private sector organizations is low by reason of its disparity. A low-quality disparate data resource cannot provide high-quality information and cannot adequately support business intelligence. All data must be inventoried, understood and integrated within a common data architecture to adequately support a business intelligence initiative.1 All data means all data megatypes, not just tabular data, because comprehensive business intelligence depends on the value of all types of data in the data resource. The data resource is the foundation for business intelligence, and the quality of the support for business strategies from an intelligent learning organization can be no better than the quality of the data resource. There must be a high-quality integrated data resource, high-quality information preparation and sharing and a high-quality human resource to discover and accumulate knowledge to achieve successful business intelligence.


1 An explanation of disparate data and its integration within a common data architecture was briefly explained in "Transforming Disparate Data." DM Review. October, 1998. Page 20. The process is explained in more detail in the author's two latest books: Data Sharing Using a Common Data Architecture (John Wiley. 1994) and The Data Warehouse Challenge: Taming Data Chaos (John Wiley. 1996).

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