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BI Capability
Maturity Model
Maturity Model
BI Comprehensive
Improvement Model
Improvement Model
Business Intelligence Defined
Voice of the
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Customer
Measuring
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Quality
The Impacting
Factors
Factors
What is Business Intelligence?*
The Business Intelligence product has a foundation comprised of Data, Information, and Intelligence.
Data* -- The raw material for the Business Intelligence product is data. This includes both structured and unstructured data (i.e. text based). The data comes from numerous sources and is usually collected in a data warehouse, or similar data integration platform.
Information -- The data is cleansed, integrated and translated into the intermediate product, which is business information. Information is the resulting product of the activities which occur within the data warehouse.
Intelligence -- The intelligence end product is a result of the integration of the information and a person or system which has the expertise to use the information to make decisions related to management and/or operations of the business.
(*Data refers to a collection of facts usually collected as the result of experience, observation or experiment, or processes within a computer system, or a set of premises. This may consist of numbers, words, or images, particularly as measurements or observations of a set of variables. Data is often viewed as a lowest level of abstraction from which information and knowledge are derived.)
In this section, we describe some of the individual products which are included in the BI product set. The list is growing rapidly and exponentially, as people find more and better ways to make organization data accessible and translate it into information of value and use it to add value. We treat each product individually and as a part of the full product set, in the same manner as we can effectively manage the product 'green peas' as a variety of sizes of cans, fresh, frozen, e tc.
The Business Intelligence product has a foundation comprised of Data, Information, and Intelligence.
Data* -
Information -
Intelligence -
(*Data refers to a collection of facts usually collected as the result of experience, observation or experiment, or processes within a computer system, or a set of premises. This may consist of numbers, words, or images, particularly as measurements or observations of a set of variables. Data is often viewed as a lowest level of abstraction from which information and knowledge are derived.)
In this section, we describe some of the individual products which are included in the BI product set. The list is growing rapidly and exponentially, as people find more and better ways to make organization data accessible and translate it into information of value and use it to add value. We treat each product individually and as a part of the full product set, in the same manner as we can effectively manage the product 'green peas' as a variety of sizes of cans, fresh, frozen, e tc.
These are a few examples of the varieties, shapes and sizes of the Business Intelligence product which comprise Business Intelligence product base. These are managed individually and collectively by the Six Sigma program for Business Intelligence (Business Intelligence Comprehensive Improvement Model - BI-CIM)
Data-Driven DSS - This type of DSS emphasizes access to and manipulation of a time-series of internal company data and sometimes external data. Simple file systems accessed by query and retrieval tools provide the most elementary level of functionality. Data warehouse systems that allow the manipulation of data by computerized tools tailored to a specific task and setting or by more general tools and operators provide additional functionality. Data-Driven DSS with On-line Analytical Processing (OLAP) or data mining tools provide the highest level of functionality and decision support that is linked to analysis of large collections of historical data. Early, very limited versions of data-driven DSS were called Retrieval-Only DSS by Bonczek, Holsapple and Whinston (1981).
Data Mining - A class of analytical applications that search for patterns in a data base. Data mining is the process of sifting through large amounts of data to produce data content relationships. Data mining tools use a variety of techniques including case-based reasoning, data visualization, fuzzy query and analysis, and neural networks. Case-based reasoning tools provide a means to find records similar to a specified record or records. These tools let the user specify the "similarity" of retrieved records. Data visualization tools let the user easily and quickly view graphical displays of information from different perspectives.
Data Visualization - This term refers to presenting data and summary information using graphics, animation, 3-D displays, and other multimedia DSS tools.
Decision Analysis tools - DA tools help decision makers decompose and structure problems. The aim of these tools is to help a user apply models like decision trees, multi-attribute utility models, bayesian models, Analytical Hierarchy Process (AHP), etc. Examples of DA software packages include AliahThink, BestChoice3, Criterium Decision Plus, DecideRight, DecisionMaker, Demos, DPL, Expert Choice, Strad, Supertree, and Which and Why.
Decision Systems are computer based programs and technologies intended to make routine decisions, monitor and control processes, and aid or assist decision makers in semi-structured and/or non-routine decision situations.
Decision Support Systems (DSS) (Systèmes d'Aide à la Décision) are interactive computer-based systems intended to help decision makers utilize data and models to identify and solve problems and make decisions. The "system must aid a decision maker in solving unprogrammed, unstructured (or "semistructured") problems...the system must possess an interactive query facility, with a query language that ...is ...easy to learn and use (Bonczek, Holsapple & Whinston, 1981; p. 19)". DSS help managers/decision makers use and manipulate data; apply checklists and heuristics; and build and use mathematical models. According to Turban (1990), a DSS has four major characteristics: DSS incorporate both data and models; they are designed to assist managers in their decision processes in semistructured (or unstructured) tasks; they support, rather than replace, managerial judgment; and their objective is to improve the effectiveness of the decisions, not the efficiency with which decisions are being made (cf., p. 9).
Document-Driven DSS - It integrates a variety of storage and processing technologies to provide complete document retrieval and analysis. The Web provides access to large document databases including databases of hypertext documents, images, sounds and video. Examples of documents that would be accessed by a Document-Based DSS are policies and procedures, product specifications, catalogs, and corporate historical documents, including minutes of meetings, corporate records, and important correspondence. A search engine is a powerful decision-aiding tool associated with a Document-Driven DSS (cf., Fedorowicz, 1993, pp. 125-136).
Enterprise-wide DSS - A DSS that supports a large group of managers in a networked client-server environment with a specialized data warehouse as part of the DSS architecture.
Executive Information Systems (EIS) - A computerized system intended to provide current and appropriate information to support executive decision making for managers using a networked workstation. The emphasis is on graphical displays and an easy to use interface that present information from the corporate database. They are tools to provide canned reports or briefing books to top-level executives. They offer strong reporting and drill-down capabilities.
Executive Support Systems (ESS) - An executive information system (EIS) that includes specific decision aiding and/or analysis capabilities.
Expert Systems are man-machine systems with specialized problem-solving expertise. The "expertise" consists of knowledge about a particular domain, understanding of problems within that domain, and "skill" at solving some of these problems.
Functional DSS - A decision support system that holds and derives knowledge relevant for decisions about
The Six Sigma Way: Defining Business Intelligence
Understanding and providing the basic definitions and metrics for BI and BI quality are arguably the most difficult part of the Six Sigma process. BI is complex and incorporates a set of products and processes along with a spider web of infrastructures, architectures, tools and complex inter-
Business Intelligence - For purposes of this Six Sigma BI program, Business Intelligence is defined as all the components which comprise Business Intelligence systems and products within an organization. When the term Business Intelligence is used alone, the reference will be to the product which is composed of the integration of Information and either people or systems which utilize that information for the good of the organization. We will refer to other aspects of Business Intelligence in the following manner, i.e. the BI systems and the BI infrastructures, etc. (There are many other definitions of BI. BI is a popularized, umbrella term introduced by Howard Dresner of the Gartner Group in 1989 and is frequently used to describe decision support systems, which assist in the business decision making process. )