This study is fundamentally based on the most common components of a Business Intelligence System, data warehouses, ETL tools, OLAP techniques and data mining, which comfort the decision making function. It further describe about the role of each component in a Business Intelligence System and how Business Intelligence Systems can be used for better business decision making at each level of management.

1. Role of Business Intelligence
In Knowledge Management

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Shakthi Fernando
BSc. Financial Management (Special)-Undergraduate
Sabaragamuwa University of Sri Lanka
2014 April

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Abstract
This study is fundamentally based on the most common components of a Business Intelligence System, data warehouses, ETL tools, OLAP techniques and data mining, which comfort the decision making function. It further describe about the role of each component in a Business Intelligence System and how Business Intelligence Systems can be used for better business decision making at each level of management.

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Content
Page
Introduction 04
Knowledge Management and Business Intelligence 05
Data warehouses 06
ETL tools 07
On-Line Analytical Processing techniques 08
Data mining 09
Role of components in a Business Intelligence System 10
Role of Business Intelligence in each level of management 12
References 14

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Introduction
There is no any controversy that the knowledge has become one of most important features in businesses. A business cannot be developed and cannot even survive without a proper torrent of knowledge. As well as businesses should manage it well in order to get the maximum results out of knowledge. As a result of that Knowledge Management has been identified as one of the fastest growing areas of software investment. Knowledge in an organization which cannot be disseminated and communicated with others is considered as useless. When it is shared throughout the organization it becomes useful to the organization. Knowledge can be disseminated within an organization via Google sites, social networking, e-mail and etc.
It is said that the major source of wealth is the production and distribution of information and knowledge since we live in an information economy. It has been calculated that 55 percent of the United States labor force consists of knowledge and information workers, and 60 percent of the gross domestic product of the United States are from the knowledge and information sectors (Laudon, 2011, pp. 417). Businesses have identified the importance of Knowledge Management. They have recognized that mostly the value of the firm depends on its ability to gain and manage knowledge. It has found that a substantial part of a stock market value of a company is related to firm’s intangible assets, which consists knowledge as an important component.
When it comes to Knowledge Management, Business Intelligence is playing a vital role. It says Business Intelligence is used to recognize the capabilities of the firm, trends, patterns, technologies, future directions in the market and the regulatory environment in which the organization operates. Same as that it is used to identify the actions of competitors. Business Intelligence basically supports to the Knowledge Management function. In fact it is ultimately backing to the decision making function, which is known as one of the most important function in an organization.

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Knowledge Management and Business Intelligence
Knowledge Management can be elaborated as a systematic process of finding, selecting, organizing, distilling and presenting information in a way that improves the organization’s understanding in a specific area of interest. Knowledge Management comforts an organization to gain the understanding from its own experiences. Certain activities of Knowledge Management focus on acquiring, storing and utilizing knowledge in an organization for strategic planning, dynamic learning, problem solving and decision making.
Knowledge Management technologies support to create, store, retrieve, distribute and analyze structured and unstructured information. Those Knowledge Management technologies enhance capabilities of helping to solve current problems, answer questions and realize new opportunities. There is a vast amount of data in larger firms including business documents, spreadsheets, databases, e-mails, reports, technical journals, news and press articles, web documents and contracts. Knowledge Management technologies are used to search, organize and extract value from these information sources and focus on development activities.
Business Intelligence focuses on the similar purpose, but from a different point of view. Business intelligence systems are used for intelligent exploration, integration, aggregation, and analysis of data originated from various information sources. It concerns with decision making using data warehousing and On-line analytical processing techniques. Data warehousing collects relevant data, where it is organized. Then it can support to decision making objectives. Same as that Business Intelligence uses data mining and ETL tools for making the decision making function into a more efficient one.
It is identified data warehouses, ETL tools, OLAP techniques and data mining as most common components of a Business Intelligence System. At the same time it highly supports to Business Intelligence System to comfort the decision making process.

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Data warehouses
A data warehouse is identified as a database, which stores current and historical data of the organization. Then it can be analyzed to comfort decision making process. Data warehouses are filled with data that has been extracted from distributed databases. It has data which has been originated from various core operational transaction systems such as manufacturing, sales, customer accounts as well as internet based website transactions. The data warehouses are filled with information from different operational databases so that the information can be used all over the organization with the purpose of decision making and management analysis.
New data warehouses are frequently being filled with business data in order to verify that the updated data is available for decision making function. As a result of that decisions which are taken by management are efficient, since those are based on historical as well as up to minute updated data. In some organizations, they have created smaller, decentralized warehouses, called data marts rather than a centralized, enterprise-wide data warehouse which serves the entire entity. Data mart is a sub set of a data warehouse, where the summarized or most important portion of entity’s data is placed.
The idea of data warehouses is practically used by Catalina Marketing, which is recognized as a global marketing firm for major consumer packaged goods, companies and retailers. It operates an enormous data warehouse, which includes purchase history of three years. It is considered as the largest loyalty database in the world. In that case customers’ buying preferences are determined by analyzing the database of customer purchase histories. When a purchase is done, it is instantly analyzed with the customer’s buying history in the data warehouse (Laudon, 2011, pp. 222-223).

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Same as that the United States Internal Revenue Service (IRS) maintains a data warehouse, which amalgamates tax payer data, in different systems. It includes personal information of tax payers as well as tax returns. In that case the warehouse integrates the data of tax payers from many sources and it makes analyzing part much easier. It provides a descriptive image of tax payers. Then Internal Revenue Service (IRS) can identify tax payers, who are most likely to cheat on their income (Laudon, 2011, pp. 223).
ETL tools
ETL tools are responsible for extracting the data from source systems. It transforms data to a common format from different formats. Then those are loaded into the data warehouses. Earlier ETL designs and implementation was identified as a supporting task for the data warehouse. It was not considered as a part of the business intelligence, but a subset of the data warehousing. But now it has become one of the most common components of a Business Intelligence System.
ETL solutions have been divided into three stages that find, convert data from various sources and transfer the results into a data warehouse. The extraction stage involves with obtaining access to data, which is originated from different sources. The transformation stage involves with transforming the extracted data. It is considered as the most complex stage of the ETL process. The load stage involves with loading the data warehouses with data.
Simply the task of ETL tools is to fulfill the requirements of a business intelligence system. It extracts data which is in different formats, convert them and load them into the appropriate data warehouse. The use of ETL tools is identified as the most efficient way to process data with frequency and timing varying according to the requirements of businesses. Same as that ETL tools can access to a large volume of data.
Eventually companies need of reporting data is rapidly increasing. On the other hand the complexity and volume of the data is rocketing. In practical business world Walmart Company is handling more than one million transactions in every hour regarding sales, products, inventory and customers by using ETL tools.

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On-Line Analytical Processing (OLAP) techniques
The utilization of On-Line Analytical Processing is originated with the difficulties arisen, when data analysis on databases are implemented that are frequently being updated during transactions via other information systems. Basically On-Line Analytical Processing is analyzing complex data on a database that are constantly being updated along with traditional data.
On-Line Analytical Processing can be identified as amelioration of earlier single dimensional analysis tools that allowed users to analyze data from only one point of view at a time. It provides a multi-dimensional tool to users. On-Line Analytical Processing enables users to analyze data from different perspectives and explore it with the intention of discovering hidden information. Simply On-Line Analytical Processing backs multi-dimensional data analysis, enabling users to view the same data in different ways. At the same time On-Line Analytical Processing enables users to exact online answers even when the data are stored in large databases.
On-Line Analytical Processing allows user access, analysis and modeling of business problems and sharing of information which is stored in data warehouses. On-Line Analytical Processing offers techniques for analyzing and drilling data. As well as it offers tools those are particularly used for generating interactive reports. Basically On-Line Analytical Processing tools use data mining techniques and statistical methods to create fast and readable reports that are used for forecasting and strategic decision making process.
As an example, a leading outsource collection agency for government debts in the United States has saved $200K in accounting software expenses by using On-Line Analytical Processing technology. Even it has supported ABC to sell some equity for $167,000,000 in just nine months.

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Data Mining
Data mining is utilized for further discoveries. Data mining techniques are used to identify relationships and rules. The data mining process includes discovering various patterns, regularities, rules and generalizations in data resources. Data mining provides erudition into corporate data that cannot be obtained with On-Line Analytical Processing by recognizing hidden patterns and relationships in large databases and rules from them to predict future behavior. The patterns and rules are used to direct decision making and forecast the consequences of those decisions.
There are several fundamental strategies for data mining. The most frequently used strategies are classification, prediction, estimation, time series analysis and market basket analysis. These strategies are aligned with the requirements of an organization and support to decision making function by breaking through varies regularities, patterns, rules and generalizations in data resources. As an example, market basket analysis can be used in a business to model retail sales. Same as that classification can be used to classify unstructured data. The types of information which can be obtained from data mining include associations, sequences, classifications, clusters, and forecasts.
As a practical example for data mining, Harrah’s Entertainment, the second largest gambling company in industry, uses data mining to recognize its most profitable customers with the intention of generating more revenue from them. The company analyzes data about its customers when they play casinos or use hotels. Harrah’s marketing department uses this information to build a detailed gambling profile. Then data mining enables the company to identify the favorite gaming experiences of a regular customer, hid preferences for room accommodations, restaurants and entertainment. This information conduct management when decisions are made about how to amplify the most profitable customers, encourage them to spend more and attract more customers with high revenue generating potential. Harrah’s profits have been increased with the use of business intelligence and it has become a core piece of the firm’s business strategy (Laudon, 2011, pp. 226).

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Role of components in a Business Intelligence System
Business Intelligence Systems chiefly extract information with the intention of supporting managers to solve their structured and unstructured issues. Each component of a Business Intelligence System can be used to extract information by acquiring information, searching and gathering information, analyzing information and delivery of information. Same as that Business Intelligence Systems eliminates communication barriers that exist at the different organizational levels within the entity by analyzing historical data. Those analysis enable decision makers to evaluate former activities and direct future actions.
Basically each component of a Business Intelligence System fulfill some tasks in order to comfort the decision making process. Simply ETL tools are acquiring and searching information, data warehouses also acquire information. Both On-Line Analytical Processing techniques and data mining components are analyzing and delivering the information.
Acquiring information
It has been more difficult to acquire information, since modern organizations use more distributed information systems to store their business data. This action is revealing the business issue. It uses ETL tools in order to direct the processes to find out the needed information and into which data warehouse that the information should be stored.
Searching information
The newly loaded data which are in high quality are mined using data mining techniques and processes after the data are extracted from operational databases. This action is basically implemented at different quality levels of data. Lower quality data are searched by using ETL tools. The data, which are in higher quality are being loaded into a data warehouse.

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Analyzing information
Managers have to create data models in order to understand and address business issues. Managers can analyze information from multiple dimensions by data preprocessing and applying On-Line Analytical Processing and data mining techniques. As an example, information derived through analysis is directly affected to decisions related to forecasting sales, promotional campaigns and financial results. It can be used in fraud detection in some cases.
On-Line Analytical Processing summarizes data and makes predictions based on historical data. On the other hand data mining reveals hidden patterns in data. Data mining operates at a detailed level instead of a summary level. In real words, data mining predicts while On-Line Analytical Processing forecasts. Data mining and On- Line Analytical Processing can be used to analyze financial data, marketing data, customer data, production data, wage related data, personal data, logistical data, etc.
Delivery of information
Data mining can be also used to deliver information within an entity. Data mining in a Business Intelligence System is not only interpreting and evaluating results generated from the analysis implemented on data stored in data warehouses. Even it can display reports that enable decision makers to discover various patterns, regularities and generalizations. On the other hand On-Line Analytical Processing creates quick reports by utilizing simple data mining techniques by summarizing data. Same as that data mining provides a descriptive report, while On-Line Analytical Processing provides a summary of information. Management will get reports that are inappropriate for the decision making function and reports which are included too much information without a well defined delivery.

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Role of Business Intelligence in each level of management
Simply a Business Intelligence System is collecting, treating and disseminating information with the intention of reducing the uncertainty of decision making function in an organization. In this case, organizations need real time data in order to make decisions. A Business Intelligence System enables users to make decisions by using real time data by monitoring competition and carrying out continuous analysis of various data. The Business Intelligence System collects data from various data sources like operational databases and customer databases, transforms into some formats. Finally it loads the newly formatted data into data warehouses that are available to all three level of decision making, operational, tactical and strategic.
Each level of management use different On-Line Analytical Processing techniques and data mining processes to analyze data and report information that is highly relevant to them. The information generated from the Business Intelligence System is being used in all decision making processes. Chiefly decisions at the strategic level are to set objectives and directed to the tactical level of the organization. At the tactical level, information is mined from the Business Intelligence System in order to develop tactics to realize the strategic objectives. On the other hand it pushes decisions to the operational level of the organization. Different levels of the organization use information for different purposes. Information provides input to senior managers at strategic and tactical levels, while information provides input to lower level managers at the operational level.

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Operational level decisions
Decisions which are made at the operational level related to the continuous operations. These decisions are basically based on up to date information. Simply operational level decisions are considered as decisions, which allow an organization to run its day today activities. Data that is identified from the operational level by Business Intelligence System is analyzed and combined with other information in order to support the strategic planning process. As an example, operational level decisions provide analysis of products, analysis of employees, analysis of regions, etc.
Tactical level decisions
Decisions which are made at the tactical level are related to planning and it is based on real time data. It is forecasting to direct the future actions of sales, finance, marketing, as well as capital management. Tactical decisions are used to comfort strategic decisions. As an example, it forecasts demand for a given product or service, makes decisions related to marketing, sales, finance, capital management, etc.
Strategic level decisions
Strategic level decisions set objectives as well as ensure that those are attained. Business Intelligence Systems provide information in order to support to strategic decisions which are related to the development of future results based on historical results, profitability and effectiveness of distribution channels. Simply strategic decisions use Business Information Systems in order to make forecasts based on historic data, combine them with current performance and estimate how conditions will be performed in the future. As an example, decisions are made at the strategic level by using the Business Intelligence System such as whether to enter into a new market, whether to launch a new product, etc.

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References
Business Intelligence case study. (n.d.). How a financial services company developed a performance report for clients, saved $200K & sold $167,000,000 of equity in just nine months. Retrieved from http://www.winmetrics. com/olap_ casestudies.html.
Cody, W.F., Kreulen, J.T., Krishna, V., & Spangler, W.S. (2002). The integration of Business Intelligence & Knowledge Management. IBM Systems Journal.
Herschel, R.T. (2005). KM & Business Intelligence: The importance of integration. Workshop on Knowledge Management & organizational memories, Edinburgh, Scotland.
Laudon, K.C., & Laudon, J.P. (2011). Management Information Systems: Managing the digital firm (12th ed.). Prentice Hall.
Lloyd, J. (2011). Identifying key components of Business Intelligence Systems & their role in managerial decision making. University of Oregon, Eugene.
Morris, H., Liao, H., Padmanabhan, S., Srinivasan, S., Lau, P., Shan, J., & Wisnesky, R. (n.d.). Bringing business objects into Extract-Transform-Load (ETL) Technology. IEEE International Conference on E-Business Engineering.
Shehzard, R., & Khan, M.N.A. (2013). Integrating Knowledge Management with Business Intelligence processes for enhanced organizational learning. International Journal of Software Engineering & its Applications.