CONTEMPORARY DECISION SUPPORT AND KNOWLEDGE MANAGEMENT TECHNOLOGIES

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1 I International Symposium Engineering Management And Competitiveness 2011 (EMC2011) June 24-25, 2011, Zrenjanin, Serbia CONTEMPORARY DECISION SUPPORT AND KNOWLEDGE MANAGEMENT TECHNOLOGIES Slavoljub Milovanovic * University of Nis, The Faculty of Economics in Nis Trg Kralja Aleksandra 11, Nis, Serbia smilovan@eknfak.ni.ac.rs ABSTRACT In latest decade of last century, turbulent technological changes occured in all fields. Dynamic technological changes influence on the type of information systems which are primary used for decision support and knowledge management. Traditional decision support systems were mainly based on models, while contemporary decision support systems are based on multidimensional analysis of huge data amount and trasformation of data to knowledge. Data is organized in data warehouses and processed by data mining tools. Datawarehousing and data mining are two most important knowledge management technologies which are used for decision support, so the paper talks about the technologies. Key words: decision support systems, knowledge management, data mining, data warehouses INTRODUCTION Decision Support Systems (DSSs) are similar and complementary to standard management information systems (MISs) and represent superstructure of the standard information systems (ISs). Characteristics of DSSs are defined in relation to classic data processing and management information systems. Therefore, DSSs are different from classic data processing and MISs because they require symbiosis between a user and the system to achieve planed business efficiency. While MISs give information for structured decision making (operational and tactical decisions), DSSs support semistructured and unstructured decision making (strategic level of management). DSSs are often defined as interactive information systems that support solving semistructured and unstructured class of problems in decision making process. In order to support semistructured and unstructured decisions, DSSs use sophisticated analytic models and tools and user-friendly software. Thus, DSSs provide to users flexible set of tools and abilities for analysis of important data. Contemporary DSSs are driven by data in contrast to old classic DSSs that were mainly driven by models. Therefore, contemporary DSSs are based on datawarehousing and data mining technologies for elicitation knowledge from data. Many organizations today install data warehouses and sophisticated tools for data analysis to better use information stored in their transaction information systems. Also, the organizations use ability of connecting transaction databases to World Wide Web and analysing data from web. (Turban et al., 2004) Characteristics and functions of contemporary technologies for decision support and knowledge management are analysed in two sections. In first section of the paper, architecture of contemporary decision support systems is presented and components of the architecture are analysed. In second section, tools for multidimensional analytical data processing and data mining are explained. 303

2 ARCHITECTURE OF CONTEMPORARY DECISION SUPPORT SYSTEMS Classic DSSs driven by models were used in eighties and nineties years of last century and were based on small sets of data. These were primarily autonomous systems isolated from main ISs of organizations and they have used some types of models to achieve what if and the other kind of analyses. Such systems was usually developed by end users without centalised control of IT (Information Technology) department. Analyitical capacity of the systems were based on rigor models combined with good user interface facilitating use of the models. IBM was developed Capacity Optimization Planning System that is typical example of DSS driven by model. Contemporary improvements in computing and databases technology have broaden definition of classic DSSs and included analysis of great amount of data from transaction ISs (TPSs Transaction Processing Systems) and data from web. DSSs driven by data analysing great amount of data from transaction ISs and other sources have emerged. The systems support decision making enabling users to extract useful information that previously was hidden in great transaction databases. Data from TPSs is colected and organised in special data bases called data warehouses. Data warehouses are essentially different from transaction databases because contain aggregative data extracted from transaction databases. Data Warehouses (DWs) provide several ad hoc and standardized query tools, analytical tools and graphical reporting capabilities including OLAP (On-line Analytical Processing) and DM (data mining) tools. DM software tools discover patterns and relations in great data warehouses and derive rules based on them. The rules can be used for forcasting future behaviour and decision making. DWs enable decision makers data access without impact on performance of operational, transaction ISs and databases. In addition, many organizations use web technology to facilitate access to DWs. (Wrembel & Koncilia, 2007) Figure 1 illustrates DW concept. Catalog of information provides to users information on data available in DW. DW must be carefully designed by business and information specialists in order to be right information for critical business decisions obtained. An organization perhaps should change its business processes to use information from DW efficiently. An organization can build DW as central database serving needs of whole organization or to create smaller, decentalised DWs called datamarts. Datamarts (DMs) are subsets of a DW. Summarized and high focused parts of whole organization data are stored in separate databases for specific population of users and these parts are DMs. For example, organization can develop datamarts of marketing and sales for efficient customer relations management. DMs are usually focused to one area and line of business, so it can be constructed faster and with lower costs than DW of whole organization. However, if an organization creates too many datamarts, complexity, costs and management problems will emerge. Organizations more and more develop DSSs driven by data in order to analyse data on customers collected from their web sites and data from other ISs, as well. DMs help organizations to engage in one-to-one marketing where personalized and individualized messages can be created on base of individual customer preferences. The systems can achieve complex analyses of patterns or trends in data to discover more details on some occurrence, when it is needed. Figure 2 illustrates architecture of contemporary DSSs. As we can see, DSSs have folowing components: 1. database that is used for generation of queries and analysis; 2. software system with models, data mining and other analytical tools and 3. user interface. 1. Data base. DSS database is set of current and historical data from many applications. DSS database can be small database stored on PC containing subset of whole organization data. The internal data is combined with external data. Alternatively, DSS database can be massive DW that is continually updated from data generated in transaction ISs and web sites. Data in DSS databases 304

3 is extracts or copies of transaction databases, so use of DSS do not impact on function of operational ISs. Figure 1: Components of DW 2. Software system. DSS software system is made of software tools that are used for data analysis. The system contains OLAP tools, data mining tools or collection of mathematical and statistical models which can be easily accessed by users. A model is abstract representation illustrating components or relations in some phenomenon. Model can be phisical model (such as aircraft model), mathematical model (such as equation) or verbal model (such as description of order making procedure). Every DSS is built for specific set of goals and has various collections of models which are available in dependence of the goals. Figure 2: Architecture of contemporary DSSs 305

4 Perhaps most usuall models are the models in form of library. There are libraries of statistical models. Library of models for specific functions (for example, financial models, risk analysis models etc.) can also be created. Libraries of statistical models usually contain full range of most used statistical functions, including average, deviation, median and dispersion graphics. This software enables forecast of future results by analysis of data series. Statistic modeling software can be used for establishing relations, such as relating of product sale with attributes of customers like age, revenue and other attributes. Optimization models usually use linear programming and determine optimal allocation of resources for maximize or minimize specific variables, such as costs or time. Classic use of optimization models is determination of right mix of products for specific market to maximize profit. Forecasting models are often used for forecast, prognosis of sale. User of the type of models gets range of historical data for projecting future conditions of business and sale which can result from the conditions. A decision maker can change the future conditions in model (for example, increasing of material costs or entry of new competitor on market with low prices) to determine how the new conditions can influence on sale. Organizations usually use the models to forecast actions of competitors. Most used models are sensitivity analysis models that ask what if questions in order to determine impact of changes in one and many factors on results. What if analysis taking into account known and assumed conditions allows users to change specific values for testing results in order to better predict outcomes of real changes if they occure. What will happen, if we increase price for 5% or increase promotion budget for $10000? What will happen if we keep the same price and budget? Desktop software for table calculations (spreadsheet software like Microsoft Excel), are often used in answer on the questions. There is software analysing sensitivity backward and use principle of goal seeking. For example: If I want to sale million units of product next year, how much I must reduce price of the product? 3. User interface. DSS user interface enables that user easy interacts with DSS software tools. Graphical, easy to use and flexible user inteface supports dialog between user and DSS. DSS users can be managers or employees without patience for learning complex tools, so the interface must be relatively intuitive. Today, many DSSs are built with interfaces based on web technology because of easy to use web interface, interactivity and potential for personalization and customization. Development of effective DSS requires high level of users participation and use of prototype as a method of system development. Accordingly, a DSSs that fully meet users needs can be developed. DATA MINING TOOLS AND MULTIDIMENSIONAL ANALYSIS OF DATA Managers sometime need to analyse data on such manner that is not possible by traditional database. Traditional queries to databases can answer on questions such as: How many units of some product was delivered in november 2008? However, managers need multidimensional analysis supporting more complex requirements for information, such as: Compare amount of the product sale in a region with quarter plan of sale in last two years? With data mining tools and technics for data analysis, users get better information for decision making. (Han & Kamber, 2006) We can take example of organization selling four products (bear, vine, brandy and juices) on east, west and central region of country. Management of the organization wants to know amount of sale by product for every region and to compare the amount with planed sale. This analysis require multidimensional view on data that can be provided on two way: 1. use of specialized multidimensional databases (data warehouses) and 2. use of tools that create multidimensional views on twodimensional relational databases. 306

5 Multidimensional analysis enable users to view same data on different ways by using many dimensions. Every aspect of information (product, price, costs, region or time period) represents one dimension. For example, manager responsible for sale of juices can use multidimensional analysis to determine how much of juices was sold on east region in june and to compare the amount of sale with sale in previous mounth and in june of previous year or with planed amount of sale. Data mining analysis of data is more oriented to explore and discover of new knowledge. Data mining tools usually have elements of artificiel intelligence (Coppin, 2004), such as neural networks, fuzzy logic, genetic algorithms, technics based on rules and other intelligent technics. Thus data mining tools are categorized in technologies for knowledge management and classic DSSs become knowledge-based DSSs. (Gottschalk, 2007) Data mining analysis provides insight into data which can not be obtained by other technics. The insights can be obtained by finding hiden patterns and relations in great databases and elicitating inferences on rules related to it. The patterns and rules than can be used in decision making and predicting effects of the decisons. Types of information that can be provided from data mining analyses incude associations, sequences, classifications, clusters and forecasts. (Wrembel & Koncilia, 2007) Associations are appearances related to an event. For example, study of sample of purchases in supermarket can discover that when potato chips is bought, cola drink is sold in 65% of all cases, but when there is promotion, cola is sold in 85% of all cases. In case of sequences, events are chronologically related. For example, we can determine if house is bought, then new refrigerator will be bought in period of two weeks, in 65% of all cases, and cooker will be bought in period of one mounth after purchase of house, in 45% of all cases. Classification recognises patterns describing some group to which an item belong by examining of existing items that are classified and by determination of rule set. For example, companies are worried due to loss of loyal customers. Therefore, they create model supporting managers to predict who are these customers and to develop special campaign for keeping the customers. Clustering is similar as classification, with difference that none of groups is not defined yet. Data mining tools discover various clusters in data, such as similar groups of bank card users. Data mining tools include forecasts which can be done on various ways. In forecasts, series of existing values are used for predicting other values. There are a lot of examples how some organizations use data mining tools for decision support and improvement of business. ShopKoStores company uses DM tools to find correlation between position of items on store shelves and habits in parchases of customers. Aim is to increase sale by arrangement of products so that are appropriate to customers. Second example is Notdstrem company using DM for analysis of data that is generated by visitors of its web site. Results of the analysis then are used for adjustment of advertising and content of advertisments to individual attributes and needs of customers and for improvement of on-line services for customers. (Han & Kamber, 2006; Lin et al., 2005) CONCLUSION Decision Support Systems are similar and complementary to standard management information systems and mainly support unstructured and semistructured processes of decision making. Components of the systems are: databases, software system with models and tools and user interface. Contemporary DSSs use special databases called data warehouses that are made by 307

6 extracting of data from transaction databases and putting data in appropriate form for decision making. The data is subject of multidimensional analysis with support of dataminig technology that uses various types of models (statistical, mathematical, financial etc.). Many examples (some of them are explained in the paper) show that great business benefits can be achieved by support of the two technologies. Business benefits follow from enhanced making of unstructured and semistructured decisions that are multidimensional in nature. REFERENCES Coppin, B. (2004). Artificial Intelligence Illuminated, Jones and Bartlett Publishers, Inc. Sudbury. Gottschalk, P. (2007). Knowledge Management Systems - Value Shop Creation, Idea Group Publishing, Hershey. Han, J. & Kamber, M. (2006). Data Mining: Concepts and Techniques, Second Edition, Morgan Kaufmann Publishers, San Francisco. Lin, T.Y., Ohsuga, S., Liau, C.J., Hu, X. & Tsumoto, S. (2005). Foundations of Data Mining and Knowledge Discovery, Springer-Verlag, Berlin Heidelberg. Turban, E., King, D., Lee, J., Warkentin, M. & Chung, H.M. (2004). Electronic Commerce: A Managerial Perspective, Prentice Hall, Pearson Education Inc., New Jersey. Wrembel, R. & Koncilia, C. (2007). Data Warehouses and OLAP: Concepts, Architectures and Solutions, Idea Group Publishing, Hershey. 308

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