The Application of Data Warehouses in the Support of Decision Making Processes. Polish Enterprises Case Studies

Transcription

1 The Application of Data Warehouses in the Support of Decision Making Processes. Polish Enterprises Case Studies Artur ROT Department of Management Information Systems Engineering, Wroclaw University of Economics Wroclaw, Poland and Leszek ZIORA Department of Business Informatics, Czestochowa Technical University Czestochowa, Poland ABSTRACT In the economy based on knowledge data warehouses constitute one of the key information technologies supporting management of the enterprise by providing data for the purpose of decision making at all levels of management i.e. strategic, tactical and operational. Data warehouse is a solution which allows for integration of different, historical and diffused information coming from transactional systems of organization. It is a key component of decision support system to which tasks belong among others: supporting managers in decision processes at resolving unstructured problems, increase of decision making effectiveness, combination of models and analytical techniques usage with the use of lots of data, allowing for testing, proposal of solutions based on rules of inference. Data warehouse is constantly developing venture on a big scale, ensuring for users appropriate data in a proper place and time. It provides aggregated information needed for decision making. Thanks to the multidimensional data structure it is possible to perform any reports and analyses which are source of in-depth information which allow for assessment of observed phenomena and constitute basis for making appropriate and quick business decisions. The main aim of the paper is to present applications of data warehouse in supporting decision making processes in the enterprise. There was presented characteristics of data warehouse, its role in supporting the process of decision making at three levels of management and benefits resulting from its implementation. There were also presented chosen case studies from application of data warehouses in decision making support in such organizations in Poland as: Getin Service Provider SA, DnB Nord Bank Poland and Atlantic company. Keywords: data warehouses, decision support systems, decision processes, Polish enterprises. 1. CHARACTERISTICS OF DATA WAREHOUSES TECHNOLOGY The author of data warehouse concept is Wilhelm Inmon. He describes it as a subject oriented, integrated, time variant and non-volatile collection of data in support of management s decision making process [7]. The aim of data warehouse is to specify certain trends and patterns of specified data objects. Data warehouses operate on historical data obtained from operational data bases, they enable for approximation of behavior of data objects included in database. For creation of data warehouse very useful are distributed systems gathering locally data according to its place of usage. Whereas, the data can be collected from all local nods of the system for the purposes global analyses [3]. Data coming from transactional systems before writing in the data warehouse have to be integrated. Data integration means its standardization, change of the way of coding, naming etc. Data included in the data warehouse are transformed into a shape to which end user has access to and thanks to which data warehouse can fulfill functions connected with decision support. These processes are called in abbreviation ETL it is: extraction, transformation and load [11]. The same effect of data synthesis is not sufficient for the management of organization and there is necessary to aggregate and analyze data gathered in data warehouse. The aim of these activities is presentation of a new relations and dependencies between data and a new context of knowledge usage. Knowledge aggregation and its multidimensional analysis enable OLAP techniques and data mining. Data processing in the data warehouse generally takes place in layers. It means that data undergo cleansing, standardization and next aggregation. In practice functional elements of data warehouse are implemented most frequently (data marts) for individual segments of organization s business activity, and then they are joined as a whole [16]. There exist four main data categories in the data warehouse, which are facts, dimensions, aggregated data and metadata. The most significant area of data is constituted by facts and on its

2 basis all analyses are directly made. Data concerning dimensions are de-normalized what enables user for its exploration drill-down and for its aggregation drill-up. Aggregated data contain facts which are summed or calculated with the help of different statistical functions. Metadata which are data about data do not directly contain data but information about its location, structure, meaning and mapping [14]. 2. THE ROLE OF DATA WAREHOUSE IN DECISION MAKING SUPPORT The process of management can be defined as a set of tasks (including planning and undertaking decisions, organizing, leadership and control) directed at organization resources (human, financial, material, informational) and executed with the aim of achieving goals of organization in an efficient way. In every step of management process there exists a need for information. It is information about conditions of a given company - its past and future. The management cannot have influence on historical events but only at the future events. Information concerning past events are deleted from operational databases and they are stored in data warehouses. Data warehouse enables for active application of information possessed by organization for the purpose of strategic, tactical and operational decision making, describing market trends or for resources management. The application of data warehouse means providing appropriate information and/or knowledge in appropriate time for appropriate decision makers in order to make the most beneficial decision for the company [15]. Data warehousing and business intelligence are fundamentally about providing business people with the information and tools they need to make both operational and strategic business decisions. The research conducted by Hugh J. Watson (Watson H. J. 2005) based on the audit of a few hundred enterprises (with revenues from $250 mln to $50 bln) indicated that 39.8% users of analytical databases are vice-presidents of organization. Data warehouse is used by 26.9% of directors, 16.7% of managers and 11% of presidents in the surveyed enterprises. 25% of users are managers of lower level and employees. Financial departments, production and operating ones perceive analytical data warehouses as tools giving helpful information in the increase of production rentability, profitability of products and increase of service quality level. Data warehouses are most often built on the demand of marketing department 36.5%, IT department 30.8%, research and development 23.1% and financial department 20.2%. Watson s research show that data warehouses are used in most cases by sales and marketing departments 51.8%, financial departments 40.7% and production and operational ones 36.1%. The basic effects of data warehouse usage in an enterprise is quick obtaining of information throughout data drilling which consists of deep analysis called drilldown, aggregation analysis - drill-up and sectional analysis slicing and dicing. Another role of data warehouse is data-mining which is a technology of knowledge acquirement by the use of neuron networks, genetic algorithms and statistical techniques Data warehouse gives to the enterprise three main benefits: ensures manageable structure for data concerning decision support, enables enterprise s employees performance of complex queries on data concerning many areas of operation, enables the use of many applications which are called BI such as OLAP and data mining. The general aim of data warehouse is enhancement of effectiveness and efficiency of the process of making decision in enterprises. And this should give them competitiveness advantage. Data warehouse is a key component of decision support system. As it was previously mentioned data warehouses are being used at three levels of management. At the strategic level they enable more precise goalsetting and their execution monitoring, they enable conducting comparisons, e.g. in the area of previous results, profitability of particular offers, channel distribution channel effectiveness, etc., they enable conducting development simulation and result forecasts based on specified assumptions. At the tactical level they provide the grounds for decision-making in the area of marketing, sales, finance and capital management and allow for optimizing future activities and modifying organizational, financial or technological aspects of the company s functioning in a proper way to ensure more effective execution of its strategic goals. At the operational level they are used in ad hoc analyses, to respond to inquiries regarding everyday operation of particular departments, the current financial standing, sales, cooperation with suppliers and customers [6]. In a successful data warehouse the data are used for creation of applications having clear business benefits directly effecting financial output of the company. To applications increasing companies profits belong [20]: Fraud detection and counteraction against frauds before losses occur, Marketing oriented systems enabling for the company to understand customers behavior and demand for the product, in such a way that marketing campaigns could be directed at those who would respond to them favorably, Profitability analysis showing to the companies which clients are profitable and which are not, Management of supplies enable producers and traders to possess appropriate products in a proper time and place, what prevents from excessive losses resulting from lack or surplus of goods, Analysis of credit risk enable companies to avoid debts by detection among customers of those who have compound credit history,

3 Making competitive prices enable companies to create new methods of calculation by recognition of demand on product, competitiveness on the market and proper margins, Prediction of certain indexes in the future on the basis of knowledge about present, past and simulation of organization s behavior. The application of data warehouses in the process of decision making in different enterprises and institutions have an impact on the increase of its work efficiency. In trade data warehouses became crucial tool supporting sales, marketing, promotions, and even the way of goods location in the shop. Thanks to basket analysis there can be specified customers preferences and correlations between products. All those activities transfer into measurable financial outcomes, significantly exceeding costs of data warehouse implementation. In telecommunication data warehouses use billing data and enable i.e. customers segmentation into groups of those who in a different way use services provided by operator. It allows for setting dedicated tariffs especially aimed at those groups. Moreover, in calculation of so called churn rate it is possible to predict who is going to resign from services of telecommunication company [9]. In the insurance field, similarly to the banking data warehouses are very useful and in a measurable way support business decisions making. Data warehouses in which there were gathered data concerning customers coming from different Information Systems and data about particular products or insurance services allow for: profit increase from existing insurance policies by limitation of risk, frauds limitation, fixing rates ensuring appropriate profit, limitation of marketing costs, launching new products onto the market and taking over part of the market from other institutions [9]. 3. PRACTICAL EXAMPLES OF DATA WAREHOUSES APPLICATION. CASE STUDIES As an example of the company where data warehouse was applied in the support of decision making can be presented Getin Service Provider SA, which was established in March 2000 year, and on 30 August 2000 year it established first e-business center in Poland aimed at small and medium sized companies. This Center is virtual market where over companies from 199 branches meets. The mission of Getin Service Provider SA is support of electronic commerce development in the sector of small and medium sized companies and providing modern tools enabling trade, communication, promotion and management support via Internet. The application of data warehouse enabled activities connected with financial control and management of enterprise. In the first half year of 2001 there was undertaken decision in Getin S.A. company to build data warehouse as a base for decision making support system. Data warehouse in its scope embraced all indispensable data in order to obtain full view of business processes. Getin decided to use Microsoft SQL Server together with Analysis Services module. Data are rejoined from production server located in Warsaw to the office in Wroclaw. For integration the company uses Data Transformation Services (DTS) module which is a part of SQL Server and enable design of cyclical operations of data import, its transformations, running any SQL procedures or functions of ActiveX components. It is also possible to import data from other SQL servers e.g. Oracle, local files (text, Excel, Access, dbase, Paradox, etc.) and remaining sources. With the help of DTS the company has the possibility to design components enabling workflow and also control of this process [5]. In Getin the data coming from different sources were classified at the beginning, and later its integration embraced registry systems of so called e-visiting-cards of customers and e-business center products. Getin Service Provider s customers may use as an interface other applications e.g. MS Access. The basic benefit resulting from building data warehouse based on Microsoft products was obtainment of homogeneous, integrated view of the company and possibility to manage customer relationship. This system allows for analysis of customers behavior who use e.g. or updating offers in Internet shops, assess if and what customers have problems with update of theirs data. These benefits have influence on lowering costs of customer s service office. Complete view of enterprise facilitate calculation of trading agents, and also conduct of current analyses e.g. phone calls and work time in connection with effects for the company. The company was able to offer for its client s high quality of services, regular monitoring of IT systems and call center. The application of a new implementation had also influence on growth of performance, both among employees, who spend less time on obtaining data for reports and also on servers performance thanks to optimization of sites generation and change of structure [5]. The other example of data warehouse application for the purpose of decision making is DnB Nord Poland bank. It started its economic activity on Polish market in 2002 year. Until the end of April 2006 year it possessed NORD/LB Bank Poland SA brand name, and it is currently member of DnB NORD Banking Group created by NORD/LB Norddeutsche Landesbank Girozentrale bank and the biggest Norwegian DnB NORD Bank. The group began its economic activity at the beginning of 2006 year and provides services to customers in the Baltic Sea region in Poland, Denmark, Finland, Latvia, Lithuania and Estonia. In the bank there was implemented management information system based on Comarch Business Intelligence, which provides coherent data simultaneously to all interested accountants, financial analysts, risk analysts, product managers, bank s board of management and to the bank s owners. All users obtain data from one source, however its scope and the way of interpretation is adapted to the needs of particular category of recipients. The users of the system see data in three dimensions: factual, factual on the background of plan/budget and prognostic one. The main

4 assumption resulting from the needs of managerial staff and owners was transfer to the data warehouse of whole ledger, and also detailed data from all transactional systems functioning in the bank. After standardization and introductory transformations data could go to domain data models, which were separated according to category of bank products. Separate models were created for credits, deposits, current accounts, securities and also for costs. Every fact in the data warehouse such as contract, transaction etc. is given a product code. Apart from it, facts are marked by accounting codes and risk codes. The same data are visible in three dimensions: accounting, analytical and from the point of risk view. The tables in data warehouse allow for application of many indispensable measures in every dimension and it allows for keeping flexibility in information interpreting after its transfer to multidimensional structures what has significant meaning for decision making. The owners may get to know from reports, what is the value of granted in a given period credits with division on its different types, terms of realization, ascribe to them risk profiles, acceptable factors of concentration and many others. Comarch Business Intelligence and SQL Server 2005 enable realization of budget analyses as the element of typical controlling process. Budget references are saved in additional dimension what facilitates whole analysis and the process of generating and interpreting management information. Thanks to it bank managers posses flexible tool for sales management, allowing also for dynamic steering of costs allocation. Thanks to implementation of corporate reporting system DnB Nord Poland bank achieved many benefits as the possibility of whole look at current economic activity and possibility of its correction according to strategy assumption. Applied here data warehouse constitutes the only source of truth and it is possible to use it as source for more advanced analyses and management reports. Created in the scope of the project central product catalogue gives to marketing and sales departments flexibility in defining its scope and automatically reflects changes in analysis and reporting layer, banks shareholders have access to reports concerning DnB Nord Poland Bank, in the scope of system there was created interpretation layer, separate for controlling, accounting and risk analysis, the needs can be steered by persons directly interested: accountants, analysts, product managers etc. [10]. Atlantic Company can be mentioned as the other example of enterprise which applied data warehouse in supporting decision making, where Comarch system was implemented. Atlantic company is a leader among companies offering underwear in Poland and on many Central and Eastern European markets. Before implementation of data warehouse the company was using Microsoft Excel spreadsheet. On the basis of functional analysis there was agreed that in the company have to be generated reports concerning the structure of sales, stocks in days, logistics control and accounting. In applied Comarch Business Intelligence system there were defined end users profiles and introduced two types of users: active and passive. Active users can have the possibility to create own definitions of reports, control over viewed reports and the possibility of its distribution to passive users. Passive user has access to interactive reports without possibility to write them in the sets of reports. Atlantic source system uses Oracle 8i data base server. Data warehouse which is integral part of Comarch Business Intelligence was based on Microsoft SQL Server. Among the layer of provided ETL solutions there was created transition area, where data from different systems are gathered and unified in such a way that they can be compared. From selected, thematically collated and aggregated data the target areas of data warehouse was created. Analytical areas took its reflection in OLAP cubes, gathering data concerning sales, accounting, finances, reserves in days, warehouses and order analysis. There was also used here Reports Register Administrator application, which gives full control over security of information provided by data warehouse. Reports register allows for conducting multidimensional OLAP analyses in graphic environment and its visualization in the form of charts. System Applied in Atlantic company allowed for improvement of company s functionality in key aspects of its economic activity [1]. 4. CONCLUSIONS Data warehouses are IT systems consisted of many elements and gathering data from other source Information Systems, organizing, integrating and arranging collected data in order to present them in a clear and logic way to end users in the form of up to date reports, analyses and statements. Data warehouse as the main component of decision making support system provides information which has to help in making right managerial decisions at all levels of management. It allows for gathering data from different business fields, giving to key managers distinctive view on situation of the enterprise and it is also advanced tool used for generation, storing and exchange of information, giving for managers access to detailed statistics. The goal of data warehouse is to increase effectiveness and performance of decision making processes in the enterprises what have to give them competitive advantage. Building data warehouse is business venture of strategic meaning. Its application ensures support for management by providing appropriate information in specified time period, what allows for achievement of many business benefits. The application of data warehouses in the enterprises representing different branches brings many benefits such as the possibility to generate multidimensional reports in a defined period of time, facilitated access to the information, the possibility to use external data which do not come from transactional systems of the enterprise. The real challenge is to make the business intelligent environment an integral part of the decision making process. Data warehouse technology is actual issue in the management domain and it is still developing. Its main aim is to support the management by supplying managers

5 with reliable information. Data warehouse is a technology which is still growing, has good future prospects. REFERENCES 19. H.J. Watson, Current practicing in data warehousing, Information Systems Management, Vol.18, R. Wojtachnik, Data warehouses in management (in Polish), Gazeta IT no 9, October Atlantic case study, Comarch company materials 2005, 2. K. Bolesta-Kukulka, Managerial decisions (in Polish), PWE, Warsaw M. Chalon, Systems of data bases. Introduction (in Polish), Wroclaw Technical University Publishing House, Wroclaw A. Czerminski, J. Czerminski, A. Latowska, Theory and practice of managerial decision making (in Polish), TNOiK Publishing House, Torun Getin Service Provider S.A. Receipt for Internet business, case study, Hogart company materials, December Data warehouses and Business Intelligence, Transition technologies, December W.H. Inmon Data Architecture: The Implementation Paradigm, Wiley-QED, New York A.M. Kwiatkowska, Decision support systems. How to use knowledge and information in practice (in Polish), PWN, Warsaw O. Morawski, Data warehouses and decision support systems, Hewlett Packard Poland materials, Bank DnB Nord customer s implementation analysis, Microsoft materials January A. Nowicki (ed.), Computer business support (in Polish), Placet Publishing House, Warsaw A. Nowicki, Management Information-Decision systems (in Polish), Wroclaw University of Economics Publishing House, Wroclaw A. Nowicki (ed.), Introduction to management information systems in the enterprise (in Polish), Czestochowa Technical University Publishing House, Czestochowa M. Nycz, Support of decision making process in the enterprise with the use of opened expert system (in Polish), Wroclaw University of Economics Publishing House, Wroclaw M. Nycz, Managerial knowledge acquirement. Technological approach (in Polish), Wroclaw University of Economics Publishing House, Wroclaw C.M. Olszak, Business knowledge (in Polish), Computerworld 3 January J. Penc, Decisions in management (in Polish), Professional Business School Publishing House, Cracow V. Poe, P. Klauer, S. Brobst, Data warehouse design, WNT, Warsaw 2000.