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COMPONENTS OF TECHNOLOGY BUSINESS INTELLIGENCE AND THEIR IMPORTANCE FOR MANAGEMENT AND DECISION-MAKING IN BUSINESS Gabriel KOMAN1, Irena KUBINOVÁ2 University of Žilina, Faculty of Management Science and Informatics, Slovak Republic
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e-mail:
[email protected] Researcher of ITC Department within University Science Park Žilina
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e-mail:
[email protected] Abstract Information and communication technologies currently affect management and decision-making in company nowadays. The managers of the factories decide on the basis of quickly available and relevant information which is distributed to them from the information systems, which the company is using. The correctness of decision of the managers provides continually available, actual and historical data in the required quality and form. The technology of Business Intelligence belongs among the complex of the technologies within the information systems for providing relevant data in decision making managers. This paper describes the architecture, components, and the importance of Business Intelligence technologies for the management and decision-making in company. Keywords: Business Intelligence, OLAP, Data Warehouse, management, decision making 1
INTRODUCTION The influence of new technologies and techniques in the area of business processes, such as production, finance, marketing, etc., arises in the companies ever larger and larger amounts of data. These data are important for the company and information is also entered into the decision process. Data that the company generates should be able to efficiently process and use within its business activity. Thanks to correct and rapid processing of data, managers can make good decisions for the efficient operation of the company. One of the possibilities how to effectively use data from all source systems company is the possibility of the using of Business Intelligence, based on data warehouse. That the system Business Intelligence is
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effective, it is necessary to ensure the efficient use across the company with links to all data sources that generate the source of the data. 2
BUSINESS INTELLIGENCE The systems Business Intelligence (BI) are getting nowadays more often the part of the informative system, mostly of the big factories but also of the medial ones. The emergence of these systems inspired particularly significant leap in the development of information technology and the continuing growth of competition. The development of the IT (information technology) is associated with generating of big data amount, which analytics must work with and offer to the managers for the needs of deciding. Using of available technology it was possible to process this amounts of informations effectively, easy and quickly make available and uncover opportunities and danger situations for the company. Because of it there were created new departments in the form of data warehouses, systems business intelligence and many more.[1] In the context of the new informative systems creation there were developed also new methods for the needs of deciding supported by new information and communication technologies (ICT). Development of these methods was incited by development of computational power (speed of 3GHz CPU), RAM capacity (2 to 8 gigabytes) and the development of data storage, it means in HDD (standard is 500GB or more). With the development of hardware it has been developing software, too. This allowed the emergence of new, more extensive and sophisticated databases. The data, which were the databases filled with, were processed using new methods and systems. Thanks to these businesses began to uncover hidden information content and opportunities with high added value. The principe of data processing systems in BI is based on secondary data, which is different from what the transaction systems work with - primary data ("data of single use"). By secondary processing of data, the data are used in a different way than they were initially generated for. It comes to linking of the informations that arise in the different activities and times. These informations are then analyzed and used in the optimization of management and decision-making. Secondary systems read data generated in large amounts, using advantage tricky methods of analysis, aggregation, statistics, etc.. In terms of performance, these IT systems are more difficult as the primary. The main advantage of these systems is that they analyze the data generated in different time intervals and different systems.[1] BI systems use different methods and tools necessary for the presentation of analysis results at different time intervals, but they are working with data that were created in other systems, in a different time and a different purpose. Older information systems (IS) were focused only on data processing in production, trade and financial transactions within the company. Content of the concept of BI are the skills, knowledge, technological equipment, application equipment, security issues, models and techniques used in business for the purposes of analyzing and understanding the market situation. Therefore the purpose of BI is to support business decisions. Applications run within the BI system allow the
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analyst to work with historical, current and predicted business operations and corresponding data using historical data warehouse or data directly from operational systems. The basic features of BI include: [1] OLAP (online analytical processing), Reporting, Support analysis, Overview mode (Dashboard, Balanced Scorecard), Data mining, Corporate Performance Management (CPM), Predictive analysis. The forms of user presentations and outcomes of BI includes, in particular: [2] Assemblies, Questions, OLAP, Control panels, Summary of the results. Within the BI system using various specific analyzes that use a lot of information to the management and decision-making. BI systems are therefore not focused on basic processing and implementation of current operations (production, trade, finance). The role of BI is to support senior management decision-making processes by providing relevant and necessary information. So therefore is its position above the rest of the IS (Figure 1). EIS
Management
MIS
BI
ERP
TPS
DWH
Customers
Suppliers
Employees
Figure 1 Position of BI in information system of the company
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COMPONENTS OF SYSTEM BUSINESS INTELLIGENCE The complexity of the structure of BI systems may differ according to the needs of the company. It may be a simple and not very expensive solution, or even more comprehensive solutions, which are knowledge, technology, implementation and costly. The general concept of BI systems (Figure 2) contains several layers: [1] [3] 1. layer ETL (Extract Transform and Load), 2. layer of data storage, 3. layer of data analysis, 4. presentation layer. Data Sources
ETL layer
Storage data
ERP
Data Analysis
BI users
EIS DWH Management
CRM
ETL DM
Analytical tools Analytical department
SCM
EAI
ODS
DSS
DWH Other sources
Other departments Other tools
DSA
Data stream
Figure 2 Architecture of the system BI ETL layer provides BI systems collection and transfer of the source data to the data storage layer. This layer contains mostly the following components: ETL systems designed for extraction, transform and data transfer. EAI systems that serve the needs of application integration. These include integration tools and applications for on-line updating of the data warehouse. ETL system, respectively data pump represents insisted that allows efficient process large volumes of data from various sources and store them in a data warehouse. ETL tool must be able to: to process various data from different data sources, propose transformation of transfer data between different data formats.
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ETL task is thus transfer data in one place and unify access to all data from various systems. Another task, which must deal with ETL, there are different registrations of individual fields, such as data entry time, which vary according to the time zone in different countries. Since the data pump fills data warehouses of data sources, this process takes place at a time when it is expected a smaller workload transaction systems (at night, weekends, etc..) It is to not to prolong the response time for users of these systems. ETL process is divided into three phases: Extract Transform and Load. Layer for data storage ensures the processes associated with storing, managing and updating data in BI systems. This layer includes the following components: [4] Data Warehouse (DW), Data Mart (DM), Data Staging Area (DSA), Operational Data Store (ODS). Data warehouse is a collection of unified, subject-oriented databases designed for the purpose of providing information for decision-making purposes. Unification means getting data from a variety of different data sources, often even within other states. In data warehouses is also ensured the immutability of data (non-volatility). While in transactional databases, the data is constantly changed and reflect the current status, the data warehouse data are permanently stored statically. Data are not changed or deleted, just to add new ones. Thanks to it, it is possible to compare current results with past and monitor trends of development in various indicators.[2] Data marketplaces includes a subset of the entire data warehouse data, used for analytical purposes, certain parts of the organization, so they can have even more of an organization (e.g. marketplace data for marketing department, human resources, etc..). Data staging area provides temporary storage of extracted data from production systems to promote rapid and efficient extraction (selection) data. So, DSA is used to store initial untransformed data from source systems.[4] Operational data store can serve a similar purpose as the data warehouse. But compared to it, the data in the ODS change (in real or near real time). ODS can serve as a quick and timely analysis of immediate development of data across multiple applications. The ODS is data in the detailed atomic form, in contrast to a data warehouse, where it may be used in certain aggregation. The amount of data and the ODS can be larger than the amount of data in the data warehouse. ODS is designed for workers performing repetitive monitoring of certain parameters, but also for incidental finding hidden connections.[4] Layer for data analysis provides data access and analysis. This layer generally contains the following components: Reporting can query the database components BI standard or ad hoc and also generate output in the form of reports and surveys.
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OLAP is used for the purposes of implementing advanced and dynamic analyses. Data mining a system designed for sophisticated analysis of large amounts of data. It is used for the needs of complex and highly structured analysis. The presentation layer is used for the communication needs of end-users with BI system components. The purpose of this layer is to collect requirements and subsequent presentation of results. The presentation layer contains the most following components: EIS systems, included management and user applications EIS type of DW or OLAP cubes. Analytical tools, provide users assigned tasks, analysis and presentation of outputs from other applications, for example: of OLAP applications. Another analytics models, applications and special tools, such as older systems, decision support and experts systems. The major components of BI include data storage layer. This layer is made up of several components, which are based on data warehouse. The processed data from all database resources, especially of transaction database systems. Their role is to ensure storing, updating data management system BI. The main task of the data warehouse is to provide input to the OLAP applications. Based on the above information, business analysts then perform various analyzes and reports, to support decisionmaking processes in the company. Therefore, it can be argued that the data warehouse and OLAP applications are the most important part of the BI system based on data warehouse. 3.1 Data Warehouse and OLAP Data Warehouse (Figure 3) is a system in which data are collected, stored, organized and shared with a time resolution that means historical data [2]. The content of the data warehouse are therefore data that were used, derived from operational systems. Data warehouse (DW) is used in an enterprise decision support users / managers. Company users run queries on the data in the data warehouse and the replies then help them in their decision. In terms of management and decision-making have a data warehouse for the holding of particular importance for the following reasons: Data is stored in one place, making it possible to eliminate the need for the acquisition and conversion of data from various data sources. Data is constantly up to date, because they are updated regularly at certain intervals. Data warehouse can store almost unlimited amount of data. The content of the data warehouse, the data contained throughout its service life. Thanks to this are in data warehouse available historical data for the needs of various time-based analyses. Because the information from data warehouses is almost never deleted, data warehouse can often contain more information than the source because it imposes a change in the data.
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Data Sources
Data Sources
Data Data Warehouse
Data acquisition
ETL
Adjusted data
Data organisation
Central data storage
Standardized data
ETL
Organized data
DM
DM
OLAP
Data distribution
Optimized data warehouses allow workers to access data quickly, making it possible to increase their performance and remove unwanted down time caused by pulling transaction system. Data warehouses are developed based on the needs and requirements of employees, allowing their rapid incorporation and understanding of applications for working with data. All data in the data warehouse are standardized. [1] [5] [6]
Reporting
Users
Figure 3 Data Warehouse model
Output information
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To make the benefits real, data warehouse must be built in accordance with the needs and requirements of businesses and users, and based on feedback. A data warehouse is appropriate to use in factories which often process large amounts of data. This data can help businesses to uncover opportunities and threats, and so to assist managers make good decisions to achieve business objectives. It is decisive that the company could tell whether it the data warehouse really needs or it just needs easier storage in a database server, and so on. In the case that the company decides to implement it in its data warehouse environment, it is important to know the needs and requirements of users on the system and ensure data quality. So the system is effective, it is necessary to appropriately select its architecture, its implementation methods and approach to implementation. In constructing the data warehouse depends only on skill and company managers, whether the data warehouse will work effectively from the beginning of implementation of the requirements of users, or will be necessary to be invested additional financial resources to staff training and system optimization. Nowadays the most valuable timely and accurate information, so the company should consider the necessity of implementation of data processing system, whether in the form of a data warehouse, or in the form of any other system. In any case, it is appropriate, in connection with the efficiency of processing and using information to a company that generates a lot of data from various systems, had established a central data repository accessible to all users of the company according to their needs and requirements. For the needs to facilitate and improve the quality of data analysis, it is possible to use various technologies and applications that work with data of the data warehouse. Among the most widely used technology for statistical analysis of data in the data warehouse is OLAP (online analytical processing). OLAP technology respectively OLAP server provides the access to multidimensional data, is actually a multidimensional database, which it called data cube, that means data managed by OLAP servers, can be viewed in a similar way, the Rubik's Cube. Different points of view on the data correspond to the various dimensions (dimensions). This enables the company to analyse data from several respects, for example in terms of products, or part of the time. Specific numbers respectively indicators or facts are on a plane view.[4] The basic unit in the data cube is a cell that represents a particular entry (value) in terms of all dimensions. Cell contains a value, such as the number of products sold and the like. The data contained in cells are often summarized in some way (for example, the volume of sales for each store in the region), depending on the degree of detail selected by the user wants, and which also allows data warehouse.[4]
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Thanks OLAP technology can work with data at the summary level, the identified problem or interest group and called. Nesting is to proceed to a level of detail that is necessary for decision making managers. Deploying OLAP changes in the company quality management processes, enables to reveal hidden facts and trends. Manager sees the company as a multidimensional way, which allows him to analyse the data to discover the economic context that means to create business knowledge intelligence. Decision-making activity often requires managers to solve problems that have not yet met with, but for that they need certain information. OLAP queries allow them interactively on the basis of legal action, the characteristics of which are not known in advance, and was not known or structure queries (ad hoc querying). Since the OLAP tools for managers and should be characterized by a simple intuitive user interface, with the possibility of interaction and the use of various visual aids, such as graphs, figures and tables. Use of OLAP applications in the company should be nowadays an essential element for the implementation of analysis and examination of data from different perspectives. Such data processing allows an organization to search for reserves in the business, which may pose to take certain competitive advantage. OLAP tools are mainly used in the companies that deal with selling large quantities of different products and have multiple branches. OLAP enables these enterprises to answer questions such as: Which product achieves the highest sales per outlet? Which branch achieves the highest profit? The sales volume of which product drops? What are the best selling products in each branch? Which is the most profitable comprehensively? [4] Through administrative queries can managers identify the essential elements that will help them in their future decision-making. With OLAP technology and each instrument can view data from different points of view, which enables an accurate and efficient analysis and provides the manager with clear and structured reports. OLAP technology is particularly suitable for large companies or companies that process and store large amounts of data. The introduction of technology into the factory with OLAP Business Intelligence gives analysts the ability to process data in the source systems BI, which allows working effectively with business data that is already stored in a RUN box company. The basis of the functionality of OLAP technology is sufficient hardware and software support. That is why this technology is recommended especially for large businesses that have sufficient funds to invest in the latest technology and infrastructure. OLAP technology is currently one of the basic system processing and data analyses. This system provides managers with timely and accurate information for their decision process. OLAP technology should be an essential technology for data
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processing in companies that need to analyse large amounts of data from their or external database systems. 4
BUSINESS INTELLIGENCE Effective Business Intelligence system implemented in the company, which it really needs, and will also use, has great importance for the company. The benefits system can be quantified generally, in terms of financial management and production, by simply comparing the results obtained before the introduction of the company with the results after the introduction of BI. Other benefits of the scheme, therefore, the benefits of a qualitative nature, which can’t be easily quantified, can be classified according to different aspects: [2] return on investment, benefits of implementing BI within business performance and its management, benefits affecting the quality and degree of ICT company and its management. For the needs of statements from recoverability benefits from investments is used in a descriptive way, which reflects the accurate quantification of benefits. The basic benefits of BI implementation in terms of return on investment include the following: [7] The benefits of automation of routine operations and compensation of complex analytical systems at the operational level, the creation of analyses and reports according to user requirements. Increase employee productivity across the company based on the system's ability to consistently and systematically share information, including accrued. The cost savings in IT and various economic benefits (e.g. increase profits) from the system implementation. The main benefits of implementing BI within business performance and management include: [8] Thanks to applications in BI is possible for the user to understand better the essence of its activities. User can better understand the complex context within business management, manufacturing and other areas of business. The user can work with information from various sources and times in real-time. Due to the time dimension, it is possible to look at data from different perspectives and to follow the development trends (e.g. Demand, competition, etc.). Within analytical BI applications can be predefined thresholds monitored indicators and dimensions. Thanks to the system in advance, quickly and clearly warns users about critical or emergency conditions in the sale, purchase, capacity, etc.
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BI systems allow solving problems comprehensively and in depth. The system supports solving hidden problems by identifying the complex links between data. Use of BI systems also has an impact on the career development of employees. New technologies and methods for detecting and solving problems enable managers and executives to develop their skills and abilities. Since BI applications can be adapted to the needs and requirements of users, eliminating dependence on IT specialist users, which gives the company room for cost savings or reduce the burden on IT staff, etc. Implementation of BI is also influenced largely technological level of current enterprise systems. In most cases strengthens the functional and technological level of ICT. Additional benefits in terms of the level of ICT company in the implementation of BI include: BI systems also support the integration of resources, which is not possible under the current IS enterprise integrating various reasons (e.g. kind, geographical and other). Applications BI managers to deliver projects and rationally manage the entire enterprise using a variety of analytical tools and comprehensive information to BI system available. The current BI systems enable users to query data directly to individual need. This makes it possible to take decisions in the decision-making process is very fast (on the order of days or minutes. 5
CONCLUSION Management and decision making of managers in companies is mainly based on maximizing the information that is available in a correct and transparent structure, in real time, with instant access with a variety of computers and mobile devices. These challenging specifics on the availability and presentation of information declares a management information system which using Business Intelligence tools collect all the data into the data warehouse, which can then be used in real contexts not only top management, but also workers at lower levels. Mutual unification of data into a single functional unit is an important and valued part, and also the necessary standard for the comprehensive management of each enterprise so as to become prosperous dynamical. Acknowledgement This article is the outcome of the project KEGA 035ZU-4/2013 (Master degree study program: Operations Management and Logistics). This paper is supported by the following project: University Science Park of the University of Zilina (ITMS: 26220220184) supported by the Research & Development Operational Program funded by the European Regional Development Fund.
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