1. INTRODUCTION 2. EIS DEVELOPMENT LIFECYCLE

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1 EXECUTIVE INFORMATION SYSTEMS: DEVELOPMENT LIFECYCLE AND BUILDING BY USING THE BUSINESS INTELIGENCE TOOLS Lungu Ion Academy of Economic Studies, Bucharest, Romania, , Vatuiu Teodora Constantin Brâncuşi University, Tg-Jiu, Romania, , Abstract: The Executive Information Systems (EIS) are designed to improve the quality of strategic level of management in organization through a new type of technology and several techniques for extracting, transforming, processing and presenting data in order to provide strategic information. These technologies are known as Business Intelligence Tools. This paper presents the development lifecycle, architecture of Executive Information Systems and also the main technologies used for designing and building an EIS. Keywords: Business Intelligence (BI), EIS, Data Integration, Data Warehouse, Data Mining, OLAP (On-Line Analytical Processing). 1. INTRODUCTION The Management Information Systems are second level information system, designed to provide information required by managers for planning and decision making. They relay on Operational Information Systems when dealing with primary data, but their main features are the flexibility and the easy to use. They are supposed to supply immediate responses to various data requests, to process collected data to get summary information. From a modern perspective, the information systems provide support for decision making and the use of the new generation of Decision Support Systems is rapidly expanding. All these information systems, took advantages of data bases, the fourth generation environments and the high technologies of modern computers. EIS is a subset of a class of technology solutions that also are referred to in the industry as business intelligence (BI) software. The main objective of EIS (Executive Information Systems) is to provide in real time representative information to the high-level management, to support strategic activities such as goal setting, planning and forecasting, and also tracking performance. Another objective of these systems is to gather, analyze, and integrate internal and external data into dynamic profiles of key performance indicators. Based on each executive s information needs, EIS can access both historical and real-time data through ad-hoc queries. EIS users can manage and manipulate multidimensional or cube-like databases. In essence, managers at every level can have a customized view that extracts information from disparate sources and summarizes it into meaningful indicators. EIS consists in a set of technology solutions that is based on business intelligence (BI) tools. EIS provide a friendly graphical interface and when this is customized for the individual manager, allow users to access corporate data and complements the executive's personal knowledge and provide quantitative diagnostics to monitor the progress of decisions. In many organizations there are implemented ERP systems for operational and transactional processing for different functional areas such as: financials, inventory, purchase, order management, production. Information from these functional areas within an ERP system is managed by a relational software database such as Oracle Database or Microsoft SQL Server. Operational levels of management require detailed reports with daily operational activities. But executive levels need information for strategic and tactical decision that often requires reports of aggregated data from ERP and non-erp application sources. 2. EIS DEVELOPMENT LIFECYCLE There are some major differences between OLTP systems lifecycle and EIS lifecycle which depends on executive systems characteristics, but the same traditional techniques and stages are used for development: justification, project planning, analysis, design, construction, and deployment (fig. 1). 837

2 Fig 1. EIS development lifecycle In these stages there are many steps used for modeling EIS characteristics such as: EIS are oriented o business opportunities rather than transactional needs; EIS have to implement strategically decisions, not only departmental or operational decisions; EIS analysis is focused on business needs. This stage is the most important of the process; Development process is cyclical, focused on evaluation and improvement of successive versions, not only building and major delivering of a singular a final version. 3. EIS ARCHITECTURE AND BI TOOLS EIS systems demand for technology solutions that can extract, analyze, and visualize information from ERP and stand-alone systems in real time and with a friendly and flexible user interface. EIS architecture is common to Decision Support System s architecture and it's structured on three distinct levels: Management represented by relational database, data warehouses and other type of data resources; Model Management, which is the level of extract, transformation and processing of data; Data Visualization Tools that provide a visual drill-down capacity that can help managers examine data graphically and identify complex interrelationships. 838

3 Presentation tools: Reports, graphs, Charts builders, web DATA MINING MIDDLE-TIER OLAP WEB TECHNOLOGIES BOTTOM TIER Metadata Central data warehouse Data Marts Data Targets Extract/Transform/Load(ETL) Integration Data sources Financials HR Files Production Logistics External Sources Fig. 2.- A complex EIS arhitecture with three distinct level A. Data Management Level This level consists of the data sources integration through data warehouses. A data warehouse collects and organizes data from both internal and external sources and makes it available for the purpose of analysis. A data warehouse contains both historical and current data and is optimized for fast query and analysis. Data are organized in another type of schema which contains fact tables and dimension tables. A fact table is related with dimension tables and contains measure measures and which enable a much easier way in finding data. Dimension tables are structured on different hierarchical levels of aggregation (e.g. Time dimension can have day, week, month and year as hierarchical levels.) Data presented in fact tables derived from different type of data sources like relational databases and user files. Data warehouses extract, transform and process data for high-level integration and analysis. Data warehouse's architecture is different for each individual organization, but in generally it consists of three levels: data sources, ETL process and data marts. All data sources can be integrated into a central source data warehouse from where data are extracted, transformed and loaded through ETL process into a final storage place which can be a central data warehouse or many data marts which are departmental data warehouses. 839

4 Although a data warehouse can make it easier and more efficient to use the EIS, it is not required for an EIS to be deployed. Organizations can extract data directly from their host system database for their analysis and reporting purposes, but in a more difficult way. B. Model Management Level At this level we can find BI tools for extracting and analyzing data such as OLAP systems, Data mining process and statistical tools. C. Online Analytical Processing (OLAP) An OLAP engine is a query generator that provides users with the ability to explore and analyze summary and detailed information from a multi-dimensional database. Traditional relational database systems handle this situation by using multiple queries. In many cases, the queries become so complex that even the developer finds them difficult to maintain. OLAP overcomes this barrier by enabling users to analyze multi-dimensional data. OLAP systems have typically been implemented using two technologies: ROLAP (Relational OLAP), where data is stored in a RDBMS and MOLAP (Multidimensional OLAP) where dedicated multidimensional DBMS is used. There are also version of HOLAP (Hybrid OLAP) and DOLAP (Desktop OLAP) systems. Managers can use an OLAP engine for typical operations, like "slice and dice" data by various dimensions and then drill-down into the source data or roll-up to aggregate levels. OLAP provide tools for forecasting data and what-if scenarios and analysis. Bat OLAP can only mark the trends and patterns within the data that was requested. It will not discover hidden relationships or patterns, which requires more powerful tools like data mining. D. Data Mining Data mining tools are especially appropriate for large and complex datasets. Through statistical or modeling techniques, data mining tools make it possible to discover hidden trends or rules that are implicit in a large database. Data mining tools can be applied to data from data warehouses or relational databases. Data discovered by these tools must be validated and verified and then to become operational data that can be used in decision process. E. Data Visualization Tools Level This level contains tools for presenting and analyzing data from previous levels. There are many graphical tools for building friendly and flexible presentations like: reports, graphics, and charts builders, web pages which can be integrated into an organizational portal or an ERP system interface such as Oracle E-Business Suite. EIS should permit the user the interface to accommodate different degrees of technical knowledge. CONCLUSIONS Information Systems are software products intended to store and handle data in an organization. They must meet the informational needs of all levels of management - operational, middle and top and they must be designed accordingly. Ann EIS should be designed to allow managers who are not trained to use query languages and advanced technologies, a fast, easy, and understandable way to navigate into data and identify trends and patterns. Developing EIS systems involves time, high-costs and human resources, efforts and an EIS must be capable to provide in real time representative information to the executive management. Deploying EIS involves many risks: system design, data quality, and technology obsolescence. System design risks stem from poor conceptualization of an enterprise s true business needs before the technology is deployed. Data quality risks relate primarily to whether or not data has been properly cleansed. Technology obsolescence refers to the failure on the part of the vendor to anticipate new technologies. Large budgets and strategic information are involved in deploying EIS systems this is the reason to establish rigorous criteria for evaluating EIS systems. These criteria are discussed below. Decisions based on business process EIS should not be viewed only as a data repository or a large set of data. Instead, system s implementation should be concern on conceptualizing new data models, processes, and indicators that form the content of EIS. EIS should provide extensive understanding of the benchmarks that are useful to evaluate business processes. This feature typically refers to the response time that a system provides to its users. Most responses should range from a few seconds to a maximum of 30 seconds for routine queries. Response times depend on the complexity of the database and the queries being requested. Flexibility determines whether an EIS solution can continually adapt to changing business conditions after the system has been delivered. An EIS should be able to accommodate changes in any type of business process and functions like personnel, services, and processes, as well as new mandates, laws, and regulations requiring the capture of different types of data. Integration involves two types of issues: data integration and system integration. Data integration is the ability to access data from much different type of systems. An EIS will be particularly effective if it can overcome the challenge of information fragmentation, allowing executives to measure features of business processes that 840

5 involve information from inside and outside of the organization. System integration refers to two things: the ability to extent the EIS software with new capabilities and modules and the system s ability to coexist with other enterprise solutions. EIS systems have a powerful impact on strategic decisions quality to reduce the time for making decisions. EIS must have the ability to allow managers to view data in different perspective, to drill-down and roll-up to aggregate levels, to navigate and online query data sets in order to discover new factors that affect business process and also to anticipate and forecast changes inside and outside the organization. EIS improve the quality of management in organization through new type of technology and techniques for extracting, transforming, processing and presenting data in order to provide strategic information. REFERENCES 1. Yonghong L., Rowan M., Dashboards and Scorecards: Executive Information Systems for the Public Sector, Government Finance Review, Dec Lungu I, Sabău G., Velicanu M., Muntean M., Ionescu S., Pozdarie E., Sandu D.,- Informatics Systems. Analyze Design and Implementation, Ed. Economică, Lungu I, Bâra A., Executive Information Systems Development Lifecycle, Economy Informatics Revue, ASE, Moss L., Atre S. Business Intelligence Roadmap The complete project lifecycle for decisionsupport applications, Addison-Wesley, Vătuiu T., Popeangă V., The building of executive information systems by using the business intelligence tools, Universitaria SIMPRO 2006, Petroşani, page Internet ressources: