Conference Paper From the traditional to the modern and complex agricultural companies that are using business intelligence tools

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1 econstor Der Open-Access-Publikationsserver der ZBW Leibniz-Informationszentrum Wirtschaft The Open Access Publication Server of the ZBW Leibniz Information Centre for Economics Balint, Antoniu Ovidiu Conference Paper From the traditional to the modern and complex agricultural companies that are using business intelligence tools Provided in Cooperation with: The Research Institute for Agriculture Economy and Rural Development (ICEADR), Bucharest Suggested Citation: Balint, Antoniu Ovidiu (2013) : From the traditional to the modern and complex agricultural companies that are using business intelligence tools, In: Agrarian Economy and Rural Development - Realities and Perspectives for Romania. 4th Edition of the International Symposium, The Research Institute for Agricultural Economy and Rural Development (ICEADR), Bucharest, pp This Version is available at: Nutzungsbedingungen: Die ZBW räumt Ihnen als Nutzerin/Nutzer das unentgeltliche, räumlich unbeschränkte und zeitlich auf die Dauer des Schutzrechts beschränkte einfache Recht ein, das ausgewählte Werk im Rahmen der unter nachzulesenden vollständigen Nutzungsbedingungen zu vervielfältigen, mit denen die Nutzerin/der Nutzer sich durch die erste Nutzung einverstanden erklärt. Terms of use: The ZBW grants you, the user, the non-exclusive right to use the selected work free of charge, territorially unrestricted and within the time limit of the term of the property rights according to the terms specified at By the first use of the selected work the user agrees and declares to comply with these terms of use. zbw Leibniz-Informationszentrum Wirtschaft Leibniz Information Centre for Economics

2 FROM THE TRADITIONAL TO THE MODERN AND COMPLEX AGRICULTURAL COMPANIES THAT ARE USING BUSINESS INTELLIGENCE TOOLS BALINT ANTONIU OVIDIU 1 Abstract: In t he c urrent c onditions of a volatile economic en vironment, t he a bility to use an in telligent w ay th e information that allows agricultural companies to cope with the challenges that they are facing regarding the current economic environment, represents the solution to strengthen their position on the global market. I explain in this paper how in the prospects of adapting intelligent business solutions they can influence not only agricultural companies but also t he w hole e conomic s ystem. T o r emain c ompetitive i n t he ne w gl obal e nvironment, c haracterized by an increasingly sharp competition, companies must adapt to new technologies and solutions. Key words: intelligent systems, agricultural companies, economic development, business intelligence, support system INTRODUCTION Business Intelligence (BI) is a t erm that designates a s et of concepts and methods used to improve t he qua lity of decision-making i n bus iness. M ore s imply, a lthough, we c an say t hat, Business Intelligence is providing relevant information in a timely manner for the people who need them [11]. We can define BI as a presentation of the information platform in a correct manner, useful and specific to each person in adequate time to be able to serve in an effective mode. In a 1958 a rticle, IBM researcher Hans Peter Luhn used the term of BI. He defined intelligence as "the ability to understand the relationship between the facts presented in a way that lead us towards the desired goal." BI a s it is understood t oday represents t he evolved f rom of a d ecision-making s upport system that began in the 1960s, and was developed through the 1980s [2]. DSS (Decision-making support s ystem) has its origins in computer mo dels de signed to assist w ith decision making a nd planning process. S ince t hen, da ta s torage, a nd t he B I Executive Information S ystem be gan to evolve rapidly [3]. In 1989 Howard Dresner has proposed that the term BI to describe: "concepts and methods to improve business decisions, using a support system based on f acts." Only in the late 1990s the term BI began to be spread throughout companies worldwide. Thomas D avenport a rgued t hat BI s hould b e di vided i n que ries, O LAP reports ( On-line Analytical Processing) and BA (Business Analysis). In this definition, business analysis is a subbranch of BI based on statistics, predictions and optimization. There are two basic interactive techniques to gather the necessary data: 1. interviews and 2. open session features. Before starting implementing a BI solution, it is appropriate that certain factors to be taken into c onsideration. T here a re t hree c ritical a reas t hat m ust be va lued i n a n or ganization be fore making a BI project: the level of commitment and sponsorship from the top management; you need a certain business level to create a new BI implementable; the quantity and quality of the existing business should facilitate implementation of a new BI. 1 PhD Student BALINT Antoniu Ovidiu, The Bucharest University of Economic Studies, 6, Romana Sq, Sector 1, , Bucharest, Romania, balint_antoniu_ovidiu@yahoo.com 178

3 Business Intelligence is not a set of printed reports or an presentation on a screen [6]. The rows of a s ales report, f or e xample, m ay contain de tailed i nformation a nd a ccurate, but i s not a Business Intelligence s olution until the y a re put into a f ormat th at c an b e e asily u nderstood a nd interpreted b y a p erson i n or der t o establish an e ffective s olution f or a pa rticular s ituation encountered in the day-to-day process by agricultural companies or any other type of companies. The analysis of Business Intelligence Source: Business Intelligence solutions have w itnessed a n unpr ecedented d evelopment i n t he l ast decade and companies t hat of fer such solutions continued developing them despite t he e conomic downturn that w e ar e f acing i n the pr esent - worldwide. T he functionalities of fered us ers, have become i ncreasingly va ried, c overing a br oad r ange of ne eds, f rom s imple t abular or g raphical reports to the ability to track key indicators of the performance of the organization in a synthetic manner and concise. MATERIAL AND METHODS Business A nalytics (BA) used t o de velop a nd i mprove Business Intelligence r educes considerably the risk of decision-making process and marketing activities that generate real value, usable with minimal resources. Business Analytics applications include decision support systems, reporting and query tools, on-line analytical processing (OLAP) and also forecasting systems and data mining. Ultimately, the results of business analytics deployments are in-depth analysis, refinement and concentration of a large number of business information, in specific performance indicators and finally, organizational knowledge. Business Analytic deployments are efforts involving multiple aspects of organizational management s trategies and processes f rom application management t o infrastructure ch anges. Business Analytic projects doe s not a im t o t each m anagers how t o t ake t he r ight de cisions, t hey help them instead to take decisions based on facts and figures and not on assumptions. Companies c ollect va st a mounts of da ta t hroughout t ransactional s ystems ( e.g. E RP - Enterprise R esource P lanning, CRM - Customer R elationship M anagement and S CM - Software Configuration M anagement) w hich ha ve been implemented over t he years and are us ed daily t o perform a variety of corporate functions. 179

4 Analytic application in Business Intelligence Source: Development of concepts and technologies for BI using BA creates an ambiance where the current data management can be used to improve the quality of decisions. In addition, the existence of l arge vol umes o f t ransactional da ta, and i n particular t ransactional d ata w ith a hi gh de gree of specificity a nd pa rticularity, creates oppo rtunities f or m anagers within agricultural c ompanies to improve the accuracy of forecasts [4]. RESULTS AND DISCUSSIONS The g lobal de mand for new a nd innovative business s olutions, especially i n a gricultural companies, has increase in the last decade and continues to grow. But despite the technology that we have access to, the evolution of intelligent systems is deficient [9]. The concept of Business Intelligence is relatively new and appeared approximately 40 years ago [5] and it has not been used in agricultural companies since a few years ago and onl y in the USA. The application of multidimensional dynamic systems that supports the decision-making processes with intelligent a nd predictive s kills, ha s l ed t o t he de velopment of t he c oncept of bus iness intelligence [12] which can be a new and revolutionary tool for the development and increase of agricultural com panies. These s ystems (BI) are be coming i ncreasingly m ore c omplex, be ing capable of m ultidimensional da ta a nalysis with statistical a nalysis c apabilities a nd software th at serves much better the decision-support systems [10]. The need for business intelligence systems can be easily explained if we study the current economic cont ext: to s urvive in today's competitive agricultural and econom ic conditions a company has to develop a s uccessful s trategy t o increase i ts r esources, to anticipate f uture development of the economic environment and to know how to analyze the strategies that are used by other companies to be able to predict the its own future. Business Intelligence does all of these things. 180

5 1. Discoverability 2. Collaboration 3. Self Service 4. Insights Data Access

6 BIBLIOGRAPHY 1. Burghelea C., 2010, Trend analysis o f t he a utochthonous a gricultural segment e volution, Supplement o f Quality-access to success Journal, No. 111, ISSN , Pp ; 2. Cebotarean E., 2011, Business Intelligence, Journal of Knowledge Management, Economics and Information Technology, Vol. 1, Issus 2, pp. 2-12; 3. Davenport T.H., P rusak L., 1998, Working k nowledge: How or ganizations m anage what t hey k now?, Cambridge, MA: Harvard Business School Press; 4. Gheorghiu A., G heorghiu A., S pânulescu I., 2009, Target m arket risk e valuation, Proceedings o f t he International Conference on Econophysics, New Economics & Complexity ENEC 2009, Victor Publishing House, Bucharest, ISSN , p. 113; 5. Greenbaum J., 2003, The Paradox of ROI, Intelligent Enterprise, pp ; 6. Luhn H. P., 1958, A Business Intelligence System, IBM Journal of Research and Development, Issue 4, pp ; 7. Mărcuţă L., Mărcuţă A., Tindeche C., 2013, Analysis of R omanian G DP dur ing c risis, 2 0th I nternational Economic Conference - IECS 2013 Post Crisis Economy: Challenges and opportunities, Sibiu, Romania; 8. Nelson G. S., 2010, Business Intelligence 2.0: Are we there yet?, SAS Global Forum April , Seattle Washington; 9. Soejarto A., 2003, Tough T imes Call f or B usiness I ntelligence Se rvices, an I ndisputable A rea of G rowth, or (acc ); 10. Waltz E., 2003, Knowledge Management in t he I ntelligence E nterprise, A rtech H ouse P ublishing H ouse, ISBN-13: , p. 211; 11. Waston H. J., Wixom B. H., 2007, The Current State of Business Intelligence, Computer Magazine, Vol. 40 Issue Whiting R., 2003, Look Within Business-Intelligence Tools have a New Mission: Evaluating All Aspects of a Company s Business, InformationWeek, p. 32; 13. *** 14. *** 182