İş Zekasının Karar Destek Sürecindeki Etkisi: Finans Sektörü Üzerine Bir Uygulama

Year 2020, , 197 - 206, 30.04.2020

Serdar Biroğul , Hasan Berk Gültekin

https://doi.org/10.17671/gazibtd.573999

Cited By: 1

Abstract

Günümüzde, veri ambarı (VA) ve şirketler tarafından sıklıkla kullanılan iş zekası kurumsal çözümleri, zengin görsel bileşenler sayesinde raporlama, analiz ve veri madenciliği hizmetlerini sunmakta ve karar vericiler için kolay anlaşılır ve anlamlı bilgiler sağlamaktadır. Bu çalışmada banka gibi kurumsal veri ambarı modelinde kar zarar ve bakiyesini işletme düzeyinde aşağıdan yukarı metodoloji kullanarak özetleme işlemi amaçlanmıştır. Aşağıdan yukarıya metodolojisini kullanarak bir veri mart oluşturmak; bireysel iş departmanı (finans) bilgi gereksinimlerine dayandığı için yüksek esneklik ve kullanım kolaylığı sağlamaktadır. Bu metodolojinin tercih edilmesinin bir diğer nedeni, boyutsal modellemenin temel kavramının yıldız şeması olması ve Oracle OBIEE 11g veri modelleme mimarisi tarafından desteklenmesidir. Bir bankanın fiyatlandırma politikasının temel dayanaklarından biri şubelerin kar ve zararını kontrol etmektir. Banka üzerinde yapılan uygulamalı çalışma neticesinde, kurumsal hafızanın artması ve raporlama açısından insanlara olan bağımlık ortadan kaldırılmıştır. Çalışma sonucunda uygulamaya alınan bu yapı ile bankanın finans bölümünde iletişim ve bilgi paylaşımı, kişisel verimlilik artmış, maliyet avantajı sağlanmış, yapısal verilerin yaygın kullanımı arttırılmış ve kullanıcıların iş zekası çözümlerine olan güvenleri artmıştır.

Keywords

İş zekası, stratejiler, veri ambarı, ETL, data mart, finans sektörü

Supporting Institution

Düzce Üniversitesi

Project Number

2016.07.02.408

Reviewing The Effect of Business Intelligence on Decision Support Process: An Application on The Finance Sector

Abstract

Nowadays, data warehouse (DWH) and the business intelligence enterprise solutions frequently used by companies blend the services of reporting, analysis and data mining by rich visual components and provide easy to interpret and meaningful information for decision makers. This study aims to summarize the bank profit loss and Balance in the corporate data warehouse model using the bottom up methodology at enterprise level. Building a data mart using the bottom up methodology allows; high flexibility and user friendliness, because it is based on the individual business department (finance) information needs. The other reason this methodology which was preferred, is that the fundamental concept of dimensional modelling, is the star schema and it also supported by data modelling architecture of Oracle OBIEE 11g .One of the main pillars of a bank's pricing policy is to control the profit and loss of branches. At the end of application of this concept’s study, Corporate memory became more mature and dependency on people was removed in terms of reporting. In addition communication and sharing of information within the finance department increased, personal Productivity increased and cost advantage was ensured and the widespread use of structural data, the users' confidence on business intelligence solutions increased by new data mart.

Keywords

Business Intelligence, Strategies, Data warehouse, ETL, Data Mart, Financial Sector

Project Number

2016.07.02.408

References

• T. H. Davenport, “BI and organizational decisions”, International Journal of Business Intelligence Research, 1-12, 2010.
• H. Ateş, Karar Vermede İş Zekasının Önemi Tekstil Sektöründe Bir Araştırma, M.S. Thesis, Department of Business, Dokuz Eylul University, 2008.
• H. J. Watson, B. Wixom, “The Current State of Business Intelligence”, In IEEE Computer, 40(9), 96-99, 2007.
• M. N. Aziz, Z. Sarsam, The impact and power of Business Intelligence (BI) on the Decision making process in Uppsala University: A case study, M.S. thesis, Dept. Information Systems, Uppsala University, 2013.
• F. Dakic, K. Markovski, Assessing the benefits of business intelligence use within an organization, M.S. thesis, Dept. Informatic, Lund University, 2017.
• H. Mintzberg, J.B. Quinn, Five Ps for Strategy. The Strategy Process, Prentice-Hall International Editions, Englewood Cliffs N.J., 12-19, 1992.
• M. P. Schultheis, The impact of Business Intelligence systems on the perceived quality of strategic decision making, PHD. dissertation, Dept. Business Administration , Curtin University, 2016.
• M. Özşahin, Stratejik Karar verme Hızını etkileyen Faktörler ve Stratejik Kararverme hızı firma Performans İlişkisi, M.S. Thesis, Department of Business, Gebze Technic University, 2005.
• A. Shollo, The Role of Business Intelligence in Organizational Decision-making, Ph.D. dissertation, Dept. IT Management, Copenhagen Business School, 2013.
• M. Biere, Business Intelligence For The Enterprise, 2nd ed., vol.2, New Jersey, USA: IBM Press, 26-29, 2003.
• Internet: D. J. Power, A Brief History of Decision Support Systems, http://dssresources.com/history/dsshistory.html, 2015.
• D. A. Bodislav, “Transferring business intelligence and big data analysis from corporations to governments as a hybrid leading indicator”, Theoretical and Applied Economics, 1(602), 257-264, 2015.
• B. T. Amoako, The Importance Of Business Intelligence as a Decision-Making Tool: Case Study Electricity Company Of Ghana, M.S. thesis, Dept. Business, University of Boras, 2013.
• G. J. Miler, D. Brautigam, S. V. Gerlach, Business Intelligence Competency Centers, 2nd ed., vol.2, New Jersey, USA: SAS, pp. 3, 2006.
• A. Yılmaz, Esnek Raporlama Aracı ve İş Zekası Uygulamaları ile Bütünleştirilmesi, M.S. Thesis, Department of Computer Engineering, Ege University, 2010.
• B. Wixom, H. Watson, “The BI-Based Organization”, International Journal of Business Intelligence Research, 1(1), 13-28, 2010.
• M. Gibson, D. Arnott, I. Jagielska, “Evaluating the Intangible Benefits of Business Intelligence: Review & Research Agenda”, Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference, 295-305, 2004.
• W. F. Cody, J. T. Kreulen, V. Krishna, W. S. Spangler, “The integration of business intelligence and knowledge management”, IBM Systems Journal, 41(4), 697-713, 2002.
• J. Reinschmidt, A. Francoise, Business Intelligence Certification Guide, 1st ed.,California, USA:IBM Corporation, 3-4, 11-13, 2000.
• P. Hawking, C. Sellitto “Business Intelligence (BI) Critical Success Factors”, 21st Australasian Conference on In-formation Systems, Australia, Brisbane, 2-14, 2010.
• Internet: Techopedia DWH, https://www.techopedia.com/definition/1184/data-warehouse-dw, 2018.
• Internet: DWH Wiki. Bottom-up Methodolgy, http://en.dwhwiki.info /best_practices/bottom_up, 2018.
• Internet: Business Intelligence Best Practices. Four Ways to Build a Data Warehouse, http://www.bi-bestpractices.com/view-articles/4770, 2018.
• E. Sidi, N. E. Merouani, E. A. Abdelouarit, “Star Schema Advantages on Data Warehouse: Using Bitmap Index and Partitioned Fact Tables”, International Journal of Computer Applications, 134(13), 11-13, 2016.
• Internet: ORACLE. Creating and Maintaining the Presentation Layer, https://docs.oracle.com/cd/ E28280_01/bi.1111/e10540/presentationlayer.htm#BIEMG266, 2015.
• Internet: ORACLE. Working with Physical Tables, Cubes, and Joins, https://docs.oracle.com/cd/ E28280_01/bi.1111/e10540/physicallayer.htm#BIEMG1377, 2015.
• A. Yusuf, “A Design Comparison: Data Warehouse Schema versus Conventional Relational Database Schema.”, 16th International Conference on Computing Research and Innovations, Nigeria, Ibadana, 217-222, 2016.
• Internet: ORACLE. Working with Logical Dimensions, http://docs.oracle.com/cd/E28271_01/fusionapps.1111/e20836/dimensions.htm, 2015.
• H. Al-Aqrabi, L. Liu, R. N. Hill, “Cloud BI: Future of business intelligence in the Cloud”, Journal of Computer and System Science, 1(81), 85-96, 2015.