Strategi Pemilihan ERP Software Dalam Implementasi Business Intelligence Dengan Menggunakan Pendekatan Analytical Hierarchy Process

Penggantian pimpinan perusahaan yang berkala mengakibatkan adanya kebutuhan jenis bentuk pelaporan yang berbeda-beda, sehingga banyaknya jenis bentuk pelaporan pada sistem informasi yang ada, yang berakibat terjadinya ketidak akuratan data pada saat proses pembuatan laporan. Dewasa ini sudah banyak jenis ERP yang bisa menterjemahkan kebutuhan bentuk pelaporan yang tidak tergantung dengan kondisi keadaan tertentu, yaitu dengan menggunakan Business Intelligence (BI) yang dapat mendukung keputusan bisnis sesuai dengan strategi perusahaan. Pemilihan aplikasi perangkat Business Intelligence (BI) menjadi hadl yang sangat kritikal bagi para pimpinan perusahaan dalam menentukan jenis aplikasi Business Intelligence (BI) yang akan digunakan sesuai dengan persyaratan yang telah ditentukan oleh perusahaan.Aplikasi Business Intelligence (BI) sudah menjadi alat bantu strategic bagi para pimpinan perusahaan dalam memimpin, mengukur, mengoptimalkan, menemukan dan melakukan inovasi untuk melakukan perubahan pada organisasi. Saat ini banyak sekali aplikasi ERP Business Intelligence (BI) yang handal serta mudah dalam penggunaannya, misalkan SAPTM , ORACLETM, MICROSOFTTM. Permasalahan akan muncul ketika perusahaan ingin memilih ERP Software yang manakah yang sesuai dengan kebutuhan perusahaan. Proses pemilihan harus mempunyai strategi yang tepat sebagai awal implementasi Business Intelligence (BI) agar terjadi adanya integrasi dengan data yang berasal dari kegiatan iransaksi ERP system yang berjalan, yang tuujuan akhirnya mampu memberikan efesiensi bagi perusahaan baik dari proses implementasi hingga proses penerapan aplikasi Business Intelligence (BI).strategi pemilihan ERP Software Dalam Implementasi Business Intelligence ini didasarkan pada teori penelitian Oyku Alanbay, ISO 9126 dan GPPM (Global Policy and Procedure Management) melalui pendekatan AHP (Analitical Hierarchy Process) dengan tool menggunakan Expert Choice 2000

Business Intelligence (BI) Critical Success Factors

Companies are increasingly focussing their information systems efforts around Business Intelligence (BI) solutions. The benefits realised from BI vary significantly from company to company. BI systems are now being used as extensions of Enterprise Resource Planning (ERP) systems as they consolidate, transform and analyse the vast amounts of data generated by the firm. Much attention has been given to the identification of critical success factors (CSF) associated with the adoption of ERP systems. However, there is only limited research that has focussed on the CSF associated with BI implementations as part of an ERP system environment. Hence, this research documents BI specific critical success factors that industry partners, venders or systems users have identified in their presentations at conferences, education forms or formal user group meetings

INFORMATION SYSTEM FOR MODELING ECONOMIC AND FINANCIAL PERFORMANCES

The analysis of the most important financial and economic indicators at the level of some organizations from the same sector of activity, the selection of performance ratios and generating a particular analysis model help companies to move from the desireperformance, financial ratios, Decision Support Systems (DSS), Business Intelligence (BI), econometric model, information system

Business Intelligence (BI) Approach for Traffic Accidents Analysis

‘FLCRASH’ is the data source used for this report that consists of crash data from the year 2008 to 2009. The purpose of this report is to find ways to reduce the number of fatal accidents on the road by identifying the main culprits on why these accidents happen so frequently. Business intelligence is used in the process of finding the relationships and trends in the major causes of the crashes. The technologies related to Business Intelligence that will help in aiding the organization to their goal are also discussed along with the supporting factors for the use of Business Intelligence for the organization. A dashboard has been created from various graphs and charts for easy viewing and understanding of the situation so logical decisions can be made based on it. This report seeks to help us better understand the nature of why traffic accidents happen in the first place and how to prevent them from happening in the future

Data-Driven Decisions: Business Intelligence (BI) Training Skills

Organizations are drowning in data and struggling to turn disparate facts into useful information. The technological capability to collect data has expanded faster than the ability to turn data points into useful information. Never before has so much data been available for users to leverage to make a decision. Data-driven metrics can help teams make informed decisions and provide a competitive advantage. When it comes to using data to answer business questions and make a data-driven decision, technology is only one part of the solution. Business Intelligence (BI) is a discipline that attempts to turn data into meaningful insights in order to make a better decision. In the last decade BI technology has evolved as the ability to process information has increased. There is a wealth of research about BI technology but less material on the human elements and skills needed to be successful developing BI solutions. BI skills and the best methods to teach those skills need to be further analyzed to improve data-driven decisions. Without a comprehensive BI strategy, data will continue to be used in a limited capacity and provide a fraction of its potential value. The purpose of this phenomenological study was to determine which BI skills professionals believe should be taught to enable better data-driven decisions. Five BI program components were categorized and analyzed during the course of this study. These components are: (a) data management, (b) calculation intelligence, (c) delivery output, (d) consumption device, and (e) business enablement. Within each of these components, skill variables were rated by importance and their effect on user adoption. Interviews and surveys were conducted to collect data and determine the skills that should be taught and the best practices on how to teach those skills. The findings highlight which skills are important and influence user adoption, ideal formats to teach the skills, and relationship dynamics between the skills that enable BI teams to be more successful

Detection of Insurance Fraud

Diplomová práca sa zaoberá problematikou odhaľovania potenciálnych podvodných poistných udalostí s využitím Business Intelligence (BI) a jeho praktickou aplikáciou na reálne dáta povinného zmluvného a havarijného poistenia. Popisuje základné termíny v oblasti poisťovníctva, jednotlivé vrstvy architektúry BI a podrobný popis procesu implementácie od dátovej transformácie, cez použitie pokročilých analytických metód, až po prezentáciu nadobudnutých znalostí.This thesis focuses on the area of detection of potential insurance frauds by using Business Intelligence (BI) and its practical application to real data of compulsory and accident insurance. It describes the basic concepts of insurance business, the individual layers of BI architecture, and a detailed description of the implementation process from data transformation through the use of advanced analytical methods to the presentation of acquired information.

Using a Semantic Model to Build and Execute Dynamic SQL Queries

Having a semantic layer to define structured query language (SQL) based metrics, allows end-users of business intelligence (BI) products to compose SQL queries on the fly, using a graphical user interface (GUI). This disclosure describes the use of a semantic layer to define SQL-based metrics which allows end-users of business intelligence (BI) products to compose SQL queries on the fly, using a graphical user interface (UI), with support for utilizing the results of an initial query to automatically compose a new query. The use of a modeling layer to enable query composition on the fly makes such operation simple, performant, and easy to execute, without requiring building an entire new application