Business Intelligence

Table of Contents

INTRODUCTION3

Business Intelligence Application4

CRITICAL FACTORS: DEFINITION AND CONCEPTS7

PREVIOUS STUDIES OF CSFS FOR IMPLEMENTATION OF BI SYSTEMS8

SUCCESSFUL SYSTEMS IMPLEMENTATION STRATEGIES9

IT AND BUSINESS INTELLIGENCE11

CONCLUSION12

END NOTES13

Business Intelligence - Literature Review

Introduction

Business Intelligence means leveraging the data in an application or system to gain improvements and efficiencies in the system as well as in the business process. For this project, by starting with a flexible software package, Business Intelligence (BI) was already incorporated in the system and was accessible to all the users if they wanted to. The article by Smith, "Analysis Everywhere" showed how accessibility of BI should be considered and this was also accomplished in this project since the software package chosen had data mining and analysis tools for managers and even low level support persons to view. It allowed managers to check workload and gave them work analysis reports to see not just what tasks are being done but also gave useful statistics on workload either to justify hiring of more staff or the need for staff reallocation [1].

In today's highly competitive and increasingly uncertain world, the quality and timeliness of an organization's “business intelligence” (BI) can mean not only the difference between profit and loss, but also even the difference between survival and bankruptcy.

BI is the conscious, methodical transformation of data from any and all data sources into new forms to provide information that is business-driven and results-oriented. It will often encompass a mixture of tools, databases, and vendors in order to deliver an infrastructure that not only will deliver the initial solution, but also will incorporate the ability to change with the business and current marketplace [2].

The purpose of investing in BI is to transform from an environment that is reactive to data to one that is proactive. A major goal of BI is to automate and integrate as many steps and functions as possible. Another goal is to provide data for analytics that are as tool-independent as possible.

Centralized centres of competency were created to provide a means for end-users to become productive quickly. The need to set corporate standards for analysis tools was one of the most significant benefits from these centres [3]. The information warehouse proved that accessing data in place are not always desirable, but capturing the metadata about existing information makes perfect sense. Before we transform current information, we need to know all we can about its current contents and form. We are entering an era where packaged BI solutions are desired. One driving force behind these is the need to deliver sophisticated metrics and analyses to top management [4].

BI decision-support applications facilitate many activities, including: multidimensional analysis, for example, online analytical processing (OLAP); click-stream analysis; data mining; forecasting, business analysis; balanced scorecard preparation; visualization; querying; reporting and charting (including just-in-time and agent-based alerts); geospatial analysis; knowledge management; enterprise portal implementation; mining for text, content, and voice; digital dashboard access; and other cross-functional activities.

Examples of BI decision-support databases include enterprise-wide data warehouses, data marts (functional and departmental), exploration warehouses (statistical), data mining databases, web warehouses ...