This post is part of Series Business Intelligence – Tools & Theory
Currently running topic for this series is listed as below :
Series Business Intelligence – Tools & Theory
>>Chapter 1 : Business Intelligence an Introduction
>>Chapter 2 : Business Intelligence Essentials
>>Chapter 3 : Business Intelligence Types
>>Chapter 4 : Architecting the Data
>>Chapter 5 : Introduction of Data Mining
>>Chapter 6 : Data Mining Techniques
>>Chapter 7 : Introduction to Data Warehousing
>>Chapter 8 : Different Ways of Data Warehousing
>>Chapter 9 : Knowledge Management
>>Chapter 10 : Data Extraction
>>Chapter 11 : Business Intelligence Life Cycle
>>Chapter 12 : Business Intelligence User Model
>>Chapter 13 : Business Intelligence Issues and Challenges
>>Chapter 14 : Business Intelligence Strategy and Roadmap<You are here>
Continuing from my previous post on this series, If you have missed any link please visit link below
We are going to Cover the Following Points in this article
- Understand Limitations of Business Intelligence
Understand Limitations of Business Intelligence
Data warehouses, that the BI systems attach to, generally do not contain all the necessary data to generate the revenue from the existing company data. The data present in the data warehouse may not be in the right format for sales. The relationships in the data warehouse may not be optimized for the revenue generating user community. Data warehouses usually do not store old data to allow the formation of trended information.
The final data quality is questionable. The queries can take a long time to run that is more than 10mins. The IT departments will have data warehouses but only experts can access them. The sales representative will not or may not use the BI tools. The queries solved out of the BI systems can be burdensome and time consuming for the end users. The data analysts and the business intelligence tools do not have to directly generate the revenue but there should be more effective and productive sales representatives.
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