Introduction

In this article, we shall be highlighting two data technologies which were not only taken into confidence by organizations but also contributed to the company’s business. The enterprise intelligent systems have been an essential tool for the management bench in a business firm to analyze its strengths and synchronize its pace with the market trends. Today, the data is nothing less than the unit of matter which has established an “Information Democracy” in the market. Humongous amount of data generation, builds up huge warehouses of data, which require behavioral analysis to conclude rational inferences. Business intelligence works with the dumps of data to report commercial performance, comparative studies, leading and lagging averages, and predict a future forecast up to a reasonable extent. Organizational management relies and makes use of the “intelligent” information to take steps in the direction of business, efficiency and productivity.

BI and DWH relationship

Data warehouse prepares the platform for the business intelligence systems. Data warehouse (DWH) is a repository which acquires the raw data from varied sources and arranges it logically. Once the BI tool takes over the operations, the DWH data is utilized for statistical analysis, process study, business forecasting and in cases, strategic decision making in an organization.

The BI DWH coupling offers multiple benefits listed as below –

• Cost effective systems reduce manual work and human intervention
• One time implementation can yield long term benefits
• Quick analysis of real time data. Historical analysis can help in measuring the delta vectors.
• Track the market trends and hence, business opportunities. Stay in competition.
• Data Warehouse

 

The famous computer evangelist from U.S.A. , Bill Inmon is regarded as the “father of data warehousing” concepts. He published his works in various journals and magazines, thereby justifying the need of a denormalized data mart which can serve as the primary source data mining, reporting and data analysis. His famous definition on data warehouses is as below

“A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process.”

The basic principle followed while building up a warehouse can be broken down into three stages – staging, cleansing and loading. Staging is the layer where the data is collected from all the sources and staged in a common area. Cleansing is the stage where data is transformed meaningfully and contextually. The final stage is the loading where cleansed data is loaded into warehouse tables following a specific schema model. The principle is flexible as per the business specific requirements and resource availability. Usually, the DWH tools in the area comply with ETL (Extraction, Transform and Loading) procedure but the procedure can be always engineered upon to fit into the specific scenarios.

Business Intelligence

As the name suggests, the Business Intelligence provides intelligent analysis on the business data. Business intelligence can be applied by the organization’s management for the below purposes –

Operational reporting – Performance management and KPI measurement. KPI stands for Key Performance Indicators.

Business forecasting – Consider the running trends and relatively predict the future forecast pertaining to the business.

Multi dimensional analysis – Diving into more deeper level to get sectoral or grouped analysis which can be crucial in tracking customer footprints or market stake.

Derive correlation among the factors – A deep dive into the data to study the factors effective in a category and derive a correlation among them.

Tools for BI and DWH

Appropriate tool selection remains an important aspect of data warehousing development and BI implementation. The tool is expected to be robust, stable and most importantly easily implementable and applicable in various projects.

The key factors decisive during the selection of DWH tool are data volume, data source compatibility, and data cleansing algorithms. Besides, the tool must provide the space for functional compatibility and schema support too. Popular data warehousing tools in the market are Oracle Warehouse Builder (Oracle) , Data Stage (IBM), Ab Initio, Informatica, and Talend.

Business intelligence can be implemented through OLAP tools and reporting tools. OLAP tools are expected to be quick, customizable, and secure. Popular OLAP tools in the market are BI Publisher (Oracle), Business Objects (SAP), IBM Cognos (IBM), and Micro Strategy. On the other hand, reporting tools must be database compatible, scheduler capabilities, secure and customizable, and exporting compatibility. Multiple OLAP tools like Oracle BI publisher suite, Micro Strategy, and Cognos offer reporting ability also. Besides, there are multiple tools in the market which can offer efficient reporting based on software operations and cost effectiveness.

Suggested References

• https://www.1keydata.com/datawarehousing/datawarehouse.html
• https://www.dwinfocenter.org/
• https://etl-tools.info/

 

Books on BI and Data warehousing

• Oracle Warehouse Builder 11g R2: Getting Started 2011 By Bob Griesemer
• Business Intelligence Strategy; A Practical Guide for Achieving BI Excellence By John Boyer, Bill Frank, Brian Green, Tracy Harris, and Kay Van De Vanter
• The Microsoft Data Warehouse Toolkit: With SQL Server 2008 R2 and the Microsoft Business Intelligence Toolset By Joy Mundy

 

Video Tutorials

• https://www.youtube.com/watch?v=OM1q3pjE_Mw
• https://www.youtube.com/watch?v=9Akvz2x0az4
• https://www.youtube.com/user/bilessons
• https://www.youtube.com/user/PCTeachME

 
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