Business Intelligence Applications in Retail Business: OLAP, Data Mining & Reporting Services

What is it about?

As a result of today's competitive business environment, companies have been trying to improve the util-ization of funds effectively in their budgets for information technology investments. These companies retrieve more information with the same set of resources by means of business intelligence methods. According to Rubin (Chabrow, 2004) IT budgets are not simply declining or levelling or, rather, companies are shifting from a pure cost-cut mode to a model that emphasizes agility and effciency. Tremendous daily growth ofthe company data requires more funds and investment for establishing the technologies and infrastructure necessary forgathering fast and crucial information that supports the decisionmaking process. This necessity gave birth to various business intelligence methods, which mainly aim to process mass amount of collected data from their existing application, and represent it in a way with which companies can apply to their daily competitive decisions. This application primarily concerns the implementation of business intelligence for a retail business company. The aim is to implement built-in business intelligence solutions of the Microsoft SQL Server that holds the commercial information of the company for the past three years. The customer company has already been using Microsoft products. The key items used for analyzing data are sales, momentary inventory and logistics information. The application can be grouped in ̄ve main areas: Building the data warehouse, constructing OLAP cubes, applying data mining algorithms on OLAP cubes, representing the results in reports with reporting services, and implementation.

Featured Image

Photo by William Iven on Unsplash

Photo by William Iven on Unsplash

Why is it important?

The ̄rst step is to create a data warehouse for each year from OLE DB to collect the necessary data specifically for business intelligence applications. Then OLAP(Online Analytical Processing) cubes, which are queried and periodically loaded from a data warehouse, were constructed. Data mining is the further step to analyze the data and ̄nding hidden patterns by applying classification, association, and forecasting algorithms. Finally, the specified data was reported with the Reporting Services tool of the MSSQL server to provide users with meaningful data in an organized way.

Perspectives