Beginners’ Guide to Business Intelligence Solutions

Current Condition of the Business Intelligence Tools Market

The sustained interest in Business Intelligence applications has driven large corporations, offshore software development centers as well as custom software development companies to focus on developing a wide range of Business Intelligence Tools suitable for each and every industry. The use of Business Intelligence tools in key industries from aerospace to iron and steel has also increased in recent years due to the uncertainty in global markets. Currently available tools including the Microsoft Business Intelligence software include numerous paid, freeware as well as open source and proprietary software, which are often customized by a custom software developer to suit the requirements of a specific client. Some of the additional categories of Business Intelligence Tools are discussed here and these constitute only a few of the business intelligence reporting tools commonly utilized by the enterprise.

Data Mining

Data mining combines key elements of statistics and computer science with the objective of identifying patterns in large data sets. Currently implemented data mining methodology includes various elements of database systems, statistics, machine learning and artificial intelligence to deliver actionable intelligence to managers, decision makers and data analysts in an enterprise. Apart from the analysis of the available raw data, additional operations performed by data mining process include online updating, visualization, discovered structure post-processing, complexity considerations, metrics to determine interest as well as data management. Data mining is distinct from information processing or large-scale data analysis as the process is based on “discovery” i.e. the detection of something new. As data mining deals with large data sets, various automated and semi-automated solutions are available to carry out the task. Data mining applications developed by any software development company focuses on performing the following tasks- anomaly detection, association rule learning, clustering, classification, regression as well as summarization. Current business applications include data mining in applications related to customer relationship management, determination of successful employee characteristics using HR department data, identification of customer purchase pattern by the marketing department as well as much more. Leading companies engaged in providing data mining tools for use in business intelligence reporting include Extra-Data Technologies, Clarabridge, Versium Analytics, emanio and Polygraph Media.

Data Warehousing

Data Warehousing in simple terms refers to any database utilized for reporting as well as analyzing enterprise data. The data in an enterprise is usually obtained from all over the organization including the HR, Marketing, Sales, Customer Support, Warehouse, administration departments. In some cases, the raw data may undergo a small degree of pre-processing prior to being used for reporting in a Data Warehouse. A traditional data warehouse (a warehouse operating on the extract-transform-load mechanism), houses the key functions by using separate staging, integration and access layers. The staging area stores all the raw data obtained from various enterprise-wide sources. In the integration layer the raw data stored in the staging area is integrated to transform it into a form suitable for analysis and stored in the data warehouse database. The data stored in the data warehouse database is arranged in hierarchical groups, which are accessible by the user through the access layer. Each data warehouse is often subdivided into data marts, which store subsets of the data integrated in the warehouse. The key objective of a data warehouse is thus to store data in a format suitable for analysis by the user using various techniques including OLAP and data mining.

The earliest data warehouses used by an organization were offline operational data warehouses. In these warehouses, the data was updated periodically (fortnightly, weekly or monthly) from operational systems and stored in a report-oriented format. In the next stage of data warehouse evolution, offline data warehouses came into existence. In offline data warehouses, the data was updated regularly from operational systems and the structure of the stored data was designed to aid the reporting process. The offline data warehouses later evolved into Online Integrated Data Warehouses, which updates the data in the warehouse in real-time by recording every transaction performed on the source data. Further evolution of data warehouses has resulted in the creation of the integrated data warehouse, which compiles the data obtained from the various departments of the enterprise to provide users with real-time access to actionable intelligence from all over the organization. Leading data warehousing solutions companies include Accenture, IBM, Igate and Infobright.