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.

Decision Engineering

Decision Engineering is defined as a framework, which unifies various leading practices in the field of enterprise decision-making to improve the overall decision-making procedure by providing a structured approach. The decision engineering process is designed to overcome problems resulting from a “complexity ceiling” of the decision-making process. This “complexity ceiling” usually results from a mismatch between the complexity of a particular situation and the sophistication of the decision-making procedure being implemented. Decision engineering acts as a framework for providing advanced analytic techniques to a non-enterprise user while simultaneously integrating machine learning and inductive reasoning techniques to streamline the organizational decision-making procedure. The use of Decision Engineering as a business intelligence tool by enterprises is still in its infancy and further development would be required before decision engineering develops into a viable business intelligence reporting tool.

Reporting and Querying Software

Reporting and querying software are designed to provide users with access to the data stored on enterprise databases subsequent to submission of user-queries. Such tools are designed to provide a logical format to the available data sets to support enterprise-wide data accessibility as well as speed-up the organizational decision-support process. Currently, various open source business intelligence tools as well as commercial business intelligence reporting software are developed by software development companies all over the world. Some of the leading reporting and querying tools are mPower, Zoho Reports, Cognos BI, GNU Enterprise and JasperReports. Many offshore software development companies in India also provide customized versions of reporting and querying software to streamline the overall enterprise-wide decision making process.


A spreadsheet is defined as an interactive computer program, which allows the analysis of available information by use of a tabular format, which originated from the use of paper-based accounting spreadsheets. On a spreadsheet, users can modify the values in each cell of the spreadsheet and are now used widely by the financial sector as a replacement of paper-based accounting methods. The digital spreadsheets allow users to automatically calculate values after making modifications to the available data as and when necessary. Apart from the standard arithmetic calculation support, currently available spreadsheets also features support for a wide range of statistical and financial operations built into this commonly used business intelligence tool. Spreadsheets are probably the most widely used and easily available among a wide range of proprietary and open source business intelligence tools. The first spreadsheet introduced for a micro computer was Visicalc, which was overtaken by Lotus 1-2-3 at a later date. Currently Microsoft Excel, available as part of the Microsoft Office Package, is the leading spreadsheet solution utilized by enterprises all over the world.