Beginners’ Guide to Business Intelligence Tools

What is Business Intelligence and what is the utility of BI Tools?

Business Intelligence (BI) is defined as any real-time, current or past information that helps various business professionals including managers analyze current/past activities to help in predicting future course of the company’s business. Business Intelligence is thus a stream of data and hence it requires sorting as well as analysis before it is suitable for use in a company’s decision-making process. Such sorting and analysis of relevant data is usually carried out by software solutions, to ensure superior speed and accuracy of the data collection and analysis procedure. Any and all software solutions aiding the process of generating Business Intelligence for an enterprise are termed as Business Intelligence Tools. So BI data can help an enterprise rapidly adjust to changing business environment, while BI tools ensure that the changing business environment is rapidly as well as correctly identified and reported to facilitate a streamlined decision-making process. In case of historic data, BI tools usually sort and analyze data, which was previously stored in the enterprise database.

Commonly Used Business Intelligence Tools

BI tools are commonly classified into the following categories:

• Local Information Systems
• Business Performance Management
• Process Mining
• Dashboards
• Online Analytical Processing (OLAP)
• Data Warehousing
• Data Mining
• Reporting and Querying Software
• Spreadsheets

Most of these Business Intelligence technology and tools apart from spreadsheets are available as part of software suitable for a specific industry, standalone solutions, ERP system components or as a BI software suite. These solutions are often developed by a custom development company in response to requirements specified by a client or detected after a thorough analysis of the company’s business model. A few open-source business intelligence tools are also available, however most enterprises prefer to use proprietary business intelligence technology to ensure adequate protection of critical data. The type of business intelligence architecture implemented by an organization varies on the industry, market conditions as well as specific market requirements. Some of the commonly available BI tool categories are described here:

Local Information Systems

The term Local Information System (LIS) originated from its use in the public sector of the UK; other terms used synonymously with LIS in different parts of the world include – Data Observatory and Community information systems. In the global business intelligence technology market, LIS applications are usually limited to providing support for geographic reporting of enterprise operations. The functions supported by LIS tools often overlap with some of the features of Geographic Information Systems and Knowledge Management tools. Unique functions of LIS include providing a region-specific database accessible by citizens, policy makers, managers as well as data experts. LIS statistics are usually compiled with respect to a small area such as the National Neighborhood Statistics projects in the UK. Currently operating Local Information Systems include the DarlingtonLIS, UK; Newcastle Council; New Zealand Ministry of Health and Fife Council, UK.

Business Performance Management

Business Performance Management (BPM) refers to a set of management as well as analytical processes designed to facilitate improvement of a company’s processes in accordance with the preset goals of the organization. Such tools are capable of handling large amounts of data and help managers in determining fruitful interventions designed to improve the functioning of specific business processes. Currently available tools for BPM are based on the balanced scorecard framework and queries supported by BPM tools include metric-related queries, customer/stakeholder queries, goal-alignment queries, cost/risk queries as well as much more.

Process Mining

Process Mining is commonly defined as a process management technique which allows decision-makers to analyze business processes on the basis of available event logs. These event logs are automatically generated by the enterprise event system and the aim of Process Mining is to facilitate improvement of overall performance by providing tools and techniques designed to identify social, organizational, control and process structures by using the event logs. The technique is preferred if other conventional techniques fail of provide adequate insight into an enterprise process. Process Mining features in certain contemporary management techniques such as Business Process Intelligence, Business Operations Management and Business Activity Monitoring. Current Process Mining techniques are classified into the following categories: extension, conformation analysis and discovery.


Dashboards are defined as an easily read, real-time interface, which provides a snapshot of the current status of key business processes as a chart or graph. Dashboards are one of the most prolific and widely used tools for supporting informed and instantaneous decision making. Dashboards are capable of displaying a wide variety of user-defined key performance indicators significant for different departments of an organization. A production dashboard can display the total number of units produced, the average rate of production per hours, number of produced units which failed inspection during a one month period and so on. The key benefit of a dashboard is its capability to be customized to show only the relevant data, which results in significant time savings during the process of decision making. Currently available dashboards are commonly classified into three categories- desktop widgets, web-based applications as well as standalone solutions, which feature spark lines, bullet graphs, pie charts and/or bar charts to represent the data. Dashboards are also capable of being integrated into mobile business intelligence solutions to ensure seamless connectivity irrespective of the user’s location.

Online Analytical Processing (OLAP)

OLAP tools are designed to help users interactively analyze multidimensional data from multiple perspectives. OLAP as a business intelligence method includes various aspects of data mining and rational reporting. The term Online Analytical Processing is a derivative of OLTP (Online Transaction Processing), which is used in reference to traditional databases. Key analytical operations performed by these tools are- consolidation, drill-down and slice ‘n’ dice. The consolidation process refers to the aggregation of data to enable its analysis on multiple dimensions. The drill-down technique enables users to navigate through large quantities on data to sort out the relevant data. The slice ‘n’ dice technique allows users to remove (slice) a particular data set to allow closer inspection (dicing) of the selected data set. Databases with OLAP support utilize a multidimensional data model for supporting rapid execution of both ad-hoc as well as complex analytical queries.

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.

Do Business Intelligence Strategies Really Boost Productivity Levels?

Facing a more cutthroat market, their battle to attain higher efficiency levels along with the latest overall collapsing economy, in conjunction with numerous other challenges, have forced many organizations to continually seek out brand new approaches as well as tools to help make their top priorities achievable.

The principle enhancements occurring during the last decade that assist businesses to make the most effective decisions include the rapid development of information engineering, the replacing many organization procedures through automation, as well as the possibility to store and handle vast amounts of raw data.

Enter Business Intelligence. Business Intelligence refers to having the ability to foresee future events, improve response times and prevent obstructions long before they’ve even taken place. Apparently a growing number of enterprises are checking out these approaches, according to Constellation, as the results of a recent study that they had conducted, Big Data along with Business Intelligence tools will be rocketing in use during 2013. According to these same results, 50% of CTO’s take into consideration that Big Data along with Business Intelligence tools will be must-haves for the majority of companies and SMB’s.

Business Intelligence. When and why do we need this?

When a business expands, logically, the methods and amount of data collected and generated expand at the same time. During this growth, frequent tasks such as creating accurate reports or even disclosing certain information at each week’s group meetings begin to develop and expose a myriad of troubles. This, combined with diminished productivity ranges as well as disappointing revenue projections, can give companies more determination to carry out distinct Business Intelligence strategies and seek out tools and resources to begin collecting along with mining all of the data into useful information swiftly. Minus the correct tools, looking for the reason for reduced efficiency levels can be an extremely hard process.

Driven by this increasing demand for Business Intelligence applications, numerous companies are bringing new services to the marketplace, centered both upon the larger organizations as well as much smaller businesses.

Principal benefits of Business Intelligence

1. Time savings. Among the key features of Business Intelligence is that most enterprise techniques are computerized and automated, which results in the generation of incredible savings in terms of both time and actual costs, which experts claim plays a role in increasing overall productivity and efficiency levels.

Let’s think about one example. It might take days for an accounting department to organize its monthly fiscal reports employing traditional resources. But, together with the right Business Intelligence application, the same department can quickly have the necessary fiscal data, and routinely create the statement using only a quick mouse click.

2. Quicker and easier access of information. It can be apparent that, over the last couple of years, the amount of organizational information has grown exponentially. For that reason, it is essential that organizations increase their attempts at digitizing and gathering their information through document management software. Nonetheless, it’s also essential that Business Intelligence tools offer you easy to get at details in which allows the company to see how the data has combined and evolved so that they can better predict future events.

3. Correct as well as pertinent decisions. In order to stay ahead of the competition, reduce costs and also enhance earnings, an organization needs to make proper decisions. To achieve this, these types of choices must realistically be based upon reliable as well as pertinent information, which is where the traditional processes cannot succeed. Most of these tools as well as underlying systems can’t ensure the relevancy and accuracy available within the company’s data.

4. A quick Return (ROI). Huge companies like McDonald’s™, Tesco™ or even Google Inc.™ are usually “intelligent companies” that were implementing Business Intelligence techniques during the last decades in order to keep up their high-level status in their markets. So far, the majority of SMB’s have not deemed employing Business Intelligence resources to be critical, possibly because of their high cost and increased complexity, or even given that they don’t even know where to begin. These days, however, a whole new era regarding Business Intelligence software programs has been directed exclusively in the direction of these types of firms. These kinds of new tools are likely to be quite simple to utilize, are usually targeted specifically for the financial market, and, on many occasions, they’re very easily incorporated using a department’s present spreadsheets.

In short, on the one hand, current Business Intelligence tools can be easily implemented and integrate well with the current software, they’re less complicated and are available at a much lower cost. Alternatively, the vast array of basic business processes and operations acquired through the implementation of this software are, certainly, beneficial strategies that can significantly improve the organization’s on-going business activities and also enhance efficiency levels with an almost immediate return on investment.

Business Intelligence – The 5 Stage Process to Maximize Business Profits

Every business works with the aim of earning profits and this can be achieved by taking the right business decisions. Business leaders take innumerable decisions that influence the work in various ways. However, the ultimate goal is to make the decisions effective enough in order to carve the path of profit for the organizations. Hence, successful implementation of plans is the foremost need of every business and Business Intelligence proves helpful in this context. Let’s get an insight to Business Intelligence and its components:

Business Intelligence:

Business Intelligence plays a primary role while implementing strategies and the right planning procedures. BI technology assists its users in gathering, storing, accessing, and analyzing the data. The set of applications covered under Business Intelligence allows the companies in effective implementation of Decision Support System, applying Online Analytical Processing (OLAP) concepts, Statistical Analysis, Forecasting, and Data Mining.

Business Intelligence serves in sending the information to the right decision makers at the right time. BI is preferred by lot of users, as it leads them in reaching the facts based on conclusion or more commonly known as ‘single version of the truth’. This gives the best end product and leads an organization to convert the raw data into useful information; thus, bringing profits.

Characteristics of a Business Intelligence Solution:

It is a single point of access to information
It gives well-timed answers to business questions
It allows effective implementation of BI tools, applications, and systems in all departments of an organization

Stages of a Business Intelligence Process:

Business Intelligence process gathers raw data and converts it into useful information; and further transforms it into knowledge that must be used with intelligence. The BI process is based on five major stages mentioned below:

Data Sourcing: This stage works on gathering the data from different sources including, E-Mail messages, images, formatted tables, reports, sounds and other relevant sources. The major role of Data Sourcing is to gather the data in digital form; therefore, the sources for collecting data are computer files, digital cameras, scanners etc.

Data Analysis: The next stage is to arrange the data collected from Data Sourcing and estimating the data depending upon the current and future trends. Also known as Data Mining, this stage also predicts the information that will be needed in future.

Situation Awareness: This stage of the Business Intelligence process helps in filtering the relevant data and using it in relevance to the business environment. The users compile the data by keenly observing the market forces or Govt. policies, so that it becomes easier to take decisions. Combinations of different Algorithms are used to aptly identify the Situation Awareness.

Risk Assessment: Taking risks is part of every business; but, if one can take precautions, it turns extremely helpful. Risk Assessment stage helps in identifying the current and future risks, including cost benefits; choosing the best options; and comparison between two decisions to identify which one will turn beneficial. It summarizes the best choice amongst varied options.

Decision Support: This last stage in BI process aids in utilizing the information with intelligence. The aim of this stage is to warn the users about various crucial events like poor performance by staff, takeovers, changing trends in market, sales fluctuations and much more. It aids in taking better business decisions for improvising staff morale and customer satisfaction.

Significance of Business Intelligence:

Business Intelligence plays a significant role in the working of organizations and helps them to continue with progression. Following is the significance of Business Intelligence:

BI helps in studying the changing demands; therefore, a company can have accurate and updated information about customer preferences
It aids the Managers to remain informed about competitors’ behavior and their actions
It assists the analysts in knowing the adjustments that need to be done for maximizing profits
It helps organizations to make future plans based upon relevant data organized to give better results.

Business Intelligence Users:

IT Users: These users make use of BI tools for development purposes, including Data Integration, Data Modeling, Report Generation, Presentation, and Final Delivery. IT users also use it for supporting the individuals in the organization and provide reports to the outside customers.
Power Users: These types of users include ‘Professional Analysts’ who have been using the BI tools. These users study the pre-defined reports and provide support in taking the right decisions, but they are not obligated to take decisions.
Business Users: They review the analysis report presented by the Power Users. These users can apply their own queries on the data, and create reports based on those queries.
Casual Users: These users have the privilege of making changes in report information and may enter the data that can help to perform further high-level research.
Extra-Enterprise Users: These users are usually not a part of an organization and are external sources that help the companies in taking more tactical decisions. These may include External Partners, Customers, Business Analysts, Suppliers etc.

Hence, Business Intelligence solutions assist the organizations to take effective decisions and get a deeper insight of business data in order to meet the specified goals. By using all the tools, applications, and systems, organizations can speed up the delivery of product by achieving the targets.