Business Intelligence

Introduction

Business Intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help executives, managers, and other corporate end users make informed business decisions. It encompasses a wide range of tools, applications, and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against the data, and create reports, dashboards, and data visualizations to make the analytical results available to corporate decision-makers as well as operational workers.

History

The term "Business Intelligence" was first used by Richard Millar Devens in the "Cyclopædia of Commercial and Business Anecdotes" from 1865. Devens used the term to describe how the banker Sir Henry Furnese gained profit by receiving and acting upon information about his environment, prior to his competitors. The ability to collect and react accordingly based on the information retrieved, an ability that Furnese excelled in, is today still at the heart of BI.

Components of Business Intelligence

BI technologies provide historical, current, and predictive views of business operations. Common functions of business intelligence technologies include reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.

Data Warehousing

The concept of Data Warehousing is a vital component of Business Intelligence. A data warehouse is a large store of data collected from a wide range of sources within a company and used to guide management decisions. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.

Data Mining

Data Mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. It is an essential process where intelligent methods are applied to extract data patterns.

Process Mining

Process Mining is a process management technique that allows for the analysis of business processes based on event logs. The goal of process mining is to improve process efficiency and understanding of processes.

Complex Event Processing

Complex Event Processing (CEP) is a method of tracking and analyzing streams of information about things that happen (events) and deriving a conclusion from them. It combines data from multiple sources to infer events or patterns that suggest more complicated circumstances.

Business Performance Management

Business Performance Management (BPM) is a set of performance management and analytic processes that enables the management of an organization's performance to achieve one or more pre-selected goals.

Benchmarking

Benchmarking is the practice of comparing business processes and performance metrics to industry bests and best practices from other companies.

Text Mining

Text Mining is the process of deriving high-quality information from text. It involves the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources.

Predictive Analytics

Predictive Analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.

Prescriptive Analytics

Prescriptive Analytics is the area of business analytics dedicated to finding the best course of action for a given situation. It is related to both predictive and descriptive analytics.

Applications of Business Intelligence

BI is used to enhance the timeliness and quality of information and enable managers to make better decisions. In the context of business intelligence, decision making often implies the selection of a course of action from various alternatives. Thus, intelligence could be conceived as the process of gathering information about decision alternatives and an intelligent decision as the selection of the best course of action.

Key applications of BI include:

- Enterprise reporting: BI can be used to generate reports and dashboards for different levels of users within an organization. These reports can be interactive, allowing users to manipulate data and customize views according to their needs.

- Sales and marketing: BI tools can help sales and marketing teams to analyze customer behavior and market trends, enabling them to make data-driven decisions and improve customer engagement.

- Financial analysis: BI can be used to analyze financial data and track performance against financial goals. It can also help in forecasting and budgeting.

- Supply chain optimization: BI can help in identifying bottlenecks in the supply chain and optimizing operations for improved efficiency.

- Risk management: BI can help in identifying, managing, and mitigating risks. It can also help in compliance reporting.

- Human resources: BI can help in analyzing employee performance, identifying skill gaps, and planning for workforce needs.

Future of Business Intelligence

The future of BI is likely to be influenced by the increasing amount of data generated by businesses and the advancement of technologies such as artificial intelligence and machine learning. Some of the key trends that are expected to shape the future of BI include:

- Artificial Intelligence and Machine Learning: AI and machine learning are expected to play a key role in the future of BI. These technologies can help in automating data analysis and generating insights, thus reducing the need for human intervention.

- Data Quality Management: As the volume of data increases, the importance of data quality management is also expected to rise. Businesses will need to invest in tools and technologies that can help in maintaining the quality of data.

- Real-Time BI: With the increasing need for real-time decision making, real-time BI is expected to gain prominence. This involves the use of tools and technologies that can provide real-time insights to businesses.

- Mobile BI: With the increasing use of mobile devices, mobile BI is expected to grow. This involves the use of BI tools and applications on mobile devices, enabling users to access and analyze data on the go.

- Self-Service BI: Self-service BI, which allows users to generate their own reports and analyze data without the need for IT support, is also expected to grow in popularity.