What is Business Analytics?  

Wilder and Ozgur (2015) state that business analytics applies processes and techniques that transform raw data into meaningful information to improve decision-making. It covers key business areas such as:

  • Marketing
  • Sales
  • Human resources
  • Finance
  • Operations
  • And much more

There are four types of business analytics, and each answers four crucial questions for any business organisation:

  • Descriptive - “What happened?” 
  • Diagnostic - “Why did it happen?”
  • Predictive - “What will happen?”
  • Prescriptive  - “How can we make it happen?”
Source: Big Data Framework

The benefits of Business Analytics

The benefits of having the answers to these questions make the difference between data-driven and non-data-driven organisations.

 According to a Forrester report, "Forrester Insights-Driven Businesses Set the Pace for Global Growth Report," - “Insights-driven businesses are growing at an average of more than 30% each year, and by 2021, they are predicted to take $1.8 trillion annually from their less-informed peers.”

Decision-makers can not overestimate the value of meaningful information to their organisation in an increasingly data-driven world. The following are some of the benefits of business analytics:

  • Revenue growth 
  • Increased employee performance 
  • Customer acquisition and retention
  • Efficient decision making

Business analytics is much more than the tools. This article will focus on a simplified evolution of some associated tools, their pros & cons, functionalities, ease of use, value proposition, point of difference, and more. 



The industrial era from 1760 to 1840 and its advanced business systems required formalised business analytics. This saw inventions such as Taylor’s System of Scientific Management by  Frederick W. Taylor, also known as “Taylorism” - a system that split job tasks into individual parts, analysed them to decide which were vital and timed the labourers. His system would go on to influence Henry Ford’s assembly line.

But the need for tracking business data to derive valuable information predates the industrial era. It goes as far back as our cave ancestors and their barter system -  tallying on cave walls and paintings to record stocks and items owned. (Structured and unstructured data?).

Before the advent of the computer, the 1900s gave rise to ‘Operational Reporting’ - siloed handwritten ledgers used for organisational analysis to support business decisions. 

The information age, which started in the mid-20th century, is characterised by an economy based on information technology. It saw the advent of systems such as the decision support system (DSS) in the 1970s; examples include GPS, Crop planning, and Clinical decision-making. And in the 1980s, some researchers at IBM developed the Data warehouse -  “A core component of business intelligence, a data warehouse pulls together data from many different sources into a single data repository for sophisticated analytics and decision support” - IBM(2015).

Technological advancements such as the data warehouse and significant growth in computer systems capabilities and functionalities led to the ever-increasing value derivation from Big Data by business organisations in the 21st century. A feat that would not have been possible on previous systems and software considering the five Vs of Big data (Velocity, volume, value, variety and veracity). The innovations in machine learning, AI and automation have also contributed to all thats possible with Business analytics and its associated tool today, especially in making its processes more intuitive. 

For an organisation to derive value from its data entirely, it must cultivate a data culture by fostering data literacy organisation-wide. Hence, factors like ease of use, technical support, functionality and integration with other apps are essential when considering an analytics tool for business analytics.