Business decisions can easily be based on personal and individual experience, expertise, and instinct. Business data analytics reduces cognitive and personal biases by using data as the primary input for decision-making. When performed well, business data analytics can create a competitive advantage for the organization.

For example, analytics models based on weather, soil, and other conditions have been found to be more accurate in predicting the price and quality of red wine after it has been aged compared to the wine experts who influence the decision-making based on their own cognitive biases as to what they enjoy and do not enjoy in a wine.

The objective of business data analytics is to explore and investigate business problems or opportunities through a course of scientific inquiry. The specific outcomes of business data analytics are dependent on the type of analysis and inquiry that is being performed.

There are four types of analytics methods:

• Descriptive: Provides insight into the past by describing or summarizing data. Descriptive analytics aims to answer the question “What has happened?”
  • Example: Aggregation and summarization of sales data based on geographic regions.
• Diagnostic: Explores why an outcome occurred. Diagnostic analytics is used to answer the question “Why did a certain event occur?”
  • Example: Investigation of dipping revenue in a particular quarter.
• Predictive: Analyzes past trends in data to provide future insights. Predictive analytics is used to answer the question “What is likely to happen?”
  • Example: Predicting profit or loss that is likely to happen in the next financial year.
• Prescriptive: Uses the findings from different forms of analytics to quantify the anticipated effects and outcomes of decisions under consideration. Prescriptive analytics aims to answer the question “What should happen if we do …?”
  • Example: What will happen to the total sales if the organization increases the marketing spend by 10%?

New modelling techniques are now available due to the advances in machine learning, deep learning, optimizations, and advanced data science. These techniques, coupled with the availability of disparate data and related data infrastructure, have increased the feasibility of deploying analytics solutions for business problems or opportunities.

Examples of Analytics Contexts and Business Use Cases 

Analytics Context	Typical Business Cases
Customer Analytics	Predicting customer churn and behaviour Understanding customer segments Developing proactive campaigns to retain them Understanding customer lifetime value
People Analytics	Understanding and predicting attrition Assessing performance
Supply Chain Analytics	Predicting and matching demand and supply Managing inventories Conducting root cause and failure analysis
BFSI Analytics (Banking, Financial Services, Insurance)	Quantifying portfolio risks and value at risk (VaR) Detecting and preventing fraud by implementing credit risk models Pricing products
Digital Analytics	Utilizing platforms and channels Assessing digital marketing and search engine optimization Analyzing web and social media engagement statistics
Healthcare Analytics	Predicting disease vectors and outbreaks Discovering new drugs and genomics Researching for lifestyle diseases
Government and Public Sector Analytics	Improving e-Governance initiatives Understanding and acting on defense and security threats Understanding public sentiment on policies