Business analytics is the practical application of statistical analysis and technologies on business data to identify and anticipate trends and predict business outcomes. Research firm Gartner defines business analytics as “solutions used to build analysis models and simulations to create scenarios, understand realities and predict future states.”
Business analytics vs. data analytics
Business analytics is a subset of data analytics. Data analytics is used across disciplines to find trends and solve problems using data mining, data cleansing, data transformation, data modeling, and more. Business analytics also involves data mining, statistical analysis, predictive modeling, and the like, but is focused on driving better business decisions.
Business analytics vs. business intelligence
Business analytics and business intelligence (BI) serve similar purposes and are often used as interchangeable terms, but BI can be considered a subset of business analytics. BI focuses on descriptive analytics, data collection, data storage, knowledge management, and data analysis to evaluate past business data and better understand currently known information. Whereas BI studies historical data to guide business decision-making, business analytics is about looking forward. It uses data mining, modeling, and machine learning to answer “why” something happened and predict what might happen in the future.
Business analytics techniques
According to Harvard Business School Online, there are three primary types of business analytics:
- Descriptive analytics: What is happening in your business right now? Descriptive analytics uses historical and current data to describe the organization’s present state by identifying trends and patterns. This is the purview of BI.
- Predictive analytics: What is likely to happen in the future? Predictive analytics is the use of techniques such as statistical modeling, forecasting, and machine learning to make predictions about future outcomes.
- Prescriptive analytics: What do we need to do? Prescriptive analytics is the application of testing and other techniques to recommend specific solutions that will deliver desired business outcomes.
Digital skills training company Simplilearn adds a fourth technique:
- Diagnostic analytics: Why is it happening? Diagnostic analytics uses analytics techniques to discover the factors or reasons for past or current performance.
Benefits of business analytics
Simplilearn says business analytics can help your business in six ways:
- Improving operational efficiency through daily activities
- Helping you understand your customers more precisely
- Providing data visualizations that offer projections for future outcomes
- Providing insights to aid in decision-making and planning for the future
- Measuring performance and driving growth
- Discovering hidden trends, generating leads, and helping you scale your business in the right direction
Examples of business analytics
Microsoft boosts collaboration
Starting in 2016, Microsoft’s workplace analytics group worked with commercial real estate company CBRE to study how the physical workspace fosters collaboration. It based its new workplace layout on the insights from the analytics project. Microsoft estimates that the changes it made as a result saved a total of 100 hours of work per week across 1,200 employees, which in turn led to an estimated cost savings of $520,000 per year in employee time and increased collaboration within teams.
Uber enhances customer support
In 2018, Uber created Customer Obsession Ticket Assistant (COTA), a tool that leverages machine learning and natural language processing techniques to help its agents deliver better customer support. Later versions would use deep learning and A/B testing to improve COTA further. Through the A/B testing, the company determined implementing version 2 of COTA would improve customer service and save it millions of dollars by streamlining the ticket resolution process.
Blue Apron forecasts orders
Blue Apron uses predictive analytics to forecast demand for its meal kits to optimize inventory and reduce spoilage, as well as to optimize staffing by determining how many personnel it will need to ship merchandise.
Business analytics tools
Business analytics professionals need to be fluent in a variety of tools and programming languages. According to the Harvard Business Analytics program, the top tools for business analytics professionals are:
- SQL is the lingua franca of data analysis. Business analytics professionals use SQL queries to extract and analyze data from transactions databases and to develop visualizations.
- Statistical languages. Business analytics professionals frequently use R for statistical analysis and Python for general programming.
- Statistical software. Business analytics professionals frequently use software including SPSS, SAS, Sage, Mathematica, and Excel to manage and analyze data.
Business analytics dashboard components
According to analytics platform company OmniSci, the main components of a typical business analytics dashboard include:
- Data aggregation. Before it can be analyzed, data must be gathered, organized, and filtered.
- Data mining. Data mining sorts through large datasets using databases, statistics, and machine learning to identify trends and establish relationships.
- Association and sequence identification. Predictable actions that are performed in association with other actions or sequentially must be identified.
- Text mining. Text mining is used to explore and organize large, unstructured datasets for qualitative and quantitative analysis.
- Forecasting analyzes historical data from a specific period to make informed estimates predictive of future events or behaviors.
- Predictive analytics. Predictive business analytics use a variety of statistical techniques to create predictive models that extract information from datasets, identify patterns, and provide a predictive score for an array of organizational outcomes.
- Once trends have been identified and predictions made, simulation techniques can be used to test best-case scenarios.
- Data visualization. Data visualization provides visual representations of charts and graphs for easy and quick data analysis.