Business Analytics refers to the practice of investigation of past business performance using data and statistical models in order to develop new insights and understanding of future business performance. Business analytics makes extensive use of statistical and quantitative analysis, explanatory and predictive modelling and fact-based management to drive decision-making.
Business Analytics is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business and an organizational commitment to data-driven decision making.
Also Read: What is Business Analytics?
Examples of Business Analytics uses include:
- Exploring data to find new patterns and relationships. (data mining)
- Explaining why a certain result occurred. (statistical analysis, quantitative analysis)
- Experimenting to test previous decisions. (A/B testing,
- multivariate testing)
- Forecasting future results. (predictive modelling, predictive analytics)
Once the business goal of the analysis is determined, an analysis methodology is selected and data is acquired to support the analysis. Data acquisition often involves extraction from one or more business systems, cleansing, and integration into a single repository such as a data warehouse or data mart.
The analysis is typically performed against a smaller sample set of data. Analytic tools range from spreadsheets with statistical functions to complex data mining and predictive modelling applications.
As patterns and relationships in the data are uncovered, new questions are asked and the analytics process iterates until the business goal is met.
Deployment of predictive models involves scoring data records (typically in a database) and using the scores to optimize real-time decisions within applications and business processes. Business Analytics also supports tactical decision making in response to unforeseen events, and in many cases, the decision making is automated to support real-time responses.
While the terms business intelligence and business analytics are often used interchangeably, there are some key differences:
|BI vs BA||Business Intelligence||Business Analytics|
|Answers the questions:||What happened? When? Who? How many?||Why did it happen? Will it happen again? What will happen if we change? What else does the data tell us that never thought to ask?|
|Includes:||Reporting (KPIs, metrics)Automated Monitoring/Alerting (thresholds)Dashboards, ScorecardsOLAP (Cubes, Slice & Dice, Drilling)Ad hoc query||Statistical/Quantitative Analysis, Data Mining, Predictive Modeling, Multivariate Testing|
Recognizing the growing popularity of business analytics, business intelligence application vendors are including some Business Analytics functionality in their products.
More recently, data warehouse appliance vendors have started to embed Business Analytics functionality within the appliance. Major enterprise system vendors are also embedding analytics, and the trend towards putting more analytics into memory is expected to shorten the time between a business event and decision/response.