Going Into Business For Yourself

What Is Business Analytics?

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Business Analytics (BA) is the study of an organization's data through iterative, statistical and operational methods. In other words, business analytics try to answer the following fundamental questions in an organization: Why is this happening? What happens if the trend continues? What are the predicted outcomes? How can we optimize for the best result?

The process analyses data and provides insights into a company's performance and expected results through predictive models. Therefore, it helps the organization to make data-driven decisions and strategic moves. BA can be used to evaluate a specific product, project or process or even the entire company.Business analytics begins with the collection of the company's data - both present and past data, then followed by statistical analysis or advanced analysis (for example cluster analysis) of the data.

Specific Types of Business Analytics

BA should not be confused with business intelligence. Business intelligence is a process of collecting and examining previous data to get a sense of how the organization performed over time. It can be considered as the first step in BA.

The different types of BA include:

- Descriptive analytics is done on historical data to help understand the business' present state.

- Predictive analytics. Predictive models are employed to evaluate current data to predict future outcome.

- Prescriptive analytics. Past data is analyzed, and insights gained help to come up with recommendations on how to optimize and handle similar situations in the future.

- Decisive Analytics. Visual analysis models are employed to reflect reasoning.

Benefits of Business Analytics

Business analytics is very essential in the future and growth of any company aspiring to remain profitable. Insights gained from business analytics are beneficial to know where a business stands, to predict future outcomes and to form the basis of making proactive tactical decisions.

Business analytics identifies weaknesses in existing products or processes and highlights essential data that will prepare the organization for future growth and challenges. The information is also used to automate decision making that will deliver real-time responses, continuous improvements and optimization models giving the company a competitive advantage.

Applications of Business Analytics

The main applications of business analytics include: Analytical customer relationship management (CRM), prediction and inventory management.

Predictive analysis is done to customers' behavior across the customer cycle in all departments and locations of the company. CRM then gives an organization insight on how to retain customers and ways to improve their customer services.

When used for prediction and inventory management, a manufacturing company evaluates the demand for its goods using BA. Insights gained are used to predict GDP figures and thus influence the level of production. A retailer operating different stores will require data from all the stores to form predictive models for inventory management.

Other Applications

Business analytics is used by insurance companies to analyze past data of assets, and predict its future value to determine the premium to charge. Banks also analyze previous data of a borrower and predict his/her capability to pay before approving a loan.

In Market Basket Analysis companies identify and analyze data from high-volume consumer purchasing patterns to allow them to predict the supply and demand of these goods.

Banking firms and also the Internal Revenue Service (IRS) employ predictive models to help them distinguish one transaction from similar ones, therefore, reducing exposure to fraud. The system can identify fraudulent transactions, false insurance claims and tax frauds.

Challenges Encountered in Business Analytics

Business analytics requires acceptance from the top management and a clear business strategy for integrating predictive models. A management structure should be put in place to deal with implementing predictive models.

The company must invest in advanced technological infrastructure that should be good enough to handle the BA process effectively. There should be an ample storage space that can accommodate all the data in the company. This storage should also react extremely fast to deliver the needed data in real-time. The implementation of the system will not be effective if these requirements are not met.