What are the 4 Core Functions of Business Analytics?

Did you know there are four core functions of business analytics? Do you know what they mean? Do you know what business analytics is?

Now, you might think business analytics is for geeks but as a manager or business owner, these four functions of business analytics are very important.

For those who are not sure, business analytics is the practice of iterative, methodical exploration of an organisation's data, with an emphasis on statistical analysis. Business analytics is used by companies committed to data-driven decision-making and has traditionally been an investigation of past business performance to gain insight and drive business planning. However, some companies are now looking at preventing something happening before it happens (or at least ameliorating its effects) on the business.

There are now a number of universities offering degrees in business analytics, and demand is growing. These business analysts, often using business analytics software that mine an organisation's data, can apply statistical methods to a specific project, process, product, or even the entire company. Business analytics are performed in order to:

• Identify weaknesses in existing processes;
• Supplement decision support systems;
• Aid continuous improvement programmes; and
• Highlight meaningful data that will help an organisation prepare for future growth and challenges.

Moving on, the four functions are:

1. What has happened?
2. Otherwise known as Descriptive Analytics.
3. Considered a reactive analytical technique.
4. With this function we use business intelligence and data warehousing to support management and operational reporting, and dashboards using aggregated data (i.e. monitoring or what is happening now?).
5. It is a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis.
6. What is Likely to Happen?
7. Otherwise known as Predictive Analytics.
8. In simple terms, what will happen.
9. With this function we use statistical models to quantify cause and effect to predict what is likely to happen or how someone is likely to react.
10. It is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. Predictive analytics does not tell you what will happen in the future, only what might happen based on the past.
11. For example, a consumer's credit score predicting likelihood to repay a loan.
12. What Should We Do?
13. Otherwise known as Prescriptive Analytics.
14. In simple terms, why it happens.
15. With this function we use optimisation algorithms to prescribe actions to improve human decision-making around outcomes.
16. It is dedicated to finding the best course of action for a given situation.
17. Prescriptive analytics is related to both descriptive and predictive analytics.
18. While all types of analytics ultimately support better decision making, prescriptive analytics outputs a decision rather than a report, statistic, probability or estimate of future outcomes.

1. What Actions Should We Take to Prevent Undesirable Outcomes?
2. Otherwise known as Preventative Analytics.
3. In simple terms, stop it before it happens (or at lest reduce its effects).
4. Considered a proactive analytical technique.
5. With this function we use deep learning and machine learning to make preventative recommendations to avoid undesirable situations and outcomes.
6. Preventative analytics exploits data to drive digital transformation and create am intelligent enterprise.
7. It can dramatically reduce, or even eliminate, costs while increasing (depending on how it is implemented and used):
8. Customer satisfaction.
9. Employee satisfaction.
10. Citizen satisfaction.
11. Quality of life.

The use of these four analytical functions across a business, and in synergy with other business functions, can lead to numerous cost reductions. For the average business, the use of analytics can lead to a positive impact on the bottom line.

For example, the big firms in the fitness industry already utilise preventative analytics, to varying degrees, in:

• Preventing customer attrition:
• In the fitness industry we would look at data such as times visited the venue over a set period (for example, in last month). A reduction in visits is generally a marker for customer attrition, and this should trigger an intervention targeted to the individual customer.
• In essence, we are trying to predict when a customer will leave based on their current behaviour rather than after they have left (by using descriptive analytics).
• New customer acquisition is 5 to 25 times more expensive than retaining an existing customer.
• Increasing customer retention rates by 5% can boost profits by 25-95%.
• Preventing employee attrition:
• Businesses spend approximately 20% of an employee's annual salary to replace that employee when they (prematurely) leave.
• That cost is even higher for high skilled and senior management posts.

We can also consider the following model:

• Reactive: Try to re-engage the customer after they have left.
• Periodic: Scheduled communications with the customer.
• Proactive: Eliminate feelings of customer attrition at an early stage.
• Predictive: Use analytics to predict (individual) customer attrition.