What are the 4 Types of Business Analytics?

Venkat Obillaneni

Cloud Data Engineer at Aezion, Inc. | + posts

Venkat Obillaneni is Aezion Cloud Data Engineer and Data Practice Lead.

Co-workers are seen collaborating on business analytics in an office setting.

What Are the 4 Types of Business Analytics?

Did you know that the estimated total value of the business analytics software market is projected to be worth over $57 million by 2023? With numbers like these, it’s clear that business analytics have earned a crucial spot in the modern-day business world. This article highlights the importance of business analytics, highlighting how it can be used to enhance any type of business operation. Read on to learn more about business analytics and discover what the four pillars of business analytics entail.

Understanding Business Analytics

The term business analytics refers to the process of transforming raw data into valuable insights to improve business decisions. However, analytics is a broad term that can mean different things depending on where it sits on the data analytics maturity model. Modern business analytics generally falls into four distinct categories: descriptive, diagnostic, predictive, and prescriptive.

The Four Types of Business Analytics

Each of the four types of business analytics can be used to enhance business operations and enable companies to make better data-driven decisions. First, it’s essential to understand the differences between these types of business analytics to determine better what application you should use them with and why. So now, let’s delve deeper into the four types of business analytics to grasp how each of the four pillars of business analytics can be used to enhance any business operation.

Pillar 1: Descriptive Analytics

Descriptive analytics refers to the process of using current and historical data to identify trends and relationships. Descriptive analytics is the most commonly used type of business analytics as it provides reporting centered on past events, which is often readily available. In addition, descriptive analytics can be considered the first pillar of business analytics in the analytics maturity model, answering the simple question, “What happened?”

In general, descriptive analytics helps companies understand elements such as the following:

How much the company sold over a specified amount of time
The company’s overall productivity rate over a specified amount of time
Customer churn over a specified amount of time
Month-over-month sales growth
Year-over-year pricing changes

With descriptive analytics being the base of business analytics, companies must build core competencies in descriptive analytics before trying to incorporate more advanced business analytics into their business operations. These core competencies include data modeling fundamentals, communicating data with the right visualizations, and basic dashboard design skills.

Pillar 2: Diagnostic Analytics

Moving up the analytics maturity model, we soon find diagnostic analytics. Like descriptive analytics, diagnostic analytics uses historical data to answer a question. Instead of focusing on “the what,” diagnostic analytics aims to determine the root cause of an occurrence or trend. In general, diagnostic analytics helps business operations understand questions such as the following:

Why did company sales increase/decrease in the previous quarter?
Why are certain products outperforming/underperforming compared to their prior-year sales figures?
Why are we seeing an increase/decrease in customer churn?

Diagnostic analytics offers much value to business operations as it addresses critical questions about why an occurrence or anomaly occurred within data. Despite this, it’s often the most overlooked and skipped step within the analytics maturity model. This is because users often attempt to jump from the “what happened” step to the “what will happen” step without addressing the “why did it happen” step.

Pillar 3: Predictive Analytics

Moving past the “why did it happen” step in the analytics maturity model, we reach the “what will happen” step. This step in the analytics maturity model is known as predictive analytics. Predictive analytics is a branch of advanced business analytics that makes predictions about future outcomes based on a combination of historical data, statistical modeling, data mining techniques, and machine learning. In general, predictive analytics can be used to help companies predict potential issues that may arise, such as:

Predicting maintenance issues
Identifying potential fraud
Identifying signs of customer dissatisfaction

Being able to predict these potential issues through historical data, statistical modeling, data mining techniques, and machine learning helps companies take preventative action, save money, and better prepare for the future.

Pillar 4: Prescriptive Analytics

The final pillar of the analytics maturity model is prescriptive analytics. The term prescriptive analytics refers to the process of using data to determine an optimal course of action. Essentially, prescriptive analytics merges descriptive and predictive analytics or the “why did it happen” and the “what will happen” phases of business analytics. In addition, merging descriptive and predictive analytics to create prescriptive analytics works to drive decision-making. In general, prescriptive analytics can be used to:

Automatically adjust product pricing based on anticipated customer demand or external factors
Flag employees for additional training based on field incident reports

With prescriptive analytics requiring strong competencies in the first three pillars of the analytics maturity model, it tends to be primarily utilized in highly specialized industries like oil and gas, clinical healthcare, and finance. Overall, business operations should keep in mind that there is no starting point in prescriptive analytics without the requisites of descriptive, diagnostic, and predictive analytics being met.

Let Aezion Handle Your Business Analytics Needs

At Aezion, our dedicated team of technical professionals can handle all of your data management and business analytics needs. Our experts can assist with data collection, transformation, real-time visualization, and reporting. If you’re ready to discover how Aezion can help elevate your company through data-driven innovation, contact us today to speak with an expert.

Venkat Obillaneni

Cloud Data Engineer at Aezion, Inc. | + posts

Venkat Obillaneni is Aezion Cloud Data Engineer and Data Practice Lead.

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