How Midsized Businesses Can Leverage the Different Types of Data Analytics

In today’s landscape, data is a powerful tool that is readily available at a staggering scale and can drive meaningful decision-making and improve performance within the organization.

As we know, harnessing the power of data entails being able to utilize it effectively, thus the need for nuanced data analysis and interpretation. But data analysis in itself is not a single process that is a one-and-done thing that organizations would go through. On the contrary, data analysis comes in different forms that organizations go through either as separate processes or together as a single, extensive process in order to harness data effectively, as well as address the issues that they face in their operations.

Depending on where one sits on the data analytics maturity model, understanding the type of data your organization is dealing with and the right form of data analysis to use is crucial in ensuring improved decision-making and the organization being able to meet its business objectives.

The Four Types of Data Analytics

Modern analytics tend to fall in four distinct categories: descriptive, diagnostic, predictive, and prescriptive. We shall have a look at each of them and see how each data analytics type can be utilized properly to analyze the right data.

Descriptive analytics

Descriptive analysis explains what occurred, often using multiple data sources. This analysis is considered the first step in identifying (and eventually fixing) potential issues, as well as in making meaningful improvements in the business process. It provides insights into which departments are consistently busier, peak admission times, and the general flow of patients.

While descriptive analytics answers the question “What happened?” and is a great tool for summarization and data visualization, it still requires a combination with other types of analytics in order to gain better insights and outcomes.

Diagnostic Analytics

Diagnostic analytics delves deeper as to why a particular event occurred by looking at historical data. The goal is to uncover details and patterns within past information related to a specific issue, thus eliminating the need to gather fresh data for each problem, saving time and money.

In essence, diagnostic analytics answers the question “Why did it happen?” and assists in troubleshooting issues or challenges. As such, it is best used in cases such as:

• Root cause analysis - finding the real reasons behind negative events

• Drill down - digging through layers of details to find the reasons behind an event.  

• Regression analysis – finding trends and connections between variables to predict future outcomes, such as assumptions about irregular behavior and relationships between events.

Predictive Analytics

Predictive analytics looks at the historical data and determines what might be the most likely outcome of a particular event. The goal is to establish the right “data models” that will simulate certain situations that will enable analysts figure which factors can affect how the problem can be triggered as well as which would help address such problems.

As such, predictive analysis has been applied by many organizations in their operations, such as:

• Predicting maintenance issues and breakdowns of certain parts of machinery

• Determining credit risk and identifying potential fraud

• Identifying signs of customer dissatisfaction.

Prescriptive Analytics

Prescriptive analytics is where the specific actions and measures that will effectively address the issues are formulated by taking into account all the information that has been gathered, including all the factors explored, during the conduct of the predictive analysis.

It is important to remember that obtaining the results of the prescriptive analytics is the ultimate objective in finding the solutions to any problem facing the organization. As such, prescriptive analytics is the output or the result of the work conducted in various stages of the problem-solving process, wherein various types of data analytics have been conducted as well.

The Greater Data Analytics Process

While the four types of data analytics discussed here are distinct from one another in their own right, they also work together as components of the greater data analytics process which is crucial in addressing the challenges facing any organization or to make strategic decisions that will facilitate the growth of the organization.

It is thus important to have a strategic and phased approach to analytics adoption in order to achieve smarter, data-driven decision-making, in order for organizations to have a competitive edge in today’s data-driven environment. The key is to start with foundational analytics and gradually evolve towards more advanced techniques to drive efficiency and business growth.