4 Types of Data Analytics to Improve Business Decision Making [With Examples]

Over the last decade, technological development has helped several industries in generating and retaining vast volumes of data. So much so that big data is one of the most popular buzzwords of the 21st century. We are currently living in a post-digital era, trying to build something with this burgeoning and varied information. And data science has emerged as a dynamic multidisciplinary field to help us do that. 

Data analysts inspect, clean, transform, and model structured and unstructured data to discover information that could be useful for decision-making. To extract such knowledge, they apply scientific methods, algorithms, and systems. In other words, they use different types of data analytics to translate complex details into insights that are understandable to the average man. 

But what is the most important use of data science? The answer to this question has four different dimensions, namely: Descriptive, Diagnostic, Predictive, and Prescriptive. So, we encounter not one, but 4 types of data analytics in data science. Let us understand these types of analytics in detail. 

Different Types of Data Analytics

1) Descriptive data analytics:  Discerning the reality

Descriptive data analytics is all about using existing raw data to paint a clear picture of what exists. For example, data from the monthly profit and loss statements of an organization could be used to know more about its performance. 

And different measures and metrics about the business could be compiled to give a holistic view of its strengths and weaknesses. 

Descriptive analytics is also useful in presenting insights for further analysis. A statistical analysis of the demographic data of customers could reveal the percentage of people in a particular age group.

Sales and pricing data could be consolidated and compared over the years or across departments. Data aggregation and data mining are some of the techniques used in this process. Analysts also use visualization tools to enhance the message. 

2) Diagnostic data analytics: Figuring out the ‘why’

After the ‘what,’ comes the ‘why.’ And diagnostic data analytics facilitates this reasoning process. Analysts read, scan, filter, and extract useful data to find out why something is happening. 

As the name suggests, diagnostic analytics is about breaking down the available information and identifying the causes behind specific problems, events, and behaviors. For example, a large organization may want to gain meaningful insights into its complex workforce issues.

With the help of data analytics, managers can search and create snapshots of employees working across multiple locations and divisions. They can also filter and compare their work attendance, performance, tenures, and succession metrics.

Business Information or BI dashboards with interactive tools are especially useful in getting to the root-cause of problems in this manner. Drill-down, data discovery, data mining, and correlations are some of the popular techniques used in the diagnostic analysis. 

3) Predictive data analytics: Getting an idea about the future

Predictive analytics is one of the most exciting types of data analytics. It helps us in learning about the future! 

The world is full of uncertainty. And we can never fully know what will happen. But, we can try to predict future events and hence, make better decisions. Predictive data analytics can help us estimate the likelihood of an event, when something might happen, or the extent of an upcoming change.

It analyzes past and present data to forecast the future. Will the sales increase or decrease? What will be the revenue situation in 2025? Analysts seek to make such projections with as much precision as they can. Data modeling and machine learning are some of the techniques that are increasingly gaining popularity in this area.

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Typically, they use variable data to predict otherwise unknown events. Let’s say that a predictive model churned out a statistic about a higher risk of heart attacks among older people. The prediction would be made after finding a linear relationship between the variable data on age and frequency of heart attacks in a population.

Such analysis can thus, improve patient care, reduce costs, and bring greater efficiencies to the healthcare industry. The financial services industry also uses predictive analytics for fraud detection, predictive investing, etc. 

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4) Prescriptive data analytics: Suggesting the way forward

If predictive analytics is about forecasting, prescriptive analytics is about using those predictions to deliver value. It provides the key to the future by prescribing the best course of action out of the available alternatives.

At this stage, analytics use the insights from the first three steps to determine the possible solution to a problem. And it is not just about picking any but comparing and selecting the most suitable recommendations for the given situation. For instance, a mobile application for road traffic can help you choose the best route to reach home from your current location.

The App would take into consideration the distance, speed, and traffic congestions to tell you the shortest or the most timely way to get there. Another example is a consulting agency using data analytics to suggest advantageous locations to roll out a new product. 

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Today, data science is delivering tremendous value across industries. And all 4 types of data analytics mentioned above will continue contributing to the transformation in their own ways!

If you are keen to get practical knowledge by attending hands-on workshops, One on one with industry experts, 7+case studies and projects, check out upGrad’s Data Science programs which is designed for working professionals.

How is predictive modeling different from predictive analytics?

Predictive modeling requires more technical skills than predictive analytics. The most effective predictive analytics software smoothly makes the transition from modelling to analytics. Modeling of statistics and other historical data is done by data analysts. After that, the model calculates the likelihood of various outcomes. Predictive analytics, on the other hand, seeks to explain why the models produce differing weighted ratings. For company managers and other professionals, they are useful in a variety of ways.

What are the limitations of using descriptive data analytics?

If you want to use data analytics for measuring something that you have generalized for your convenience, then it’s quite a task. This is so because descriptive data analytics can be applied only to the items or people that are accurately measured and not generalized.

What are the advantages of using diagnostic data analytics?

Data analytics is all about understanding the data in a better and more accurate way by converting it into visualized data. Diagnostic data analytics is more helpful in a way that after visualization of data, it asks the right question thus providing in depth answers. Thus, it is very helpful in artificial intelligence and also in businesses.

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