Types Of Data Analytics And Suitable Preferences Among Companies

Proper business analytics has to go through different business processes. Depending on the workflow and requirement for analysis companies have had varied preferences for data analytics considering the four major types namely - Descriptive, Diagnostic, Predictive and Prescriptive Analytics.

data analytics

With changing time and business requirements, data analytics had been one the most considerable matters for smooth running business processes and steady growth even amidst unsteady trends and conditions. Keeping in mind this present day requirement we are going to follow up with some basic guidelines in respect to data analysis and its consequences for your business.

What is Data Analytics?

Also known as Data Analysis, Data analytics refers to the quantitative and qualitative techniques and procedures meant to boost business gains and productivity. In order to analyze and identify behavioral data and pattern, data is first extracted and categorized. However, techniques for doing so are known to vary as per various organizational requirements.

To fetch the best of something it is often necessary to move to the depths just as the words quoted by John Dryden say “He who would search for pearls must dive below”. Therefore, fact-based insights for this topic are also necessary so that you can make out the most from various data analytics tools for your pacing up your business’s productivity.

Types of Data Analytics

types of data analysis

Ranging from simple to more complicated and sophisticated, there are 4 different types of data analytics. More complex the analytics is the better value it is known to bring. Well, we are here going to discuss all these types of data analysis starting with the simplest and moving further to the most complex.

Descriptive Analytics

Descriptive analytics is suitable for finding appropriate answers to what happened? Consider a healthcare provider for instance; he will search for how many patients were hospitalized during the last month. Similarly, a manufacturer would look for a rate of products that returned for past month and a retailer would strive for average weekly sales.

For a clearer example, based on factors like monthly revenue for each product, revenue analysis, income be product group and quality of things produced per month, a manufacturer would focus on a particular product category.

Descriptive analysis is considerate about organizing raw data from different sources to fetch out valuable insights from the past. However, these researches just let you figure out whether something went wrong or right. The reason behind the happening is not the focus here. This is hence not suitable for companies that are highly data-driven. Alternatively, companies look in to combine descriptive analytics with other types of data analysis.

Diagnostic Analytics

data analytics tools

This category of data analysis moves to a higher level of complexity for bringing up the answers to why something happened? Under this historical data is compared with other data in order to find the appropriate reasons for a happening. Diagnostic analytics helps to drill down deep and identify patterns and fetch out dependencies.

Diagnostic analytics is the best when you are looking for deeper insights into a considered issue. At the same time, it is important that the company holds detailed information for clearance on their part. Otherwise, individual data collection for all different issues will make the process time-consuming.

Let’s again consider an example from different industries; a healthcare provider would compare the response of patients from various promotional camps held in different regions. Similarly, a retailer would run down the sales as per different subcategories. All this would consider measuring the consequences of something that has taken place.

Predictive Analytics

As the name suggests, predictive analytics look forward to predicting and answering the future interrogations of what would likely happen? The results fetched through descriptive and diagnostic analytics are gathered here in order to identify clusters, exceptions, and tendencies. Being highly predictable for future tendencies, this is among the ideal data analytics tools for companies for forecasting.

Despite the numerous advantages that prevail with predictive analysis, it is important to recognize that forecasting is just an estimate and estimates are not guaranteed for being accurate. The accuracy of estimates is determined by the quality of data and stability of the situation. Therefore, it is important to consider continuous optimization and careful treatment as the major elements. The proactive approach of predictive data analytics makes the go easier.

Consider the example of a telecom company; they will try to identify the number of users who are likely to reduce their expenses to carry forward the target marketing activities for the same. A management team will recognize the risks involved in investments for the company’s expansion with the help of forecasting and cash flow analysis.

Prescriptive Analytics

prescriptive analytics

Prescriptive analytics focuses on finding the next step to be taken answering the questions like what action to perform? With a view of optimum utilization of promising trends or eliminating a problem that would hold power in future this is an important element of analysis. For instance, with the help of customer analytics and sales records, a multinational company can easily identify opportunities and trends for repeat purchases and take a further suitable step.

This up-to-the-mark data analysis technique requires historical data along with other external information as per the respective state of statistical algorithms. Besides this, prescriptive analytics utilizes sophisticated technologies and tools including algorithms, business rules, and machine learning. Things, therefore, become easier to manage and implement. Before a company actually adopts prescriptive analytics, expected added value and required efforts should be compared.

What Types Of Data Analysis Do Companies Prefer?

types of data analysis  

In order to identify whether there had been any analysis trends, we will have to have a look at the results of several recent surveys.

As per the Global Data and Analysis survey, over 2,000 employees were asked about the most appropriate category that can well define the decision-making process of their company. Also, they were asked the type of analytics they rely the most on. The results for different categories were as below:


Dominant Analytics

Percentage Share

Rarely data-driven decision-making Descriptive analytics 58%
Somewhat data-driven decision-making Diagnostic analytics 34%
Highly data-driven decision-making Predictive analytics 36%

As per the analysis from the survey, at different stages of a company’s development, there may be a need for one or more types of data analytics models. Moreover, the companies striving for detailed decision-making would find Descriptive analytics to be deficient hence they will have to add up diagnostic and predictive analytics to the list.

There is a different face to the results of the same survey. Executives looking for sophisticated and faster decision-making are increasing and this would gradually increase the preference for predictive analytics among different companies.


There are varied types of analytics and companies are hence free to choose their sphere of work and the depths till which they need to dive into an analysis. They can pick the one which allows them to satisfy business needs in the most appropriate manner.

On one hand where descriptive and diagnostic analytics allow working with a reactive approach, predictive and prescriptive analytics avail proactive approach for the users. However, as per current trends, more and more companies are posed with the need to adopt advanced data analytics and are known to adopt it.