Type of Data Analysis

The most successful businesses are those that constantly learn and adapt. No matter what industry you are in, it is important to understand what has happened in the past, what is currently going on, and anticipate what might happen in the future. Organizations that learn from past performances and current trends are more likely to perform better in the future. But how do successful organizations achieve this?

The answer is “Data Analytics.” The insights and learnings drawn from data depend on the type of analysis performed on the data. Broadly, in data science, there are four types of data analysis, namely, Descriptive, Diagnostic, Predictive, and Prescriptive. In this blog, we will explain each of the four types of analysis and see its usefulness.

1.?????Descriptive Data Analysis

2.?????Diagnostic Data Analysis

3.?????Predictive Data Analysis

4.?????Prescriptive Data Analysis

DESCRIPTIVE DATA ANALYSIS (WHAT HAPPENED?)

As the name suggests, the purpose of descriptive analysis is to describe what has happened, without giving inputs on what led to it or establishing a cause-and-effect relationship. The aim is to simply provide an analysis of the past in an easy, readable format.

Google Analytics is a good example of descriptive analysis. It gives an overview of the number of visits to the website, what pages have been browsed the most, visitor information, etc. HubSpot is another tool that gives a descriptive analysis of any digital campaign that you run, be it an email campaign, or a social media platform campaign, giving information about those who opened the email or clicked on a particular campaign.

In a nutshell, descriptive analysis condenses large volumes of data in a simple, clear overview of what has happened and serves as the starting point of in-depth analysis.

DIAGNOSTIC DATA ANALYSIS (WHY DID IT HAPPEN?)

Diagnostic analysis is the process of using data to determine the causes of trends and correlations between variables. Diagnostic analytics can be viewed as a logical next step after descriptive analysis because it aims at identifying and responding to anomalies within the data, as presented by descriptive analysis. For example, if descriptive analysis shows a 20% rise in website visits, the diagnostic analysis will try to find the factors that led to the rise. Diagnostic analysis can be performed either manually, using an algorithm, or by applying statistical software such as a spreadsheet application.

To summarize, the diagnostic analysis provides distinct insights on minor aspects of data, provides evidence on certain events which help form and test hypotheses, identifies anomalies and outliers to establish if they represent significant findings or are just inaccurate data, and helps determine the cause of past events which helps organization to avoid repeating mistakes and replicate actions that led to positive outcomes.

PREDICTIVE DATA ANALYSIS (WHAT IS LIKELY TO HAPPEN?)

Predictive analysis uses the findings of descriptive and diagnostic analysis to detect clusters and exceptions, and to predict future trends, and hence is a valuable tool for forecasting. Predictive analysis is a form of advanced analytics and is based on machine and deep learning and a proactive approach that is enabled due to predictions. It examines historical data in conjunction with other variables such as industry trends, consumer behavior, economic forecasts, etc., to make informed forecasts. The information gathered from predictive analysis helps companies formulate a set of actions that can positively impact operational effectiveness, profitability, customer satisfaction, branding, and many more aspects. For example, an eCommerce business can identify customers who have been passive and offer specialized discounts or incentives to make them active.

In short, the key motive behind the predictive analysis is to make data-driven decisions, thereby reducing the risks associated with the “what if we are wrong?” scenario.

PRESCRIPTIVE DATA ANALYSIS (HOW TO MAKE SOMETHING HAPPEN?)

While other types of data analytics determine what and why things happened, and what is likely to happen, prescriptive analysis prescribes actions to make something happen. Considering all variables that can be known or logically anticipated, the role of prescriptive analysis is to discern how to proceed, based on the analysis of likely scenarios. The purpose of the prescriptive analysis is to prescribe what action to take to eliminate a future problem or take full advantage of a promising trend. For example, if predictive analysis shows that consumers will behave in a certain manner due to a change in fashion, prescriptive analysis prescribes new products or changes in products required to ensure that the company uses the changing trends as an opportunity and profitability is maintained. Prescriptive analysis heavily relies on artificial intelligence, machine learning, computational modeling, optimization techniques, and rules-based techniques such as inference engines, decision trees, and scorecards.

However, compared to other types of data analytics, prescriptive is least utilized by organizations primarily because it needs advanced technologies and tools to fully implement, and hence is expensive and complicated.

The need for data analytics varies from business to business and organizations can have a mix of any or all the four types of analytics, to ensure that data is used to improve operations and increase profitability