Types of Data Analysis

Data analysis is highly essential for any organization these days for making informed decisions. When data is used adequately, it can lead to a complete understanding of the business process, previous performances, and better decision-making in the interest of the business. Because of the growing importance of data analysis, professionals seek a comprehensive data analyst course that will help them learn in detail about data analysis and understand the business better.

There are multiple ways of making use of the available data depending upon the business operations. The data analysis process can be broadly categorized into four types: descriptive analysis, diagnostic analysis, predictive analysis, and prescriptive analysis. Although they seem to be different, they are very much related and differentiated based upon the complexities involved.

So without wasting any time, let’s learn in detail about these four types of data analysis methods.

Descriptive Analysis

It’s the fundamental type of data analysis and forms the foundation of data analysis. It’s the simplest and used more widely than any other data analysis method in the world. This type of analysis helps find answers to “what happened” by briefing the past data in the form of dashboards. Descriptive analysis is largely used for tracking Key performance indicators or KPIs.

KPIs help in measuring the business performance based on set targets. Descriptive analysis is broadly used in generating KPI dashboards, monthly revenue reports, and sales lead overviews

Diagnostic Analysis

While the descriptive analysis stresses “what”, diagnostic analysis tries to determine the “why” behind a particular event. While performing descriptive analytics, insights are derived for descriptive analytics to find the root cause behind the outcomes. These days, organizations prefer to use diagnostic analysis to find connections between the available data, make sense out of it, and identify behavior patterns.

The main aim of the diagnostic analysis is to collect detailed information and statistics so that whenever a problem arises, it’s easy to find solutions in a short period of time. Some of the typical applications of diagnostic analysis are finding out why orders have not been shipped on time, determining marketing strategies that can boost the business.

Predictive Analysis

As the name suggests, predictive analysis stresses answering questions about the future like “what is likely to happen in the upcoming days”. This type of analysis involves collection of historic data to draw inferences about the future. This majorly involves summarizing the available data and making logic-based predictions about the likely events. Statistical modeling forms the base of predictive analysis involving technology and the workforce for making future predictions.

It’s essential to understand that forecasting is only about making an estimation, and the accuracy of the forecast depends largely on the quality and accuracy of the data collected. Predictive analysis often requires the involvement of a large manforce, which is sometimes not feasible for businesses and organizations and this acts as a limitation for the implementation of predictive analysis. Some of the common predictive analysis applications involve risk assessment, sales forecasting, lead conversion evaluation, and predictive analytics.

Prescriptive Analysis

The prescriptive analysis is one of the most sought-after analysis methods; however, only a handful of organizations are well equipped to perform it. It primarily involves taking insights using all the analysis methods discussed and making actionable recommendations to solve the present problems. It involves huge infrastructure, and therefore, organizations need to be well equipped to perform it. These days a majority of the data-led organizations are using prescriptive analysis methods to analyze their data. Furthermore, it has also got a huge application in the field of artificial intelligence.

After going through the article, you must have learned that though these data analysis methods are different in their approach, they are somewhat interrelated. You can choose the type of analysis based on the type of insight you wish to gain and the type of project you are dealing with.

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