Types of Data Analysis

Before I jump in to Types first let’s understand what is data analysis and why its important in every industry.

 

What is Data Analysis?

Data analysis is a comprehensive method of inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It is a multifaceted process involving various techniques and methodologies to interpret data from various sources in different formats, both structured and unstructured.

The Importance of Data Analysis in Today’s Era.

Data analysis is the key to unlocking the potential of big data. It helps organizations to make sense of this data, turning it into actionable insights. These insights can be used to improve products and services, enhance experiences, streamline operations, and increase profitability.

The vast amount of Data, if analyzed correctly, can provide invaluable insights that can revolutionized businesses.

Through data analysis, E-Commerce company can understand their customers' buying behavior, preferences, and patterns. They can then use this information to personalize customer experiences, forecast sales, and optimize marketing strategies, ultimately driving business growth and customer satisfaction.

In Healthcare industry through data analysis, healthcare providers can predict disease outbreaks, improve patient care, and make informed decisions about treatment strategies. Similarly, in the finance sector, data analysis can help in risk assessment, fraud detection, and investment decision-making. Each & Every Industry need their data analysis.

Now let’s understand 4 types of Data Analysis.

Data analysis can be categorized into four main types, each serving a unique purpose and providing

different insights. These are descriptive, diagnostic, predictive, and prescriptive analyses.

 

1.Descriptive Analysis

Descriptive analytics answers the question, “What happened?”

For example, imagine you’re analyzing your company’s data and find there’s a seasonal surge in sales for one of your products: ABC. Here, descriptive analytics can tell you, “This product ABC experiences an increase in sales in October, November, and early December each year.”

Descriptive analysis is all about trying to describe or summarize data. Although it doesn’t make predictions about the future, it can still be extremely valuable in business environments. Descriptive analysis makes it easier to consume data, which can make it easier for analysts to act on.

 

There are two main techniques used in descriptive analytics: Data aggregation and data mining.

Data aggregation:

Data aggregation is the process of gathering data and presenting it in a summarized format.

Let’s imagine an E-commerce company collects all kinds of data relating to their customers and people who visit their website. The aggregate data, or summarized data, would provide an overview of this wider dataset—such as the average customer age, for example, or the average number of purchases made.

 

Data Mining:

Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

2.Diagnostic Analysis

Main purpose of Diagnostic Analysis is to identify “Why did this happen?”

Let’s say for example product: ABC drop 20% sale for last month then why this happened? Diagnostic analytics seeks to go deeper in order to understand why something happened. The main purpose of diagnostic analytics is to identify root cause and then diagnostic analysis will take place. 

Diagnostic analytics isn’t just about fixing problems, though; you can also use it to see what’s driving positive results. 

While descriptive analytics looks at what happened, diagnostic analytics explores why it happened. A step further than descriptive analysis. It involves more detailed data exploration and comparing different data sets to understand the cause of a particular outcome.

For instance, if a company's sales dropped in a particular month, diagnostic analysis could be used to find out why.

 

3.Predictive Analysis

Predictive analysis is to predict what is likely to happen in the future.

based on past patterns and trends, data analysts can devise predictive models which estimate the likelihood of a future event or outcome.

Predictive models use the relationship between a set of variables to make predictions; for example, you might use the correlation between seasonality and sales figures to predict when sales are likely to drop. 

New and existing companies tend to function better when they have a visual reference that provides an overview of expected outcomes and trends. Successful companies often incorporate forecasting models when planning for the future.

Example: You want to predict how many takeaway orders you’re likely to get on a typical on festival. Based on what your predictive model tells you, you might decide to get an extra inventory on hand. 

In this image shaded part of graph showing predictive analysis of sales and profit for next upcoming year based on past data.

4.Prescriptive Analysis:

Finally, Prescriptive Analysis answers the question, “What should we do next?”

looks at what has happened, why it happened, and what might happen in order to determine what should be done next.

In other words, prescriptive analytics shows you how you can take advantage of the future outcomes that have been predicted. What steps can you take to avoid a future problem? 

Prescriptive analytics takes into all possible factors in a scenario and suggests actionable takeaways. This type of analytics can be especially useful when making data-driven decisions.

Machine learning makes it possible to process a tremendous amount of data available today. As new or additional data becomes available, computer programs adjust automatically to make use of it, in a process that is much faster and more comprehensive than human capabilities could manage.

Prescriptive analytics works with another type of data analytics, predictive analytics, which involves the use of statistic and modeling to determine future performance, based on current and historical data. However, it goes further: Using the predictive analytics' estimation of what is likely to happen, it recommends what future course to take.

In this analysis data analyst use ‘If’ and ‘Else’ condition to predict some outcomes. This enables you to see how each combination of conditions and decisions might impact the future, and allows you to measure the impact a certain decision might have. Based on all the possible scenarios and potential outcomes, the company can decide what is the best “route” or action to take.

Prescriptive analytics is the most complex type of analysis, involving algorithms, machine learning, statistical methods, and computational modeling procedures. Essentially, a prescriptive model considers all the possible decision patterns or pathways a company might take, and their likely outcomes.

Knowing what actions to take for the best chances of success is a major advantage for any type of organization, so it’s no wonder that prescriptive analytics has a huge role to play in business.

So: Prescriptive analytics looks at what has happened, why it happened, and what might happen in order to determine the best course of action for the future.

Key Takeaways:

In some ways, data analytics is a bit like a treasure hunt; based on clues and insights from the past, you can work out what your next move should be.

With the right type of analysis, all kinds of businesses and organizations can use their data to make smarter decisions, invest more wisely, improve internal processes, and ultimately increase their chances of success. To summarize, there are four main types of data analysis to be aware of:

Descriptive analytics: What happened?

Diagnostic analytics: Why did it happen?

Predictive analytics: What is likely to happen in the future?

Prescriptive analytics: What is the best course of action to take?