4 Types of Data Analysis

DXDirections

In this post, we’ll look at the 4 main types of data analysis that companies use to gather business intelligence and make data-driven decisions: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.

Information is one of the most highly sought-after resources today. Many businesses use technology to gather information on a variety of things that they can then use to their advantage.

Companies collect data about their targeted audiences, their operations’ performance, and the reception of their services or products.

However, data collection by itself would be pointless if organizations didn’t try to learn from them and extract meaningful insights. This is where data analysis comes in.

Table of Contents

What Is Data Analysis?

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Data analytics is the process of studying raw data and interpreting them. Today, many companies use data analytics to learn valuable information about their customers, evaluate the efficiency of their functions, compare their performance with those of their competitors, and much more.

In summary, organizations integrate data collection and analytics into their decision-making, attempting to pre-plan for their desired outcomes.

While each organization uses data analytics according to its needs, the general scope of this process, as it is often used today, is to improve the overall procedures of an organization by diagnosing issues, recommending alternative actions, and foreseeing results.

It is also used to identify trends, analyze performance, and answer questions about possible dilemmas. Because data analytics is used so often in business, it tends to be referred to as “business analytics.”

The Types of Data Analysis

Data analysis can achieve different things depending on how it is used. Therefore, there are a few different types of data analysis, each producing a different result.

Let’s take a look at the four main types of data analysis that a data analyst may use.

Descriptive Analytics

A descriptive data analysis basically describes what happened. It offers all the basic facts about something that happened in the past or is currently happening. This type of analysis is the simplest one, but it gives you the initial information you need to know to proceed to the next one.

With this kind of analysis, you will not be able to see if there are any issues, understand why specific results appear, or predict any future outcomes. For this reason, it might be hard for some to interpret the information from descriptive analytics clearly.

However, there are some visualization tools available that might give you a better perspective on the results produced from the descriptive analysis. These tools include tables, bar charts, pie charts, and line graphs.

For example, a simple sales report, presented in a spreadsheet or data dashboard, that shows how many purchases happened in the past month is considered a descriptive analysis in data science.

Google Analytics also offers a form of descriptive analytics, as it offers simple data about how many people visited your website, how many times, etc. Google Analytics also uses visualization tools to present its findings more clearly.

Essentially, descriptive analytics can give you information from multiple data sources to describe and summarize what is happening in the business.

Diagnostic Analytics

Diagnostic analytics compares different data to find cause and effect and create hypotheses. This process answers the question of why something happened. In this process, past data is also examined in relation to the new data to hypothesize why something might have happened.

Some of the information you might get through this type of analysis is observing patterns, detecting causes and effect relationships, uncovering correlations, and identifying outliers.

The primary use of diagnostic analytics is to assess the cause of an organizational problem. Many techniques are used to perform diagnostic analytics, such as data mining, data discovery, and data drill-down.

Diagnostic analytics can often be used to determine customer satisfaction.

For example, while descriptive analytics might tell you how many people are subscribing to your newsletter from a particular campaign, a diagnostic analysis why people tend to unsubscribe from the company’s newsletter.

Companies may also use log files, IoT sensors, and software applications to derive data from machines, troubleshoot and make diagnostic conclusions as to why machines are down.

Predictive Analytics

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Predictive analytics takes descriptive and diagnostic analytics one step further and attempts to make future predictions. Predictive analytics helps organizations become proactive in their business approach by using older data.

Using this type of analysis and industry trends, a data scientist can help a business improve its performance.

However, it should be noted that predictive analytics results are only an estimation and do not necessarily guarantee specific outcomes. It tries to predict what is likely to happen based on data.

Regardless, predictive analysis can help a company make better strategies and be more prepared to take advantage of industry trends, as many of those could have also been predicted.

Predictive analysis is part of many existing business processes today that help companies run their operations more smoothly. A couple of these business processes are sales forecasting and risk assessment.

Predictive analysis also presents patterns that can possibly be recreated in the future.

For example, if a food chain sees sales decrease during July and August for two consecutive years, predictive analytics might show why the sales could decrease during that time and why it may happen this year.

In another example many of us are familiar with, streaming services like Netflix use predictive analytics to recommend shows and movies we might like based on usage patterns.

Prescriptive Analytics

With the three aforementioned types of data analytics, you can learn what has happened or is currently happening, why it happened, and if it will happen again in the future. With prescriptive analytics, you learn what you can do to avoid an issue or gain from a future circumstance.

It essentially gives recommendations on what your strategy should be, based on the predicted outcomes. It helps businesses navigate future changes in the market more easily and continue their operations steadily.

With this type of analysis, it is very common to use some advanced tools better to facilitate the management of large volumes of data.

Machine learning, artificial intelligence, business rules, and neural network algorithms allow companies to better utilize the various recommended actions produced from prescriptive analytics.

It is an advanced process that relies on computational analysis and algorithms. As such, many companies are just in the early stages of understanding and applying this type of analysis to their business operations.

A basic example of prescriptive analytics is web mapping applications that people use as a guide while driving to different places. An app like that finds the best route you should follow to get to your destination faster.

Based on previous data, these apps often consider distance, speed limits, traffic at that specific time of day, and more to predict what route should be faster for you.

Conclusion

Data analysis is a critical aspect of most business operations today, and one of the fundamental components of digital transformation. Companies often employ them to investigate possible issues, check their performance, and prepare their next strategies.

Each of the four types of data analysis offers something different, but businesses can actually benefit quite a lot when used together.