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A Quick Guide to the Four Types of Data Analytics

Last updated on March 4th, 2023 at 01:21 am

Are you an aspiring data scientist looking to brush up on the four types of data analytics? You’ve come to the right place!

Data analytics is the process of analyzing raw data in order to identify patterns and relationships that can be used to make informed decisions.

It involves various techniques such as machine learning, predictive modeling, and statistical analysis to draw meaningful insights from data. Businesses use data analytics to gain a better understanding of customer behavior, market trends, and potential opportunities.

The six most commonly used tools for data analytics in business to wrangle all that beautiful data are:

  • Google Analytics — a web-based platform that helps businesses analyze website traffic.
  • Microsoft Power BI — can be used to visualize data and generate reports.
  • Tableau — provides access to data exploration and powerful information visualizations.
  • SAS Visual Analytics — a cloud-native platform that helps uncover hidden patterns in datasets quickly.
  • Qlik Sense — a self-service analytics tool that supports interactive dashboards and stories.
  • Splunk — can be used for real-time monitoring of applications and infrastructure with advanced search capabilities.

Data analytics has become increasingly important in today’s business environment. According to shrm.org, 95% of organizations that are utilizing data-driven decision-making are experiencing strong financial performance.

Companies across all industries rely on data analytics to gain a better understanding of customer behavior, market trends, and potential opportunities.

Data analytics can help businesses:

  • Increase customer satisfaction by uncovering their needs and preferences.
  • Identify areas for improvement within the organization and create strategies for growth.
  • Reduce risk by helping identify potential fraud and mistakes before they happen.

By taking advantage of data analytics capabilities, companies can also gain an edge over competitors and remain competitive in the ever-changing business landscape.

What Are the Four Types of Data Analytics?

In the data science world, there are four major types of analytics.

Whether you choose to use one or all, each has its unique strengths that can be leveraged to make a substantial impact on your business’s success.

Here’s an overview of what each type has in store for you.

Pic courtesy of Tom March

Descriptive analytics

Descriptive analytics is a type of data analysis that focuses on describing and summarizing the characteristics of a given dataset.

It involves looking at past and present data from various angles to gain insights about trends and patterns, as well as drawing conclusions or making predictions about future events.

Descriptive analytics can be used to inform decisions and even create predictive models.

How To Get Started With Descriptive Analytics

Getting started with descriptive analytics is fairly simple.

First, identify the goals you want to achieve through the analysis, such as understanding customer preferences or finding ways to increase sales.

Then, collect and organize the relevant data—this requires having access to reliable sources like databases, surveys, and questionnaires.

Finally, use appropriate tools and techniques (such as tables and graphs) to analyze the data, draw conclusions, and make decisions.

With descriptive analytics, you can quickly understand your audience better and improve your business outcomes.

Diagnostic Analytics

Diagnostic analytics is a type of data analysis that provides insights and explanations about the causes behind observed patterns or behaviors.

This kind of analysis may be used to investigate the reasons behind a decrease in sales or a decline in customer satisfaction.

It involves digging deeper into data, putting it in context, and using tools like correlations or regressions to determine root causes.

How To Get Started With Diagnostic Analytics

To get started with diagnostic analytics, begin by defining the problem you want to solve—this includes researching known facts as well as prior studies on the topic.

Then collect data relevant to the issue at hand—for example, customer behavior information or transaction logs.

Next, analyze the data and apply different techniques such as A/B testing to identify causal relationships between variables.

It’s important to pay close attention to any assumptions made during the process since they might impact your results.

With diagnostic analytics, you can gain valuable insights that provide actionable guidance for informed decision-making.

Predictive Analytics

Predictive analytics is a type of data analysis that uses historical and current data to forecast future trends and behaviors.

It’s based on the idea that past events can be used to make educated guesses about what will happen in the future.

Predictive analytics typically involves developing machine learning models which take into account many variables to make accurate predictions.

How To Get Started With Predictive Analytics

Getting started requires careful planning and consideration.

First, determine your end goal—this should include factors such as accuracy, cost, and timeframe.

Then identify the variables that are likely to have an effect on the outcome you want to predict.

Collect data from reliable sources like customer databases or surveys, clean it up if necessary, and then export it for further analysis.

Finally, build a model using suitable algorithms and optimize it using different techniques so that it can yield accurate results. With predictive analytics, you can gain invaluable insights about potential outcomes and plan accordingly for success.

Prescriptive analytics

Prescriptive analytics is a type of data analysis that uses predictive models and optimization techniques to produce recommendations about optimal solutions for a given problem.

Unlike traditional analytics which only offers insights into the past and present, prescriptive analytics can provide guidance about the best possible future course of action.

It combines various elements such as business objectives, data patterns, and decision-making tools to arrive at optimal solutions.

How To Get Started With Prescriptive Analytics

Begin by clearly defining the goals you want to achieve and then examine all potential strategies or solutions.

Once you have identified different options, determine which ones are most likely to deliver the desired outcomes.

Then collect relevant data from internal or external sources including market trends, customer feedback, etc., analyze it using machine learning algorithms or other statistical methods, and gain insight into which solutions are better than others.

Finally, use optimization techniques such as linear programming to generate recommendations regarding what should be done next in order to maximize results. With prescriptive analytics, you can make informed decisions that lead to tangible results.

A Quick Guide to the 4 Types of Data Analytics [Infographic]

Pretty, pretty pictures!


Relatively short, but sweet:

Descriptive analytics deals with summarizing historical data in order to understand what has happened in the past.

With diagnostic analytics, companies dive deeper into the data by identifying the root causes of certain events or anomalies.

Predictive analytics uses machine learning models to forecast future outcomes based on current trends.

Finally, prescriptive analytics provides guidance about the best possible course of action by combining business objectives, data patterns, and decision-making tools.

Data-driven decision-making enables organizations to remain competitive by embracing change and taking advantage of opportunities that arise in a rapidly evolving world.

By utilizing all four types of analytics – descriptive, diagnostic, predictive, and prescriptive – organizations can make more informed decisions that will have a positive impact on their bottom line.

Dani Lehmer is the Founder and Head Honcho of Dani Digs In.

She is a Quality Assurance Analyst and blogger whose natural curiosity allows her
to dig in (pun intended) to help people build their businesses and satiate curiosity
in regard to data science, analysis, and crypto.

Connect with Dani on LinkedIn

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