What Is Data Analytics? (Definition, Types and Steps)

By Indeed Editorial Team

Updated 15 December 2022

Published 27 April 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Data analytics is the process of making important decisions for a company based on analytics results. Nowadays, it has become more prevalent in every industry. Understanding and mastering data may help to add value to your resume, as it's a highly-sought skill in many companies. In this article, we discuss the definition of data analytics, explore their importance and provide tips on using them effectively.

Related: 10 Most Essential Data Analysis Skills

What is data analytics?

Data analytics is a collection of techniques that help you draw inferences from data models. Many data analytics processes today come in the form of algorithms that experts can tweak and automate. Stakeholders may use real-time business insights to make important decisions about a company's growth.

Data analytics insights appear as business trends and metrics drawn from massive quantities of data. This information is crucial for companies to remain competitive in their markets. Data analytics correlates with big data in the business world, which is data with volume and variety that's important to make quick business decisions.

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Why is data analytics important?

Data analytics helps stakeholders and business leaders understand and interpret extensive amounts of big data. Typically, data analytics insights allow stakeholders to see the critical context, trends and metrics. Professionals can create models from the data and provide interpretations.

Results of data analytics help business decision-makers make important decisions regarding customer service and satisfaction. It can also help companies prepare and adapt to changes in the market. For example, companies can use data analytics to design innovative and cost-saving products and exciting advertisements to attract new customers.

Types of data analytics

The main types of data analytics that companies use are:

Predictive analytics

Predictive analytics uses modelling and statistical analysis. It also makes use of both diagnostic and descriptive data. It's a standard type of analytics that can help to forecast inventory and manage resources. These predictions can guide companies to make accurate business decisions that optimise performance and increase revenue.

This type of analytics uses data on a company's performance in the past to make inferences that may predict trends about future growth and market behaviour. The results of predictive analysis can show a company's current and future performance if they choose to change certain business variables. This data result can help companies decide if they want to make improvements or even change their business strategy entirely.

Diagnostic analytics

This type of analytics uses various data components to analyse why something happened. For example, you can look into a company's performance concerning other data to see why they perform better or worse than expected. Companies can also use data from diagnostic analytics to make decisions regarding the allocation of costs and resources.

Descriptive analytics

Descriptive analytics uses data to explain and visualise what happened during a particular time. Data from descriptive analytics typically aim to describe events that need context. For example, descriptive analytics provide current performance and financial data that can show whether a company is gaining profit or not. Business decision-makers can use the data to decide whether a change in their business strategies is necessary.

Prescriptive analytics

To perform this type of analytics, you can use diagnostic, descriptive and predictive data analytics. Statistical results of prescriptive analytics provide stakeholders with directions that may help them make decisions on what to do next. Companies can also use prescriptive analytics to assess impacts from predictive analytics.

For example, a fashion company can look at past data on fashionable items and evaluate current fashion trends. Prescriptive analytics may recommend what items may become fashionable in the future. It may also help to suggest what kind of marketing campaign is suitable to promote it and show how to better reach the target audience.

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Where is data analytics used?

Various industries benefit from data analytics, including:

  • Finance: It uses data analytics to provide better management, reduce risks and avoid fraud.

  • Communications: Data analytics improves consumer targeting and reduces operational costs and risks.

  • Health care: It uses data to provide better administrative services, identify current diseases and implement efficient medical treatments.

  • E-commerce: Companies gather data on targeted market audiences and their competitors. They can then develop strategies to grow sales.

  • Entertainment: They use data to understand which songs, movies or series are trending. This data can help companies compile a list of recommended media types for users.

  • Marketing: They gather data to personalise and improve advertisements to generate more income and increase sales.

  • Education: Using data, schools can improve lesson plans and courses, and be able to help students discover their preferred major and college.

Related: 35 Health Care Business Analyst Interview Questions

What's the data analysis process?

These are the steps in data analysis:

1. Collect data

Collecting data is the first step to data analytics, as you can only start analysing when you have the relevant data. If you're a decision-maker, consider important processes that may support your marketing channels and generate customer data leads. You can also collect data for various segments, including accounting and manufacturing. Make sure the data you collect correlates with the company's needs.

Related: Examples of Data Mining Applications and Selection Criteria

2. Analyse data

In this phase, it's important that you verify the accuracy of your data. If you collected your data from various unknown sources, you may want to check for errors. It's also critical that you compile the data in a way that's readable and consistent. Data analysis is crucial in many businesses, but it's costly. Thus, ensure your compiled data is complete and accurate before you invest in data analytics.

Related: What Is a Data Engineer? (With Guide on How to Become One)

3. Gain insights from the data

To better understand the data, you want to draw information and conclusions from the data that you have. Learn about the data-collecting process and if there are any security concerns during it. You may also want to check if IT professionals have analysed the data recently and review how it complements the company. This information helps you understand the crucial insights you may get from your data pool.

4. Store your data well

After getting the necessary data, it's vital that you store them well. Storing data is a complex process that requires time, money and resources. Consider using data tools like data warehouses and databases. It's important to ensure that the tools you choose help you store and understand the data. This is so that you can easily make reference to past data in the future, whenever needed. The tools you use depend on the company's size, the size of your data and the total number of data users.

5. Create visualisation and model

The previous steps help ensure that your data is clean, accurate and well-stored. Now that you've gathered, compiled and understood your data, it's time to visualise insights from the data. A highly effective and common way to gain insights from your data is by creating visuals and building a suitable model to showcase your data.

There may be people in the company who aren't comfortable with statistics and heavy data. In this situation, effective data visualisation helps to translate your data into a form that's simple and easy for everyone to understand. By creating a suitable model, you can communicate the data that you've collected to others. There are various free and paid tools to enable you to model your data and create visuals that are easy to understand.

6. Make an action plan

Having the correct data and understanding them is important in a business. After you've gathered meaningful insights from your data, it's time to put it into action. You can now form an action plan that tells the organisation how to use data and what to do with insights that are gained. No matter what your goals are, to be successful, a business can convert data into insights and insights into action.

An insight can become a powerful idea for a company. For example, CEOs can make budgetary decisions that impact the whole company. The marketing department can gain insights into consumer habits and bring in more customers into the business. The insights from data analytics may also produce results at every level of the organisation. They help businesses to improve the way they communicate with consumers, strategise ways to reach their target audience and understand their preferences.

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