What Is Data Analytics and What Is It Used For?

Data analytics is one of the foundations of data science, and it can help businesses and organizations make data-driven decisions. Having a strong data analytics team is useful for any company. Data analysts organize data and use it to provide insights and create plans for the future.

An understanding of data analytics is one way to thrive as a data science professional. There are many uses for data analytics in a wide variety of industries. Scientific research, government institutions, marketing firms, and more use big data to make decisions. Read our guide to answer the question: What is data analytics?

What Are the Types of Data Analytics? 

The four types of data analytics are predictive, descriptive, prescriptive, and diagnostic. Each type differs in terms of what result they achieve and provides broad insights so businesses can discover new analytics techniques in real-time. Take an in-depth look at the four types of data analytics.

Predictive Analytics

This type of data analytics uses historical data to predict what can happen in the future. It combines statistics with machine learning to generate data models that can be used in forecasting. One of the common uses of predictive analytics is market analysis for stock trading and business strategy.

Aside from the stock market, you can also use predictive analytics in business operations. For instance, you can project how many items you will sell and whether you need to amp up your operation in anticipation of a bigger demand. Predictive analytics is also useful in cyber security to detect potential fraud.

Descriptive Analytics

Descriptive analytics is used to represent what has happened in the past. Analysts use this type to understand what caused the current situation or outcome. This is commonly used in business intelligence, particularly for data visualization. Such visualization techniques include creating charts and graphs for presentations.

Descriptive analytics will help businesses determine if their strategy worked or if they need to switch things up. With the help of statistical analysis, you can gain an accurate insight into your past performances. Descriptive analytics can include profits and revenue, percentage of sales, or customer satisfaction.

Prescriptive Analytics

Prescriptive analytics is used to define what can be done to achieve a specific result. It is similar to prescriptive analytics because they both use historical data to determine future performance. However, where predictive analytics is focused on monitoring data, prescriptive analytics is used to create concrete, actionable insights.

One of the fields that uses prescriptive analytics is business intelligence. A business intelligence analyst uses statistical models to find the right strategy to achieve specific future outcomes. Predictive analytics provides you with models for future outcomes, and prescriptive analytics tells you how to achieve them.

Diagnostic Analytics

Diagnostic analytics answers why a certain outcome happened. It is different from descriptive analytics because diagnostic analytics use data mining and other techniques to uncover deep reasons behind the data. Many business analysts use analytics software to diagnose issues and help make informed decisions in the analysis process.

One way to perform diagnostic analytics is to combine or correlate various data sets to uncover a pattern. For instance, if you want to know why a set of employees are performing better than a different set, you can check the trend for salary increases and other benefits. In this case, you are not just reporting an outcome but understanding the cause.

What Does a Data Analyst Do?

Data analysts organize numbers and big data to create insights. These professionals cover the entire data process from collecting information to applying statistical techniques to analyze the data. The analytics process includes past, present, and future performances as all of these can affect the future of an organization.

Data analytics is a discipline that any industry can use. It takes up a huge chunk of any analytics process of a business. Statistical analysis, data visualization, and data presentation are just some of the common tasks for a data analyst. Read below to learn more about data analyst skills, responsibilities, and tools.

Data Analyst Skills

Being a data analyst requires skills in mathematics, statistics, data management, and programming. Mathematics and statistics are required skills because you will be dealing with a lot of numbers. With data management, you also need to have strong data visualization techniques. Data storytelling is important to communicate with the intended users. 

To understand data findings, you need to have critical thinking skills. Your interpretation should be backed by supporting evidence that is taken from reliable sources or other data sets. Finally, being technically skilled is key for data analysts. Tools like Python, R, and SQL are vital if you want to work in data analysis.

Data Analyst Responsibilities

As a data analyst, your responsibility spans from collecting data to presenting the outcome. You need to make sure that your data is clean to avoid anomalies and inaccurate findings. You are also responsible for making sure that the right tools and techniques are applied to get accurate results.

Data has to be secured and safe from potential breaches because it can contain sensitive materials. You are also responsible for creating reports for non-analysts in your company. Especially in a large organization, you are likely to work with a lot of different people. Being able to communicate will help the quality of your work.

Data Analyst Tools

The first tool that you will learn and master as a data analyst is Excel. It is widely used by almost every organization for organizing data and keeping it safe. Some organizations utilize databases provided by Microsoft BI, SAP, Oracle, and more. Different organizations use different analytics tools, so you might learn these on the job.

Programming languages like Python and R are also useful tools when it comes to data analysis. R is useful for cleaning data, while Python is used for modeling and visualization. For database management, the first programming language you should learn is SQL. As a data analyst, you must also learn Tableau, a popular platform for data visualization.

How to Become a Data Analyst

A data analyst job is demanding, so you need to have at least a bachelor’s degree to qualify. You need to have skills in handling data, performing statistical analytics, and using data tools and techniques. If you want to prepare for a data analyst career, you can pursue a degree at a university or further your education through coding bootcamps.

It will also be advantageous to earn data analytics certifications from reputable institutions. You can take an exam and get certified by IBM, Google, or the International Institute of Business Analytics. These institutions will test your knowledge and skills to earn a certificate that will give you an advantage over other job candidates.

Data Analysis Degree

Most companies prefer applicants who have completed a four-year degree at university. Some universities offer a Bachelor’s Degree in Data Science, and you can also pursue a master’s or doctoral degree. According to PayScale, the average salary for people with business data analysis degrees is about $84,000.

If your desired university does not have a data analytics program, you can earn another related degree. Other options include statistics, applied mathematics, and computer science. If you have an interest in the business side of data analytics, then your best option would be a degree in finance or economics. 

Data Analyst Coding Bootcamp

A data analyst coding bootcamp is an alternative studying option. This is ideal for students who completed a bachelor’s degree in a different field that are looking to further their education or switch careers. Coding bootcamps are an excellent choice for those who do not have the time to commit to a four-year degree. 

Within a few months, you will learn the fundamentals of data analytics for a lower cost compared to university tuition. Popular bootcamps like General Assembly and NYC Data Science Academy offer programs that are focused on data analytics. You can learn everything you need to know about predictive models, analysis tools, and more.

How Much Does a Data Analyst Make?

According to PayScale, a data analyst earns an average salary of $61,617. Aside from this job title, you can also explore other careers that make use of data analytics. For instance, according to the Bureau of Labor Statistics (BLS), operations research analysts earn $86,200 per year.

Other options such as marketing analysts and management analysts earn yearly median salaries of $65,810 and $87,660 respectively, according to the BLS. No matter what career you choose, you will most likely earn a salary that is above average.

Why Is There a Demand for Data Analytics?

There is a demand for data analytics because more businesses are becoming aware of its value. According to the Bureau of Labor Statistics, for example, the demand for operations research analysts is rising by 25 percent by 2029. This is over five times the normal growth rate for all occupations.

If you are interested in numbers and analysis, then you should consider a career as a data analyst, marketing analyst, or other analytical career paths. Every organization needs valuable insights to hit its business objectives. You can join this growing field and earn a high salary if you learn the types of analytics and more in-demand skills.

Data Analytics FAQ