What Is Data Analytics? The Key Things to Know

Every single marketer or business owner on the planet has already heard about data analytics in one form or another.

However, how much of what you know is the surface understanding behind the word, versus a truly solid understanding of the concept in its multi-dimensional form?

Well, if you can’t separate between data analytics and the concept of big data, no worries. We’ve got you covered. Keep on reading for our full breakdown of what data analytics means, and the different types you’ll want to keep in mind.

What Is Data Analytics?

In the simplest of terms, data analytics is the scientific method behind analyzing raw data, so organizations can make solid conclusions about the wealth of gathered information.

Most of the current algorithms responsible for processing data and performing data analytics have been automated, so people can make sense of the data and get answers to specific questions without getting lost in the minute data and the smaller data.

The Common Types of Data Analytics

Needless to say, the field of data analytics is rather broad in nature. However, businesses and the majority of industries just need to know about the four primary types of data analytics. These are descriptive, predictive, diagnostic, and prescriptive analytics.

Every type is conducted to reach a different goal, and it also belongs to a different place in the data analysis process. Also, you’ll want to check out this article to learn about the benefits of each type.

Descriptive Analytics

This type of analytics is named appropriately. It’s simply the type of analytics that helps answer questions about something that happened.

For instance, it encompasses techniques that specialize in summarizing huge datasets, so a business can describe outcomes to stakeholders.

Examples of this type of data analytics are key performance indicators (KPIs), as well as the return on investment (ROI) metric that’s used in a plethora of industries.

Predictive Analytics

This is the type of analytics that’s responsible for answering questions about the future. By using techniques like historical data mapping to identify trends, predictive analytics tools give businesses valuable insight into what might occur in the future.

Besides, it uses a variety of machine learning and statistical techniques, like neural networks, regression algorithms, and decision trees.

Diagnostic Analytics

If you’re looking for analytics that can help you understand the effects of something that already happened, then diagnostic analytics is the way to go.

It involves techniques that take the results of descriptive analytics and nail down the “why” and the causes.

Prescriptive Analytics

Now, you might already feel pretty good about your current analysis, but you’re not sure about potential strategies and whether they should be implemented in the first place or not.

In that case, you’ll want to take advantage of prescriptive analytics. This will help you make data-driven decisions, which can be a godsend during times of uncertainty.