The Different Types of Data Analytics

Data analytics involves the inspecting, cleansing, transforming, and modeling of data to find information that is used to make informed decisions. An algorithmic or mechanical process is applied, and it identifies correlations between data sets. With the endless amounts of data everywhere, many industries such as healthcare, travel, gaming, and energy management rely on data analytics to better operate, and they take on data analytics consulting.

Data analytics companies focus on 4 main types of data analytics:

Predictive Data Analytics Prescriptive Data Analytics Diagnostic Data Analytics Descriptive Data Analytics

Predictive Data Analytics

Predictive analytics is the most common category of data analytics that is used by data analytics consulting firms. Predictive analytics is used to identify trends, correlations, and causations. Within this category, there are two more specific subcategories: predictive modeling and statistical modeling.

Statistical modeling can determine things like the relationship between conversion rate and a target audience’s geographic area, income bracket, and interests. Then, predictive modeling can be used to analyze the statistics for different target audiences. Through predictive analytics consulting, a company can determine the possible revenue sales for each demographic.



Prescriptive Data Analytics

Prescriptive data analytics deals with AI and big data in order to predict outcomes and which actions should be taken. Subcategories within prescriptive data analytics are optimization and random testing.

By using machine learning, prescriptive analytics can help determine what the result of an action could be, or what action should be taken in the first place. This can be done without having to spend long periods of time trying out each variable, and positive outcomes can be generated.

Diagnostic Data Analytics

Data analytics consulting firms use diagnostic data analytics to examine data in order to determine why something happened. There are various techniques with diagnostic data analytics, including drill-down, data discovery, data mining, and correlations.

Diagnostic data analytics can be further broken down into two specific categories as well: discovery and alerts and query and drill-downs. Query and drill-downs are used to pull out more detail from a report, which helps a company better understand why something happened. Discover and alerts help identify a potential issue prior to it happening, and it can find information like who has the best skillsets for a specific position.

Descriptive Data Analytics

The last main category of data analytics is descriptive data analytics, and this addresses the questions of how many, when, where, and what. Two subcategories of descriptive data analytics are ad hoc reporting and canned reports. A canned report contains information about a given subject, like a monthly report sent by an ad agency.

Ad hoc reports are used to obtain more in-depth information about a specific query. For example, it can examine the types of people that liked a specific page on social media and what other specific pages they have liked as well. Ad hoc reports are hyperspecific and give a good idea about an audience.

Each one of these categories and subcategories shows the different aspects of data analytics. Data analytics companies use each one to better understand data, which is the foundation of almost all operations today.