If you don’t know, every second, a huge amount of data is being collected.
Whether you are shopping online, socializing on social media platforms or simply browsing the Internet, data is constantly getting collected.
So, what to do with such a huge amount of data?
Raw data is a big pile of unstructured information. To make sense of this information, data analysts use their expertise to derive crucial information from the big pile of data!
This is where types of data analysis in research methodology come into play.
Below given are a brief description of the types of data analytics that are extensively used today.
Descriptive analysis or statistics summarizes raw data and converts them into a form that humans can understand.
It describes in detail about an event that has happened in the past.
This kind of analytics is helpful in drawing interpretations or deriving a pattern from past events so that effective strategies can be formed for the future.
You could say that this is the most popularly and frequently used type of analytics across industries. It plays a key role in revealing key measures and metrics within a business.
Diagnostic data analytics
Diagnostic analytics is the successor to descriptive analytics.
With the help of diagnostic analytical tools, analysts are able to dig deeper into issues at hand and be able to unearth the source of the problem.
Drill-down, data mining, data discovery and correlations are some of the tools used in this form of data analytics.
In a structured business environment, both diagnostic and descriptive analytics tools go hand-in-hand.
Predictive data analytics
In the process of data analytics, this is the most extensively used model.
This format of data analytics is used for identifying correlations, trends and causation. It is further divided into two types – predictive modelling and statistical modelling. Both of these work in correlation to each other.
In this type of analysis, various co-dependent variables are assessed to predict a trend. For instance, in the healthcare sector, potential health risks can be predicted on the basis of an individual’s diet, habits and genetic composition.
Prescriptive data analytics
In any situation, this format of data analytics explains the step-by-step process.
For example, a prescriptive analytics is what is used when an Uber driver gets the fastest and easiest route from Google maps. This route was selected after considering the distance of every available route from the pick-up point to the destination, along with traffic constraints.
For predictive data analytics, Big Data and AI are used to help predict possible outcomes. It is further broken down to Random Testing and Optimization.
Are you interested in securing a successful career in the domain of data analytics? If so, you need to get yourself enrolled in data analytics training provided by any good Data Science Training Institute. Such training programs are designed to impart knowledge and expertise in the basics, as well as, types of digital data in business analysis and more.
Make sure to undertake training from a professional company for the best results.