Data Analytics


Data Analytics

Data Analytics focuses on processing and maintaining statistical analysis of existing datasets. Data Analytics depends on the methods and techniques that drive the raw data to the primary goals of the business and drill this information to convert metrics, facts, and statistics into improvement initiatives

There are various methods for data Analytics, most commonly there are two: quantitative data Analytics and qualitative data Analytics.

Quantitative Data Analytics

Quantitative data refers to information collected from each individual of a large group about the opinions of a particular group of services. This type of data is the result of conducting surveys. Quantitative data analytics is all about analyzing number-based data. There are two methods of quantitative Data Analytics descriptive statistics and inferential statistics.

Quantitative data analysis may include the calculation of the frequencies of variables and the differences between the variables

Qualitative Data Analytics

Qualitative Data Analytics involves the identification, testing, and interpretation of patterns and topics in textual data. Data is collected from a variety of sources, including social media platforms, customer feedback, and various solutions to various problems. There are several methods to analyze data obtained through exploration. The most commonly used methods are content analysis, narrative analysis, discourse analysis, etc.


5 Steps of Qualitative Data Analysis

  • Preparing and organizing the data.
  • Review and explore data.
  • Creating initial codes.
  • Revising codes and incorporating them into themes.
  • Present themes in a cohesive manner.

There Are 4 Types of Data Analytics

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

Descriptive Data Analytics

Descriptive Data analytics is the interpretation of historical data to better understand changes that have occurred in a business. example: Summarising past events such as sales and operations data or marketing campaigns.

Diagnostic Data Analytics

Diagnostic Data Analytics is a form of advanced analytics that examines data or content to answer the question of what has happened in the past. During the discovery process, analysts identify data sources that help interpret the results.


Predictive Data Analytics

Predictive Data Analytics encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning that analyses customer responses or purchases, as well as promote cross-sell opportunities.

Prescriptive Data Analytics

Prescriptive Data Analytics is the third and final phase of business analytics. the prescriptive analysis can predict the possible consequences based on a different choice of action.


Once you set out to collect the data for analysis, you will be overwhelmed by so much information that makes it hard to handle. But if you go through the following steps you will get clear data for analysis.

The analysis refers to the division of the individual test into its individual components as a whole.


Process of Data Analytics:

  • Data Requirement Specification
  • Data Collection
  • Data Processing
  • Data cleaning
  • Data Analysis
  • Modeling and algorithms
  • Infer and Interpret Results

The Data Analytics process uses analytical and logical reasoning to obtain information from the data. The main purpose of data analytics is to find meaning in the data that can be used to make informed decisions.

In your business data analytics, you must begin with the right question. We at Analytic Era guarantee to resolve the problems and questions of our clients as quickly as possible.