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All You Need to know about Data Analytics

All you need to know about Data Analytics



  1. Introduction and flashback
  2. Benefits and uses of Data Analytics
  3. Types of Data Analytics
  4. Process of Data Analytics
  5. Techniques used for Data Analytics
  6. Skills and qualifications
  7. Who hires YOU and what payscale?
  8. Roles and responsibilities on a daily basis
  9. Future forecast of the field
  10. References



Companies all over the world, be they small or medium, or large, store huge amounts of data. This data is usually raw. To be precise, raw data is that form of data that is vast, unorganized, and not yet processed by a machine or a human. The sources of raw data include satellite images, mail, audio, video, or texts.

Data Analytics is all about converting raw data into a well-organized meaningful form of data and effectively interpreting them. In this process, unimportant data is cleansed or eliminated. Whenever a business identifies particular questions or challenges affecting its business, data analysts address these questions or challenges by visualizing their findings in the form of dashboards, charts, or graphs.



Data was manually analyzed in the past, i.e., till the era of black-and-white cinema. This was often expensive and time-consuming. It was only from the 1970s, businesses started using technology in the field of data analytics. As years passed, machine learning algorithms, data warehouses, data visualization, and other tools were involved in the process.


Benefits and Uses of Data Analytics:

Now that we understand what data analytics is, let us find out the benefits a company can enjoy because of data analytics:

  • Predict future sales of the organization
  • To protect the organization against fraud
  • To analyze the effectiveness and efficiency of ad and marketing campaigns
  • To improvise supply chain efficiency
  • To boost customer retention

After understanding the benefits of Data Analytics, let us now try to find out what are the types and tools used for Data Analytics.


Types of Data Analytics

There are mainly four types of data analytics. They are

Descriptive Data Analytics

Diagnostic Data Analytics

Predictive Data Analytics

Prescriptive Data Analytics

It is important to note that all these types are interconnected and interdependent. Let us try to understand each of the types in detail.

Descriptive Data Analytics: It is a type of analytics that addresses the question of ‘what happened in the past?’ It involves studying present and historical data to identify trends and observe changes over time. It is considered the simplest form of data analytics. Descriptive Data Analytics can be applied to Demand trend analysis, or financial statement analysis are some examples of descriptive analysis.

Diagnostic Data Analytics: This type of analytics tries to answer the question of ‘why did it happen?’ In other words, it is also called root cause analysis. Methods like time series, probability theory, and regression theory are used in this type of data analytics. It tries to get more information from any report by identifying the root cause of several issues, changes, etc. Diagnostic Data Analysis can also help in alerting or notifying some issues even before it occurs in an organization.

Predictive Data Analytics: This type of analytics tries to answer the question of ‘what might happen in the future?’ Historical data is used to understand and forecast future trends and events. Regression analysis is one of the tools used.

Prescriptive Data Analytics: This type of analytics addresses the question of ‘what can be done in the future?’ Based on the results of predictive data analytics, organizations can make informed decisions on what can be the best course of action to tackle risks and improvise their efficiency in the future. It is considered a complex type of data analytics as it involves the usage of algorithms.




Process of Data Analytics  

Data Analysts follow 5 main steps when they are working on a project. They are

  1. Define the question that the organization wants to answer:

A Data Analytics professional should know why they are conducting a particular analysis. Which problem to address? The analyst will make a research question or hypothesis based on this. Here, the analyst should be very clear on what type of data he/she requires and what is the source of that data. For example, a data analyst may want to know why the new product of their organization is not able to increase their sales.

  1. Collect the data:

In order to collect the data, they may use primary or secondary sources. They can use CRM measures to know the opinions of the customers, analyze trends, conduct online surveys, or try to get data from government websites, or international websites like IMF, World Bank, WHO, etc.


  1. Cleansing of the data:

Once the data is collected, it should be prepared for analysis. But before analyzing, the collected data might be unorganized and raw. The raw data can contain some unwanted information, or missing information that can be unnecessarily misinterpreted. Data cleaning is a tedious and time-consuming task.

  1. Analyzing the data:

Once the data is edited, the data will be ready for analysis. In this step, analysts make sure that their research questions will be answered. Techniques like Regression Analysis, Cluster Analysis, Cohort Analysis, Factor Analysis, and Time Series Analysis are used. These techniques are explained in the subsequent section.

  1. Interpret and share the result:

It is the final step where the data analysts interpret their understanding in the form of a chart, a map, a table, etc after their research. This process is called Data Visualization. With this step, organizations will make their decisions on how to move forward.



Techniques used in Data Analytics

Data Analytics can either be qualitative or quantitative.

Quantitative data can be measured. For example, analyzing the number of responses answered by customers in an online survey. Qualitative data cannot be measured. For example, analyzing how people answer an interview.

Though data analysts mainly work with quantitative data, they are expected to have an understanding of both methods. Broadly, the field of Data Analytics uses 5 methods. They are

Regression Analysis

Cohort Analysis

Time Series Analysis

Cluster Analysis

Factor Analysis


Regression Analysis: It is a technique used to estimate the relationship between dependent and independent variables. Such insights can be very helpful to study historical trends and develop forecasts. An example of regression analysis can be, analyzing the exam scores of students who study for 6 hours a day. Based on these two variables (study hours and scores), analysts can understand if 6 hours of study is fruitful for the students or not.

Cohort Analysis: It is a technique of data analytics where a specific group based on common characteristics is analyzed over a period of time. The behavior of this group is studied. For example, organizations use cohort analysis to understand the customer life cycle which helps them recognize important trends. This type of behavioral analytics can save a lot of time as cohorts with common characteristics rather than analyzing individuals.

Time Series Analysis: It is a technique where data that is recorded over a regular time period is analyzed to discover causes of certain patterns or trends. Time series data is widely used in studying stock prices or weather forecasts. By studying time series data, analysts can come to a conclusion to understand the behavior of different industries.

Cluster Analysis: In this technique, data points are grouped into clusters based on common characteristics, and the patterns of these homogeneous groups are studied. This technique does not require prior knowledge. For example, customers can be divided into different groups based on their geographical location, age, or different factors and the purchasing behavior of each group can be analyzed. This can help business organizations find costumers of high value.

Factor Analysis: In simple words, it is the technique of condensing lots of data variables into a few data variables and a smaller set of factors. It is heavily used in the field of marketing and psychology as it helps in identifying undiscovered and inactive variables which influence customers’ preferences. For example, an organization wants to understand customer preferences for denim jeans, cargo jeans, ripped jeans, slim-fit pants, joggers track pants, gym-wear track pants, and solid track pants. Factor analysis can reduce all these variables into two main variables- Preference for Jeans pants and Track pants.


Tools used in Data Analytics:

At Net Connect Global, we mainly use 4 tools for data analytics. They are; Power BI, Alteryx, Tableau, and MS Excel. With the help of these tools, raw data is simplified and organized into effective visual dashboards, charts, maps, tables, and worksheets by our talented Data Analysts.

Find out more about NetConnect Global


Skills and Qualifications

As we understood how the field of Data Analytics works, let us now try to find out how to become a Data Analyst and what are the skills organizations expect. Fortunately, there is actually NO SET QUALIFICATION to become a data analyst. Any student from a commerce, science, or humanities background can get into the field.

However, the candidate is expected to have knowledge of Mathematics and Statistics as they heavily work with numbers. This job can be a very good choice for any ‘math lover’. Along with mathematics, the candidate should acquire knowledge of programming languages like Oracle, SQL, and Python. Thanks to EdTech platforms, learning these languages is now easy for any student living in any corner of the world.

But wait, being a talented programmer is still not enough. Organizations look forward to seeing Analytical Skills in a candidate. What is ‘Data Analytics’ without analytical skills? Data Analysts should be able to understand what the data is trying to say. They should understand what exactly is going on and how to address various issues. Here, Problem-Solving skills are also needed. When analysts analyze the data, they should be able to find solutions for the research questions they are working on.

Lastly, Good Communication Skills is a must. It is a common skill that almost every job role prefers. Data Analysts closely work with important stakeholders in the organization. Data Analysts should be able to confidently present their analysis with solutions as it heavily impacts the decision-making of the organization.

To sum it up, the skills required for Data Analytics are;

  • Mathematical and Statistical knowledge
  • Oracle, SQL, Python
  • Analytical Skills
  • Problem-Solving skills
  • Good Communication skills

These skills can be moulded with the increase in one’s work experience.

Here are some of the top coaching centers for Data Analytics all over India:

Top Data institutions in India


Who hires YOU and what Payscale?

The top industries that require Data Analysts are;

  • Healthcare
  • Finance
  • Entertainment
  • Business Intelligence

Healthcare: A data analyst in this sector might have to study how much a particular disease or a virus has spread. To be precise, healthcare analysts study the number of victims of a particular health hazard. They might also study the sales of vaccines or other pharmaceutical drugs, etc.

Finance: In this sector, financial analysts are required to make predictions on the prices in t   stock, crypto, or bond market. They have to make risk-return analyses or several investment decisions, recognize uptrends or crashes, etc.

Entertainment: In this sector, data analysts are also labeled as marketing intelligence analysts where they analyze the increase or decrease in the number of subscriptions for a particular OTT platform, which genre of movies people prefer nowadays, or if a particular advertisement has created an impact on a consumer or not.

Business Intelligence: Data Analysts in this sector are called marketing analysts, or business analysts. Business Intelligence emerged in the 90s which involves gathering and storing data for the organizations’ decision-making.



The salary earned by a data analyst is very subjective. Highly talented individuals who upskill themselves as much as they can in this sector can earn more than anyone can expect. However, the average package for a data analyst is;

0-2 years: 3.5 to 8 LPA

3-5 years: 8 to 14 LPA

6+ years: >14 LPA

Salary levels can vary depending on the individual’s skills, the institution he/she studied in, the company that is hiring them, the work experience of the individual, geographical location, etc.


 Roles and Responsibilities on a daily basis

Now that we have also understood the skills required and the top industries that hire data analysts, let us now try to understand what will be the work of a data analyst on a daily basis in an organization.

A data analyst has to;

  • Manage the delivery of user satisfaction surveys and report results using data visualization software.
  • Interact with business owners to develop requirements, manage analytics projects, and evaluate results. Follow policies, processes, and systems to improve opportunities.
  • Proactively communicate and collaborate with stakeholders, business units, and technical and support teams to define concepts and analyze needs and functional requirements.
  • Turn important questions into specific analytical tasks and collect new data to answer customer questions, and gather and organize information from multiple sources.
  • Come out with innovative insights and present them to clients with effective dashboards by using analytical tools.
  • Communicate complex concepts and data in visualizations and collaborate with data scientists and other team members to find the best product solutions.
  • Implementing the process of data quality by designing, testing, and maintaining backend code and creating data processes.
  • Take ownership of the code base, including suggestions for improvements and rework
  • Create data validation models and tools to ensure the accuracy of the data recorded
  • Work as part of a team to evaluate and analyze key data to inform future business strategies


Future forecast of the field

No doubt, data analytics is a career option with a huge opportunity to excel. Data is as precious as oil. We all know how powerful are the countries rich in crude oil. Similarly, data is as precious as oil. Every organization in every country needs data. An organization without data is a body without a soul.

Data is something that will always be in demand. The higher the data, the more the analysis, and with this, the higher the demand for data analysts. As of 2022, global data analytics is valued at a whopping amount of 271.83 billion USD. By 2030, it is predicted to grow to 745.15 billion USD.

Data Analytics is a great career option that is a need for every organization. Be it public, or private, or large, or medium, or small, or non-profit. Data Analytics is an ever-growing career in demand.

NetConnect Global is hiring data analysts! Send us your CV to sara@netconnectglobal.com

Click here to find out other career opportunities at NCG