“Most of the world will make decisions by either guessing or using their gut. They will either be lucky or wrong.” – Suhail Doshi, Chief Executive Officer, Mixpanel

“Data is raw information provided by or to a user. Analysis of that data and making conclusions out of it is simply termed as Analytics”. 

Data Analytics is the process of pulling out raw data or information, cleaning it, transforming it, and finally putting in front, the meaning out of it. Data is indeed everywhere, anywhere you look will consist of a particular data and those who know how to handle it are in a very powerful position. Top CEOs and Entrepreneurs from around the world have emerged as successful because they have mastered the art of handling data and using them for raising wealth.

Technological development has seen a boom and is keeping up today by developing new software, hardware, and applications to make meaningful insights and provide better solutions to consumers. After COVID in 2020, the rise of technology was seen around the world and especially in India.

With the rise of start-ups in India after the lockdown, a trend of the rise of data analysts too has been observed. With time to time and innovation, Data Analytics has developed into a wide aspect of all branches and sectors in the development industry. 

So why Data Analytics is important? The answer can be given in a very easy way. Data Analysis helps in reducing extra efficiency required in a business and boosts its productivity and growth. It has shaped every sector and industry be its IT companies, school, colleges, telecom, airlines, medical, or any other. Different companies have also created business models that focus on cost reduction and also doing good business along with storing large data sets. Nowadays, in the services sector, there is a tremendous increase in data analytics jobs all over as new facilities or start-ups are coming up together.

In Education sector also, students are being taught the course of Data Analytics and its importance. Especially in management colleges, the students are being trained in analytical tools like Excel, Power BI, Tableau, Quicksight, and Alteryx, and software like R and Python.

Management colleges like ICFAI Business School or IBS have been successful in making students learn about data analytics from the very first year in their Master in Business Administration (MBA) or Post Graduate Program in Management (PGPM). In the 1st year, students are taught the subject of Business Analytics, and later on, in the 2nd year, IBS offers Analytics as a minor subject along with major subjects like Finance, Marketing, and Human Resources (HR).

Now let us understand, how data is collected and processed. There are a few steps before raw data is converted to meaningful information.

Data Requirement: Why is this data important? Is there any usefulness after the data is transformed? We need to answer these questions first before collecting any form of data. 

Data Collection: After knowing why data is required, it’s time to collect the data from sources. Sources can include case studies, interviews, surveys, questionnaires, direct observation, and focus groups. We have to make sure to organize the collected data for analysis.

Data Cleaning: Not all types of data we collect will be useful, so we need to clean them first. In this process, we need to remove any sort of white spaces, duplicate records, and basic errors like unorganized dates, columns, or headers. Data cleaning is mandatory before sending the information to the next level for analysis.

Data Visualization: Data visualization is a new way of saying, “graphically show your information in a way that even a layman can read and understand it.” We need to put the analyzed report using charts, pivot tables, bullet points, or a host of other methods.

Visualization helps us derive valuable insights by helping and comparing datasets along with observing relationships.

Data Analysis: Here we are required to analyze the data loaded after it is been cleaned and then load it using the Data analysis tools including Excel, Python, R, Looker, Rapid Miner, Metabase, Redash, Microsoft Power BI, Tableau, Alteryx, and many more.

Data Interpretation: Now that we have our results, we need to interpret them and come up with the best courses of action, based on our findings.

Coming to the next part is what are the type of data analysis that is generally involved in a dataset. There are four major types of data analysis methods:

• Descriptive Analysis: Whether the data type or dataset is describing something. An example can be, are sales good or bad for this month as compared to the previous month? Are there any sales metrics that have gone up?
• Diagnostic Analysis: Diagnostic analysis focuses on the data which tells why an event occurred and it requires a huge number of diverse datasets. An example can be, why there is so much increase in population.
• Predictive Analysis: Predictive analysis forecasts future events or what is going to happen in the future. For example, forecasting a product’s sales.
• Prescriptive Analysis: It suggests more of a course of action that will occur. An example can be an investment in stocks.

After discussing the types of data analysis involved, let us look at the types of Data Analytics Tools involved in processing data.

In earlier times, a dataset was processed majorly in the form of spreadsheets in Microsoft Excel. But now due to rapid increase in technology and innovation, there has been the rise of new data analytics tools in the market. Some notable mentions can be of Python which helps in transforming raw datasets and programs into feasible data. R is used for statistical analysis and graph modeling. Data can be visualized using tools like Power BI and Tableau where reports are generated and results are distributed on dashboards. SAS is an analytical platform tool used for data mining, whereas Apache Spark is used for processing large datasets. Now, there are many more tools and technologies available in the market which furthermore enhances the skills of an individual. Hence, in a nutshell, we can conclude the fact that data analytics is the new generation future of data gathering. Data Analytics is ready to change the future and the outlook that people used to have in organizations or either as an individual also. It is responsible to shape the generation of data to come.