What is Data Analysis – Attempt for In-depth Understanding

Data analysis in depth understanding, internet revolution, geography distribution, team size, per-requisites, history, analytics industry etc.

An effort on explaining “What is Data Analysis – Attempt for In-depth Understanding”.

Definition:
Data analysis is nothing but analysis of data and the data can be of any type numerical, text, image etc.

Is data analysis simple or difficult?

Depending on the type of information one wants to extract out of data, the data analysis can be simple or difficult.

Is data analysis expensive?

If the dataset is small and can be handled in googlesheets then one can do the analysis without much expense, but if dataset is huge and complex from which one wants to extract useful information then lot of processing capacity will be required and skilled workforce as well to do that, hence then it can somewhat expensive.

So, in data analysis requirement of big data storage and/or processing machines leads to lots of cost.

e.g. of some tools softwares required in data analysis are AWS machines, pyspark, snowspark, amazon redshift etc. to name a few.

Internet revolution and data generation?

With digital revolution the data generation has increased massively and hence this field is of very prime importance in IT industry like software business, e-commerce business, online food delivery, cab services app, fintech and any other such businesses dependent on internet.

Refer these articles to understand more about it’s requirement:

• Data Analytics या Database का Industry level पर क्या उपयोग है | Data Analytics or Database application at Industry Level
• Data Mining Application – Real Life Applications of Data Mining in Sales, Marketing, NLP, Text Mining etc.

Geography wise distribution:

It is popular in every country, every continent as everywhere we have internet and so data is getting generated in every corner of this world.

Also, now-a-days everyone is connected to everyone, gone are those days when talking or connection to people in different city or country was not possible.

With internet revolution and advancement in communication technologies whole world is connected and hence data generation is happening at every place and every corner of the world.

Other relevant synonyms of data analysis:

It is also called as data wrangling, data analytics, business intelligence, data crunching etc. which are more or less same things that has same objective of generating useful relevant insights or information from data.

Ultimate objective is to extract the relevant information out of the data to improve your business decision process.

Team size of a data analytics team:

Talking about the number of people required to do data analysis in a company then it depends on the need of the company, but for a company of say 1000 employees in IT industry typically a team of 20-30 analysts will help them manage their workload related to data analysis and insights generation.
But, again this can increase or decrease depending on the company and the field in which it is working.

What happens in data analysis exactly?

Data analysis is mainly better structuring of data only and finding big buckets in which data is lying for e.g. suppose if you have data of e-commerce company then just sorting the data date wise will tell day over day sale, sorting the product category by age will tell which age group is buying which product and so on.

So, it’s better structuring of data or better structuring of the problem that solves most of the problem.

Pre-requisites of data analysis:

Having knowledge of mathematics and a mind full of curiosity is enough to start data analysis. Earlier also it was happening but then we were lacking powerful machines to do this huge calculations easily, now we have resources to store the data and do powerful calculations over it using computer.

Also, if you have some domain knowledge of the industry you are going to work in then that can be really advantageous for you.

History:

The use of analytics by business can be found as far back as the 19th century, when Frederick Winslow Taylor initiated time management exercises. Another example is when Henry Ford measured the speed of assembly lines. In the late 1960s, analytics began receiving more attention as computers became decision-making support systems.

Relational databases were invented by Edgar F. Codd in the 1970s and became quite popular in the 1980s. Relational databases (RDBMs), in turn, allowed users to write in sequel (SQL) and retrieve data from their database.

In the late 1980s, the amount of data being collected continued to grow significantly, in part due to the lower costs of hard disk drives.

Big companies utilising data analytics on day to day basis:

Some of the major companies using data analytics at a bigger scale are Google, Amazon, Microsoft, Netflix, Apple, Uber, OpenAI and what not.

And recently with CHatgpt everyone is not aware of the importance of data and what magic can happen using data.

Analytics industry in India:

India in last few decades has become one of the hub of IT industry and it is because of huge number of colleges India has that is providing qualified graduates who are competent enough to learn this skill and can work in this industry.

Some big companies dependent on Analytics Industry completely:

Some of the relevant companies whose business solely depends on providing analytics services are Fractal, EXL, Tableau, Looker, Mu Sigma, Absolutdata etc.

Usage of Data Analysis in different fields:

There are various fields in which this skills is required for e.g. analysing software product to make a world class software by solving all the problems that customers are facing, analysing customers to find out which customers actually buys their product and hence focusing on that customer base more from profit point of view etc.

Now-a-days more and more companies are looking for automated solutions of data analysis that takes in data, crunches it and provides some readymade solution, hence dependency on actual humans is decreasing.

Other relevant links:

• What is noise in data mining MCQ?
• Data Cleansing and Data Transformation Benefits / Explanation in Data Mining or Machine Learning Tasks
  

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