Data Analytics vs Data Analysis, Are they similar?
If you have a basic knowledge of tech, you must have come across the terms: Data Analytics and Data Analysis. Have you ever thought of the difference or the similarity between them? Many times people have a misconception that both are the same. Where do you stand in this argument? Do you think they are the same or similar?
The answer is ‘No! they are not the same. They have considerable differences between them like the two chutneys: Onion Chutney and Coconut Chutney. They both are used as a side dish for the well-known South Indian dish Idli. Since both are chutneys in general that doesn’t mean they are the same in-depth. Without Idli, there is no worth for both. Similarly, there is not much importance for both the terms Data Analytics and Data Analytics without the Data.
What are Data Analytics and Data Analysis?
Literally, “Analysis” is the detailed examination of the elements or structure of something. On the other side “Analytics” is the systematic computational analysis of data or statistics. In detail, Data Analytics is a wide area involving handling data with a lot of necessary tools to produce helpful decisions with useful predictions for a better output, while Data Analysis is actually a subset of Data Analytics which helps us to understand the data by questioning and to collect useful insights from the information already available.
In simple terms, Data Analytics is the process of exploring the data from the past to make appropriate decisions in the future by using valuable insights. Whereas Data Analysis helps in understanding the data and provides required insights from the past to understand what happened so far.
The reason behind why the Data receives that much attraction
Now let’s have a small and quick discussion in between this discussion to know why they are the hot tech topics in recent days. Both the concepts run around the information called the Data. Everyone knows that Data is a collection of information, but nowadays information is the richest wealth when compared to all the other wealth including Gold, Diamond, Fuel, etc.
It is because, with Data, one can rule the world only if they know how to use it. Even the world-famous tech giants like Google, Microsoft, Amazon, and other companies collect data and analyze it for various purposes, primarily to improve customer’s feed by analyzing customer preferences and their mindsets, the reason is customers are the wealth givers for any commercial industry.
Hot tech talks of 21st century
That’s why the craze for handling, understanding and effectively analyzing the data is increasing like a summer temperature nowadays. And hence the craze behind the two terms of our discussion Data Analytics and Data Analysis and they have become one of the notable hot topics in the tech world in this twenty-first century.
Understanding the insights hidden behind the datasets, the analysis and analytics patterns play a major role in fetching and showcasing a lot more about the data, they attain various transformations and cross numerous stages to produce valuable output.
Tools and Process involved
The journey of Data Analytics consists of various stages including identifying the problem, finding the Data, Data Filtering, Data Validation, Data Cleaning, Data Visualization, Data Analysis, Inference, Prediction, etc. The most common tools employed in Data Analytics are R, Python, SAS, SPARK, Google Analytics, Excel, etc.
Similarly, the journey of Data Analysis comprises Data gathering, Data validation, Interpretation, Analysis, Results, etc., shortly it tries to find what the data is trying to express. The most common tools employed in Data Analysis are Tableau, Excel, SPARK, Google Fusion tables, Node XL, etc.
Analytics is commonly used in many distinct ways to find some strange patterns like finding the preferences, compute various correlations, trend forecastings, etc. The most common real-life findings found through analytics are market trend forecastings, customer preferences, and effective business decisions.
With the help of Analysis, it is quite simple and easy to explore more valuable insights from the available data by performing the various types of Data Analysis such as Exploratory Data Analysis, Predictive Analysis, and Inferential Analysis, etc. They play a major role by providing more insights in understanding the data.
Which is beneficial?
In general, the outputs from the Data Analysis are the affordable equipment for a user to understand the actual reality behind the Data and also easy to produce better pictorial and graphical representations in the presentation to make even an illiterate understand the information hide behind the dataset much better and quicker.
But it is quite struggling for a common person to understand the analysis and process made by the Analytics person to produce predictions and the inference. Because the post-process like creating something new from the dataset for producing a better and expected output may be difficult for a third person to understand without similar background.
Examples for better understanding
Let’s try to understand the concepts with the following real-life examples,
Almost every one of us has at least some little knowledge about the Share Market. Just think if you are a beginner and you want to start your trade with some profit there. Now say what you will do initially?
Most probably before starting trading you just try to examine the past trend records of the shares in the share market to understand what happened so far in order to frame your strategies to get more profit right? This kind of process is an example of Data Analysis.
After understanding the trend of the shares, now you may use different techniques to predict the future price trend of the shares, and based on that you buy some shares right? This is an example process of Data Analytics.
I hope you should gain some more extra knowledge about the difference between Data Analytics and Data Analytics. I believe I’ve given some useful insights for you to enrich your tech desire.
I request you to share your valuable thoughts about this article. It will be more useful for me during my future works.
About the author:
I’m Shankar DK, a Data Science student. Connect with me on Linkedin https://www.linkedin.com/in/shankar-d-k-03470b1a2
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