Data science

Data Analytics v/s Data Analysis: How do they Differ?

The magical world of data has got every reason to enjoy every bit of our attention. With every industry relying heavily on data, it is quite obvious for organizations to look out for ways to make the best out of the data available. Data analysis and data analytics are the two most common terms that we get to hear as far as the magnificent world of data is concerned. However, many assume that both these terms can be used interchangeably. In reality, this is not the case. This article will throw light on Data analytics v/s data analysis: How do they differ?

First things first – data analytics is a broader term of which data analysis is a subcomponent. Data analysis is the process of examining, transforming and arranging data in a manner that one can study its individual parts and extract useful information. On the other hand, Data analytics is a discipline that encompasses the complete management of data. Data analytics includes not just analysis but data collection, organisation, storage, and all the tools and techniques used as well. Now that we have a brief understanding of what exactly makes the two fields different from each other, let’s delve deeper into these concepts.

 

Data analytics
Data in its raw form doesn’t serve any purpose. This is exactly where data analytics comes into play. Data analytics comprises of all the steps both human- and machine-enabled, to discover, interpret, visualize, and identify patterns in the data, if any, in order to achieve the business objectives. Data analytics is that process in which data from the past is explored deeply to make appropriate decisions in the future by using valuable insights. When done properly, with data analytics, one can predict actions, find trends and make informed decisions. This field of data has a series of processes that follow. The first step, needless to say, is collection of data. Now is the time to categorise the data into structured and unstructured. This is followed by managing the data. Then, this data is stored. The next steps i.e performing ETL (extract, transform, load), analysing the data and sharing the data to business users or consumers are considered to be the most important ones from the business point of view.

 

Data analysis
Data analysis is concerned with cleaning, transforming, modelling, and questioning data in order to find useful information. There are many data analysis techniques that one can make use of in order to fetch the desired results. Out of these, the most common ones are – predictive analysis, prescriptive analysis, text analysis, diagnostic analysis, statistical analysis, etc. When the objective is finding the preferences, computing various correlations, trend forecasting, etc., then there cannot be a better process to rely on than data analysis. Data analysis is not only simple but also way easier than other processes and techniques to derive valuable insights from the data that is available.

The most common tools that can be used for both, data analysis as well as data analytics are – Excel, Tableau, R analytics, Python, etc.

 

Bottom line
The major difference between data analysis and data analytics lies in their approach. Simply put, data analysis looks towards the past whereas data analytics towards the future.

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