WHAT IS IT?
Data analytics is the process of examining and manipulating data in order to gain insights and draw conclusions. It involves collecting, cleaning, and organizing data, as well as performing statistical and machine learning techniques to analyze the data. The data analytics life cycle is a structured approach that guides organizations through the various stages of the data analytics process.
APPROACH?
1 OBJECTIVE :
First and foremost, Understand the Objective, and the purpose. Why
are you doing this? What outcome do you want from it?
If these questions are not clear, the rest is in vain. It is the same way
that we do in SDLC (Software Development Life Cycle) model, If the requirement
is not clear, then you might develop or test the software wrongly.
2 UNDERSTANDING THE DATA :
Once you have understood the “Objective”, understanding the data
is crucial. Suppose your data contains rows and columns in excel or on a
server, check what each row comprises with respect to the columns. If you have a bulk dataset, take a sample, and then do some wear and tear to understand what
it embraces. And most importantly, check if the data fulfills the objective or
not. Sometimes the data provided to you might have some crucial information
missing in the data.
3 DATA CLEANING AND DATA TRANSFORMATION
:
These two go hand in hand. Data cleaning includes removing and
replacing junk data and filling in some gaps if present. Whereas data
transformation consists of transforming the data as per your requirement to
achieve the objective. Like if you create some metrics like Weekend vs Weekday
sales, Seasonality like Spring, Summer, etc. for Sales data, and many more.
Sometimes after you transform and clean the data you might have to transform it
more or vice versa. If you have a Large Dataset, try cleaning the data before
you start the transformation, it will reduce your efforts.
4 DATA ENHANCEMENT :
Data enhancement is adding value to the data given to you by
looking for other external sources or non-traditional data. Today many new
forms of data channels are available which can be leveraged for meeting the
business objective.
5 DATA ANALYTICS :
When you have all the data in the desired format, you will perform
Analytics which will give you insights into the business and help in
decision-making. For this, you can use Linear Regression, Clustering, and Decision Tree techniques to come to a conclusion and many more as per
requirement. This can be done with help of R language (open source).
6 DATA VISUALISATION :
What good it would do if you were not able to present it well.
So, do take time to Visualise your data, for this you can use Microsoft
PowerBI. The way you present the outcome of Analytics does matter. The more it appeals, and is user-friendly, the more user will indulge in it.
Data Analytics is Storytelling, it tells you what is happening
and what you should do to reach your objective.
So start building your story…
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