Automating business insights using Analytics

Data analytics (DA) is the process of examining data sets to draw conclusions about the information it contains, increasingly with the aid of specialized systems and software. 

Over the past few years, we’ve seen a remarkable growth of data analytics. organizations like, professional sports franchises, medical research institutions and much more, not even a single industry is untouched the use of data analytics. The analytics has changed the face of the market in every term.

Every organization is acknowledging the power of data, they are driving their business to the new heights with actionable decisions. Precisely, all the IT giants are not only getting the benefits from it also doing research and development to get the better prediction accuracy.

Using analytics and other data mining techniques, you can get the best insights from the data. So, let’s have a deep dive into it with their use cases.

Descriptive Analytics:

Descriptive Analytics are used to describe the basic features of the data with the help of analytical tools (i.e. R, SAS, Tableau) and other statistical methods. It provides simple summaries about the sample and measures.

Use case – I

Let’s start working with “Online Retail” dataset which is well known and very popular among us. we are taking this dataset for the sake of understanding.

Dataset reference link:

So, below is the head of the retail transactional data which consist over 500 thousand records, having the daily transaction with eight different variables which can be seen below.


So, by using some basic visualization tools and techniques, now we have the basic information about the data, what data is about?


In the top left plot (i.e. time series – monthly sale), we can observe that in the month of November-2011 highest sale recorded compare to other months. The very next month Dec-2011 has the marginal drop in a sale. By analyzing this plot we can at least have the basic idea that there was something untrendy in Dec-2011. If we look at trend line which shows an upper trend, that means over the past months we have significant growth rate.

In the top right plot (i.e. customer wise sale report), the top customers are there on the list. We can focus to serve them as good as possible.

In the lower plot (i.e. country wise sale report), it’s a geographical plot for different countries based on sale generated for the region. The highlighted region “United Kingdom” is the country who is having the highest sale over the past year.

The table below gives us the basic idea about dataset with statistical attributes.

Summary table (transaction dataset):


Below plot shows, the highest selling product in decreasing order.


Predictive Analytics:

Predictive Analytics is the branch of advanced analytics which is used to make the prediction of unknown future events. It uses many statistical techniques, machine learning, artificial intelligence, data mining and modeling to make the prediction for unknown future events.


To understand the techniques of predictive analytics, let’s work with another use case.

Use Case – II

Here, I am using the “German Credit” data, having 1000 number of records and 21 columns, let’s see what information we can get from this data using predictive analytic techniques?

data reference link:

Below, the structure of the data gives us the overview, what data is about?


Applied first phase of analytics i.e. descriptive analytics. Now, here we are going to apply some popular predictive analytic techniques like regression model, decision tree etc.

Model – I

Decision trees: These are classification models that partition data into subsets based on categories of input variables. So, let’s apply DT on credit data.


Initially, we decide the target variable i.e. Creditability, later looking the impact of other variables on target variable and we can treat them accordingly.

Below, the DT plot from which it can easily identify the different levels and nodes of DT. The classification criteria for the different levels are calculated with the help of entropy.


Model – II

Regression (linear and logistic) is one of the most popular methods in statistics. Regression analysis estimates relationships among variables. So, let’s work with another model i.e. “Regression” on credit data.

Regression modeling in R, codes are below:


The regression output is shared below with some highlighted rows, which means those variables have the significant impact on credibility (i.e. variable of interest).


The highlighted variables have p-values less than 0.05 (significant level), which indicates that the variables have the significant impact on target variable.

Prescriptive Analytics:

Prescriptive Analytics is basically the area of business analytics which leads to increase the number of suggestions (options) for a current situation. It is related to both descriptive and predictive analytics. It optimizes decision and helps us to maximize profitable growth and mitigate the risk as well.

Case Study – III: Product Recommendation

E-commerce websites and applications, for example, make recommendations based on user data—such as Amazon or Netflix suggesting a product based on our interest or on our purchase history.

How does it work? Behind the whole process, the team of data scientists monitors model results. The analytic results, associated content, and delivery of said content are all controlled through an automated workflow which is nothing but an algorithm.

The brilliant part about this case example is that the models prescribe what to do (i.e., which product to recommend), and an automated system actually does it (i.e., the website tells you what to buy next).

This insight is already here, but the next step is even more exciting: solutions will also offer actual recommendations based on data to ensure good business decisions that will help organizations to reach their goals.


The major goal of analytics is to help organizations and make them more informed business decisions in the form automated insights. After analyzing the large volume of different forms of data which may be untouched due to their complexity, this can include Internet clickstream data and weblogs, internet contents and social activity, survey responses and emails, sensor data connected with IoT.

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