Visualizing Modi Foreign Visits Data

Visualization of Prime Minister Narendra Modi’s foreign visits from the time he took charge as the Prime Minister of India.

A simple quick activity of visualizing the data to come up with a dashboard summarizing all of Narendra Modi’s 35 foreign tours till Jan 2015 on Ideata Analytics Platform .

We collected the information from documents and facts available on different website and reconciled in to an excel sheet.

Uploading the excel sheet was an easy task — After choosing excel as my datasource, I just have to choose my excel file and click on upload which gives me preview of my data present in the sheer. I did some quick fixes using the wrangling interface and then created some wonderful visualizations as below.

  • Number of Modi’s country visits per month 

This chart shows the number of countries per month visisted by Modi. It shows that on an average he visited 2–3 countries per month in his tenure. The highest number of visits was in July -2015 when he visited around 6 nations in a single month

  • Country Visited

He almost visited countries in all the 5 major continents with major focus on countries in Asia.

  • Days spent in each country

To get in to more depth we also calculated and plotted the number of days which modi spent in each visit in any country. His longest stay was in USA which is a 7 days trip. He also spent 4 days in USA in one of earlier trips which makes it a total of 11 days in USA.


  • Reason of Modi’s Visit

From all his trips, around 67.6% of them are state visits. For 18.9% of time he visited the country to attend confrences like BRICS, G20, SAARC etc.

  • Total Budget of the trips

We do not have all the data, we plotted the amount of money spent on his visits for which we have the information. The actual value of these 16 foreign trips has been calculated to Rs 37.22 crore, among which the US and Australia have been the most expensive ones, amounting to about 40 per cent of the total expenditure.

  • Agreement signed and Speeches given

To take a more 360 degree view of things, we added in information on speeches given by Mr. Modi (excluding media interactions) and agreements signed (not including joint statements) on each visit.

We can indeed call Modi a frequent flyer.

Ideata Analytics Achieves “Yarn Ready” Certification on Hortonworks Data Platform



Ideata Analytics is now certified on HDP


We are excited to announce that Ideata Analytics is now certified on Hortonworks data platform (HDP). Users can now use Ideata analytics fast cycle analytics application on HDP with Hadoop and Spark.


Ideata Analytics provides an easy to use analytics platform for users to source, prepare and analyze data from various sources. With its extensive connector library, it gives users direct access to their data to perform deep dive exploration. It provides a self-service data preparation engine for users to perform data cleaning and enrichment on the fly. They can build advanced machine learning models to drive predictions and segmentation on their data. With inbuilt drag and drop functionality, users can quickly visualize their data and interactively slide and dice it to find hidden insights.




With the newly achieved certification, Ideata will further simplify and accelerate deployment of its advanced analytics application on HDP platform. HDP enables high intensity workloads on it’s yarn based platform with optimal efficiency. HDP’s secure, scalable and reliable enterprise data platform is an important component of the modern data architecture and users can now run Ideata Analytics on it and utilize it to process and analyze large amount of structured and unstructured data sets.

We’re hoping to enable HDP users to derive insights more rapidly from their data lakes without having to write a single line of code” said Pranjal Jain, founder, Ideata Analytics. “We’re very pleased to be able to work with Hortonworks and take our product to customers with an enterprise-ready, productionablespark and hadoop distribution


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For more information about Ideata Analytics and Hortonworks, please visit:

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Visualizing YouTube Data in 5 easy steps

In this blog post, we will quickly upload, analyze and visualize the data of YouTube. The full analysis cycle will be performed using Ideata Analytics interface and will only take 10 minutes.

This exercise will help you understand how easy and fast it is to analyze your datasets in apache spark a using Ideata Analytics.

We have a sample youtube data file with us and we will try to bring out some insights like top rated videos, highest rated video, top users etc


Step 1 : Upload your data

We have a sample youtube file in tab delimited format downloaded. Ideata Analytics provide in box connector to upload the delimited file. The step is straight forward, we will create a new connection and click on the delimited file and provide necessary details like “tab” as a separator and click on upload  to upload  the file in the system.


Step 2 : Clean your data

Once the data is uploaded successfully we can see the preview of the data. It seems pretty structured data and do not need any formatting. The only thing missing in the file is column names which we can quickly provide on the preview screen. We will rename all the columns to what it represents like video id,   etc so that it is easy for us to understand the

Column Rename : The only thing missing in the file is column names which we can quickly provide on the preview screen. We will rename all the columns to what it represents like video id,   etc so that it is easy for us to understand the data on analysis screen, Once done, we will click on finish to create the dataset and make it available in the system for analysis.


Step 3 : Find out Answer of Question 1 :

We will now try to figure out which are the top video categories with the most number of  videos uploaded.

In order to answer the question, we will go to analysis screen and drag category column from the left panel into dimension(x-axis). This will show us a quick bar chart with all the categories along with its number of times it is present. As we are only interested in top 10 we can just specify that in right panel by selecting the checkbox of “limit” as top 10.




Drag the column “category” from left panel here


Limit the results to top 10 by selecting this checkbox


We will see the above chart which shows us the top categories of videos. We can see the actual number by clicking on the underlying data checkbox, which will show us the data table


Read more

Acquisition to Retention

“Who cares if we find out we lost a customer after he/she left?”

Using analytics to compete and innovate is a multi-dimensional issue. It ranges from simple (reporting) to complex (prediction). We are now in an era of man + machine interactions.
There is a drift from “Serving customers with few channels” to “integrating multi-channels, devices”, from “lone demographic segmentation” to “complex behaviour segmentation”

You have acquired a customer. Now what? Retain the customer, as the chances you can get existing customers to do more business with you is much more likely than what it takes to go out and get somebody new. An Organisation needs passion and precision when it comes to customer retention. The objective of performing analytics for ‘Retention’ is not just to understand why you lost a customer; but how to prevent you from losing one before it happens. In this blog we want to tell how important customer retention is and the ways to improve customer retention.

Here are few important ways to improve customer retention:

1. Customer Segmentation – Know The Customer

Customer segmentation is the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests, spending habits, and so on. Customer segmentation allows a company to target specific groups of customers effectively and allocate marketing resources to best effect. Better customer segmentation for customized services.

Customer segmentation includes:
I. Collection of data
II. Integrating data from various sources (Our product ‘Connect’)
III. Data analysis for segmentation (‘Analyze’)
IV. Effective communication among business units (‘Share’)

2. Customer funnel optimization and improvement

Identify the blockage points –
· Are you getting enough leads at the top of funnel?
· How many visitors have converted to registered users?
· Are you able to catch the decision trigger?
· Are you able to bring back the customer?

Ideate on the causes of blockage points, solve them. Removing the blockage points will increase the conversion rates in your funnel.

3. Suggest products

You now got a customer through the funnel. What next? Suggest products which customers might like based on their previous buying history. Companies are now interested in understanding every aspect of customer interaction, websites purchasing patterns, social media, support calls, transactions etc. The combination of customer’s transactional history across channels with their online and social behaviour is the ‘Holy Grail’ here.

Machine intelligence is getting increasingly married to human insight. Let us look at following interesting applications:

  • Recommendation based on social media relationships: “Several of your Facebook friends have recently enjoyed visits to our restaurant, so we’re offering you 15% off to try it yourself”
  • Recommendation with regard to cross-sell sales: “We hope you’ve liked the 55 inch LED TV purchased with us, as a token of loyalty we want to extend the gratitude and here’s a coupon for 20% off valid across all Home theatres”
  • Recommendation based on customer behaviours: “We are sorry we missed you this Sunday at Baskin Robbins after nine straight weeks of enjoying your company! Here is a free ‘Warm Brownie Sundae’ for you”
  • Recommendation based on location: “We see you have just landed in New York, and your final destination is The Plaza. Here is a $30 Uber voucher to get you there”

We identify the patterns of past purchases, browsing history, social media behaviours and build an algorithm using a training set to train the model and implement the algorithm thus developed on the desired set which gives us “Machine learning based recommendations‘’; Checkout our product ‘Ideata Analytics’ for ‘self service analytics’ with ‘easy to use’ interface.

4. Customer Satisfaction

The most important factor that drives to retain a customer. If you are successful in making your customer happy on their first purchase, chances are high in gaining them back. An organization needs to focus on the value and support provided to the customer during their engagement.

The following helps in maintaining healthy CSAT score:

  • Improved NPS (Net Promoter Score)
  • Increased customer satisfaction and fewer calls to call centers
  • First call resolution
  • Social Media engagement

As they say, “water water everywhere not a drop to drink”; Most of the data is unstructured which is hard to clean and bring into shape for analysis to work. Customer data analytics can unleash significant financial rewards for an organization’s sales, marketing and customer services. With so much data to contend with, companies often struggle with making sense of information from customers (segmentation), public records (social media data) and external databases (web history, buying pattern etc.); The aggregation of data renders any analysis on the individual customer level impossible. Profit and revenues are determined by a multitude of variables, which in addition are highly correlated. Data aggregation and correlation from these sources are crucial to find hidden insights. Ideata can help you achieve these goals. Lets ‘Ideata’