India still the Top Destination for Outsourcing

SAP Labs India Pvt. Ltd. in Bangalore
SAP Labs India Pvt. Ltd. in Bangalore

Asian countries, especially countries in South Asia and Southeast Asia, keep on being favored picks among organizations interested in contract out business processes offshore. India remains the top outsourcing destination, with its unrivaled advantages in scale and people skills, said the 2014 Global Services Location Index (GSLI) released by A.T. Kearney. China and Malaysia are second and third respectively.

The GSLI, which tracks offshoring patterns to lower-cost developing countries and the ascent of new locations, measures the underlying fundamentals of 51 nations focused on measurements in three general classifications, such as financial attractiveness, people skills and availability, and business environment.

Distributed since 2004 the GSLI, revealed that leading IT-services companies in India, to whom IT-related functions were outsourced, are extending their traditional offerings to incorporate research and development, product development and other niche services. The line between IT and business-procedure outsourcing there is obscuring, as players offer packages and specialized services to their customers and are developing skills in niche domains.

Furthermore, the GSLI identified a trend of multinationals reassessing their outsourcing strategies, after having aggressively outsourced back office operations in the mid-2000s; it has been noted that some companies are starting to reclaim some of these functions and undertaking them in-house again.

Data Science Toolbox: How to use R with Tableau

Recently Tableau released an exciting new feature: R integration via RServe. Tableau with R seems to bring my data science toolbox to the next level! In this tutorial I’m going to walk you through the installation and connecting Tableau with RServe. I will also give you an example of calling an R function with a parameter from Tableau to visualize the results in Tableau.

1. Install and start R and RServe

You can download base R from Next, invoke R from the terminal to install and run the RServe package:

> install.packages("Rserve")
> library(Rserve)
> Rserve()

To ensure RServe is running, you can try Telnet to connect to it:


Protip: If you prefer an IDE for R, I can highly recommend you to install RStudio.

2. Connecting Tableau to RServe

Now let’s open Tableau and set up the connection:

Tableau 10 Help menu

Tableau 10 External Service Connection

3. Adding R code to a Calculated Field

You can invoke R scripts in Tableau’s Calculated Fields, such as k-means clustering controlled by an interactive parameter slider:

Calculated Field in Tableau 10

4. Use Calculated Field in Tableau

You can now use your R calculation as an alternate Calculated Field in your Tableau worksheet:

Tableau 10 showing k-means clustering

Feel free to download the Tableau Packaged Workbook (twbx) here.

Further reading: Hands-On with R

[Update 26 Jun 2016]: Tableau 8.1 screenshots were updated with Tableau 10.0 (Beta) screenshots due to my upcoming Advanced Analytics session at TC16, which is going to reference back to this blog post.

Goodbye Academia, Hello Capgemini

CERN Main Auditorium
CERN Main Auditorium

I have enjoyed research for the last four years. Yet, I have decided to resigned from my postgraduate position at CERN, and to move to Capgemini. I will continue on the areas I love: Data and Analytics!

Capgemini is one of the world’s largest consulting corporations. Like many other consulting company, Capgemini does not yet have a dedicated team to offer effective strategies and solutions employing Big Data, Analytics and Machine Learning.

I love these technologies, and I am very confident that I will elaborate a business development plan to drive business growth, through customer and market definition, including new services such as:

  • Data Science Strategy (enable organizations to solve business problems increasingly with insights from analytics)
  • Consulting (answering questions using data)
  • Development (building custom data-related tools like interactive dashboards, pipelines, customized Hadoop setup, data prep scripts…)
  • Training (across a variety of skill levels; from basic dashboard design to deep dive in R, Python and D3.js)

This plan is also accompanied by a go-to-market strategy, which I don’t want to unveil on my blog. Maybe retrospective in some years, so stay tuned…

Challenges of Big Data Analytics in High-Energy Physics

Challenges of Big Data Analytics: volume, variety, velocity and veracity
Screenshot of CERN Big Data Analytics presentation

There are four key issues to overcome if you want to tame Big Data: volume (quantity of data), variety (different forms of data), velocity (how fast the data is generated and processed) and veracity (variation in quality of data). You have to be able to deal with lots and lots, of all kinds of data, moving really quickly.

That is why Big Data Analytics has a huge impact on how we plan CERN’s overall technology strategy as well as specific strategies for High-Energy Physics analysis. We want to profit from our data investment and extract the knowledge. This has to be done in a proactive, predictive and intelligent way.

The following presentation shows you how we use Big Data Analytics to improve the operation of the Large Hardron Collider.

Displaying Dimuon Events from the CMS Detector using D3.js

Physicists working on the CMS Detector
Physicists working on the CMS Detector

I became a Python geek and GnuPlot maniac since I joined CERN around three years ago. I have to admit, however, that I really enjoy the flexibility of D3.js, and its capability to render histograms directly in the web browser.

D3 is a JavaScript library for manipulating documents based on data. This library helps you to bring data to life leveraging HTML, CSS and SVG, and embed it in your website.

The following example loads a CSV file, which includes 10,000 dimuon events (i.e. events containing two muons) from the CMS detector, and displays the distribution of the invariant mass M (in GeV, in bins of size 0.1 GeV):

Feel free to download the sample CSV dataset here.

Further reading: D3 Cookbook