Telecommunications is complex business. As users we are mostly faced with one question- how to get the best deal out of the many telecom players in the market? But as an analyst in a Telecom company one may be faced with any one or more of the following question -

• How many Network Towers should be installed in a particular area to meet the rising demand given the rise in population, rising need of data connectivity and competition?
• What should be the optimum call rate…

Why do organizations seek to design effective multi-channel customer journeys? Customers demand it.…

All the hard work we put into the “model” on the right hand side of the equation, is only as accurate as the dependent variable was to start with in reflecting the business problem at hand.  Yet…. modeling efforts typically focus almost exclusively on the prediction of the objective variable, while often accepting the dependent “AS IS” with all that that…

In this post, I've tried to capture some of the common aspects of working in the analytics industry. While we occasionally hear about India growing fast into this space, there are a lot of things happening in India that might transform this field further. While some of these aspects are specific to what I've observed in India, a lot of them are generic.

As in previous posts, I try to classify these aspects under different heads:

1. The "cost" reduction trap…

Both R & Python should be measured based on their effectiveness in advanced analytics & data science. Initially, as a new comer in data science field we spend good amount of time to understand the pros and cons of these two. I too carried out this study solely for “self” to decide which tool should i pick to get in depth of data science. Eventually, i have started realizing that both (R & Python) has its space of mastery along with their broad support to data science. Here some…

The term "Data Science" has been evolving not only as a niche skill but as a niche process as well. It is interesting to study "how" the Big data analytics/Data Science/Analytics can be efficiently implemented into the enterprise. So, along with my typical study of analytics viz. Big data analytics I have been also exploring the methodologies to bring the term "Data Science" into mainstream of existing enterprise data analysis, which we conventionally know as "Datawarehouse & BI". This…

Over 100 years ago, the great science fiction writer H. G. Wells was credited with saying, "Statistical thinking will one day be as necessary for efficient citizenship as the ability to read or write." It is clear that this statement is probably more true today than ever, as Big Data and Analytics are paraded before every aspect…

Today’s organizations are challenged to gain insight into most productive marketing and sales actions across multiple channels they use. Doing this requires multi-channel marketing attribution approach.

Facing this topic I have made a personal research, and realize a synthesis, which has helped me to clarify some ideas. The attached presentation does not intend to be exhaustive on the subject, but could perhaps bring you some useful insights:  …

A Social Network is a theoretical construction useful in the social sciences to study social relationships.

Social network analysis refers to methods used to analyze social networks, social structures made up of individuals or organizations, which are connected by one or more specific types of interdependency, such as friendship, common interest, financial exchange, or relationships of beliefs, etc.…

Facing this new domain I have make a personal…

Percolator, Dremel and Pregel: Alternatives to Hadoop

Hadoop (MapReduce where code is turned into map and reduce jobs, and Hadoop runs the jobs) is great at crunching data yet inefficient for analyzing data because each time you add, change or manipulate data you must stream over the entire dataset.

In most organizations, data is always…