Name: Harish Kotadia, Ph.D.

Bio: Dr. Harish has about ten years’ work experience as a hands-on CRM Program and Project Manager implementing CRM solutions for Fortune 500 clients in the US. He also has about five years’ work experience as a Research Executive in Marketing Research and Consulting industry working for leading MR organizations. Dr. Harish currently lives in Dallas, Texas, USA and works as a Consultant focusing on Social CRM, E2.0 and Analytics.

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Why Big Data Mining / Analytics is the New Gold Rush

February 14th, 2012

Just mention the words “Big Data” to any technology entrepreneur or investor and observe how his/her face lights up with excitement. Given the perceived opportunity in Big Data, tech entrepreneurs and investors want to capitalize on it by starting /investing in a Big Data Management, Mining and Analytics business. Is this perceived opportunity in Big Data for real or is it a bubble that will burst soon?

I think the perceived opportunity in Big Data is real and is here to stay as Big Data Mining/Analytics will fundamentally change the way business is done not only online but also offline. Here’s why: Big Data is a key enabler for Social Business and without Big Data Mining/Analytics, a large or medium sized company can neither make sense of all the user generated content online nor can collaborate with  customers, suppliers and partners effectively on Social Media channels. And both of these activities, namely gaining insights from user generated content online and collaborating with customers and partners on Social Media channels are critical for success in the age of Social Media.

Can you imagine any large or medium sized business without e-Business in the internet age? Similarly, it is impossible to run a large or medium sized business in the age of Social Media without leveraging user generated content or without collaborating with customers and partners. And for this to happen, Big Data Management, Mining and Analytics are critical/key enablers.

Being a key enabler and catalyst in Social Age, Big Data Management, Mining and Analytics companies are going to command a premium valuation and hence Big Data Mining and Analytics has triggered a new ‘Gold Rush‘, not to mine gold this time but something even more valuable – knowledge and insights.

What do you think? Do you agree that Big Data has triggered a new Gold Rush?

 

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Big Data and Rise of Predictive Enterprise Solutions

February 10th, 2012

Given the three Vs of Big Data, namely Volume, Variety and Velocity (read this for more), challenge before large and medium sized companies is how to unlock the potential of Big Data and productively leverage its value in running the business.

In “traditional” Data Analytics or Business Intelligence, focus is more on analysis and reporting of “historic” or past data stored in the database. Take for example how most organizations use data from their CRM or ERP applications. Almost all the reports that are generated pertain to past or “historic” information. Running a business based on “historic” or past data is like driving a car looking in the rear view mirror and is not going to work.

Instead, companies must analyze all the available information in real time, apply statistical modeling techniques to available information in order to predict future outcomes and take action/run the business based on predicted outcome rather than analysis of historic data as is being done currently.

Since Big Data is characterized by not only Volume, but also Velocity and Variety, it is very important that Big Data is used for analysis in real-time to predict the future and take corrective action based on that analysis. How about using Causal Path analysis on Social Media data (like Twitter and Facebook) to predict Churn or Customer Attrition in Telecom industry and taking corrective action to prevent Churn/Attrition rather than analyzing “historic” attrition rate, call volumes or average response time as being done currently. The real value is in using predictive analytics and taking corrective action before it is too late, rather than just reporting historical information.

Techniques like Multiple-regression analysis coupled with Factor analysis, Cluster analysis and Causal Path analysis can be used very effectively with Big data – now that we have many variables and multiple observation for each variable at a customer level to generate statistically significant difference in analysis.

In future, no ERP or CRM system will be complete without Predictive Analytics functionality that will enable companies take preventive steps (rather than reactive) in real time. For example, rather than analyzing “historic” attrition rates, Predictive CRM application will make it possible for companies to identify critical incidents leading to customer attrition so that steps can be taken to retain the customer before he/she defects.

And thanks to Predictive Analytics, ERP or CRM applications will no longer be just a repository of “historical” information but will be transformed into a Predictive Knowledge base or Engine driving business decisions looking forward and not backwards/in the rear view mirror of “historic” information.

What do you think? Do you agree that Big Data will result in rise of Predictive Enterprise Solutions? Please do share your thoughts.

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Big Data: Will Open Source Software Challenge BI & Analytics Software Vendors

February 3rd, 2012

Predictive Analytics has been billed as the next big thing for almost fifteen years, but hasn’t gained mass acceptance so far the way ERP and CRM solutions have. One of the main reason for this is the high upfront investment required in Software, Hardware and Talent for implementing a Predictive Analytics solution.

As a result, only a handful of very large enterprises such as mega banks or top telecom companies have made the required investments and have benefited from power of Predictive Modeling and advanced Statistical techniques that are in existence for well over five decades. Most of the other companies have not been able to levarage power of business analytics as they cannot afford investing in specialized harware, database and BI/Analytics software applications being marketed by enterprise software vendors such as SAS and Teradata.

Well, this is about to change – thanks to technologies such as Apache Hadoop (which supports Big Data distributed applications under a free license), HBase (an open source, non-relational/distributed database) and the freely available R programming language (which is part of the GNU project). Using R, HBase and Hadoop, it is possible to build cost-effective and scalable Big Data Analytics solutions that match or even exceed the functionality offered by costly proprietary solutions from leading BI/Analytics software vendors at a fraction of the cost. And since R programming language is a freely available Open Source Software, users can leverage work done by others for specific analytics functionality and don’t have to re-invent wheel by rewriting the code. This reduces cost of developing analytics solution significantly.

Established BI and Analytics software vendors have no option other than offering their solution under SaaS (Software as a Service) model so that it is cost effective for their customers to implement analytics solution without requiring large upfront investment. This is all the more important for Big Data as the field is evolving rapidly. And if any BI or Analytics software vendor fails to adapt to this changing technological environment, they risk losing their market share.

 

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Big Data Analytics a Key Enabler for Social CRM – Airlines Case Study

February 1st, 2012

Big Data Analytics is a hot topic of discussion these days. But many feel that it is more of a “hype” and less of substance. In my opinion, Big Data Analytics is the real deal and if used correctly, can deliver great business results at a fraction of cost compared to other alternatives.

Here’s a simple yet very effective example of using analytics for understanding consumer attitudes towards airlines in real time. In this study, Jeffrey Breen has used the R programming language to analyze consumer sentiments about major U.S. airlines expressed on Twitter. For more on the methodology and results, see the following embedded Slideshare presentation (if you cannot see the embedded file, click here to view it on Slideshare website).

As you can see on slide no. 27, the twitter sentiment scores obtained  for many of the airlines are “comparable” to results of The American Customer Satisfaction Index (ACSI). What is important to note here is that by analyzing few tweets using freely available R programming language (which is part of the GNU project), it is possible to achieve results similar to that of an elaborate and expensive market research study such as ACSI at a fraction of a cost, that too in real time. Isn’t that a game changer?

Now imagine using Big Data Analytics to analyze Social Media data – not only text data but also pictures, video, GPS , Social Graph/Social Media linkages and using that information for engaging customers on Social Media channels. That in my opinion is the ‘holy grail’ (if I may use that term) of Social CRM.

In one of my earlier post, I have defined Social CRM as the business strategy of engaging customers through Social Media with goal of building trust and brand loyalty. Big data analytics is a key enabler for engaging customers on social media channels for building trust and loyalty (Social CRM). What do you think?

R by example: mining Twitter for consumer attitudes towards airlines

View more presentations from Jeffrey Breen

 

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Why Big Data Analytics is The Next Big Opportunity for Outsourcing Companies

January 8th, 2012

Big Data Analytics is making big headlines these days. Just check out a few from recent past:

• Big Data — Big Money Says It Is A Paradigm Buster - Forbes.com
• For Start-Ups That Aim at Giants, Sorting the Data Cloud Is the Next Big Thing - The New York Times
• Accel Makes Big Commitment To Big Data With $100M Fund - The Wall Street Journal
• Five startup predictions for 2012 - c|net
• Big Data: Investments Flow into the Next Big Thing - a great post by Dick Weisinger

So what is Big Data and why it is in the news so much these days?

According to Philip Russom, Director of TDWI Research, Big Data has three defining attributes – three Vs as he calls them. They are Data Volume, Data Variety and Data Velocity and together they constitute a comprehensive definition of Big Data. So Big Data is not just about Data Volume, but also the variety of data (mostly unstructured) and the velocity with which the data is generated and need to be analyzed. (for more, check out following posts by Philip Russom and the TDWI Best Practices Report titled Big Data Analytics):

• Big Data Analytics: Frequently Asked Questions (FAQ)
• The Three Vs of Big Data Analytics: VOLUME
• The Three Vs of Big Data Analytics: VARIETY
• The Three Vs of Big Data Analytics: VELOCITY

Given three Vs of Big Data, ‘traditional’ data storage, retrieval and analytics methodologies are no longer going to work. Cloud Computing is going to play a key role when it comes to Big Data Management and Analytics. And here in lies the opportunity for Outsourcing companies.

Traditionally, data collected by organizations is ‘safely’ stored in massive relational database accessible to only few within the organization and requires elaborate infrastructure both in terms of hardware and software for storage, retrieval and reporting/analytics. In such an environment, it is not possible to easily outsource Data Analytics function/processes alone given the heavy investments made in terms of hardware and software.

Because of the business requirement of analyzing vast amount of ever changing structured and unstructured Big Data almost instantaneously, companies will be hard pressed to do this on their own. But given the fact that Big Data stored in cloud can be accessed from anywhere the internet is available and can be analysed almost instantaneously by third party service providers,  outsourcing companies can offer to their clients value added services in the area of Big Data analytics without heavy investments on the part of clients in specialized hardware and software as was the case with ‘traditional’ data analytics. This will bring down significantly costs (especially fixed costs) associated with building and maintaining analytics infrastructure and solution center.

Just to give an example in the area of Social CRM, Social Media has empowered customers like never before as they can discuss about brands/products on Social Media channels. The best any marketer can do is to Listen, Learn and Engage customers. Given the three Vs of Big ‘Social’ data and the fact that most of the user generated content resides in the cloud, outsourcing companies can offer cost effective analytics solution to their clients to enable them effectively engage their customers/prospects in real time.

What do you think? Do you agree that Big Data Analytics is The Next Big Opportunity for Outsourcing Companies? Look forward to hearing your thoughts and comments:

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