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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There is a lot of discussion around artificial intelligence and robotics these days and many consider AI and robotics to be more of a hype than of any real substance.
Given this, one question that is often asked is how far have we progressed when it comes to AI or Robotics.
Well, here is a very good proof. First video embedded below is that of Tiger Woods acing hole No. 16 at TPC Scottsdale in 1997.
The second video embedded below is that of a robot called Eldrick, which aced the exact same shot as Tiger Woods – 16th hole at TPC Scottsdale.
What do you think, isn’t this amazing progress? Please do share your thoughts …
Here are three great videos on effective public speaking by Dananjaya Hettiarachchi, the 2014 World Champion of Public Speaking.
These 3 videos together offer a great 10 minute crash course on effective public speaking and in making impactful presentations.
Here are five great resources for those interested in Machine Learning that provide a good starting point if you are interested:
Here are three great presentations on Apache Flink by Slim Baltagi, Director of Big Data Engineering, Capital One. First Slideshare presentation embedded below is titled “Step by step introduction to Apache Flink”
Second Slideshare presentation embedded below is titled “Flink vs. Spark”
Third Slideshare presentation embedded below is titled “Apache Flink: What, How, Why, Who, Where?”
As many of you may already know, there are three levels of listening. They are:
- Internal Listening – when we listen to our thoughts and opinions
- Focused Listening – when we focus on “what” is being said, summarizing and analyzing
- Global Listening – when we don’t just focus on what is being said, but also focus on emotions behind what is said, body language, tone, gestures and all other environmental factors while being aware of our own thoughts and intuitions.
Although we are truly “in the moment” with speaker only when “Global Listening”, most of us, most of the time resort to either Internal Listening or Focused Listening. This is because we either focus on our own thoughts or focus just on words of speaker, missing out on so much of what is being communicated.
Remember, communication is not limited just to spoken words alone. And to be a good listener, one has to practice global listening by focusing not just on what is being said, but also on emotions, body language, feelings, tone, gestures and all other environmental cues, at the same time being aware of one’s thoughts and intuitions.
Please read Co-Active Coaching: New Skills for Coaching People Toward Success in Work and Life by Henry Kimsey-House, Karen Kimsey-House, Phillip Sandahl, Laura Whitworth, Nicholas Brealey Publishing if you want to know more about this.
Would love to know what do you think of Global Listening. How often do you practice global listening vis-a-vis Internal or Focused Listening? Please leave a comment:
“Verizon to Pay $4.8 Billion for Yahoo’s Core Business” reads the headline in today’s New York Times website. This is a stunning fall for a company which epitomized all things internet and which had a peak valuation of more than $125 billion dollars just over a decade ago. So what are the reasons for this stunning fall of Yahoo so fast and are there any important lessons to be learned from Yahoo’s downfall?
I think that there are two main reason for this dramatic a change in Yahoo’s fortunes and some important lessons to be learned. They are:
Customer Focus: Yahoo started off as a search engine but quickly lost focus of its core service and wanted to be everything to everyone on the internet. Just take a look at Yahoo’s home page even today (on July 24, 2016) and you can see why. Home page is cluttered with so many competing services trying to catch our attention and lost on the page is core functionality customers want – web search.
Compare it with Google’s home page and focus on the core service of web search – see pictures below:
User or Customer Experience Management is the second important reason for Yahoo’s decline. While trying to be everything to everyone on the internet, Yahoo failed to focus on the user experience – quality and speed of search results. And it quickly lost ground to the “newcomer” Google in early 2000s. Yahoo had some great services like Yahoo messenger etc. and it failed to capitalize on those strengths, especially when social media took off in a major way in 2008 and in 2009. As a result, Facebook and WhatsApp capitalized on the opportunity and have become so dominate in the market.
Lesson to be learned from Yahoo’s downfall is that:
a) Never lose focus of what your customers want and
b) Customer Experience matters more than anything else
And if you don’t focus on the above two critical aspects of your business, you will face decline and downfall.
One of the key challenge facing CMOs today is establishment of long term relationship or partnership with their customers, one that will result in continued growth of the brand. Thanks to Big Data tools and technology, it is possible to solve this challenge through Customer Journey Analytics.
Embedded below are two great presentations from McKinsey & Company on the topic and a YouTube video featuring David Edelman, Partner and Global Co-Leader of McKinsey Digital, Marketing & Sales.
A lot has been written about success and failure of Big Data and Analytics projects in recent times. Unfortunately, most of the articles and blog posts on this subject fail to highlight real reasons why Big Data projects fail. Given below are top 5 reasons, in my opinion, why most Big Data and Analytics project fail. They are:
1. Failure to define use case in objective terms
2. Failure to use right technology
3. Failure to focus on business requirements first, technology next
4. Failure to leverage all available data sets and assets
5. Failure to effectively use power of advanced analytics
In my next post, I will elaborate on these five reasons because of which Big Data projects fail and recommend ways you can avoid these pitfalls.
When it comes to Big Data and Analytics solution implementations for enterprise clients/large business organizations, I see a big divide between what functionality business users want and are demanding from their CTO or IT organization and the functionality that CTO or IT organization can deliver. I would like to call this gap as “Big Divide of Big Data“.
Given the exponential increase in number of sources and volume of Big Data being generated, thanks to digital and sensor based productivity revolution, this gap is growing wider every passing day. And one wonders if twain shall ever meet and if this divide can ever be filled or bridged. This is one of the key challenge being faced by CIO and CTO of most large business organizations.
What is a problem for CIOs and CTOs is an opportunity for vendors and solution providers. I see a great opportunity for System Integrators (SIs) and Consulting organizations when it comes to bridging this big divide of big data. And this can be done through development of industry or vertical specific platform solutions for leveraging Big Data and Analytics.
I see this already happening in Health Care and Life Sciences industry/vertical and expect this trend to catch on in all other industries too, especially Banking/Financial Services, Insurance and Retail. Thanks to availability of industry specific platform based solutions, large and medium-sized enterprises will be able to leverage Big Data and Analytics without making heavy upfront investments.
What do you think? Please do share your opinion or respond on Twitter, my ID is @HKotadia.
A lot has been written recently criticizing Goolge’s Flu Trends – a flu tracker service that predicts flu activity based on specific search terms using aggregated Google search data and estimates current flu activity around the world in near real-time. For more, read How does this work?
Science magazine has recently published an article titled “The Parable of Google Flu: Traps in Big Data Analysis” and Steve Lohr has published a great piece in BITS blog of New York Times titled “Google Flu Trends: The Limits of Big Data.”
It is important to note that over-estimation of flu activity in Google Flu Trends is NOT a limitation of Big Data or Analytics used for estimating the flu activity as some of the writers have suggested. Rather, it highlights importance of fourth “V” of Big Data – Veracity.
It is often mentioned that Big Data has three defining attributes – three Vs as they are called, namely Data Volume, Data Variety and Data Velocity. (for more, check out TDWI Best Practices Report titled Big Data Analytics). But this definition of Big Data misses a very important dimension or element of Big Data, namely Data Veracity.
I think Google Flu Trends estimates will be much more realistic if we were to incorporate Data Veracity, the fourth dimension of Big Data into estimation models and adjust estimates based on “Veracity Score”.
In other words, inaccurate estimates of flu activity as reported by Google Flu Trends is NOT a limitation of Big Data or Analytics, rather we need to incorporate the Data Veracity element into the estimation model.
What do you think? Do you agree that inaccurate estimates of flu activity as reported by Google Flu Trends is NOT a limitation of Big Data or Analytics?