Data Science Monetization: Focus on Innovation, Not Effectiveness
“I have over 1,200 Data Analysts, so we have it nailed.”
When I heard this being uttered by the head of their “analytics” group, I
knew the meeting was over. I knew that I could safely close my laptop, put
away my notebook, and gracefully thank them for their time.
It didn’t matter that others in the room didn’t agree with that
assessment. It didn’t matter that others could see the benefit of a
“think differently” collaborative engagement with key business
stakeholders in envisioning how to broaden the organization’s thinking with
respect to the how to leverage data and analytics to power the business.
Nope, their analytics leader made the statement with such authority and
confidence that any further conversation was just going to frustrate both him
and me. He already had all the answers, even to pr... (more)
Special thanks for the help on this blog to the coolest, most hip group of
industry experts that I have ever met: the Pathfinders. The Pathfinders is an
elite forces group of master system engineers inside of Dell EMC who tackle
our customers’ most difficult and inspiring challenges. I am honored to be
part of that club!
Suppose an autonomous vehicle learns of a more efficient route and wants sell
this knowledge to other autonomous cars for a fee (using blockchain to handle
machine to machine transaction). Suppose the autonomous vehicle could start
to monetize itself; to self-f... (more)
Many times, sports have been at the leading edge of data analytics. The
book “Moneyball” was one of the first popular books to bring the basic
concepts behind data analytics and data science to the general audience.
Fantasy leagues, sabermetrics and even games like “Strat-O-Matic”
baseball and basketball provided an introduction into basic statistical
And it now seems that sports, in this case the National Basketball
Association (NBA), are breaking new ground with another data analytics topic:
who owns the data? The National Basketball Players Association recently ... (more)
I have seen the future! Of course, I seem to say that every other month
(maybe that’s because the future keeps changing?), but this is a good one.
The future is a collision between big data (and data science) and application
development that will yield a world of “intelligent apps.” These
“intelligent apps” combine customer, product and operational insights
(uncovered with predictive and prescriptive analytics) with modern
application development tools and user-centric design to create a more
compelling, more prescriptive user experience. These intelligent apps not
only know ho... (more)
A recent argument with folks whose intelligence I hold in high regard (like
Tom, Brandon, Wei, Anil, etc.) got me thinking about the following question:
What does it mean, as a vendor, to say that you support the Internet of
Things (IoT) from an analytics perspective?
I think the heart of that question really boils down to this:
What are the differences between big data (which is analyzing large amounts
of mostly human-generated data to support longer-duration use cases such as
predictive maintenance, capacity planning, customer 360 and revenue
protection) and IoT (which is aggreg... (more)