As Hitachi Vantara CTO of IoT and Analytics, Bill drives Hitachi Vantara’s “co-creation” efforts with select customers to leverage IoT and analytics to power digital business transformations. With his breadth of experience delivering advanced analytics solutions, the “Dean of Big Data” brings a balanced approach regarding data and analytic capabilities that drive business and operational outcomes. See more of his posts on his LinkedIn handle.
Move aside “Monopoly,” “Risk,” and “Snail Race!” Time to teach the youth of the world of an important, career-advancing game: how to leverage data and analytics to change your life! Introducing the “Big Data Game Board™”!
Now you are ready to take the next step from a Big Data MBA perspective by building off of the Business Model Canvas to flesh out the business use cases – or hypothesis – which is where we can become more effective at leveraging data and analytics to optimize our the business.
The terms ‘true condition’ (‘positive outcome’) and ‘predicted condition’ (‘negative outcome’) are used when discussing Confusion Matrices. This means that you need to understand the differences (and eventually the costs associated) with Type I and Type II Errors.
Both athletes and machines deal with inter-twined complex systems (where the interactions of one complex system can have a ripple effect on others) that can have significant impact on their operational effectiveness.
Why is this distinction important? Because it’s critical to understanding how leading-organizations are investing in new data engineering skills that exploit advanced analytics to create new sources of business and operational value.
The best data scientists have strong imaginative skills for not just “thinking outside the box” – but actually redefining the box – in trying to find variables and metrics that might be better predictors of performance.
Democratization is defined as the action/development of making something accessible to everyone, to the “common masses.” AI | ML | DL technology stacks are complicated systems to tune and maintain, expertise is limited, and one minimal change of the stack can lead to failure.
It seems Isaac Asimov didn’t envision needing a law to govern robots in these sorts of life-and-death situations where it isn’t the life of the robot versus the life of a human in debate, but it’s a choice between the lives of multiple humans!
While I have talked frequently about the concept of Analytic Profiles, I’ve never written a blog that details how Analytic Profiles work. So let’s create a “Day in the Life” of an Analytic Profile to explain how an Analytic Profile works to capture and “monetize” your analytic assets.