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.
Optimizing a single objective, or a single point, is actually quite easy because there are no conflicting objectives. The real business challenge, and the source of much innovation, is trying to optimize a decision across multiple variables. Let’s explore this further.
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.