Leveraging Advanced Analytics to Power Digital Transformation

“You can’t stop the incessant march of economics” – Bill Schmarzo
Okay, so it’s probably not cool to quote oneself, but hey, this is my blog and I get to do what I want.  And for anyone who follows me knows, I love to “riff” on the game-changing power of economics.  The “economics of big data” – where the cost to store, manage and analyze data is 20x to 100x cheaper than traditional analytics – started this big data and data science craze.  But ultimately it is the economics of value, or to be specific, “value in use” where the economics really become a game changer.

I recent article titled “ The Simple Economics of Machine Intelligenc e” from the Harvard Business Review highlights very well the role that economics (maybe even more than data science) is going to play in separating the winners from the losers in digital transformation.  To quote the article:
Machine intelligence is, in its essence, a prediction technology, so the economic shift will center on a drop in the cost of prediction.

When the cost of any input falls so precipitously, there are two other well-established economic implications. First, we will start using prediction to perform tasks where we previously didn’t. Second, the value of other things that complement prediction will rise.

Using the language of economics, judgment is a complement to prediction and therefore when the cost of prediction falls demand for judgment rises.
So how will the “economics of machine learning” – or the “economics of advanced analytics” – impact your business model?  We developed the Big Data Business Model Maturity Index as a framework help organizations understand where and how they can leverage data and analytics to power their business models.

The Maturity Index provides a roadmap to guide customers in integration data and analytics into their business models.  See “ Big Data Business Model Maturity Index Guide ” for a “How To” guide on leveraging data and analytics to advance along the Maturity Index.
Advanced Analytics Continuum
Recent conversations with Walker Stemple of Intel’s @intelAI organization got me thinking about where and how organizations can leverage “advanced analytics” to power their business models.  Now “advanced analytics” is a broad definition, but I have included the following analytics in that definition: Regression, Clustering, Neural Networks, Machine Learning, Deep Learning, Artificial Intelligence and Cognitive Computing.  And while these “classifications” seem to change on a regular basis (sometimes due to us getting smarter; sometimes due to non-value-add marketing hype), it is critical that tomorrow’s business leaders understand where and how to apply these advanced analytics to power their business models.
My conversation with Walker helped me to understand how organizations can leverage the clusters of advanced analytics to advance along the Maturity Index.

The Advanced Analytics Continuum covers the following classifications:
Descriptive Analytics is not really advanced analytics, but it is foundational in helping organizations understand “What happened?” to their business. This is traditionally the domain of Business Intelligence.  Business Intelligence is primarily focused on “Comparative Analytics” such as Current Period versus Previous Period reporting, Period-to-date cumulative calculations and projecting trend plots.  The primary analytic tools in Descriptive Analytics are reports, dashboards and alerts.

Predictive Analytics is focused on uncovering insights about what happened in order to create foresight, or predictions, about what is likely to happen. Predictive Analytics seek to quantify cause-and-effect – and measure the analytic model’s goodness-of-fit – in order to drive those predictions.  Predictive analytic algorithms include Statistics, Clustering, Classification, and Regression Analysis.