The buzz of the “Big Data” revolution had been unnerving CIOs for more than half a decade, when it was suddenly dropped from the Gartner hype-cycle in 2016.Sure, Businesses are still collecting more and more data these days, but is that what matters?At Predictive Analytics World we have always argued that making the right use of what Companies gather matters, and the signs indicate that they weren’t, just creating more data and complexity. Instead, they should focus on using data to make better decisions using new Analytics algorithms and new Business Applications! And it seems that Gartner finally agrees: Machine Learning, (self-service) Advanced Analytics and Neurobusiness have entered the Gartner hype-cycle, with full momentum of only 2 to 10+ years into productivity. Gartner now predicts an annual growth rate of 34 percent by 2017, with revenues projected to reach $48 billion, and venture capitalists have been eager to invest in burgeoning Predictive Analytics startups (after dropping the Big Data ones). Is this finally the rainbow on the horizon which the Analytics community has been chasing for years, possibly since before it was called Data Mining, or even Statistics? Or is it just another dot on the hype-cycle to be banished? Or is it not even real? In Predictive Analytics for Time Series (an area of Predictive Analytics growing in importance with more data gathered continuously over time), aka Forecasting, the corporate reality looks rather different.The elusive crystal ball into the future is often powered by simple and elderly algorithms, many of them around since the 1960s or earlier.Industry as software vendors are slow to adapt machine learning, or indeed even anything contemporary from the 90s. In our presentation, we show evidence from an industry survey of 200+ companies and their reality of algorithms used, and measure the substantial gap between research and practice. To contrast this, we showcase a selection of state-of-the-art algorithms available in Predictive Analytics for time series today, from Neural Networks to Support Vector Machines and from Random Forests to Boosting, and how they could be applied to time series Analytics to drive a revolution. We will give examples how these have been implemented by a few industry though-leaders, from Electricity & Utilities companies to Call-Centres, Manufacturers and Container Shipping lines, who were willing to bridge the gap and lead the hype-cycle onwards.

Political bombshells, unimaginable terrorist attacks, epidemic outbreaks, natural disasters, technological disruptions, fractured markets, transitory advantages, multifarious competitors, increasingly demanding customers and fickle consumers. Thanks to the military, we have a useful descriptor for the conditions and environment these drivers create; ‘VUCA’ – Volatility, Uncertainty, Complexity and Ambiguity… a combination of the magnitude and speed of change, the lack of predictability and prospect of surprise, the multitude of forces and confounding issues, and the lack of ‘one right answer’ or single course of action. Yet against this backdrop many organisations are still carrying the early, often ill-formed, baggage of implicit promises and expectations of Big Data and Analytics, and the mindset of operating in more stable conditions. And that’s before we get to sentiment mining, machine learning, edge analytics and the like. There is no question about the power, pervasiveness, further potential and applicability of predictive analytics. But it is at the edge of this applicability that things get interesting; where a VUCA environment lays cognitive traps for those focused on ‘getting to the right answer’ rather than ‘asking the right questions’. In the context of strategy development, deployment and delivery in the real world, this edge – between prediction and insight, between extrapolation and choice, between algorithm and decision – is critical. This session explores some of the typical problems and hidden traps in creating strategy against a VUCA backdrop, considers the tensions that exist across our unavoidably uneven knowledge of the world, offers some mental models to help leaders grapple with this wicked problem, and to help you drive the most impact and value in support of your strategy.