Crone is an Assistant Professor in Management Science at Lancaster University, UK, where his research focuses on business forecasting with artificial intelligence and time series data mining.
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 are bridging the gap to lead the hype-cycle onwards.īiography – Sven F. To contrast this, we showcase a selection of state-of-the-art algorithms available in Data Science 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. 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.
#GARTNER HYPE CYCLE 2016 BIG DATA SOFTWARE#
Industries as software vendors are slow to adapt machine learning, or indeed even anything contemporary from the 90s. The elusive crystal ball into the future is often powered by simple and elderly algorithms, many of them around since the 1960s or earlier. However, in Time Series Prediction (an area of Data Science growing in importance with more data gathered continuously over time), aka Forecasting, the corporate reality looks rather different. Machine Learning, (self-service) Advanced Analytics and Neurobusiness have entered the Gartner hype-cycle, promising the future breakthroughs. Although businesses are still struggling to store more and more data, the emphasis has shifted to making better use of the data through new Data Science algorithms and new Business Applications. Mind the Gap! Hype-Cycle versus Business Reality in Data Science (a forecasting perspective)Ībstract – 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. Datalab - The ZHAW Data Science Laboratory (active).Accommodation possibilities for incoming staff.
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