Since the dawn of analytics we have strived to improve both the quality and volume of our data, with no other ambition to ensure the largest possible dataset – not because we need it, but because we might need it. GDPR have temporarily put a wrench in our original approach, but it takes more that the law to keep a good analyst away from his data and with Machine Learning as an active part of the toolbox the value of data have grown exponentially. The sessions in a reflection on how we have done things so far and where we might end if you don’t stop doing business as usual and instead calibrate our efforts in a more strategic and ethical direction.

These days there is a strong case for Machine Learning in Analytics and everywhere you turn you are presented with case and examples on how ML have made the analysis better, faster and more consistent. But for a man with a new hammer everything looks like a nail and sometimes we forget to draw a line when machine learning is the right tool and when an analyst actually is required to do the job right. Join this session to dive into some big thoughts on how to distinguish what is needed when.

The client seems happy enough just to collect the data. Our job is to make them understand what the data is for. Once they lose their data virginity, they won't want to stop. Doug describes a brand new set of personal use cases where the awesome power of data was liberated once the data was actually used. Learn even more tricks and techniques of the stealth change agent leading to that threshold moment in a data empowered career. Machine learning, data science, optimisation, professional jiu-jitsu and good old political persuasion are on the menu. Learn how to convince clients (internal or otherwise) to actually take your advice on board and do something other than just stare at a dashboard.