Marketing automation, machine learning models, customer data platforms; at least one of these is included in your company’s ambitions for 2020. But is you analytics implementation prepared for it? Many companies struggle to implement ambitious data projects because of a lack of data standards and ownership, specialy in the digital analytics sphere.

The majority of nowadays attributions are very biased. Some of them do not reflect the whole customer journey, while others are based on some algorithms defined by people (heuristics), while the reality is usually much more complex.

Stéphane is a veteran of Superweek and each year he brings a forward-looking topic which challenges the way we do our work - he brought us his famous digital analytics maturity model and the radical analytics manifesto, and last year he scared the sh*t out of us by demonstrating how machine learning could be leading us closer to an impeding doomsday. This year, Stéphane will continue where he left last year and dig into real-life examples of questionable ethics and malpractice which will hopefully make you think about your limits and where the red line is for you, as an analyst and as a consumer.

Even before GDPR, savvy analysts were aware of privacy considerations and concerns as they went about their work of analyzing consumer behavior. But, increasingly, the convergence of regulatory changes (GDPR, CCPA,...), technological changes (ITP, ETP,...), publicized data breaches (Desjardins, Adobe, Capital One,...), and increased consumer awareness and action (ad blocker installations, browser configurations) have driven limitations to the data that is automatically available to marketers and analysts. Simultaneously, the rise of CDPs, machine learning, and AI are driving an increased desire to gather and activate broader, richer, and more integrated customer data. In this session, the panelists will grapple with the tensions between these competing forces.

Data science, machine learning and AI all require a specific background in mathematics that very few people in the industry possess. Julien Coquet looks back at a 20-year career in digital analytics and discusses why data science isn't for everyone and how to prepare for such a line of work.