The previous The Data Innovation Summit: 5 years of data and analytics journey series was dedicated to practical case studies on “how” and real examples of putting data innovation into practice of the Data Innovation Summit 2017. Now we’ll see how pioneering companies utilised analytics and data science to innovate through their data and their best analytics practices.
The first 2016 edition laid out the foundation for data-driven innovation and brought light to the macro drivers, the challenges, the implications and the solutions. Whereas, the 2017 edition showcased more practical case studies on “how” and real examples of putting data innovation into practice.
At the 5th edition of the Data Innovation Summit, we are going to welcome on stage some of the biggest names in the game development industry and hear their innovative ways of using data: Massive Entertainment (a Ubisoft Studio), Rockstar Games and Zynga.
This year 2020 we have a reason at Hyperight to be truly proud and excited to host the 5th edition of the Data Innovation Summit that […]
Together with Goran, we went on a reminiscence journey to 2016 – the year when the Data Innovation Summit was born, but also the year in which Data Science was an emerging discipline.
Meltem Ballan, Data Science Fellow at General Motors, will give an overview of this topic in her Data Innovation Summit presentation Vehicles as data platforms: Telemetry meets vehicle identity and explain the neural components of data and human behaviour.
When your business is serving customers in 6000 cities, in 64 countries and millions of riders, you have to make sure you live and breath customer satisfaction. Uber, the global tech leader in ride-sharing, can teach us a thing or two with how they manage to keep all their riders and drivers happy.
Many industrial and manufacturing companies have their data stored in file systems and ERPs. But, if data scientists and engineers are to use this data to provide value, it has to be available and accessible in a consolidated cloud space.
And as data is at the core of their digital offerings, ensuring high data quality along their whole value chains is Grundfos’ most critical priority.
Data science in banking may sound a bit boring. However, data scientists have had their hands full with working on some really interesting challenges that banks have been experiencing these past several years.
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