Todd Mostak is the CEO and Co-Founder of OmniSci, our Data Innovation Summit 2020 partner and the pioneer in accelerated analytics, redefining speed and scale in big data querying and visualization. Todd talked to us about how the idea of a GPUs-accelerated analytics platform originated during his graduate research at Harvard on the role of social media in the Arab Spring.
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.
As everyone working with HR becomes aware of the importance of data analytics and its impact on enhancing recruiting, workforce management and performance, it’s a matter of time when the question of an HR dashboard is put forward.
Establishing analytics in an organisation may seem like reinventing the wheel and make business leaders feeling like they’re out of their depth.
The telco and the retail industry have much more in common in terms of data and analytics than you would think.
The person who brings together these two worlds and can give an insightful overview of the two industries is Fredrik Backner, a speaker at Data Innovation Summit 2019.
Predictive maintenance and maintenance analytics allow machine operators to save on resources unnecessarily spent on frequently scheduled maintenance checkups and part repairments that are ineffective.
What happens when a company grows so rapidly and its employee base increases three-fold? If you don’t do it right, “the culture will eat your strategy for breakfast,” according to Mark Hayton, responsible for HR analytics and employee surveys at Nokia.
Everyone agrees that there is value in data science and advanced analytics. But still, companies are struggling to see that value in their business. Francisca Zanoguera from Expedia at her presentation at the Data Innovation Summit 2019, draws attention to a McKinsey study showing that only 8% of companies have managed to implement machine learning into their processes and only 12% have managed to go beyond the experimentation phase.
Companies operating in a hyper-growth mode need to scale fast enough to meet business goals, but at the same time establish a hiring process, correctly estimate their hiring needs and attract the best candidates.
Much has been said on the subject of building a good data science team, but it seems like companies are still struggling when it comes to putting together a data team.
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