Scotland Data Science & Technology Meetup and Merkle Aquila are hosting this debate in Merkle Aquila's brand new Edinburgh office.
There will be two teams of two (one arguing either side), each team will cover an opening argument, rebuttal and Q&A.
We are all very used to the idea of bringing together multiple open source tools and solutions to solve client problems. The cost of this approach is usually in low-level technical expertise and time. There are more and more proprietary software solutions that aim to make this experience more accessible, slick, rapid, and integrated. Will these two approaches co-exist in future? Are we moving towards a situation where data science requires financial buy-in to take part at a high level?
Team Free-to-Play
Jasmina Lazić - Chief Data Technologist, Bayes Centre - https://www.linkedin.com/in/jasmina-lazić-850aa9a0
Cathy Mitchell - Assistant Director at Scottish Funding Council - https://www.linkedin.com/in/cathy-mitchell-642a074b
Team Pay-to-Win
Robert Hamlet - Analytical Consultant at Merkle | Aquila - https://www.linkedin.com/in/robert-hamlet
Michael Mulholland - Analytics Consultant at Merkle | Aquila - https://www.linkedin.com/in/michael-mulholland-a781a779
Talking points:
· Are we at or moving towards a situation where data science requires financial buy-in to take part at a high level? Is data science becoming a two-tier system of those skilled enough to build their own technical solutions, and those who must pay for proprietary solutions to compete?
· Are open-source tools sufficient to meet increasing client demands? Are they slick, integrated, rapid, and sufficiently-well supported? Is the gap between the capabilities of open-source and proprietary widening?
· How free is free? Does it represent your best interests? If you didn’t pay for it then perhaps you are the product. Can you rely upon free in an enterprise setting?
· Freedom isn’t free – GDPR saw walled gardens emerge in the digital analytics world. Is it in our best interests long term to pay for software and demand interoperability and competition?
· Are we losing valuable skills by replacing general-purpose low-level hacking skills turn into proficiency in proprietary software suites? Or does paid software allow us to step over the time-consuming groundwork to get at the true technical and/or business challenge?
· We’re used to the idea of bringing together the hard work of many different people to create our solutions for free – will this last?
· Are developers and data scientists sufficiently rewarded by recognition and indirect altruistic benefits to keep releasing their tools/libraries/neural network architectures for free, or will this come to an end?
