DeveloperWeek Austin 2018

Instead of treating data science projects as Ph.D dissertations, they need to be treated as what they are, entrepreneurial software projects. Build an MVM (Minimum Viable Model), get it in front of users and keep iterating on it. The implications of approaching data science from an entrepreneurial path are immediate and far reaching. I’ll briefly highlight three big points to keep in mind. - Build the Minimum Viable Model: The first model should be terrible, consider it a Minimum Viable Model. - Mitigate Risk with Funding Rounds: Risk mitigation through repeated project valuation. - Achieve Growth through Competition: Data science is evolving too fast for top down organizations to keep up. DataGig is perfectly poised to provide the entrepreneurial way described here. We push up the likelihood of success on individual projects by enabling a lean model. Data science has produced some of the greatest tech innovations in the past decade, but as practiced in many organizations it’s also completely unsustainable. Producing more relevant models, mitigating risk and keeping up with the pace of the field will require organizations to rethink how they do data science. Structuring data science as entrepreneurship makes it a great investment once again.