Businesses rarely approach data scientists with well-defined problems to solve. Sometimes, the problems businesses devise aren’t appropriate for solving using data science at all. This makes it difficult for data science projects to succeed. In this episode, Dr Genevieve Hayes is joined by Rob Deutsch to discuss strategies businesses and data scientists can employ to identify data science use cases and maximise their probability of success.
Guest Bio
Rob Deutsch is the Chief Operating Officer of AkuShaper, a company that uses advanced modelling algorithms and software to build better surfboards faster. He is also a data science consultant with Parity Analytic, and previously founded Boxer, which built software for creating better financial models.
Talking Points
- Processes for identifying and understanding business problems, and determining whether a data science solution is appropriate and what that solution should look like.
- The different ways in which people from different backgrounds can look at a data science problem and how that influences the questions they ask of data/the way they tackle problems.
- The role of the business vs the role of the data scientist in defining/scoping data science projects.
- How to maximise the probability of success of a data science project.
- How the data science/data analytics skill set can be transferred to areas outside of technical data analysis, such as running a SaaS company.
Links
- Connect with Genevieve on LinkedIn
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