Analytics: How do you measure the performance of data science?
Should data science operate as a true R&D function with a remit to fail-fast in the pursuit of rare breakthrough opportunities?
Or is data science becoming more like deep-dive analytics, looking beyond the optimisation of business as usual projects to find more transformative models, yet still with a careful eye on near-term business deployment?
Given the high cost and scarcity of data science as a resource, even the best-funded practice needs to have a clear view of how it will measure its impact and return on investment. But setting the right metrics and expectations within the business is crucial in order to maintain belief and support, while avoiding that difficult question, what have you done for us lately?
With a current senior data science leader providing a practitioner’s view into what appropriate metrics and true ROI look like for data science, this session is ideal for any member who is in the early stages of building their data science capability or where difficult questions are being asked about how the cost of this function can be justified.