These four experts also highlight just how important it is to motivate teams around an ongoing process of learning and discuss how to deliver a dynamic understanding of the changing role of data across the organisation. Ram Radhakrishnan, General Manager Customer Analytics, AI & Data Science at Woolworths Group Kendra Vant, Executive GM Data, ML & AI, Xero Each of these companies have been successful in productionalising ML across their enterprises, and discuss their strategies and successes in an open and free-flowing discussion:Īgustinus Nalwan, Head of AI and Machine Learning, įarhan Baluch, Principal Data Scientist, Apple (USA) To dive deeply into this critical discussion, Data Futurology recently brought together a panel of some of the leaders in ML strategy and execution. Questions that data leaders need to be asking themselves now include: What steps do organisations need to take to deliver ML maturity, how can they take the leap from experimentation to production, and how ML teams be effectively organised and motivated around this goal?
#DINER DASH HOMETOWN HERO TRAINER FULL#
Read the full podcast episode summary here.įor the most innovative and forward-thinking organisations, the next frontier forward for data is focused on machine learning, and specifically the role that MLOps plays in driving outcomes. Thank you to you our sponsor, Talent Insights Group! Tune in to the full podcast for more of Papush’s thoughts on the history and future of data science. But analytics and AI could make it much more seamless and unlock real-time care.” “It’s (data) a huge part of how we move healthcare to be more preventive and more interactive,” she said. Once the maturity is there, she says, data scientists can start looking at real life-changing innovations. A “one size fits all” approach cannot work, she says, and organisations need to build their strategies based on the maturity of their own data practice, rather than the hype in the market. Many have the technical skills to excel in analytics, but building skills in influence and thought leadership, and being a partner to the other stakeholders of the organisation, is the next critical step for the CDO.įinally, Papush also shares her insights on how value is extracted from data.
Being an effective agent for change has been important to that evolution, she says on the podcast, and data executives need to look to the “blind spots” that they might have. She has also seen the role of the CDO and their remit evolve, from one that focused on governance and controls, to being a value creator within the organisation. In the years since, she has observed that enterprises have become siloed across computer science, data science, and other roles, and that the next stage of data science evolution now is to now break those silos down and find ways to bring cohesion across the organisation. Papush has a long history in data science, having been involved in modelling and coding from before the time where “data scientist” was a defined role. For our milestone 200th Data Futurology podcast, we have the immense fortune of being able to host Gina Papush, the Global Chief Data & Analytics Officer of wellness and insurance company, Cigna.
0 kommentar(er)
