At Data Futurology’s OpsWorld conference in March, a panel of experts came together to discuss the importance of getting measurements, processes and methodologies right to drive DataOps and MLOps across the organisation.
The panel consisted of Katherine Fowler, Head of Business Transformation at L’Occitane Australia, Amar Poddatooru, Head of Data and Technology at Australian Ethical, and Emyr James, Head of Data at Resolution Life and moderating the discussion was Andrew Aho, Regional Director, Data Platforms at InterSystems. It became a far-reaching discussion that started with methods to define and measure the ROI of data and analytics initiatives and how to get those projects off the ground. The discussion moved on to overhyped technologies in the data space, and then looked forward to what is on the horizon for the years ahead.
As the panel discussed, there is a lot of interest among consumers in some innovative technologies, including ChatGPT. This is in turn driving a lot of interest at the executive level at rolling out solutions that use these tools. However, without the right foundations in place, and without proper concern for the privacy and regulatory risks associated with these tools, they will cause the data team more headaches than they’re worth.
This panel discussion is essential for understanding how to structure a foundation for data success, be disciplined in deploying the available resources across the data team, gain executive buy-in, and then steadily build the practice up.
Enjoy the show!
Thank you to our sponsor, Talent Insights Group!
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What we discussed
2:07: Felipe introduces the Measurements Thought Leaders panel and moderator, Andrew Aho.
3:48: How do you define and measure data and analytics ROI?
7:21: A discussion on metrics that help get data initiatives off the ground.
9:41: How a data leader needs to focus on the data platform, and articulate both the “big picture” view and the details.
12:35: As more organisations adopt ops, processes and methodologies, what challenges might people anticipate arising, and how can those be addressed?
17:24: What can data professionals do to help solve the change management challenge?
18:34: What are the challenges and impact of upcoming “silver bullet” technologies like ChatGPT?
20:16: What is currently overhyped in the data space (and why)?
24:03: What can we as data scientists do to ensure that we’re looking at the right risks and drawing accurate conclusions on what is right for the business?
26:13: If the goal is to focus on data science, how can we also keep experimentation and creativity going?
29:49: How do you estimate the value of change to get executive buy-in?
31:18: What upcoming developments and trends will emerge over the next five to ten years?
