Recap: Operational Efficiency Summit
About the Conference
I recently attended the Operational Efficiency Summit conference in NYC sponsored by WatersTechnology, which focused on gathering data and technology professionals to unleash the power of data, technology, and the cloud.
While the focus of all recent business conferences has been centered on Artificial Intelligence and Generative AI, it was certainly not the intention for this conference.
Instead, data professionals need to focus on the what, when, and how of implementing business use cases for AI. Without any vendor pitches or presentations at the conference, the content centered around industry leaders including non-capital market presenters, sharing their current implemented programs and roundtable topics on mock company situations to share best practices in our respective industries.
The Presentations
The themes of the conference loosely aligned with the presentations of the conference.
With an opening keynote from the Head of Development at the Artificial Intelligence Finance Institute, Dr. Miquel Noguer i Alonso presented a foundational overview of artificial intelligence including the history of machine learning development. Miquel also discussed different aspects of a workflow from business use case selection, task identification, and data preparation to deployment and where data techniques need to be properly utilized.
Automation and governance was another key theme with a keynote speech on encouraging operational efficiencies through automation with Linda Powell, Enterprise head of data governance and deputy chief data officer at BNY Mellon. Data quality is an area where technology can help improve the deduplication, profiling, and rule-building part of the data management process.
Another key theme of the conference was data use and management. This topic was supported by a best practices overview of data use and management by Subbiah Subramanian. Although the successful ingredients of a data strategy are rooted in people, practice, and technology, the data culture in the organization is also important to the successful implementation of Artificial Intelligence and Machine Learning.
The Discussions and Takeaways
Between the thematic keynote presentations, the conference agenda encouraged participants to break out into smaller groups to discuss mock situations.
The first “war room” roundtable discussion was the best example of what companies are facing today with AI. The first group had to discuss if and when organizations are ready to embark on AI.
The second group focused on what organizations need to have in order to be successful.
And lastly, I was placed in a group that assumed that an AI project is imminent – but the next question is how do you get started? Interestingly the discussion outcomes did not center around technology.
Instead, we discussed following a business value/impact framework to guide companies to measure their readiness and develop KPIs to quantify their success. Some of the conference delegates shared different approaches that their organizations are taking with AI readiness which ranged from a wait-and-see approach to a ready aim fire program to quickly identify immediate business wins.
It would not be a data conference if the recommendations did not return to a discussion on governance, metadata and data quality. Ensuring proper data availability and accuracy for the various AI requirements resonated with the attendees in the room because this rang similar to other data initiatives required for regulatory compliance.
Viewing the holistic usage of data across teams is a concept advocated by Elena Alikhachkina, former Global Chief Data Officer of Danone. The power of AI can only be optimized and streamlined through shared data ownership and accountability. The traditional silos of technology as seen by data warehouse, big data, and cloud is a different evolution with the pervasiveness of AI. With these considerations, data needs to be viewed as activated products combined with user experience, product management and technology.
Lastly, all conferences need to continue to educate and highlight the importance of diversity. Artificial Intelligence specifically is an area where a lack of diversity negatively impacts the results of the model.
From ethical concerns to unconscious bias, there are many additional challenges that data practitioners need to advocate for. A deliberate all-men panel moderated by Prosasty Chaudhuri, former Chief Data Officer of Silicon Valley Bank, she coordinated an in-depth discussion to identify that diversity comes in all types—not just gender. Subbaiah Maneyapanda, SVP Enterprise Data Management and Analysis of Northern Trust, shared a story about a colleague that came from a non-technical background that made her a successful part of the team. Dan Power, former Managing Director and Business Unit Chief Data Officer at State Street Global Markets also shared how he has actively mentored and successfully guided female colleagues in their career paths.
A Reflection
The Operational Efficiency Summit was tiny in size but mighty in the wealth of information it provided to the conference attendees for today’s viewpoints in data, technology, and cloud. I hope that more conferences can be as intentional in offering informative sessions and networking opportunities.
Over the next 6-12 months, I would like to see more discussions from the same organizations on actual implemented use cases. Now is the time to implement the AI data strategies that data practitioners are experienced in operationalizing. It is an exciting time for us to be leading the charge with our unique experience in technology implementation, cross-functional team leadership, data management knowledge and our ability to collaborate!
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