A-Team Group’s 16th annual Data Management Summit London brought together data leaders from the world’s largest financial institutions to discuss the biggest data and technology issues and trends within their industry.
Hundreds of delegates from all over the world gathered to hear the latest thoughts of practitioners in keynote addresses and panel discussions before breaking off into groups to look more deeply into specific topics during the summit’s innovative new feature, Champagne Roundtable discussions.
Here’s a roundup of the key discussion points through the day.
At the Forefront of AI
Ari Cohen, chief data and AI officer, EMEA and Americas at Macquarie Group, kicked off proceedings with his Practitioner Keynote address, which looked at how organisations could become leaders through AI.
Cohen said the use of AI agents, collaboration between models and high-performing individuals, a robust risk mitigation policy and a strategy for limiting the social and cultural impact of change would define AI “leaders”.
Based on the experience of Macquarie, he said leadership would also require teams to balance opportunities and risks, overcome silos, build strong governance policies and prepare workforces for change.
Data Products in Focus
In the first panel session of the summit, Niresh Rajah, expert chief data and AI officer as well as board adviser and non-executive director, led a discussion on delivering business value with data products. This is best achieved by adopting a “data product mindset” in which data is regarded as a reusable asset that meets consumer requirements and improves business processes.Continual measurement of products’ effectiveness is key to establishing their value, the panel said, while ownership structures could affect their business value.
It’s All About Data
“When Your Data Works, Everything Works” was the title of the keynote delivered by Ray Sullivan, vice president, data modernisation at Rocket Software. In her address, Sullivan said data departments, often perceived as cost centres, could become engines for business growth by bridging the “data messaging gap”.
This is done by moulding the work they do – using modern data management and architectures to streamline data operations – into the sorts of KPIs the C-suite demands; such as cost reductions and improved processing.
Unstructured Challenges
The next panel gathering, a session entitled “Governing the Unstructured Frontier: AI, Data and Risk”, dwelt on the fundamentals necessary to draw full value from unstructured data and the guardrails that requires.
Moderated by Joanne Biggadike, head of data governance at Schroders, the panel recognised that robust governance of such data – which accounts for 80 per cent of all data – is difficult but necessary because AI tools were core elements of the process. Fortunately, the panel said, vendors and customers are gradually forming a consensus on governance policies covering structured and unstructured data.
De-Fragging the Tech Stack
In the discussion “Beyond Silos – Building A Unified Data Ecosystem for Access, Agility And ROI”, panellists articulated that the shortcomings of fragmented legacy data architectures were only a problem because organisations that still possessed them lacked clear data ownership policies.
Unified data is a benefit, especially as the use of generative AI increases, but it isn’t always easy to achieve, the panel moderated by Duncan Cooper, former chief data officer at Northern Trust, agreed. The surest way to unify data is to transition its ownership from IT teams to the business divisions that will use it, the panel concluded.Addressing Failures
It takes a brave data leader to be subjected to questioning in a keynote fireside entitled “Failures of Chief Data Officers; How to Reboot: Data Strategy, AI Readiness, Organisational Alignment and Leadership Lessons”. But Jennifer Courant, chief data officer at DWS Group was up to the task, highlighting that AI-ready data, a strong AI governance strategy, a commitment to data quality along with accountability and oversight were among the key ingredients to avoiding costly data management errors.
Interviewed by Tina Salvage, independent data, AI and governance adviser, Courant also said leaders need to recognise that clean data is essential to innovation. Leaders should draw future plans to identify priorities and to ensure the enterprise skillsets are in place to meet those objectives.
Walk the Lineage
The penultimate panel of the summit took on the theme “End-To-End Data Lineage In Action: Case Studies And Discussion” and was moderated by Naomi Clarke, data innovation and strategy, independent expert.
Clarke led discussion on what is driving automation of end-to-end lineage structures, how to measure their effectiveness, the challenges of deployment and how to overcome resistance to change and reject misinformation about outcomes.
Quality Over Quantity
The final session took on the topic “Beyond The Dashboard: AI-Powered Approaches For Proactive Data Integrity”. This debate was framed by the shift from dashboards to data observability as being the best way to monitor data quality. This enables the deployment of AI to solve for the pain points of lineage and governance, which are foundational for AI applications.
The panel, moderated by independent adviser Chad Giussani, agreed that data issues needed to be fixed at source, humans had to remain in the loop of AI deployments and that cultural barriers, including the perception of data as a cost rather than an investment, had to be overcome.
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