Data catalogues can be time-consuming and arduous to implement, but their worth and potential shouldn’t be underestimated for long-term data quality initiatives. By providing a central point for data discovery, the data catalogue, integrated directly into the company’s system, can help improve transparency and collaboration and productivity amongst different teams. Overall, it can also enhance your data security, privacy and governance initiatives and decision-making cross-functionally as data becomes easier of access. But implementation is nevertheless not something that should be rushed into. Whether you have implemented one already or are looking to, it is invaluable to consider all angles of this organizational tool, and most importantly, to have the tools necessary to monitor and maintain it once you have brought it onboard.

-   Evaluate the pros and cons of buying vs. building, addressing questions of resources and project goals, to better assess what is most useful and valuable for your company

-   Home in on the various options available to you, from stand-alone solutions to cloud-native data catalogues, in order to determine what is most suited to your company, people and processes

-   Assess what your company structure currently looks like, and the needs and responsibilities of each team in order to establish a data catalogue strategy suited to all

Thomas Hughes

Senior Vice President, Technology Architecture & Data Platforms Engineering
Navy Federal Credit Union

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Real-time data is the competitive edge in the Financial Services industry. It enables faster decisions in fraud detection, customer engagement, compliance monitoring, and more. But delivering secure, reliable data at low latency remains a major challenge for organizations dealing with legacy systems and fragmented pipelines.

Join this session to discuss how infrastructure modernization is the key to enabling secure, real-time analytics at scale.can accelerate business-critical use cases. We will explore how data leaders are balancing the growing demand for real-time delivery, driven by growing AI use cases, with the need to maintain strong data governance principles. Join to compare strategies and learn how others are solving for real-time data and security.

Oz Whitesell

Senior Sales Engineer
FiveTran

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The US still has very little regulatory guidance for AI in 2025. Implementing this new tool in a compliant manner is one difficulty, but one must also be tuned in to the question of ethics thrown into the mix too. Ethical practices ensure that AI systems are fair, transparent, and accountable, minimizing the risk of bias or unintended harm. In-house data teams must focus on maintaining high ethical standards in the development, training, and implementation of AI systems to foster trust with stakeholders and protect the organization's reputation.

-   Assess the importance of implementing bias audits and regular monitoring to identify and mitigate potential risks and ensure all systems are fair

-   Establish a set of clear principles for the entire business to abide by and ensure transparency in AI decision-making processes

-   Weigh in on the importance of implementing private-enhancing technologies and conducting privacy impact assessments

Diego de Aragão

SVP, Balance Sheet Management & Analytics
Citi

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With the constant influx of providers and solutions, it can often be difficult to assess which one is most suited to your needs and model. What’s more, with capacities accelerating quickly in-house, it can be even trickier to determine whether buying offers more than building. External solutions are, of course, necessary in certain cases, but each situation will require a tailored solution to make sure it is just right for you and your team and aligns in the long term with your business model and goals. Join this session to consider the decision-making behind the scenes of each solution, and the implications of efficient collaboration with third parties. 

-   Evaluate the pros and cons of building vs. buying solutions according to a range of scenarios

-   Assess the questions to factor into your decision-making when selecting a third party provider in order to ensure your collaboration is as fruitful as possible

-   Understand what is needed from you, your team and your current infrastructure, and build a strategy according to what you have available

With the rapid pace of transactions and the increasing sophistication of fraud tactics, relying on traditional, batch-processed methods for identifying fraudulent activity is no longer sufficient. Real-time data processing enables financial institutions to detect suspicious activities as they happen, reducing the window of opportunity for fraudsters and minimizing potential losses. They must, however, learn how to harness these real-time insights in order to be able to unlock these opportunities, drive competitive advantage and keep customers safe in an increasingly fraudulent world.

-   Assess the role AI and ML has to play in helping you analyze patterns, and detect anomalies and potential fraud based on historical data

-   Weigh in on the integration of cross-channel data streams as a strategy, combining data from transactions and social media amongst others to help you detect fraud that may have easily been missed if only individual data sources had been analyzed

-   Discover the role real-time analytics plays to strengthen authentication processes in customers, and to prevent unauthorized access instantly