Every day, banking institution Capital on Tap is calculating thousands of credit scores, directly impacting how their customers receive credit cards or additional lines of credit. Data quality is paramount – incorrect credit scores can set off a wide range of long-lasting financial implications for their customers, which is why the team turned to data observability with Monte Carlo, to improve their data – and credit score – reliability. 

But, as with any new tool in your tech stack, onboarding new processes for key users is just as important as onboarding the tool itself. 

Join this session with Ben Jones and Soren Rehn, to hear why the Analytics Engineering team at Capital on Tap decided to invest in a data observability tool, how their processes play a critical role in maximizing the tool’s value (including a few missteps and recalibrations along the way), and the strategies employed to garner widespread success and buy-in over time.

Join Experian, Sainsbury’s, The Nottingham, UST and British Business Bank discuss how better data quality and better data governance leads to improved AI. Hear real business examples of how AI is being implemented and the lessons our panellists wished they’d known sooner. Also learn key takeaways on how to have a better Data Governance strategy and why having trust in your data is more important than any new emerging technology.

Enterprises who deploy data observability report fewer and shorter incidents due to data quality issues. However, deploying data observability widely within an enterprise can be daunting, especially for teams who have experienced a heavy lift when rolling out other data governance technologies. This talk will review the top challenges enterprises will face when pursuing a data observability initiative, and a mix of process and technology solutions that can mitigate them to speed up time to value so data governance teams can show business-facing results quickly.

As the hype for AI grows, organizations are still wrestling with the fundamentals of data governance. The ambitions of executives and boardrooms to implement next-gen AI use cases hinges on a solid data foundation including cataloging, ownership, and data quality. Join Collibra’s Chief Data Citizen, Stijn Christiaens and Vodafone’s Sr. Data Governance Manager, Fede Frumento, to learn how Vodafone has used data governance fundamentals to increase the scalability and collaboration of GenAI use cases.

In today's data-driven world, data mastery is crucial for success. Enter Data Observability, a revolutionary approach that tackles complex challenges and unlocks new possibilities in the age of AI. This session explores the transformative power of Data Observability through compelling use cases across various industries, including retail, finance, manufacturing, healthcare, and more.

As a leader in Data Observability, Acceldata will showcase how organizations can:

Detect and resolve data issues in real-time

Ensure data integrity throughout complex transformations

Maintain consistent data quality across diverse systems

See how retail giants optimize supply chains and enhance customer experiences, how financial institutions achieve superior compliance and risk management, and how manufacturers leverage data for efficiency and innovation.

This presentation goes beyond theory, showcasing the immense potential of Data Observability.

Many companies are under pressure to implement Gen AI ASAP, but not everyone sees the risks clearly. New Actian research shows nearly 80% of respondents think their data quality is up to the task. But real data prep takes more work than most business leaders think. How can you avoid the data prep pitfalls that can tank a Gen AI initiative? How can you move quickly—and confidently—into the Gen AI era? Actian’s SVP of Engineering & Product, Emma McGrattan and CMO Jennifer Jackson will share research and customer perspectives to explain true data readiness and how to optimize your Gen AI journey.

As organizations are exploring and expanding on their AI capabilities, Chief Data Officers are now responsible for governing the data for responsible and trustworthy AI. This session will cover 5 key principles to ensure successful adoption and scaling of AI initiatives that align with their company?s business strategy. From data quality to advocating for ethical AI practices, the Chief Data Officer?s mandate has expanded to compliance of new AI regulations. Peggy Tsai, Chief Data Officer at BigID and adjunct faculty member at Carnegie Mellon University for the Chief Data Officer executive program, will provide insights into the AI governance strategies and outcomes crucial for cultivating an AI-first organization. Drawing on her extensive experience in data governance and AI, this session will be an invaluable guidance for all participants aiming to adopt industry-leading practices.

The data engineer role has expanded far beyond data pipeline management. Data engineers are now tasked with managing scalable infrastructure, optimizing cloud resources, and ensuring real-time data processing, while keeping costs in check - which continues to be quite challenging.

In this session, Revefi will demonstrate Raden, the world’s first AI data engineer. Raden augments data teams with “distinguished engineer level” expertise in data architecture, system performance, optimization, and cost management.

Raden uses GenAI and AI to address these challenges by working with your team as an 👩‍✈️ AutoPilot and/or 👨‍✈️ CoPilot by automating critical functions such as Data Quality, Data Observability, Spend Management, Performance Management, and Usage Management, allowing your data team to tackle complex use cases with ease.

Join us to discover how you can revamp your data engineering practices and dramatically improve the ROI from your data investments 

We'll explore innovative strategies to shift DQ from a technical to a business-centric mindset. This session will guide the audience in transforming DQ into a tool that business owners won't just use, but will rely on every morning to kickstart their day. The focus will be to reinvent how your organization perceives and interacts with Data Quality, making it an integral part of your business narrative.

Finding anomalies and significant deviations in high quality business-critical data in real-time is key to any data-intensive business, especially in the financial sector where trust and reliability are paramount. Join us for a deep-dive on how global real-time payment network Volt leverages Validio’s data quality and observability platform, to catch deviations in key metrics such as traffic and payment volume, payment initiation and conversion.

Data quality issues due to lack of clear data definition ». Example of unclear surface areas definitions.

• Solution: Set up data governance & organization to support it. Implement a data catalog & the choice of DataGalaxy
• Results: Data Catalog usage within Cofinimmo. Efficiency gains and risk reduction thanks to clear data definitions and data quality improvement.

The success of AI initiatives hinges on DATA. According to recent research, only 10% of enterprises will achieve the expected ROI from their Generative AI deployments, with data quality issues being the most cited reason for failure. The core message is clear: 'You are as AI-ready as your data.' This session will explore practical approaches to overcoming common data challenges and ensuring your data meets the specific requirements of AI techniques.

Key Takeaways:

• Understanding AI Readiness & how to assess it?

• AI-Ready Data: Two core Foundations

• Build a scalable data infrastructure that accelerates AI deployment and innovation. DQLabs Framework & Practical Approaches to fix your data problems.

Data Observability is the new frontier of modern data management. Leading enterprises rely on Acceldata to ensure data quality, streamline operations, optimize costs, and maintain compliance. Join us to discover how to implement Enterprise Data Observability across on-prem and cloud environments, creating a single source of truth for data leaders, engineers, scientists, and business users. Learn from a seasoned industry expert who has successfully operationalized data governance at petabyte scale and delivers reliable data for AI and analytics initiatives.

Lunar, a leading Nordic digital bank, successfully implemented a data governance framework to enhance data quality and secure C-level buy-in by using SYNQ, a data reliability and observability tool. 

Their framework focuses on data ownership, criticality, and monitoring. Lunar's data team, leveraging tools like SYNQ, ensures high standards against financial crime, personalisation through AI, and reliable reporting. 

They maintain oversight through automated monitoring, use of data products, and a robust ownership model, which enhances data quality and accelerates issue resolution for their reports to executives. 

This approach enables Lunar’s data engineering and data governance teams to work in harmony, and operate efficiently without having to increase headcount.

There are many things to think about on your Generative AI journey, but in this talk we’ll focus on two key ones. Have you identified use cases that will solve real business problems? Secondly, is your data platform prepared? 

From this session, Cynozure’s Solution Architect, Tom Wilson will share his experience of what it takes to successfully integrate GenAI across your data platform, touching on data quality, governance and model management. With this, he’ll also share the practical applications of GenAI, both now and the near-future, that will positively shake up the way we do business and deliver value to organisations that embrace them.  

Join us to learn about: 

• The important role your data platform plays in unlocking GenAI’s potential 

• Lessons, experiences and watch-outs from doing this 

• Use cases GenAI is best suited to right now 

• The complex and evolved use cases we can expect to see going forward  

This talk will explore a platform strategy that emphasizes the decentralization of data and analytics, aiming to achieve an optimal balance between autonomy and governance, thereby increasing iteration and innovation speed while ensuring compliance with regulations. Attendees will learn how to support the entire data product lifecycle, enabling teams to operate independently while adhering to governance and architectural standards. 

The discussion will highlight the following key areas:

1. Autonomy and Innovation: How decentralized data platforms empower teams to innovate faster by reducing dependencies and bottlenecks. Examples of successful implementations will be provided, illustrating how autonomy can lead to increased iteration and innovation speed.

2. Governance and Compliance: Strategies for maintaining robust governance frameworks that ensure data quality, security, and compliance with regulations such as GDPR and HIPAA. The talk will cover tools and best practices for monitoring and enforcing compliance in a decentralized environment.

3. Data Product lifecycle: A comprehensive approach to supporting the data product lifecycle, from data product prototyping to the data product operations, monitoring and change management. 

4. Adoption: Real-world scenarios where organizations have navigated the trade-offs between autonomy and governance, creating the right condition for platform adoption.

The success of any AI strategy hinges on the quality, accessibility, and relevance of the data that powers it. Data products play a crucial role in this context by transforming raw data into valuable, trusted, and purpose built data assets that fuel AI-driven innovation and decision-making.

By integrating data products into our AI initiatives, we can:

- Accelerate AI Development

- Enhance Decision-Making

- Foster Innovation

- Ensure Data Quality

Join us to learn how Starburst Data Products are feeding data hungry AI strategies across the enterprise to improve productivity, unlock new opportunities, drive competitive advantage, and lead in the era of intelligent business.