Metadata, data quality and data observability tools provide significant capabilities to ensure good data for your BI and AI initiatives. Metadata tools help discover, and inventory your data assets. Data quality tools help business users manage their data at sources by setting rules and policies. Data observability tools give organizations integrated visibility over the health of data, data pipeline and data landscape. Together the tools help organizations lay good foundation in data management for BI and AI initiatives.

Organizations struggle to make sense of numerous programs and projects that overlap or operate in silos. This research will weave together data and analytics governance, MDM and data quality into one organized initiative that every CDAO should be interested in.

As organisations scale AI and move towards Data Products, success depends on trusted, high-quality data underpinned by strong governance. In this fireside chat, Chemist Warehouse shares how domain-aligned metadata, data quality, and governance, powered by Alation, enable a unified delivery framework using Critical Data Elements (CDEs) to reduce risk, drive self-service, and build a foundation for AI-ready analytics and future data product initiatives.

Data architects are increasingly tasked with provisioning quality unstructured data to support AI models. However, little has been done to manage unstructured data beyond data security and privacy requirements. This session will look at what it takes to improve the quality of unstructured data and the emerging best practices in this space.

Three out of four companies are betting big on AI – but most are digging on shifting ground. In this $100 billion gold rush, none of these investments will pay off without data quality and strong governance – and that remains a challenge for many organizations. Not every enterprise has a solid data governance practice and maturity models vary widely. As a result, investments in innovation initiatives are at risk of failure. What are the most important data management issues to prioritize? See how your organization measures up and get ahead of the curve with Actian.

Traditional approaches and thinking around data quality are out of date and not sufficient in the era of AI. Data, analytics and AI leaders will need to reconsider their approach to data quality going beyond the traditional six data quality dimensions. This session will help data leaders learn to think about data quality in a holistic way that support making data AI-ready.

In our recent study, an overwhelming majority—80% of respondents—reported using AI in their day-to-day workflows. This marks a significant increase from just a year ago, when only 30% were doing so.
But what about data quality? Can you trust your data?
In this session, we’ll discuss how dbt can help organizations increase trust in their data, improve performance and governance, and control costs more effectively.
dbt is widely regarded as the industry standard for AI on structured data. Its Fusion engine, with deep SQL comprehension, powers the next generation of dbt use cases.

Data observability helps to detect and diagnose unknown unknowns in data quality and pipeline performance—but it doesn't fix them. Learn how Bigeye is leveraging AI to enable data teams to resolve data quality and pipeline issues, and even prevent them occurring in the first place.