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Webinar recap – Why data freaks out your team (and how AI can fix it) 

Data should empower teams, not overwhelm them. Yet, too often, systems force users to adapt to the data instead of tailoring data to the user.

This February, DataGalaxy hosted a tell-all webinar for data leaders learning to work with AI tools. Together, AI expert and Product Manager Kseniia Ilichenko and data governance leader Laurent Dresse discussed how AI-powered solutions like DataGalaxy’s Data Knowledge Catalog transform data management by adapting to user needs.

Keep reading to learn more about what you might have missed from this unique webinar.

How is data blocking data teams?

Often, raw data can be seen as overwhelming, even by skilled data teams. Common questions can include:

  • Can I trust this data?
  • Who should I talk to about these numbers?
  • Where is this data coming from?
  • Is this data up-to-date?
  • How was this data collected or processed?
  • Are we compliant with relevant regulations (e.g., GDPR, CCPA)?
  • What does this data actually mean for our business decisions?
  • How can I ensure everyone is aligned on these metrics?

Unclear processes, fragmented ownership, and a basic lack of understanding can slow down and even stop progress for data projects. Questions around these topics can vary from simple to complex. There can be a basic need to understand, locate, and share data.

There can also be a basic lack of understanding of what is in the data set, where it lives, and who owns it.

Finally, there can be questions around the clarity of data definitions and sharing a common meaning of data points and data sets.

Data governance & self-service analytics solutions

In a perfect world, each data company has a single source of truth to validate and trust data. Also, in a perfect world, teams have outstanding collaboration and processes, and ownership of data is clearly defined. 

Though the world is far from perfect, data governance platforms and self-service analytics solutions can still provide many benefits for teams looking to bring clarity to their data projects. 

Check out the video below to see what our data governance and AI experts had to say about using a data governance platform for reducing data confusion.

DataGalaxy for data clarity and understanding 

Though traditional data catalogs can provide some clarity into data definitions and solve common frustrations, DataGalaxy brings data to the people with our advanced AI tools

DataGalaxy’s commitment to making data knowledge accessible drives our innovation. By integrating advanced translation and multilingual AI-based search capabilities into our Data Knowledge Catalog, we’re breaking down barriers in data understanding and use, fostering a truly global, data-driven culture.

Our data governance platform translates your data elements into your chosen language, streamlining communication and collaboration with a single, consistent language platform across your entire organization.

Effortless governance and compliance with embedded AI

Intuitive, collaborative tools to bridge the knowledge gap

Are you interested in joining a future webinar? Discover our upcoming virtual events to save your free seat!

FAQ

Do I need a data catalog?

If your teams are struggling to find data, understand its meaning, or trust its source — then yes. A data catalog helps you centralize, document, and connect data assets across your ecosystem. It’s the foundation of any data-driven organization.
👉 Want to go deeper? Check out:
https://www.datagalaxy.com/en/blog/what-is-a-data-catalog/

Yes. We provide detailed comparisons vs. Alation, Collibra, Atlan, and others — or you can request a personalized assessment.

Building a successful data product begins with a clear business need, trusted data, and user-focused design. DataGalaxy simplifies this process by centralizing data knowledge, fostering collaboration, and ensuring data clarity at every step. To create scalable, value-driven data products with confidence, explore how DataGalaxy can help at www.datagalaxy.com.

To launch a data governance program, identify key stakeholders, set clear goals, and define ownership and policies. Align business and IT to ensure data quality, compliance, and value. Research best practices and frameworks to build a strong, effective governance structure.

Improving data quality starts with clear standards for accuracy, completeness, consistency, and timeliness. It involves profiling, fixing anomalies, and setting up controls to prevent future issues. Ongoing collaboration across teams ensures reliable data at scale.


About the author
Jessica Sandifer LinkedIn Profile
With a passion for turning data complexity into clarity, Jessica Sandifer is an experienced content manager who crafts stories that resonate across technical and business audiences. At DataGalaxy, she creates content and product marketing messages that demystify data governance and make AI-readiness actionable.