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Last updated: 9/11/2026valid
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# DataGalaxy

## Sitemaps
[XML Sitemap](https://www.datagalaxy.com/sitemap_index.xml): Includes all crawlable and indexable pages.

## Posts
- [The Top 10 Data Product Platforms in 2026: A Buyer&#8217;s Guide](https://www.datagalaxy.com/en/blog/top-data-product-platforms/): Compare the top 10 data product platforms of 2026 on governance, AI readiness, product management, and business value, and find the right fit for your stack.
- [Product Update June](https://www.datagalaxy.com/en/blog/product-update-june/): Every release should make your catalog easier to run, not harder to keep up with. This round covers five updates: simpler user offboarding, an AI agent that can now update your catalog directly, more control over your connectors, clearer activity logs, and flexible session timeouts for admins. There&#039;s also a fresh batch of MCP Server setup guides if you&#039;re connecting DataGalaxy to your favorite AI tools.
- [The 14 leading data catalogs in 2026: a buyer&#8217;s guide based on approach, price, and perception](https://www.datagalaxy.com/en/blog/the-14-leading-data-catalogs-in-2026/): Evaluating data catalog solutions may sound like a straightforward task, until you&#039;re confronted with multiple options promising similar outcomes in different ways. In addition to the numerous factors you need to consider, there&#039;s also the question of what&#039;s real and what&#039;s overhyped.
- [The missing layers: Why trust and value decide who wins at enterprise AI](https://www.datagalaxy.com/en/blog/trust-and-value-are-the-missing-layers-in-enterprise-ai/): The most expensive line item in enterprise AI is no longer compute – it is the gap between what AI promised and what it is actually delivering. After two years ofpilots, boards have stopped asking what AI can do and started asking what itreturns.
- [Outputs vs Outcomes in AI: The Distinction That Separates Strong Data Leaders from the Rest](https://www.datagalaxy.com/en/blog/outputs-vs-outcomes-in-ai-the-distinction-that-separates-strong-data-leaders-from-the-rest/): A conversation with Paloma, Senior Data &amp; AI Strategist at DataGalaxy Portfolio, on why the most expensive failure in AI today is not a technical one — it is a communication one.
- [Making AI Value Visible: How to Communicate ROI Without Losing Your Audience in the Process](https://www.datagalaxy.com/en/blog/making-ai-value-visible-how-to-communicate-roi-without-losing-your-audience-in-the-process/): A conversation between Nicolas, Chief Product Officer at DataGalaxy, and Edosa Odaro, author of “The Values of AI” and former consultant for Barclays, Lloyds, AXA, AIG and Allianz, on why most organizations can&#039;t articulate the value of their AI initiatives.
- [The AI Accountability Model: Why Explainability Alone Is Not Enough and What Ownership at Decision Time Really Means](https://www.datagalaxy.com/en/blog/the-ai-accountability-model-why-explainability-alone-is-not-enough-and-what-ownership-at-decision-time-really-means/): A vision from Nicolas, Chief Product Officer at DataGalaxy, on why technical explainability has become a comfortable illusion and how to build an accountability framework that actually keeps pace with AI.
- [Product Update May](https://www.datagalaxy.com/en/blog/product-update-may/): This month, we&#039;re rolling out several improvements that give you better visibility into your data, simpler metadata management, and smarter AI-driven experiences.
- [Product Update April](https://www.datagalaxy.com/en/blog/product-update-april/): At DataGalaxy, every update is designed to help teams move faster with more confidence in their data knowledge. This latest release focuses on three key areas: improving how you interact with your data through Blink, strengthening lineage visibility, and enabling more direct actions from the chatbot.
- [Regain control of your AI portfolio: visibility, cost governance, and lifecycle management](https://www.datagalaxy.com/en/blog/regain-control-of-your-ai-portfolio/): AI investment is accelerating fast, but for most organizations, the biggest blocker to scaling AI isn’t model capability. The biggest blocker for CDAOs and Heads of Data and AI is the lack of organizing their AI investments like an AI portfolio. Fundamentals of portfolio management like visibility, accountability, cost governance, and product lifecycle management.
- [AI Portfolio Management: The &#8220;Ghost Portfolio&#8221; Risk in Data &#038; AI Initiatives](https://www.datagalaxy.com/en/blog/ai-portfolio-management-the-ghost-portfolio-risk-in-data-ai-initiatives/): Most data leaders in 2026 are not struggling because they lack AI activity. They are struggling because they have too much of it, tracked in too many places, owned by too many teams, and connected to business outcomes by almost nobody. What is missing is the one thing that makes all of it defensible: a clear, shared view of what the AI portfolio is actually worth.
- [Solvency II compliance in 2026](https://www.datagalaxy.com/en/blog/solvency-ii-compliance-in-2026/): In 2026, Solvency II compliance is no longer just a regulatory exercise. It is a data challenge at scale.
- [GDPR compliance in 2026: why most companies still fail (and how to fix it)](https://www.datagalaxy.com/en/blog/gdpr-compliance-in-2026-why-most-companies-still-fail-and-how-to-fix-it/): In 2026, GDPR compliance is no longer a legal checkbox. It is a baseline requirement for operating in Europe.
- [Governance hype vs. business reality: Moving toward trust models](https://www.datagalaxy.com/en/blog/governance-hype-trust-models/): In this post, we explore Gartner&#039;s finding that while nearly 89% of data and analytics leaders say governance is essential for innovation, far fewer deliver it consistently. Where an organization falls on that spectrum often reflects its data maturity model, the broader framework that charts the path from fragmented practices to data excellence. We introduce trust models as a turning point. These governance frameworks are built on accountability, ethics, and stakeholder trust, not just control.
- [Product Update March](https://www.datagalaxy.com/en/blog/product-update-march/): Our latest updates focus on making data governance easier to manage, improving collaboration around data knowledge, and strengthening technical foundations for integrations and lineage. Here is what’s new and what’s coming soon.
- [Product Update February](https://www.datagalaxy.com/en/blog/product-update-february/): This latest release sharpens how teams explore metadata, trust AI answers, and keep integrations running without friction. Each update is designed to make data knowledge more accessible, more connected, and more resilient across your ecosystem.
- [Big data vs. smart data: How to turn volume into value in 2026](https://www.datagalaxy.com/en/blog/big-data-vs-smart-data-how-to-turn-volume-into-value-in-2026/): The evolution of the data landscape has been relentless: data warehouses, big data, data lakes, data fabrics, and now data mesh. Each wave promised better scalability, faster insights, and a competitive advantage.
- [DataGalaxy introduces Portfolio to close the AI velocity gap at Gartner Data &amp; Analytics Summit 2026](https://www.datagalaxy.com/en/blog/datagalaxy-introduces-portfolio-to-close-the-ai-velocity-gap-at-gartner-data-analytics-summit-2026/): Orlando, Fla., March 9-11, 2026 – DataGalaxy, a leading provider of data and AI value governance solutions, today announced its expanded positioning to coincide with the Gartner Data &amp; Analytics Summit 2026 in Orlando.
- [DataGalaxy MCP server: now speaks fluent AI](https://www.datagalaxy.com/en/blog/datagalaxy-mcp-server-now-speaks-fluent-ai/): DataGalaxy launched an MCP server that changes how AI tools work with your data catalog. Instead of guessing, your AI can:
- [Implementing effective data governance in 8 easy steps](https://www.datagalaxy.com/en/blog/implementing-effective-data-governance-in-8-easy-steps/): Today, organizations recognize the critical role of data governance in managing and leveraging their data effectively.
- [AI and Traditional Data Practices in 2026: What Still Works, What Doesn&#8217;t, and What Leaders Are Doing About It](https://www.datagalaxy.com/en/blog/ai-and-traditional-data-practices-in-2026-what-still-works-what-doesnt-and-what-leaders-are-doing-about-it/): A conversation with Joe Reis, author and host of The Joe Reis Show, on why the unsexy fundamentals of data management have become the most strategic investment leaders can make in 2026.
- [DataGalaxy vs Coalesce (2026): From AI Data Catalog to Enterprise Data Governance Platform](https://www.datagalaxy.com/en/blog/datagalaxy-vs-coalesce-2026-from-ai-data-catalog-to-enterprise-data-governance-platform/): As the data governance landscape evolves, organizations often compare DataGalaxy with newer, AI-driven tools like Coalesce.
- [DataGalaxy vs Atlan (2026): Choosing the Right Modern Data Governance Platform](https://www.datagalaxy.com/en/blog/datagalaxy-vs-atlan-2026-choosing-the-right-modern-data-governance-platform/): When evaluating modern data platforms, DataGalaxy and Atlan are often compared for their cloud-first approach and strong user experience.
- [DataGalaxy vs Alation (2026): The Best Data Catalog and Governance Platform for Enterprise Adoption](https://www.datagalaxy.com/en/blog/datagalaxy-vs-alation-2026-the-best-data-catalog-and-governance-platform-for-enterprise-adoption/): Choosing between DataGalaxy and Alation is a common step for organizations looking to scale their data governance strategy.
- [DataGalaxy vs Collibra (2026): The Modern Data Governance Platform Showdown for Data Leaders](https://www.datagalaxy.com/en/blog/datagalaxy-vs-collibra-2026-the-modern-data-governance-platform-showdown-for-data-leaders/): Choosing between DataGalaxy and Collibra is not just a tooling decision. It is a strategic call on how your organization will scale data trust, adoption, and AI readiness.
- [Data governance in 2026: Benefits, business alignment, and essential need](https://www.datagalaxy.com/en/blog/data-governance-in-2026-benefits-business-alignment-and-essential-need/): In a world where data has become more vital than ever, understanding how to effectively manage and utilize this resource is essential.
- [Organizing your data with a data catalog in 3 easy steps: The business need &amp; essential company asset (2026)](https://www.datagalaxy.com/en/blog/organizing-your-data-with-a-data-catalog-in-3-easy-steps-the-business-need-essential-company-asset-2026/): Acting as a comprehensive inventory for an organization’s data assets, data catalogs facilitate easy access, understanding, and governance of large datasets.
- [What is data lineage? Definition, types, and examples](https://www.datagalaxy.com/en/blog/what-is-data-lineage-2026/): We’ve all been there. You’re standing by the office coffee machine, debating a data glitch with a colleague, and someone throws out the term data lineage. Suddenly, the conversation descends into a confusing mix of technical jargon and blank stares.
- [DataGalaxy’s Auto Description: Documenting your data has never been easier](https://www.datagalaxy.com/en/blog/datagalaxys-auto-description-documenting-your-data-has-never-been-easier/): Smarter documentation. Less manual work. Same trusted governance.
- [Data culture: How to build the high-performing, data-driven organization your teams need](https://www.datagalaxy.com/en/blog/data-culture-how-to-build-the-high-performing-data-driven-organization-your-teams-need/): More than a buzzword, data culture is the shared mindset and operating system that determines whether your organization treats data as a strategic asset or an untapped byproduct.
- [Gartner’s top 5 data &amp; analytics predictions for new wave data teams in 2026](https://www.datagalaxy.com/en/blog/gartners-top-5-data-analytics-predictions-for-new-wave-data-teams-in-2026/): Did you know that Gartner estimates that by 2026, 90% of current analytics content consumers will become content creators enabled by AI?
- [Data governance &amp; data quality: Two interconnected foundations of data-driven success (2026)](https://www.datagalaxy.com/en/blog/data-governance-data-quality-two-interconnected-foundations-of-data-driven-success-2026/): Data quality and data governance go hand-in-hand, and it’s virtually impossible to have one without the other.
- [Data intelligence meets value governance: Inside DataGalaxy’s next-gen platform](https://www.datagalaxy.com/en/blog/data-intelligence-meets-value-governance-inside-datagalaxys-next-gen-platform/): Enterprises expect more than simply knowing what data assets exist. Today, they demand clear insight into how those assets are being used and what value they deliver.
- [The evolution of data catalogs: From card systems to AI governance](https://www.datagalaxy.com/en/blog/the-evolution-of-data-catalogs-from-card-systems-to-ai-governance/): While data catalogs have been around since the 1960s, those early systems are incomparable to the business intelligence tools they have become. However, the roots of this tool can be traced well before computers and digital data management.
- [Governance hype vs. business reality: Moving toward trust models](https://www.datagalaxy.com/en/blog/governance-hype-vs-business-reality-moving-toward-trust-models/): Gartner’s 2025 Hype Cycle for Data &amp; Analytics Governance report evaluates the progress of D&amp;A governance innovations across all use cases. The report also includes additional research that helps form a holistic view of D&amp;A. Download your free copy!
- [Modern data catalogs explained: The 7 must-have features for data &amp; AI governance](https://www.datagalaxy.com/en/blog/modern-data-catalogs-explained-the-7-must-have-features-for-data-ai-governance/): A modern data catalog is a centralized system that organizes, governs, and activates your organization’s data knowledge. It provides searchable visibility into data assets, metadata, lineage, quality, and usage context — helping teams trust, understand, and confidently use data for analytics and AI.
- [The complete guide to metadata management in 2026: Definition, benefits, challenges, &amp; why it’s now a business imperative](https://www.datagalaxy.com/en/blog/the-complete-guide-to-metadata-management-in-2026-definition-benefits-challenges-why-its-now-a-business-imperative/): In the age of big data, sound metadata and&nbsp;data management&nbsp;are essential for organizations. But what does metadata management exactly entail?
- [Marketplace: Where data products meet business strategy](https://www.datagalaxy.com/en/blog/marketplace-where-data-products-meet-business-strategy/): Most data catalogs stop at organization.
- [What is a data maturity model, and why is it important? (2026)](https://www.datagalaxy.com/en/blog/what-is-a-data-maturity-model/): A data maturity model is a strategic framework to assess an organization’s current capabilities, highlight critical gaps, and chart a deliberate path toward data excellence. Rather than being a static benchmark, it acts as a compass to direct businesses from fragmented data practices to a state where information fuels agility, foresight, and measurable value creation.
- [The top 3 data observability solutions for modern data teams](https://www.datagalaxy.com/en/blog/3-data-observability-solutions/): Do you know the top three modern data observability solutions for modern data teams?
- [The top 8 best practices for successful data governance implementation](https://www.datagalaxy.com/en/blog/8-best-practices-data-governance/): Data may be the fuel of modern business, but without governance, it can just as easily create chaos as it can create value.
- [Meet Blink, your AI copilot](https://www.datagalaxy.com/en/blog/meet-blink-your-ai-copilot/): Are you looking for a faster way to understand, trust, and use your data?
- [Why is data governance important in an AI-first world?](https://www.datagalaxy.com/en/blog/why-is-data-governance-important/): So why is data governance important, and why do these components matter so much right now? Let’s talk about it.
- [AI governance &amp; stewardship: The next era of data value realization](https://www.datagalaxy.com/en/blog/ai-governance-gartner-hype-cycle/): In this article, we’ll explore two of the hottest trends in Gartner’s 2025 Hype Cycle: AI governance and augmented stewardship.
- [AI governance best practices: Policies, teams, and more](https://www.datagalaxy.com/en/blog/ai-governance-best-practices-risk-policies/): New regulatory milestones, fresh guidance from standards bodies, and maturing internal controls have made AI governance best practices a board-level priority.
- [How DataGalaxy Portfolio connects to Jira to structure data initiatives beyond ticket management](https://www.datagalaxy.com/en/blog/datagalaxy-portfolio-jira/): Jira is everywhere in modern data teams. It powers agile delivery, manages backlogs, tracks bugs, and structures project execution.
- [2025&#8217;s top 10 data governance best practices for modern data teams](https://www.datagalaxy.com/en/blog/top-10-data-governance-best-practices/): With this in mind, here are 10 data governance best practices every modern data team should adopt, whether building from scratch or adapting to the demands of AI and analytics.
- [The data observability market: Players &amp; future outlook (2025)](https://www.datagalaxy.com/en/blog/data-observability-market-players/): From early niche adoption to mainstream business necessity, the data observability market has transformed into one of the fastest‑growing sectors in the data and analytics landscape. In 2025, organizations face unprecedented pressure to ensure the reliability, accuracy, and transparency of their data pipelines.
- [A pathway to AI governance: From principles to implementation in 5 steps](https://www.datagalaxy.com/en/blog/a-pathway-to-ai-governance-5-steps/): Yet, without rigorous oversight, AI can introduce ethical risks, regulatory exposure, and operational failures that jeopardize an organization’s reputation and bottom line. Therefore, establishing a structured pathway to AI governance is now a strategic imperative.
- [Data ROI: How to measure the business impact of data](https://www.datagalaxy.com/en/blog/measuring-business-impact-with-data-roi/): If you want your program to thrive, you must demonstrate results. Not vague wins or a list of technical accomplishments, but real business impact. That’s what data ROI is all about.
- [The 3 pillars of data observability: Metrics, traces, and logs](https://www.datagalaxy.com/en/blog/3-pillars-of-data-observability/): Far superior to simply monitoring uptime or triggering random alerts, data observability provides deeper insights into the health and reliability of your systems. Keep reading to discover the top 3 pillars of data observability.
- [Building a scalable data quality framework: The top 4 best practices](https://www.datagalaxy.com/en/blog/building-data-quality-framework/): You&#039;ve got the data. You&#039;ve got the tools. But do you have a data quality framework that’s ready for what’s next?
- [The top 3 KPIs for measuring &amp; monitoring value governance](https://www.datagalaxy.com/en/blog/3-kpis-for-value-governance/): Value governance represents a strategic shift in focus from managing data to realizing enterprise value from it.
- [Top AI governance software tools compared (2025)](https://www.datagalaxy.com/en/blog/top-ai-governance-software-tools/): AI governance software gives data teams and business leaders the tools to monitor, manage, and keep their AI systems trustworthy and compliant. 
- [Top 5 Atlan competitors for data catalog management (2025)](https://www.datagalaxy.com/en/blog/top-5-atlan-competitors/): While Atlan is a popular choice, it&#039;s far from the only option. In fact, many organizations are now evaluating other Atlan competitors that better fit their needs for usability, governance, or AI readiness.
- [Align your data strategy with business outcomes in 3 easy steps](https://www.datagalaxy.com/en/blog/align-your-data-strategy-with-business/): Data strategy can no longer exist in isolation.
- [Preparing your data for machine learning: Top 6 best practices](https://www.datagalaxy.com/en/blog/data-preparation-for-machine-learning/): In this article, we’ll walk through the best practices for data preparation for machine learning – Why it matters, how to do it well, and how a solution like DataGalaxy can make your workflow much simpler and smarter.
- [The 3 most crucial observability metrics for data pipelines](https://www.datagalaxy.com/en/blog/3-observability-metrics-data-pipelines/): Introducing the top three observability metrics every data leader needs to master for trustworthy AI.
- [Top data quality tools in 2025: Options compared](https://www.datagalaxy.com/en/blog/top-data-quality-tools/): Over time, data quality tools have evolved to meet these rising demands by offering advanced features that cater to the needs of modern data management professionals.
- [Top data observability tools in 2025: Features &amp; more](https://www.datagalaxy.com/en/blog/top-data-observability-tools/): Data observability tools help teams monitor the state of their data across systems, providing clear visibility into its quality, freshness, and lineage. These tools make it possible to detect anomalies early, trace their origins, and maintain a high standard of trust in data products.
- [How DataGalaxy Portfolio connects to Alation to drive real governance impact](https://www.datagalaxy.com/en/blog/datagalaxy-portfolio-alation/): Many organizations rely on Alation as their central data catalog. It promises discovery, collaboration, and visibility into datasets.
- [Top 3 data management strategies for working with AI tools (2025)](https://www.datagalaxy.com/en/blog/data-management-strategies-with-ai/): Data management strategies define how information is collected, stored, governed, and secured. Traditionally, the primary focus of data management was on handling volume, variety, and velocity.
- [Why companies are switching from Atlan to DataGalaxy](https://www.datagalaxy.com/en/blog/switch-from-atlan-to-datagalaxy/): In this article, we’ll discuss why more and more organizations are making the switch from Atlan to Datagalaxy, including sharing what the two platforms offer, and which is truly the more powerful, business-first data management and governance platform.
- [7 key considerations when building an AI governance framework](https://www.datagalaxy.com/en/blog/ai-governance-framework-considerations/): It’s time for a dedicated AI governance framework. One that’s squarely rooted in your business realities, expands with your ambitions, and sustains AI with confidence, not chaos.
- [AI risk management: How to monitor &amp; control AI systems](https://www.datagalaxy.com/en/blog/ai-risk-management/): Let’s talk AI risk management: How to scrutinize AI in production, where risk tends to emerge, and which tools will help you mitigate risk.
- [Building an AI governance framework: 3 real-world examples](https://www.datagalaxy.com/en/blog/building-an-ai-governance-framework/): Keep reading to learn more about AI governance – the strategic layer that ensures AI isn’t just powerful, but responsible.
- [Data governance &amp; observability: 3 steps to combined value](https://www.datagalaxy.com/en/blog/governance-observability-combined-value/): Did you know that data governance and data observability are interdependent?
- [Value governance: Ensuring data-driven business value](https://www.datagalaxy.com/en/blog/value-governance-business-value/): This concept, known as value governance, is emerging as a pivotal framework for organizations seeking to align data, analytics, and AI investments directly with business outcomes.
- [How DataGalaxy Portfolio connects to ServiceNow to align governance strategy with operational execution](https://www.datagalaxy.com/en/blog/datagalaxy-portfolio-servicenow/): Many enterprises rely on ServiceNow to manage workflows across IT, risk, compliance, and operations. It is the engine behind tickets, approvals, controls, and enterprise processes.
- [DataGalaxy launches first-ever value governance platform at the Gartner Data &amp; Analytics Summit 2025](https://www.datagalaxy.com/en/blog/datagalaxy-launches-first-ever-value-governance-platform/): Learn how DataGalaxy introduces the first-ever value governance platform to bridge the gap between data assets and business value.
- [Data readiness: The real foundation for AI &amp; data governance](https://www.datagalaxy.com/en/blog/data-readinessdata-governance/): But let’s be clear: AI doesn’t magically work on its own. Behind every smart model or automation is something far less glamorous, but absolutely essential: Data readiness.
- [5 reasons why data governance must connect to a data quality tool](https://www.datagalaxy.com/en/blog/5-reasons-why-data-governance-must-connect-to-a-data-quality-tool/): When it comes to data governance and data quality, many companies assume that an all-in-one solution is ideal. After all, having an integrated data quality tool within your data governance platform sounds convenient, right?
- [The increasing need for data trust: 2 real-world examples](https://www.datagalaxy.com/en/blog/the-increasing-need-for-data-trust/): Artificial intelligence is becoming increasingly crucial for businesses. However, to fully leverage AI’s potential, organizations must ensure data readiness and data trust.
- [Data governance &amp; observability: 3 steps to combined value](https://www.datagalaxy.com/en/blog/data-governance-observability/): Data governance and data observability are interdependent. While governance establishes the rules and standards for data management, data observability ensures those rules are being followed in real-time. Together, they create a feedback loop that reinforces data trust and AI readiness, a foundation that starts with a solid AI governance framework. This article will discuss the basics of data governance and observability, discuss the best options to implement both in your organization, and how DataGalaxy and Bigeye offer a best-of-breed platform for creating a winning strategy.
- [Why data literacy starts at the ground level (and how to do it right!)](https://www.datagalaxy.com/en/blog/data-literacy-at-ground-level/): What happens when the people using those tools aren’t confident or equipped to work with data? Teams get underused technology, unrealized potential, and decisions made on instinct rather than insight. Thankfully, data literacy can solve the majority of these issues in your teams.
- [Data products: Define, build, and deliver real value](https://www.datagalaxy.com/en/blog/data-products-build-deliver/): According to Gartner, 50% of Chief Data and Analytics Officers (CDAOs) say they&#039;ve already deployed data products. But the real question is: what a data product is, and how do you build one that delivers tangible value? In this blog, we&#039;ll explore how to define, design, and deliver data products that go beyond the hype – Ones that have the potential to actually move the needle for your business.
- [How to create &amp; sustain a data quality management process](https://www.datagalaxy.com/en/blog/data-quality-management-process/): You need a data quality management (DQM) process to deliver trusted, business-ready data at scale. So, what makes a great data quality management process tick?
- [Identifying &amp; engaging data stewards in 3 easy steps](https://www.datagalaxy.com/en/blog/data-stewards-engagement-identification/): Data stewards.
- [3 key pillars for AI readiness according to Gartner](https://www.datagalaxy.com/en/blog/3-key-pillars-for-ai-readiness/): According to Melody Chien, Sr. Research Director at Gartner, organizations are undergoing a significant shift in how they approach data and analytics – And specifically AI readiness.
- [Natural language for unlocking analytics&#8217; true potential](https://www.datagalaxy.com/en/blog/natural-language-insights/): Cloud data platforms, analytics tools, and machine learning models have all proven to be invaluable for deriving insights from data. However, one barrier continues to limit their ROI: Language – Not the programming kind, but the human kind.Despite years of digital transformation, many organizations still struggle to make analytics accessible and actionable across their workforce. Natural language processing (NLP) bridges technical data structures and business users across all levels, roles, and regions.Studies show that embedding natural language interfaces within analytics environments can radically improve data discoverability, literacy, and governance. More importantly, it creates a universal data language.Let&#039;s explore how this shift empowers users and strengthens the AI governance framework that keeps our data ecosystems secure, compliant, and scalable.
- [Why your teams need data observability with their AI models](https://www.datagalaxy.com/en/blog/data-observability-ai-tools/): Much like observability in software engineering, data observability offers a window into the health of your data systems. This enables teams to proactively monitor, detect, and resolve issues before they snowball.
- [Our top 3 takeaways from the Gartner D&amp;A Summit in Orlando](https://www.datagalaxy.com/en/blog/3-key-learnings-gartner-da/): Each year, the Gartner Data &amp; Analytics Summit in Orlando brings together industry leaders, analysts, and innovators to discuss the latest trends and challenges shaping the data, analytics, and AI landscape.
- [Webinar recap &#8211; Building AI readiness &amp; data trust with governance &amp; observability](https://www.datagalaxy.com/en/blog/ai-readiness-with-observability/): CDOs and AI leaders face big risks when business opportunities get derailed by a lack of data trust due to low levels of data governance and observability.
- [5 reasons why governance must connect with data quality](https://www.datagalaxy.com/en/blog/data-quality-data-goverance/): When it comes to data governance and data quality, many companies assume that an all-in-one solution is ideal. After all, having an integrated data quality tool within your data governance platform sounds convenient, right?
- [Gartner&#8217;s field guide for successful change management initiatives](https://www.datagalaxy.com/en/blog/gartners-field-guide-change-management/): For many organizations, “Becoming data-driven” is a long-term goal with no real path set to achieve it. Often, even starting the journey of organizational data management can be a daunting task that doesn’t offer a one-size-fits-all first step. Implementing the roles of Chief Data Offers (CDOs) and Chief Data Analytics Officers (CDAOs) is essential for accelerating organizational change toward a data-centric culture working to achieve data-driven business goals.
- [Multilingual AI governance: Why language &amp; culture matter](https://www.datagalaxy.com/en/blog/multilingual-ai-governance/): Industry reports from analysts like Gartner, Forrester, and IDC highlight a gap in data and AI governance technologies that address the unique challenges posed by language and cultural barriers. Many organizations with global operations report that their data growth will maintain an upward trajectory as they build more systems to effectively collect, store, and manage data to drive business decisions. Building a robust AI governance framework is increasingly recognized as a prerequisite for scaling these efforts responsibly. This blog post will discuss the importance of a shared language and culture in a modern data-driven organization and discuss how leading-class solutions like DataGalaxy can help solve the multilingual divide.
- [3 ways generative AI is transforming data management solutions](https://www.datagalaxy.com/en/blog/gen-ai-transforms-data-management/): More and more, data and analytics leaders around the world are seeking ways to transform data access and reduce the technical skills barrier using generative AI.
- [Webinar recap &#8211; Why data freaks out your team (and how AI can fix it) ](https://www.datagalaxy.com/en/blog/webinar-recap-ai-for-data-understanding/): 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.
- [Privacy &amp; information governance in an AI-first world](https://www.datagalaxy.com/en/blog/privacy-and-information-governance/): AI has rewritten the rules of data governance.
- [DataGalaxy’s 15 essential data management resources](https://www.datagalaxy.com/en/blog/essential-resources-data-management/): There is arguably nothing more valuable to your company and central to its success than data. However, diving into the vast world of data and data management doesn’t have to be a daunting task, even for non-technical data users. Anyone can increase their data knowledge by simply taking the time to understand the ins and outs of data terminology and management techniques.
- [The top 3 required skills &amp; expertise for Chief Data Officers](https://www.datagalaxy.com/en/blog/3-required-skills-expertise-for-chief-data-officers/): The Chief Data Officer (CDO) role has evolved dramatically in recent years, shifting from a compliance-focused function to a strategic leadership role that drives business growth and innovation. 
- [Building an AI-ready data management strategy: 3 key considerations](https://www.datagalaxy.com/en/blog/ai-ready-data-management-strategy/): AI and AI-ready data are changing data management. Have you adjusted your strategy to keep up?
- [DataGalaxy announces YOOI acquisition](https://www.datagalaxy.com/en/blog/datagalaxy-announces-yooi-acquisition/): DataGalaxy acquires YOOI, accelerating their strategy to become the global leader in value-driven data &amp; analytics and AI product governance.
- [Snowflake brings enterprise AI, but who’s pushing the semantic layer?](https://www.datagalaxy.com/en/blog/snowflake-enterprise-ai-semantic-layer/): For the past 12 years, the Snowflake data platform has been making waves in the data world, promising to bring enterprise AI to the masses. With its array of powerful tools for processing and querying data, Snowflake sets the stage for businesses to unlock their data&#039;s potential.
- [End-to-end data governance platforms are replacing traditional data catalogs](https://www.datagalaxy.com/en/blog/data-governance-replacing-data-catalogs/): However, as data ecosystems grow more complex, traditional data governance platforms – while still valuable – are no longer sufficient on their own.
- [The 6 most important data governance platform tools for unlocking value](https://www.datagalaxy.com/en/blog/data-governance-platform-tools/): Do you know the essential data governance platform tools for making data-driven decisions? Discover their game-changing potential in this article.
- [Everything you need to know about the new Gartner Magic Quadrant for Data &#038; Analytics Governance](https://www.datagalaxy.com/en/blog/gartner-mq-data-analytics-governance/): Everything you need to know about the latest Gartner Magic Quadrant rankings and how DataGalaxy stacks up in the world of data &amp; analytics governance.
- [Overcoming the 3 most common metadata management problems](https://www.datagalaxy.com/en/blog/common-metadata-management-problems/): Metadata is the key to managing data – But what’s the key to metadata management?
- [Understanding semantic layers: Where data context meets AI](https://www.datagalaxy.com/en/blog/data-context-meets-ai/): Connect raw data to meaningful insights using your data platform&#039;s semantic layer - An integrated tool made to ease data translation.
- [How DataGalaxy Portfolio connects to Collibra to turn governance into measurable business impact](https://www.datagalaxy.com/en/blog/datagalaxy-portfolio-collibra/): Many large enterprises rely on Collibra as the backbone of their data governance ecosystem. It centralizes metadata, policies, workflows, and stewardship processes.

## Pages
- [The AI Value Layer](https://www.datagalaxy.com/en/the-ai-value-layer/): Most teams stop at documentation. DataGalaxy helps you go further: give AI the context it needs, prove your data can be trusted, and connect every initiative to business value that leadership can actually see.
- [Value Layer](https://www.datagalaxy.com/en/datagalaxy-platform-overview/value-layer/): Context and trust make your data usable. Value is where it pays off. DataGalaxy connects every data and AI initiative to the outcomes the business cares about, so you can prioritize what matters, track real impact, and show the return.
- [Trust Layer](https://www.datagalaxy.com/en/datagalaxy-platform-overview/trust-layer/): Trusted data means knowing the impact of every change. Trace one field downstream and see exactly what it touches, so nothing breaks by surprise.
- [Context Layer](https://www.datagalaxy.com/en/datagalaxy-platform-overview/context-layer/): Before AI can be trusted or deliver value, it has to understand your data: what it means, where it comes from, and how it should be used. DataGalaxy turns scattered data into shared, reusable context for every team and every AI initiative.
- [CDO Masterclass-duplicate-1](https://www.datagalaxy.com/en/community/cdo-masterclass-duplicate-1-2/): seasons since 2022
- [DataGalaxy Pricing](https://www.datagalaxy.com/en/datagalaxy-pricing/): Build trusted foundations for data and AI. Starter gives teams the essential context, documentation, and visibility needed to support confident usage across the organization.
- [404](https://www.datagalaxy.com/en/404/): Sorry, the page you’re trying to access does not exist!
- [Press](https://www.datagalaxy.com/en/press-2/): 2026 2025 2024 2023
- [Connectors](https://www.datagalaxy.com/en/connectors/)
- [Finance &amp; Banking](https://www.datagalaxy.com/en/finance-banking/)
- [Regulatory Compliance](https://www.datagalaxy.com/en/regulatory-compliance/)
- [Data Catalog](https://www.datagalaxy.com/en/data-catalog/)
- [On‑Demand Webinars](https://www.datagalaxy.com/en/on-demand-webinars/)
- [Partners](https://www.datagalaxy.com/en/partners/): and trusted by 200+ global brands
- [](https://www.datagalaxy.com/en/test-slug/): werwerwer
- [Page Template — Product: Integrations / Connectors](https://www.datagalaxy.com/en/page-template-product-integrations-connectors/)
- [Page Template — Industry: Finance &amp; Banking](https://www.datagalaxy.com/en/page-template-industry-finance-banking/)
- [Page Template — Solution: Regulatory Compliance](https://www.datagalaxy.com/en/page-template-solution-regulatory-compliance/)
- [Page Template — Product: Data Catalog](https://www.datagalaxy.com/en/page-template-product-data-catalog/)
- [Component Rebuilt](https://www.datagalaxy.com/en/component-rebuilt/): Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Duis placerat arcu quis nibh pellentesque, scelerisque tempus elit pulvinar. Cras rutrum enim at mi interdum, at consectetur nisi laoreet. Duis eget efficitur odio. Nam fringilla faucibus lorem, laoreet ornare neque scelerisque vitae. Fusce condimentum sapien nisi, quis commodo urna tempus at. Donec nec metus enim.
- [Contact us](https://www.datagalaxy.com/en/contact-us/): Contact us Talk to one of our experts today and see how DataGalaxy can turn your metadata into measurable value.
- [Resource center](https://www.datagalaxy.com/en/resource-center/): White paper
- [Data catalog RFI template](https://www.datagalaxy.com/en/lp/data-catalog-rfi-template/): This data catalog RFI template includes:
- [CDO Masterclass](https://www.datagalaxy.com/en/community/cdo-masterclass/): Ahmer NassimData Strategy &amp; Gov.Peoples Trust
- [Data Governance Kitchen](https://www.datagalaxy.com/en/community/data-governance-kitchen/): Helping organizations create &amp; manage their data governance strategies with delicious data.
- [Industries](https://www.datagalaxy.com/en/industries/): Discover the transformative potential of DataGalaxy’s Data Knowledge Catalog across a diverse range of industries
- [Solutions](https://www.datagalaxy.com/en/solutions/)
- [Community](https://www.datagalaxy.com/en/community/): Countries
- [DataGalaxy Learn Hub](https://www.datagalaxy.com/en/learn/): Your trusted reference for understanding the language, roles, and real-world applications of modern data and AI governance.
- [Customer stories](https://www.datagalaxy.com/en/learn/customers-stories/)
- [Common questions](https://www.datagalaxy.com/en/learn/common-questions/): Answering your most common questions about what a data catalog is, what it does, and how it fits into your data ecosystem.
- [Use cases](https://www.datagalaxy.com/en/learn/definitions-use-cases/): Explore real-world solutions to common data challenges — from siloed systems and low data trust to AI readiness and compliance gaps.
- [Data roles](https://www.datagalaxy.com/en/learn/definitions-data-roles/): Explore executive and managerial roles that define data strategy, prioritize investments, and align data initiatives with business goals.
- [AI Terms](https://www.datagalaxy.com/en/learn/definitions-ai-terms/): Explore the end-to-end journey of AI model development — from data preparation and training to monitoring and continuous improvement.
- [Data terms](https://www.datagalaxy.com/en/learn/definitions-data-terms/): Understand how data is classified, described, and connected to improve discoverability, lineage, and governance across your ecosystem.
- [New Homepage Summer 2026](https://www.datagalaxy.com/en/): Empower AI-ready data teams with value governance: align strategy, govern responsibly, and scale trusted Data &amp; AI products.
- [Extension is now active](https://www.datagalaxy.com/en/extension-install-welcome/)
- [DataGalaxy part of the Gartner’s Magic Quadrant™](https://www.datagalaxy.com/en/gartner-mq/): DataGalaxy is proud to announce its breakthrough to be among one of the&nbsp;first companies to be recognized in Gartner’s Magic Quadrant™ for Data &amp; Analytics Governance Platforms, a milestone that reflects our commitment to innovation and excellence. As a trusted global player, DataGalaxy continues to deliver on its promise: Transforming the greatest governance challenge, user adoption, into a competitive advantage.
- [AI Policy](https://www.datagalaxy.com/en/ai-policy/): This page complements our Privacy Policy and outlines DataGalaxy&#039;s use of Artificial Intelligence (&quot;AI&quot;) systems that visitors and customers may encounter on our websites and services.
- [DataGalaxy VS Microsoft Purview](https://www.datagalaxy.com/en/data-catalog-tools/datagalaxy-vs-microsoft-purview/): Explore the distinct methodologies of DataGalaxy and Microsoft Purview when it comes to active metadata management, and gain a clear overview of the business implications of each.
- [DataGalaxy VS Informatica](https://www.datagalaxy.com/en/data-catalog-tools/datagalaxy-vs-informatica/): Explore the distinct methodologies of DataGalaxy and Informatica when it comes to active metadata management, and gain a clear overview of the business implications of each.
- [DataGalaxy VS Atlan](https://www.datagalaxy.com/en/data-catalog-tools/datagalaxy-vs-atlan/): Explore the distinct methodologies of DataGalaxy and Atlan when it comes to active metadata management, and gain a clear overview of the business implications of each.
- [DataGalaxy VS Collibra](https://www.datagalaxy.com/en/data-catalog-tools/datagalaxy-vs-collibra/): Explore the distinct methodologies of DataGalaxy and Collibra when it comes to metadata management, and gain a clear overview of the business implications of each.
- [Legal Notice](https://www.datagalaxy.com/en/legal-notice/): DataGalaxy, a simplified joint stock company with a capital of €420,061 , whose registered office is located at 129 rue Servient 69003 Lyon, France is registered under number 811 288 034 RCS Lyon.
- [Cookies Policy](https://www.datagalaxy.com/en/cookies-policy/): A cookie is a small text file placed and stored for a limited time on your computer, tablet, smartphone or any other device that allows you to browse the Site and/or the DataGalaxy platform (hereinafter “Cookies”).
- [About us](https://www.datagalaxy.com/en/about-us/): About us More Than Governance. A Catalyst for Value. Take a Trip Down Memory Lane Our co-founders, Seb and Lazhar, meet while working on a business intelligence project &#x2014; and experience firsthand the frustration of disconnected data.
2009 DataGalaxy 1.0 goes live. Our first client signs on &#x2014; powered by hard work and a lot of coffee &#x2615;
2017 Over 150% growth. The momentum is real.
2021 DataGalaxy acquires Yooi and expands its global reach &#x2014; evolving into the value governance platform.
2025 2015
DataGalaxy is born. The first office opens in Lyon, France. 2019
Selected for Facebook Startup Garage. A second office opens in Paris. DataGalaxy becomes the leading data catalog in France. 2023
DataGalaxy enters the U.S. market. Take a Trip Down Memory Lane Our co-founders, Seb and Lazhar, meet while working on a business intelligence project &#x2014; and experience firsthand the frustration of disconnected data.
DataGalaxy is born. The first office opens in Lyon, France.
DataGalaxy 1.0 goes live. Our first client signs on &#x2014; powered by hard work and a lot of coffee &#x2615;
Selected for Facebook Startup Garage. A second office opens in Paris. DataGalaxy becomes the leading data catalog in France.
Over 150% growth. The momentum is real.
DataGalaxy enters the U.S. market.
DataGalaxy acquires Yooi and expands its global reach &#x2014; evolving into the value governance platform. 2025
2023
2021
2019
2017
2015
2009
- [Press](https://www.datagalaxy.com/en/press/): 2026 2025 2024 2023
- [Partners](https://www.datagalaxy.com/en/partners-old-2/): DataGalaxy partners with the world’s leading technology and service providers to provide best-in-class portfolio management and governance for the modern data stack.
- [Events](https://www.datagalaxy.com/en/events/)
- [Platform overview](https://www.datagalaxy.com/en/datagalaxy-platform-overview/): DataGalaxy Catalog and Portfolio work together to close the gap between data knowledge and business impact.
- [Product comparison](https://www.datagalaxy.com/en/data-catalog-tools/datagalaxy-vs-alation/): Explore the distinct methodologies of DataGalaxy and Alation when it comes to metadata management, and gain a clear overview of the business implications of each.
- [Customer stories](https://www.datagalaxy.com/en/customer-stories/)
- [Data catalog comparison guide](https://www.datagalaxy.com/en/data-catalog-tools/): We’ve compiled a comprehensive review of leading data catalog providers to shed some light on what makes for a well-rounded metadata management tool. Deepen your knowledge and be confident you’re choosing the right tool for your teams based on your specific requirements.
- [Blog](https://www.datagalaxy.com/en/blog/): DataGalaxy Blog
- [Book a demo](https://www.datagalaxy.com/en/get-demo/): Book a demo
- [DataGalaxy | Data &amp; AI Product Governance Platform](https://www.datagalaxy.com/en/homepage/): DataGalaxy is the data &amp; AI product governance platform that connects strategy, product management, discovery, and business impact in one seamless experience.
- [Terms of use](https://www.datagalaxy.com/en/terms-of-use/): DataGalaxy is a simplified joint stock company with a capital of 309,075€, registered in the Lyon Trade and Companies Register under the number 811 288 034, whose head office is located at 129 rue Servient 69003 Lyon, France, represented by its legal representative (hereafter “DataGalaxy“). It publishes, develops and markets a software solution for collaborative governance of enterprise information system data in SaaS (Software as a Service) mode (hereafter the “Solution“), on which it holds all intellectual property rights.
- [Privacy Policy](https://www.datagalaxy.com/en/privacy-policy/): DataGalaxy (hereinafter referred to as “DataGalaxy,” “we,” or “us”) is committed to the protection of personal data (hereinafter referred to as “PII”) and privacy of the users of its web site, software solution, and services (hereinafter referred to as “Services.”)

## Agora
- [Samples WIP](https://www.datagalaxy.com/en/agora/v5/samples-wip/): Extra Small
- [V5](https://www.datagalaxy.com/en/agora/v5/)
- [V4](https://www.datagalaxy.com/en/agora/v4/)
- [Test Xavier](https://www.datagalaxy.com/en/agora/v4/test-xavier/)

## Events
- [Big Data is Dead, Long Live Smart Data!](https://www.datagalaxy.com/en/events/big-data-is-dead-long-live-smart-data/): Fill in the form to unlock the full session — instant access, no live sign-up.
- [How Leaders Decide Which AI Use Cases Matter?](https://www.datagalaxy.com/en/events/how-leaders-decide-which-ai-use-cases-matter/): Fill in the form to unlock the full session — instant access, no live sign-up.
- [CDO Masterclass NYC](https://www.datagalaxy.com/en/events/cdo-masterclass-nyc/): Join us to level up your leadership skills in one focused session!
- [The Missing Piece: Why AI Needs Your Business Context (And How to Add It)](https://www.datagalaxy.com/en/events/the-missing-piece-why-ai-needs-your-business-context-and-how-to-add-it/): Fill in the form to unlock the full session.
- [The AI Accountability Model: From explainability to ownership at decision time](https://www.datagalaxy.com/en/events/the-ai-accountability-model-from-explainability-to-ownership-at-decision-time/): Fill in the form to unlock the full session.
- [Inside Gartner&#8217;s CDAO Boardroom](https://www.datagalaxy.com/en/events/inside-gartners-cdao-boardroom/): AI investment is accelerating across every industry, yet many organizations are struggling to translate this spend into measurable business impact. As AI portfolios expand, CDAOs and CIOs face growing challenges around cost transparency, value tracking, and maintaining control over increasingly complex, vendor-driven initiatives.
- [Making AI Value Visible Without More Slides](https://www.datagalaxy.com/en/events/making-ai-value-visible-without-more-slides/): Fill in the form to unlock the full session.
- [Data Catalog: From Implementation to Global Adoption](https://www.datagalaxy.com/en/events/data-catalog-from-implementation-to-global-adoption/): Replay our insightful webinar, &quot;Data Catalog: From Implementation to Global Adoption,&quot; where industry experts will discuss best practices for effectively deploying a data catalog. Discover strategies for overcoming challenges, ensuring users adoption, fostering a culture of data-driven decision-making, and maximizing the impact of your data catalog and data governance projects.
- [Data Innovation Summit 2026](https://www.datagalaxy.com/en/events/data-innovation-summit-2026/): DataGalaxy is thrilled to be part of the Data Innovation Summit! As the world of data moves faster than ever, we’re focused on helping you bridge the AI Velocity Gap and turn complex data landscapes into measurable business outcomes.
- [Gartner EMEA Data &#038; Analytics Summit London 2026](https://www.datagalaxy.com/en/events/gartner-data-analytics-summit-london-2026/): 5:00 CET&nbsp;l Day 1
- [DataGalaxy at Gartner US Data &amp; Analytics Summit Orlando 2026](https://www.datagalaxy.com/en/events/gartner-data-analytics-summit-us-2026/): 2:00 PM&nbsp;l Room Sun A
- [Outputs vs Outcomes in AI; How Leaders Communicate AI Impact](https://www.datagalaxy.com/en/events/outputs-vs-outcomes-in-ai-how-leaders-communicate-ai-impact/): In this webinar, we will break down outputs versus outcomes, unpack the most common reasons AI impact gets misreported, and introduce a practical “impact chain” framework to link each AI use case to operational outputs, business outcomes, and financial impact. We will also show how to move from value claims to value proof, so AI investments can stand up to scrutiny and earn continued support.
- [Turning governance into adoption with DataGalaxy AI](https://www.datagalaxy.com/en/events/governance-adoption-datagalaxy-ai/): Join us for a live session to see how DataGalaxy AI brings intelligence to cataloging and governance in your daily work.
- [DataGalaxy x Big Data &amp; AI Paris 2025](https://www.datagalaxy.com/en/events/big-data-ai-paris/): For the past 13 years, Big Data &amp; AI Paris has been accelerating the transformation and industrialization of AI and data for French companies, offering them rich, cutting-edge programs and bringing together the crème de la crème of the data world.
- [Big Data LDN 2025](https://www.datagalaxy.com/en/events/big-data-ldn-2025/): Big Data LDN (London) is a premier data industry event that brings together the brightest data minds from around the world, and we are so excited to be a part of it.
- [Snowflake Summit 2025](https://www.datagalaxy.com/en/events/snowflake-summit-2025/): Join DataGalaxy at one of the data industry&#039;s top events, Gartner Data &amp; Analytics Summit in Orlando, FL, March 3rd - 5th
- [Data Innovation Summit 2025](https://www.datagalaxy.com/en/events/data-innovation-summit/): Join DataGalaxy at the Nordic&#039;s biggest data &amp; analytics event of the year: Learn about creating a value-driven, product-centric analytics approach and more!
- [Gartner Data &amp; Analytics US Summit Orlando 2025 Wrap Up](https://www.datagalaxy.com/en/events/gartner-da-summit-orlando-recap/): Discover everything you might have missed at the Gartner D&amp;A Summit in Orlando.
- [Gartner Data &amp; Analytics Summit London 2025](https://www.datagalaxy.com/en/events/gartner-data-analytics-summit-london-2025/): Join DataGalaxy at one of the data industry&#039;s top events, Gartner Data &amp; Analytics Summit in London on May 13th and 14th
- [Building AI readiness &#038; data trust with governance &#038; observability](https://www.datagalaxy.com/en/events/building-ai-readiness-data-trust-with-governance-observability/): CDOs and AI leaders face big risks when business opportunities get derailed by a lack of data trust.
- [Why data freaks out your team (and how AI can fix it)](https://www.datagalaxy.com/en/events/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. Join us for a 45-minute session where we’ll uncover how AI-powered solutions like DataGalaxy’s Data Knowledge Catalog transform data management by adapting to user needs
- [CDO Masterclass Season 14](https://www.datagalaxy.com/en/events/cdo-masterclass-season-14/): DataGalaxy’s CDO Masterclass is an immersive, interactive three-day online course for data professionals to learn from the world’s top brands leading data-driven transformation like Airbus and LVMH.
- [Big Data &#038; AI Paris 2024](https://www.datagalaxy.com/en/events/big-data-ai-paris-2024/): Big Data &amp; AI Paris is one of the premier events for data enthusiasts and AI professionals worldwide, and represents the best of French data innovations.
- [Big Data LDN 2024](https://www.datagalaxy.com/en/events/big-data-ldn-2024/): Big Data LDN (London) is a premier data industry event that brings together the brightest data minds from around the world, and we are so excited to be a part of it.

## Industries
- [Higher Education](https://www.datagalaxy.com/en/industry/higher-education/)
- [Healthcare](https://www.datagalaxy.com/en/industry/healthcare/)
- [Public sector](https://www.datagalaxy.com/en/industry/public-sector/)
- [Insurance](https://www.datagalaxy.com/en/industry/insurance/)
- [Retail](https://www.datagalaxy.com/en/industry/retail/)
- [Finance &amp; banking](https://www.datagalaxy.com/en/industry/finance-banking/)

## Partners
- [Wenvision](https://www.datagalaxy.com/en/partners/wenvision/)
- [DecideOm ](https://www.datagalaxy.com/en/partners/decideom/): For more than 15 years, DecideOm has been advising its customers on defining their Data strategy
- [Sopra Steria](https://www.datagalaxy.com/en/partners/sopra-steria/): Sopra Steria, one of Europe&#039;s leading players in the tech sector, is recognised for its consulting, digital services and software publishing activities.
- [Micropole](https://www.datagalaxy.com/en/partners/micropole/): Micropole is accelerating the transformation of companies through Data.
- [Collaboration Betters The World](https://www.datagalaxy.com/en/partners/collaboration-betters-the-world/): CBTW is a global tech company with a presence in 21 countries and a team of more than 3,000 professionals. We believe in the power of collaboration and create and deliver innovative tech and business solutions to better the world. Through our Collaboration Operating Model, we empower our clients and coworkers to unlock their full business and tech potential.
- [Artefact](https://www.datagalaxy.com/en/partners/artefact/): Artefact is an international data services company specialising in data transformation and data &amp; digital marketing consultancy
- [DataBuilders](https://www.datagalaxy.com/en/partners/databuilders/): DataBuilders provide solutions to automate data flows, visualise information and build data insights to drive business value.
- [Data-major](https://www.datagalaxy.com/en/partners/data-major/): The Data-Major team is first and foremost a team of passionate, committed and demanding data specialists working together for the success of our clients and the success of their projects.To learn more, visit their website:&nbsp;www.data-major.com
- [Aubay](https://www.datagalaxy.com/en/partners/aubay/): Nowadays, a majority of companies already use their data through data architectures and BI tools. However, to make the best possible decisions, it is essential to fully control the data that is at the origin of these decisions. This is why data governance is one of the major challenges facing companies today. A challenge that is even more important with the Data Democratization and the growing ambition to make data accessible to the largest number of people within organizations. 
- [ACSSI](https://www.datagalaxy.com/en/partners/acssi/): ACSSI helps more than 400 customers to capitalize and enhance their data with a “Data-Driven” approach. Based in Lille, Paris, Nantes and Bordeaux, our expertise focuses on Data Management (integration, catalog, quality…), Cloud (database, providers…) and Data Intelligence/Analytics (dataviz, reporting, ia…).
- [ASI](https://www.datagalaxy.com/en/partners/asi/): ASI puts data at the heart of companies’ digital transformations. Digital services are the basis of daily business activity and ASI uses data to bring a positive impact to all stakeholders: organisations, employees and users.
- [Solution BI](https://www.datagalaxy.com/en/partners/solution-bi/): Our consultants cover all facets of business intelligence, big data and data science: business needs, software solution selection, architecture &amp; infrastructure deployment, turnkey outsourcing. More than a mission, our passion is to help companies get the most out of their data, to become more efficient and more effective in their decisions.To learn more, visit their website:&nbsp;Solution BI
- [Data Consulting Group](https://www.datagalaxy.com/en/partners/data-consulting-group/): DCG is an Ivorian ESN, consisting of a network of national and international experts in governance and security of information systems, data governance, digital transformation and innovation based on disruptive technologies.As a service and consulting firm, we respond to your growth, innovation and value creation challenges. We advise you and we accompany you in the realization of your projects.
- [Saegus](https://www.datagalaxy.com/en/partners/saegus/): Our mission is to help people discover, try and adopt the best of digital uses, without a slide. For real.
- [Eulidia](https://www.datagalaxy.com/en/partners/eulidia/): Created in 2008, EULIDIA has positioned itself as your Data Innovation Partneroffering a large range of services in AI, Business Intelligence and Data Management.
- [Apgar](https://www.datagalaxy.com/en/partners/apgar/): Supporting its customers in their journey to build a foundation for trusted data.
- [Equancy](https://www.datagalaxy.com/en/partners/equancy/): We are convinced that digital and data are two essential levers to guarantee the success of a sustainable growth strategy.
- [Next Decision](https://www.datagalaxy.com/en/partners/next-decision/): As an expert in Business Intelligence &amp; Big Data, Master Data Management (MDM), Organization, Human Resources Management, Data Governance, Budgeting and Business Apps, Next Decision has been successfully supporting small, medium and large companies in the implementation of Big Data platforms and decision support tools since 2010.
- [Jems](https://www.datagalaxy.com/en/partners/jems/): The group has developed a leadership position in creating disruptive use cases. We have materialized this data maturity model through 3 offerings: Data to set up the data lake, the associated governance, the cloud computing solutions and the artificial intelligence algorithms; Digital to create the web platforms and mobile apps and Design to facilitate innovation. The DevOps &amp; agile methodology part underlies all the offerings.
- [Synotis](https://www.datagalaxy.com/en/partners/synotis/): Synotis specializes in the implementation of data projects for organizations engaged in digital transformation. They help their clients adopt a “data-driven” mindset by revealing the value of the company’s data. By unlocking the value of data, Synotis enables companies to make the best decisions in an increasingly digitalized and competitive environment.
- [Orange Business](https://www.datagalaxy.com/en/partners/orange-business/): Leading international management consulting and system integrator, Business &amp; Decision has expertise in master data management, data quality management, Business Glossary, and enterprise metadata management solutions. Together, these aspects support and structure corporate data governance policies.
- [KPC](https://www.datagalaxy.com/en/partners/kpc/): KPC is a consulting and expertise company specialized in Data Intelligence and business performance management that supports business departments in their digital transformation projects.
- [Talan](https://www.datagalaxy.com/en/partners/talan/): For more than 15 years, Talan has been advising companies and government agencies and helping them implement their transformation projects in France and abroad.
- [Thélio](https://www.datagalaxy.com/en/partners/thelio/): Data is anything but a technical subject. It has become a performance lever and is indeed a business issue!

## Products
- [Microsoft Fabric](https://www.datagalaxy.com/en/product/integrations-connectors/microsoft-fabric/)
- [OneLake](https://www.datagalaxy.com/en/product/integrations-connectors/onelake/)
- [AI Data Steward](https://www.datagalaxy.com/en/product/ai-data-steward/)
- [AI Maturity Assessment](https://www.datagalaxy.com/en/product/ai-maturity-assessment/): root.innerHTML = `
- [ROI calculator](https://www.datagalaxy.com/en/product/roi-calculator/): Fill in the form to unlock the full calculator and estimate the annual value of better data visibility and governance.
- [Jira](https://www.datagalaxy.com/en/product/integrations-connectors/jira/)
- [ServiceNow](https://www.datagalaxy.com/en/product/integrations-connectors/servicenow/)
- [Collibra](https://www.datagalaxy.com/en/product/integrations-connectors/collibra/)
- [Alation](https://www.datagalaxy.com/en/product/integrations-connectors/alation/)
- [Explore DataGalaxy Portfolio](https://www.datagalaxy.com/en/product/portfolio/): DataGalaxy Portfolio provides the operating framework to manage every data and AI use case, from strategy and prioritization to delivery and value realization, ensuring alignment, visibility, and measurable outcomes at every stage.
- [Explore DataGalaxy Catalog](https://www.datagalaxy.com/en/product/catalog/): Activate context, trust, collaboration and engagement across your data ecosystem A single source of truth to solve your critical data issues Deep dive into DataGalaxy Catalog capabilities
- [AI demand management](https://www.datagalaxy.com/en/product/ai-demand-management/)
- [AI value tracking](https://www.datagalaxy.com/en/product/ai-value-tracking/)
- [Data &#038; AI product management](https://www.datagalaxy.com/en/product/data-ai-product-management-2/)
- [AI use cases portfolio](https://www.datagalaxy.com/en/product/ai-use-cases-portfolio/)
- [MCP Server](https://www.datagalaxy.com/en/product/mcp-server/)
- [Campaigns](https://www.datagalaxy.com/en/product/campaigns/)
- [Visual Knowledge Studio](https://www.datagalaxy.com/en/product/visual-knowledge-studio/)
- [Business Glossary](https://www.datagalaxy.com/en/product/business-glossary/)
- [Product tour](https://www.datagalaxy.com/en/product/product-tour/): Our promises Build confidence in every decision Maximize the value of your data Scale data adoption across the organization
- [Browser extension](https://www.datagalaxy.com/en/product/browser-extension/)
- [AI copilot](https://www.datagalaxy.com/en/product/ai-copilot/)
- [Automated data lineage](https://www.datagalaxy.com/en/product/data-lineage/)
- [Data Quality Monitoring](https://www.datagalaxy.com/en/product/data-quality-monitoring/)
- [Data catalog](https://www.datagalaxy.com/en/product/data-catalog/)
- [Data &amp; AI product management](https://www.datagalaxy.com/en/product/data-ai-product-management/)
- [Data governance](https://www.datagalaxy.com/en/product/data-ai-governance/)
- [Marketplace](https://www.datagalaxy.com/en/product/marketplace/)
- [Portfolio &amp; value management](https://www.datagalaxy.com/en/product/portfolio-value-management/): Prioritize initiatives, align resources, monitor delivery, and measure outcomes all in one place.
- [Data Lineage](https://www.datagalaxy.com/en/product/data-lineage2/)
- [Snowflake](https://www.datagalaxy.com/en/product/integrations-connectors/snowflake/): DataGalaxy is now a Snowflake AI Data Cloud Products Partner
- [Looker](https://www.datagalaxy.com/en/product/integrations-connectors/looker/): Trusted by data leaders worldwide - Rated 4.8/5 on G2
- [Power BI](https://www.datagalaxy.com/en/product/integrations-connectors/power-bi/): Trusted by data leaders worldwide - Rated 4.8/5 on G2
- [Databricks](https://www.datagalaxy.com/en/product/integrations-connectors/databricks/): DataGalaxy is a technology partner validated by Databricks
- [Data Knowledge Catalog connectors](https://www.datagalaxy.com/en/product/integrations-connectors/)

## Questions
- [Is a data catalog enough to achieve BCBS 239 compliance?](https://www.datagalaxy.com/en/question/is-a-data-catalog-enough-to-achieve-bcbs-239-compliance/)
- [How can a business glossary support BCBS 239?](https://www.datagalaxy.com/en/question/how-can-a-business-glossary-support-bcbs-239/)
- [How does data lineage help meet BCBS 239 requirements?](https://www.datagalaxy.com/en/question/how-does-data-lineage-help-meet-bcbs-239-requirements/)
- [How does DataGalaxy support BCBS 239 compliance?](https://www.datagalaxy.com/en/question/how-does-datagalaxy-support-bcbs-239-compliance/)
- [What is BCBS 239?](https://www.datagalaxy.com/en/question/what-is-bcbs-239/)
- [Does using DataGalaxy guarantee HIPAA compliance?](https://www.datagalaxy.com/en/question/does-using-datagalaxy-guarantee-hipaa-compliance/)
- [How does data lineage support HIPAA risk analysis and audits?](https://www.datagalaxy.com/en/question/how-does-data-lineage-support-hipaa-risk-analysis-and-audits/)
- [Can DataGalaxy help organizations identify ePHI?](https://www.datagalaxy.com/en/question/can-datagalaxy-help-organizations-identify-ephi/)
- [How does DataGalaxy support HIPAA-aligned data governance?](https://www.datagalaxy.com/en/question/how-does-datagalaxy-support-hipaa-aligned-data-governance/)
- [What role does data governance play in HIPAA compliance?](https://www.datagalaxy.com/en/question/what-role-does-data-governance-play-in-hipaa-compliance/)
- [Does DataGalaxy replace Solvency II calculation or reporting software?](https://www.datagalaxy.com/en/question/does-datagalaxy-replace-solvency-ii-calculation-or-reporting-software/)
- [Can DataGalaxy help manage Solvency II data quality?](https://www.datagalaxy.com/en/question/can-datagalaxy-help-manage-solvency-ii-data-quality/)
- [How can data lineage improve Solvency II reporting?](https://www.datagalaxy.com/en/question/how-can-data-lineage-improve-solvency-ii-reporting/)
- [How does DataGalaxy support Solvency II compliance?](https://www.datagalaxy.com/en/question/how-does-datagalaxy-support-solvency-ii-compliance/)
- [Why is data governance important for Solvency II compliance?](https://www.datagalaxy.com/en/question/why-is-data-governance-important-for-solvency-ii-compliance/)
- [Does DataGalaxy make an organization GDPR compliant?](https://www.datagalaxy.com/en/question/does-datagalaxy-make-an-organization-gdpr-compliant/)
- [Can DataGalaxy help identify and classify personal data?](https://www.datagalaxy.com/en/question/can-datagalaxy-help-identify-and-classify-personal-data/)
- [How does data lineage help with GDPR compliance?](https://www.datagalaxy.com/en/question/how-does-data-lineage-help-with-gdpr-compliance/)
- [How does DataGalaxy support GDPR compliance?](https://www.datagalaxy.com/en/question/how-does-datagalaxy-support-gdpr-compliance/)
- [What is GDPR data governance?](https://www.datagalaxy.com/en/question/what-is-gdpr-data-governance/)
- [À quelle fréquence faut-il mettre à jour une roadmap ?](https://www.datagalaxy.com/en/question/a-quelle-frequence-faut-il-mettre-a-jour-une-roadmap/)
- [What is value lineage and how does DataGalaxy support it?](https://www.datagalaxy.com/en/question/how-does-datagalaxy-manage-value-across-the-data-and-ai-lifecycle/)
- [Why is value management important for Data and AI initiatives?](https://www.datagalaxy.com/en/question/what-is-value-management-for-data-and-ai/)
- [How is a data product different from a dataset in the catalog?](https://www.datagalaxy.com/en/question/how-is-a-data-product-different-from-a-dataset-in-the-catalog/)
- [Can DataGalaxy track adoption and performance of data and AI products?](https://www.datagalaxy.com/en/question/can-datagalaxy-track-adoption-and-performance-of-data-and-ai-products/)
- [How does DataGalaxy support ownership and federated roles?](https://www.datagalaxy.com/en/question/how-does-datagalaxy-support-ownership-and-federated-roles/)
- [What is included in a data product canvas?](https://www.datagalaxy.com/en/question/what-is-included-in-a-data-product-canvas/)
- [How does DataGalaxy help teams manage the full lifecycle of a data product?](https://www.datagalaxy.com/en/question/how-does-datagalaxy-help-teams-manage-the-full-lifecycle-of-a-data-product/)
- [What is Data and AI product management in DataGalaxy?](https://www.datagalaxy.com/en/question/what-is-data-and-ai-product-management-in-datagalaxy/)
- [Who benefits from the Use Cases Portfolio?](https://www.datagalaxy.com/en/question/who-benefits-from-the-use-cases-portfolio/)
- [Does the portfolio support reusability of Data and AI work?](https://www.datagalaxy.com/en/question/does-the-portfolio-support-reusability-of-data-and-ai-work/)
- [Can I connect use cases to data assets and metadata?](https://www.datagalaxy.com/en/question/can-i-connect-use-cases-to-data-assets-and-metadata/)
- [How do teams track progress and value realization?](https://www.datagalaxy.com/en/question/how-do-teams-track-progress-and-value-realization/)
- [Can the portfolio help prioritize Data and AI initiatives?](https://www.datagalaxy.com/en/question/can-the-portfolio-help-prioritize-data-and-ai-initiatives/)
- [How does the portfolio help build a strategic view of Data and AI initiatives?](https://www.datagalaxy.com/en/question/how-does-the-portfolio-help-build-a-strategic-view-of-data-and-ai-initiatives/)
- [What is the DataGalaxy Use Cases Portfolio?](https://www.datagalaxy.com/en/question/what-is-the-datagalaxy-use-cases-portfolio/)
- [Does Demand Management help prioritize Data and AI initiatives?](https://www.datagalaxy.com/en/question/does-demand-management-help-prioritize-data-and-ai-initiatives-2/)
- [Does Demand Management help prioritize Data and AI initiatives?](https://www.datagalaxy.com/en/question/does-demand-management-help-prioritize-data-and-ai-initiatives/)
- [How do teams collaborate during the qualification process?](https://www.datagalaxy.com/en/question/how-do-teams-collaborate-during-the-qualification-process/)
- [Can Demand Management improve the quality of submitted use cases?](https://www.datagalaxy.com/en/question/can-demand-management-improve-the-quality-of-submitted-use-cases/)
- [How does Demand Management help centralize data and AI requests?](https://www.datagalaxy.com/en/question/how-does-demand-management-help-centralize-data-and-ai-requests/)
- [What is Demand Management in DataGalaxy Portfolio?](https://www.datagalaxy.com/en/question/what-is-demand-management-in-datagalaxy-portfolio/)
- [Do I need technical skills to create diagrams?](https://www.datagalaxy.com/en/question/do-i-need-technical-skills-to-create-diagrams/)
- [How does the Studio support collaboration?](https://www.datagalaxy.com/en/question/how-does-the-studio-support-collaboration/)
- [Can I create business diagrams with the Studio?](https://www.datagalaxy.com/en/question/can-i-create-business-diagrams-with-the-studio/)
- [Who uses the Visual Knowledge Studio?](https://www.datagalaxy.com/en/question/who-uses-the-visual-knowledge-studio/)
- [How does the Visual Knowledge Studio help simplify complex data?](https://www.datagalaxy.com/en/question/how-does-the-visual-knowledge-studio-help-simplify-complex-data/)
- [What is the Visual Knowledge Studio in DataGalaxy?](https://www.datagalaxy.com/en/question/what-is-the-visual-knowledge-studio-in-datagalaxy/)
- [Can DataGalaxy replace manual spreadsheet based glossaries?](https://www.datagalaxy.com/en/question/can-datagalaxy-replace-manual-spreadsheet-based-glossaries/)
- [What is glossary certification?](https://www.datagalaxy.com/en/question/what-is-glossary-certification/)
- [How do teams collaborate on business terms?](https://www.datagalaxy.com/en/question/how-do-teams-collaborate-on-business-terms/)
- [Does DataGalaxy support glossary automation?](https://www.datagalaxy.com/en/question/does-datagalaxy-support-glossary-automation/)
- [Can I link glossary terms to datasets, KPIs, and reports?](https://www.datagalaxy.com/en/question/can-i-link-glossary-terms-to-datasets-kpis-and-reports/)
- [Why is a business glossary important for data and AI teams?](https://www.datagalaxy.com/en/question/why-is-a-business-glossary-important-for-data-and-ai-teams/)
- [What is a business glossary in DataGalaxy?](https://www.datagalaxy.com/en/question/what-is-a-business-glossary-in-datagalaxy/)
- [Can Campaigns help certify critical business terms or assets?](https://www.datagalaxy.com/en/question/can-campaigns-help-certify-critical-business-terms-or-assets/)
- [How do Campaigns support data quality and compliance?](https://www.datagalaxy.com/en/question/how-do-campaigns-support-data-quality-and-compliance/)
- [Can I customize governance workflows with Campaigns?](https://www.datagalaxy.com/en/question/can-i-customize-governance-workflows-with-campaigns/)
- [Who should use DataGalaxy Campaigns?](https://www.datagalaxy.com/en/question/who-should-use-datagalaxy-campaigns/)
- [How do Campaigns improve data governance workflows?](https://www.datagalaxy.com/en/question/how-do-campaigns-improve-data-governance-workflows/)
- [How do we start?](https://www.datagalaxy.com/en/question/how-do-we-start/)
- [Is MCP Server hard to integrate?](https://www.datagalaxy.com/en/question/is-mcp-server-hard-to-integrate/)
- [How does MCP Server stay secure?](https://www.datagalaxy.com/en/question/how-does-mcp-server-stay-secure/)
- [Does MCP Server expose data?](https://www.datagalaxy.com/en/question/does-mcp-server-expose-data/)
- [Who can use the MCP Server?](https://www.datagalaxy.com/en/question/who-can-use-the-mcp-server/)
- [What is the MCP Server?](https://www.datagalaxy.com/en/question/what-is-the-mcp-server/)
- [What are DataGalaxy Campaigns?](https://www.datagalaxy.com/en/question/what-are-datagalaxy-campaigns/)
- [Is DataGalaxy a full alternative to Microsoft Purview?](https://www.datagalaxy.com/en/question/is-datagalaxy-a-full-alternative-to-microsoft-purview/)
- [Can I migrate from Microsoft Purview to DataGalaxy?](https://www.datagalaxy.com/en/question/can-i-migrate-from-microsoft-purview-to-datagalaxy/)
- [Does DataGalaxy integrate with the same tools as Microsoft Purview?](https://www.datagalaxy.com/en/question/does-datagalaxy-integrate-with-the-same-tools-as-microsoft-purview/)
- [What makes DataGalaxy easier to adopt than Microsoft Purview?](https://www.datagalaxy.com/en/question/what-makes-datagalaxy-easier-to-adopt-than-microsoft-purview/)
- [What makes DataGalaxy easier to adopt than Informatica?](https://www.datagalaxy.com/en/question/what-makes-datagalaxy-easier-to-adopt-than-informatica/)
- [Does DataGalaxy integrate with the same tools as Informatica?](https://www.datagalaxy.com/en/question/does-datagalaxy-integrate-with-the-same-tools-as-informatica/)
- [Can I migrate from Informatica to DataGalaxy?](https://www.datagalaxy.com/en/question/can-i-migrate-from-informatica-to-datagalaxy/)
- [Is DataGalaxy a full alternative to Informatica?](https://www.datagalaxy.com/en/question/is-datagalaxy-a-full-alternative-to-informatica/)
- [Is DataGalaxy a full alternative to Atlan?](https://www.datagalaxy.com/en/question/is-datagalaxy-a-full-alternative-to-atlan/)
- [Can I migrate from Atlan to DataGalaxy?](https://www.datagalaxy.com/en/question/can-i-migrate-from-atlan-to-datagalaxy/)
- [What makes DataGalaxy easier to adopt than Atlan?](https://www.datagalaxy.com/en/question/what-makes-datagalaxy-easier-to-adopt-than-atlan/)
- [Does DataGalaxy integrate with the same tools as Atlan?](https://www.datagalaxy.com/en/question/does-datagalaxy-integrate-with-the-same-tools-as-atlan/)
- [What makes DataGalaxy easier to adopt than Collibra?](https://www.datagalaxy.com/en/question/what-makes-datagalaxy-easier-to-adopt-than-collibra/)
- [Does DataGalaxy integrate with the same tools as Collibra?](https://www.datagalaxy.com/en/question/does-datagalaxy-integrate-with-the-same-tools-as-collibra/)
- [Can I migrate from Collibra to DataGalaxy?](https://www.datagalaxy.com/en/question/can-i-migrate-from-collibra-to-datagalaxy/)
- [Is DataGalaxy a full alternative to Collibra?](https://www.datagalaxy.com/en/question/is-datagalaxy-a-full-alternative-to-collibra/)
- [What makes DataGalaxy easier to adopt than Alation?](https://www.datagalaxy.com/en/question/what-makes-datagalaxy-easier-to-adopt-than-alation/)
- [Does DataGalaxy integrate with the same tools as Alation?](https://www.datagalaxy.com/en/question/does-datagalaxy-integrate-with-the-same-tools-as-alation/)
- [Can I migrate from Alation to DataGalaxy?](https://www.datagalaxy.com/en/question/can-i-migrate-from-alation-to-datagalaxy/)
- [How does DataGalaxy differ in terms of user experience?](https://www.datagalaxy.com/en/question/how-does-datagalaxy-differ-in-terms-of-user-experience/)
- [Is DataGalaxy a full alternative to Alation?](https://www.datagalaxy.com/en/question/is-datagalaxy-a-full-alternative-to-alation/)
- [How does the marketplace support governance?](https://www.datagalaxy.com/en/question/how-does-the-marketplace-support-governance/)
- [Who can publish and manage data products in the marketplace?](https://www.datagalaxy.com/en/question/who-can-publish-and-manage-data-products-in-the-marketplace/)
- [What is a marketplace in this context?](https://www.datagalaxy.com/en/question/what-is-a-marketplace-in-this-context/)
- [Can users request access to data directly from the marketplace?](https://www.datagalaxy.com/en/question/can-users-request-access-to-data-directly-from-the-marketplace/)
- [What is a data product in this context?](https://www.datagalaxy.com/en/question/what-is-a-data-product-in-this-context/)
- [How does DataGalaxy help manage data products?](https://www.datagalaxy.com/en/question/how-does-datagalaxy-help-manage-data-products/)
- [Can I connect data products to business domains or KPIs?](https://www.datagalaxy.com/en/question/can-i-connect-data-products-to-business-domains-or-kpis/)
- [What is a data contract and why does it matter?](https://www.datagalaxy.com/en/question/what-is-a-data-contract-and-why-does-it-matter/)
- [Who is this platform designed for: compliance teams or technical teams?](https://www.datagalaxy.com/en/question/who-is-this-platform-designed-for-compliance-teams-or-technical-teams/)
- [Does this help with regulatory frameworks like the EU AI Act?](https://www.datagalaxy.com/en/question/does-this-help-with-regulatory-frameworks-like-the-eu-ai-act/)
- [What does it mean to have AI-ready data?](https://www.datagalaxy.com/en/question/what-does-it-mean-to-have-ai-ready-data/)
- [What tools and platforms does the catalog integrate with?](https://www.datagalaxy.com/en/question/what-tools-and-platforms-does-the-catalog-integrate-with/)

## Reviews

## Solutions
- [BCBS 239 solution](https://www.datagalaxy.com/en/solution/bcbs-239/)
- [Solvency 2](https://www.datagalaxy.com/en/solution/solvency-2/)
- [HIPAA](https://www.datagalaxy.com/en/solution/hipaa/)
- [GDPR](https://www.datagalaxy.com/en/solution/gdpr/)
- [CPRA](https://www.datagalaxy.com/en/solution/cpra/)
- [FISMA](https://www.datagalaxy.com/en/solution/fisma/)
- [Cloud migration](https://www.datagalaxy.com/en/solution/cloud-migration/): Cloud migration projects often overrun because organizations underestimate the complexity of their data. Without proper preparation, they move everything by default, break critical processes, and expose themselves to compliance risks. What looks like a shortcut quickly turns into delays, hidden costs, and loss of trust.
- [Data discoverability](https://www.datagalaxy.com/en/solution/data-discoverability/)
- [AI ready data](https://www.datagalaxy.com/en/solution/ai-ready-data/)
- [Regulatory compliance](https://www.datagalaxy.com/en/solution/regulatory-compliance/)

## Speakers
- [Etienne Scholly](https://www.datagalaxy.com/en/speaker/etienne-scholly/)
- [Camille Farineau](https://www.datagalaxy.com/en/speaker/camille-farineau/)
- [Edosa Odaro](https://www.datagalaxy.com/en/speaker/edosa-odaro/)
- [Zamir Abdul](https://www.datagalaxy.com/en/speaker/zamir-abdul/)
- [Andy Petrella](https://www.datagalaxy.com/en/speaker/andy-petrella/)
- [Paloma Rubio Klerian](https://www.datagalaxy.com/en/speaker/paloma-rubio-klerian/)
- [Benjamin De Reus](https://www.datagalaxy.com/en/speaker/benjamin-de-reus/)
- [Alexey Belichenko](https://www.datagalaxy.com/en/speaker/alexey-belichenko/)
- [Emmanuel Dubois](https://www.datagalaxy.com/en/speaker/emmanuel-dubois/)
- [Robert Seiner](https://www.datagalaxy.com/en/speaker/robert-seiner/)
- [Ashwin  Kamath](https://www.datagalaxy.com/en/speaker/ashwin-kamath/)
- [Akhilesh Kale](https://www.datagalaxy.com/en/speaker/akhilesh-kale/)
- [Nicolas Averseng](https://www.datagalaxy.com/en/speaker/nicolas-averseng/)
- [Laurent Dresse](https://www.datagalaxy.com/en/speaker/laurent-dresse/)
- [Kyle  Kirwan](https://www.datagalaxy.com/en/speaker/kyle-kirwan/)
- [Kseniia Ilchenko](https://www.datagalaxy.com/en/speaker/kseniia-ilichenko/)
- [Kirsten Kerr](https://www.datagalaxy.com/en/speaker/kirsten-kerr/)
- [Ethan  Aaron](https://www.datagalaxy.com/en/speaker/ethan-aaron/)
- [Lazhar  Sellami](https://www.datagalaxy.com/en/speaker/lazhar-sellami/)
- [Bob Ridings](https://www.datagalaxy.com/en/speaker/bob-ridings/)
- [Rich Zagon](https://www.datagalaxy.com/en/speaker/rich-zagon/)
- [Thomas  Mitrevski](https://www.datagalaxy.com/en/speaker/thomas-mitrevski/)
- [Danielle Conklin](https://www.datagalaxy.com/en/speaker/danielle-conklin/)
- [Nicholas Ursa](https://www.datagalaxy.com/en/speaker/nicholas-ursa/)
- [Sébastien  Thomas](https://www.datagalaxy.com/en/speaker/sebastien-thomas/)
- [Jennifer Mezzio](https://www.datagalaxy.com/en/speaker/jennifer-mezzio/)
- [Chris Larsen](https://www.datagalaxy.com/en/speaker/chris-larsen/)
- [Joe Reis](https://www.datagalaxy.com/en/speaker/joe-reis/)
- [Wajdi Fathallah](https://www.datagalaxy.com/en/speaker/wajdi-fathallah/)
- [Julien Le Dem](https://www.datagalaxy.com/en/speaker/julien-le-dem/)
- [Claire Lerbarz](https://www.datagalaxy.com/en/speaker/claire-lerbarz/)
- [Laurent Leturgez](https://www.datagalaxy.com/en/speaker/laurent-leturgez/)
- [Chris Sean](https://www.datagalaxy.com/en/speaker/chris-sean/)
- [Alexandre Bergere](https://www.datagalaxy.com/en/speaker/alexandre-bergere/)
- [Kenten Danas](https://www.datagalaxy.com/en/speaker/kenten-danas/)
- [Seifeddine Saafi](https://www.datagalaxy.com/en/speaker/seifeddine-saafi/)
- [Julien Laguilhomie](https://www.datagalaxy.com/en/speaker/julien-laguilhomie/)
- [Victor Coustenoble](https://www.datagalaxy.com/en/speaker/victor-coustenoble/)
- [Martina   Balazsova](https://www.datagalaxy.com/en/speaker/speaker-256166/)
- [Christopher Bergh](https://www.datagalaxy.com/en/speaker/christopher-bergh/)
- [Charlotte Ledoux](https://www.datagalaxy.com/en/speaker/charlotte-ledoux/)
- [Mathias Vercauteren](https://www.datagalaxy.com/en/speaker/mathias-vercauteren/)
- [Ciaran Kirk](https://www.datagalaxy.com/en/speaker/ciaran-kirk/)
- [Frédéric ROBERT](https://www.datagalaxy.com/en/speaker/frederic-robert/)
- [Dr. Thomas C. Redman](https://www.datagalaxy.com/en/speaker/dr-thomas-c-redman/)
- [Chad  Sanderson](https://www.datagalaxy.com/en/speaker/chad-sanderson/)
- [Kash Mehdi](https://www.datagalaxy.com/en/speaker/kash-mehdi/)
- [Nicola  Askham](https://www.datagalaxy.com/en/speaker/nicola-askham/)
- [Irina Nikiforova](https://www.datagalaxy.com/en/speaker/irina-nikiforova/)
- [Jonathan Reichental](https://www.datagalaxy.com/en/speaker/jonathan-reichental/)
- [Tiankai  Feng](https://www.datagalaxy.com/en/speaker/tiankai-feng/)
- [Marie  Gepel](https://www.datagalaxy.com/en/speaker/marie-gepel/)
- [Irina  Steenbeek](https://www.datagalaxy.com/en/speaker/irina-steenbeek/)
- [Jan-Willem Nieuwenhuys](https://www.datagalaxy.com/en/speaker/jan-willem-nieuwenhuys/)

## Stories
- [How DataLab Group built 100+ AI-ready assets on one shared context](https://www.datagalaxy.com/en/customer-stories/datalab/): DataLab Group felt that gap firsthand. It&#039;s Crédit Agricole&#039;s center of expertise for designing industrial-grade, innovative AI solutions across the entire group, 145,000 people and 53 million customers strong.
- [How Sage built an award-winning Data &amp; AI operating model across 2M customers and 26 countries](https://www.datagalaxy.com/en/customer-stories/sage/): For most enterprises, the question is no longer whether to invest in AI — it is whether the underlying data is ready to be trusted at scale. Sage, a global leader in AI-powered business software serving 2M+ customers across 26 countries, built its answer around a deceptively simple premise: treat data as a product, govern it as a portfolio, and let AI inherit that discipline. DataGalaxy is the platform that made this possible.
- [How Roche scaled 300+ data &amp; AI initiatives with full visibility and value tracking](https://www.datagalaxy.com/en/customer-stories/roche/): With initiatives spread across geographies and teams, Roche faced challenges in prioritizing investments, understanding value, and connecting use cases to strategic objectives.
- [Building trusted AI with governed data](https://www.datagalaxy.com/en/customer-stories/datalab-group/): DataLab Group is a center of expertise dedicated to designing innovative and industrial-grade data and AI solutions. Its teams develop a wide range of AI use cases across multiple domains, relying on diverse types of data.
- [Turn your catalog into a governance engine](https://www.datagalaxy.com/en/customer-stories/onet/): Onet is a global services leader with more than 66,000 employees. As the organization evolved, it needed to modernize how data was governed and accessed across its business lines.
- [Strengthen global data governance with trusted data](https://www.datagalaxy.com/en/customer-stories/eramet/): Eramet is a global leader in mining and metallurgy, operating across 20 countries. As the group expanded internationally and diversified into high-growth, sustainability-driven activities such as lithium and battery recycling, it faced increasing complexity in managing its data ecosystem.
- [Modern data strategies to streamline operations](https://www.datagalaxy.com/en/customer-stories/arte/): ARTE is a leading European cultural broadcaster committed to delivering high-quality content across multiple platforms. As viewer habits evolved and digital channels multiplied, ARTE needed to modernize its data strategy to gain clearer insights into audience behavior and improve operational efficiency.
- [Structured knowledge &amp; autonomous access to trusted information](https://www.datagalaxy.com/en/customer-stories/floa-bank/): FLOA is a leading online financial services company partnering with major e-retailers, travel industry players, and fintechs across Europe. As its ecosystem expanded, FLOA needed to structure its growing data environment and align its teams around shared definitions and governance practices.
- [Enable self-service &amp; reduce operational bottlenecks](https://www.datagalaxy.com/en/customer-stories/garance/): Garance is a French mutual insurer serving more than 350,000 customers. As its information systems evolved, data became increasingly dispersed across back-office applications, CRMs, and technical repositories.
- [Driving adoption &amp; business impact through shared data knowledge](https://www.datagalaxy.com/en/customer-stories/bouygues-telecom/): Bouygues Telecom is one of France’s leading telecom providers, serving over 27 million customers through an extensive mobile and fixed-line network. As data volumes and operational complexity increased, the company recognized the need for a clearer and more consistent approach to managing its data assets across teams.
- [Accelerating AI &amp; aligning teams around trusted data](https://www.datagalaxy.com/en/customer-stories/swisslife/): Swiss Life is a leading European provider of life insurance and retirement solutions, employing nearly 10,000 people. As the organization increased its focus on AI-driven innovation and regulatory compliance, it recognized the need to modernize how data was documented, governed, and shared across teams.
- [Enabling data clarity &amp; confident decision-making at scale](https://www.datagalaxy.com/en/customer-stories/getlink/): Getlink is a leader in sustainable transportation and infrastructure, employing over 3,500 people across subsidiaries, including Eurotunnel and Europorte. As demand for data-driven decision-making increased, the organization recognized the need to transform how data was structured, accessed, and shared across France and the UK.
- [Creating a shared data language across global teams](https://www.datagalaxy.com/en/customer-stories/canal/): CANAL+ is a global media group serving 25 million subscribers across more than 50 countries. As its international footprint expanded, so did the volume and complexity of its data. However, much of this information remained fragmented, under-documented, and difficult to govern effectively.
- [Malakoff Humanis scales governance to unite 300+ users with trusted, AI-ready data](https://www.datagalaxy.com/en/customer-stories/malakoff-humanis/): Malakoff Humanis is one of France’s leading mutual insurance and social protection providers. As regulatory pressure increased and data usage expanded across the organization, the company recognized that its existing data catalog and governance practices were no longer sufficient.

## Glossary
- [BCBS 239 (Basel Committee on Banking Supervision – Principle 239)](https://www.datagalaxy.com/en/?lasource_term=bcbs-239-basel-committee-on-banking-supervision-principle-239): BCBS 239 is a set of principles issued by the Basel Committee to improve risk data aggregation and reporting in banks. Applicable to systemically important financial institutions, it aims to enhance governance, data architecture, accuracy, and timeliness of risk reporting for better decision-making and regulatory compliance.
- [Solvency II](https://www.datagalaxy.com/en/?lasource_term=solvency-ii): Solvency II is a European regulatory framework for insurance companies, in force since 2016. It sets out capital requirements and risk management standards to ensure insurers remain financially stable and can meet their obligations to policyholders, while also promoting market transparency and consumer protection.
- [Data Security](https://www.datagalaxy.com/en/?lasource_term=data-security): Data security refers to the practices, tools, and policies used to protect digital information from unauthorized access, corruption, or theft. It encompasses encryption, access controls, threat detection, and compliance with regulations to ensure the confidentiality, integrity, and availability of data.
- [FISMA (Federal Information Security Management Act)](https://www.datagalaxy.com/en/?lasource_term=fisma-federal-information-security-management-act): FISMA is a U.S. federal law enacted in 2002 (updated in 2014 as the Federal Information Security Modernization Act) that mandates government agencies and their contractors to implement comprehensive information security programs. It aims to protect federal data and systems from cyber threats through risk management, continuous monitoring, and compliance with NIST standards.
- [CPRA (California Privacy Rights Act)](https://www.datagalaxy.com/en/?lasource_term=cpra-california-privacy-rights-act): The CPRA is a California state law that expands and amends the CCPA (California Consumer Privacy Act). Effective from January 2023, it enhances privacy rights for California residents, including the right to correct personal data, limit its use, and opt out of automated decision-making.
- [HIPAA (Health Insurance Portability and Accountability Act)](https://www.datagalaxy.com/en/?lasource_term=hipaa-health-insurance-portability-and-accountability-act): HIPAA is a U.S. federal law enacted in 1996 that establishes national standards for protecting sensitive patient health information. It applies to healthcare providers, insurers, and their business associates, requiring safeguards for data privacy, security, and breach notification.
- [GDPR (General Data Protection Regulation)](https://www.datagalaxy.com/en/?lasource_term=gdpr-general-data-protection-regulation): The GDPR (General Data Protection Regulation) is a European Union regulation that governs the collection, processing, storage, and sharing of personal data. Enforced since May 2018, it aims to protect the privacy and rights of individuals within the EU and imposes strict requirements on organizations that handle EU residents&#039; personal data, including transparency, user consent, data minimization, and breach notification.
- [Stakeholder Alignment](https://www.datagalaxy.com/en/?lasource_term=stakeholder-alignment): Stakeholder alignment means ensuring all key parties — from executives to data teams — are aligned on goals, expectations, and definitions around data initiatives.
- [Data Portfolio Management](https://www.datagalaxy.com/en/?lasource_term=data-portfolio-management): Data portfolio management applies portfolio thinking to data products and initiatives — balancing investments, risks, and value across multiple data assets or programs.
- [Metadata Manager](https://www.datagalaxy.com/en/?lasource_term=metadata-manager): A Metadata Manager oversees the governance, standardization, and curation of metadata across systems. They ensure that metadata is accurate, contextual, and accessible to support discoverability, lineage, and semantic clarity.
- [Machine Learning Engineer](https://www.datagalaxy.com/en/?lasource_term=machine-learning-engineer): A Machine Learning Engineer operationalizes ML models by building scalable training pipelines, deploying models into production, and monitoring their performance over time. They bridge the gap between data science and engineering.
- [Data Scientist](https://www.datagalaxy.com/en/?lasource_term=data-scientist): A Data Scientist uses statistical methods, programming, and domain knowledge to extract insights and build predictive models. They often work on advanced analytics, forecasting, and machine learning projects that inform business decisions.
- [Data Quality Analyst](https://www.datagalaxy.com/en/?lasource_term=data-quality-analyst): A Data Quality Analyst monitors and evaluates datasets to ensure they meet defined quality standards — including accuracy, completeness, consistency, and timeliness. They often define and implement rules using quality monitoring tools.
- [Data Engineer](https://www.datagalaxy.com/en/?lasource_term=data-engineer): A Data Engineer builds and maintains the pipelines and systems used to ingest, transform, and deliver data. They ensure scalability, performance, and reliability in the data infrastructure — enabling downstream analytics, AI, and operational use cases.
- [Data Custodian](https://www.datagalaxy.com/en/?lasource_term=data-custodian): A Data Custodian is responsible for the technical management and safekeeping of data. This includes implementing security policies, controlling access, performing backups, and ensuring data availability and integrity at the system level.
- [Catalog Administrator](https://www.datagalaxy.com/en/?lasource_term=catalog-administrator): A Catalog Administrator manages the setup, configuration, and day-to-day operation of the enterprise data catalog. This includes overseeing user roles, metadata ingestion, and data asset curation to ensure visibility, governance, and usability across teams.
- [Solution Architect](https://www.datagalaxy.com/en/?lasource_term=solution-architect): A Solution Architect designs tailored technical solutions that solve specific business problems, typically integrating various tools, platforms, and data sources. They balance feasibility, scalability, and cost — often partnering closely with Data Architects and Engineers.
- [Platform Engineer](https://www.datagalaxy.com/en/?lasource_term=platform-engineer): A Platform Engineer builds and maintains the infrastructure that enables efficient data operations, often focusing on CI/CD pipelines, orchestration tools, and scalable compute environments. They ensure the data platform is robust, secure, and automation-ready.
- [Enterprise Architect](https://www.datagalaxy.com/en/?lasource_term=enterprise-architect): An Enterprise Architect oversees the entire IT and data ecosystem to ensure alignment with business strategy. They connect applications, infrastructure, data, and security standards across the organization, often guiding large-scale transformation initiatives.
- [Data Architect](https://www.datagalaxy.com/en/?lasource_term=data-architect): A Data Architect designs the structure and flow of enterprise data systems, defining how data is stored, integrated, and accessed. They create blueprints for databases, data lakes, and warehouses, aligning technical systems with governance and business requirements.
- [Data Analyst](https://www.datagalaxy.com/en/?lasource_term=data-analyst): A Data Analyst collects, cleans, and interprets data to uncover trends, answer business questions, and support operational or strategic decisions. They often build dashboards, run ad hoc analyses, and work cross-functionally with data producers and consumers.
- [BI Analyst](https://www.datagalaxy.com/en/?lasource_term=bi-analyst): A Business Intelligence (BI) Analyst transforms structured data into actionable insights using reporting and visualization tools. They work closely with stakeholders to track KPIs, identify trends, and support business decision-making.
- [Analytics Engineer](https://www.datagalaxy.com/en/?lasource_term=analytics-engineer): An Analytics Engineer builds and maintains the data transformation layer between raw data and business insights. Using tools like dbt, they design reliable, documented, and reusable data models — often serving as a bridge between data engineering and BI teams.
- [Domain Owner](https://www.datagalaxy.com/en/?lasource_term=domain-owner): A Domain Owner manages a specific data domain (e.g., customer, product, finance) and is responsible for its governance, quality, and cross-team alignment. This role often overlaps with or complements that of a Data Owner in domain-oriented data organizations (like data mesh).
- [Data Steward](https://www.datagalaxy.com/en/?lasource_term=data-steward): A data steward ensures data quality, integrity, and proper management. They uphold governance policies, maintain standards, resolve issues, and collaborate across teams to deliver accurate, consistent, and trusted data for the organization.
- [Data Owner](https://www.datagalaxy.com/en/?lasource_term=data-owner): A Data Owner is accountable for the overall quality, access, and usage of a specific dataset or data domain. They have authority to define data policies and delegate responsibilities to data stewards within their scope.
- [Data Governance Officer](https://www.datagalaxy.com/en/?lasource_term=data-governance-officer): A Data Governance Officer oversees the strategic implementation of governance frameworks across the enterprise. This includes setting policies, driving adoption, and coordinating with data owners, stewards, and compliance teams to ensure sustainable, secure, and trustworthy data use.
- [Data and Analytics Stewardship](https://www.datagalaxy.com/en/?lasource_term=data-and-analytics-stewardship): The set of responsibilities and processes that ensure data and analytics assets are properly managed, defined, and used ethically and effectively across the organization. This includes business data stewards and analytics stewards who align business goals with data practices.
- [Compliance Officer](https://www.datagalaxy.com/en/?lasource_term=compliance-officer): A Compliance Officer ensures that the organization adheres to regulatory, legal, and internal policy requirements. In the context of data, this includes overseeing GDPR, CCPA, and industry-specific regulations like HIPAA or FERC.
- [Business Data Steward](https://www.datagalaxy.com/en/?lasource_term=business-data-steward): A Business Data Steward is responsible for defining, managing, and ensuring the proper use of data within a specific business domain. They bridge the gap between business users and technical teams by maintaining high data quality, consistency, and documentation.
- [Head of Data Governance](https://www.datagalaxy.com/en/?lasource_term=head-of-data-governance): The Head of Data Governance leads the implementation of policies, frameworks, and standards that ensure data quality, privacy, security, and usability across the organization. This role connects strategic business needs with operational data stewardship.
- [Chief Data Officer (CDO)](https://www.datagalaxy.com/en/?lasource_term=chief-data-officer-cdo): A Chief Data Officer (CDO) ensures data is well-managed, trusted, and drives business value. They lead data strategy, governance, and quality, helping teams turn data into actionable insights.
- [Chief AI Officer (CAIO)](https://www.datagalaxy.com/en/?lasource_term=chief-ai-officer-caio): The Chief AI Officer is an executive responsible for defining and overseeing an organization’s AI strategy. The CAIO ensures AI initiatives align with business goals, ethical standards, and regulatory compliance — often working across data, IT, and product teams to scale AI responsibly.
- [Zero-shot / Few-shot Learning](https://www.datagalaxy.com/en/?lasource_term=zero-shot-few-shot-learning): Zero-shot learning allows models to generalize to tasks they haven&#039;t been explicitly trained on, using only the prompt. Few-shot learning improves accuracy by including a handful of examples in the prompt, guiding the model&#039;s response behavior.
- [Fine-tuning](https://www.datagalaxy.com/en/?lasource_term=fine-tuning): Fine-tuning is the process of taking a pre-trained foundation model and retraining it on domain-specific data to improve performance on targeted tasks — balancing general language skills with organizational context.
- [Context Window](https://www.datagalaxy.com/en/?lasource_term=context-window): A context window defines how much information (tokens) an LLM can &quot;see&quot; at one time during inference. It limits how much data can be used in a single prompt, affecting performance in long documents or multi-turn conversations.
- [Inference](https://www.datagalaxy.com/en/?lasource_term=inference): Inference is the process of using a trained machine learning or AI model to make predictions or generate outputs based on new input data — such as prompting an LLM to summarize a document or answer a question.
- [Embedding](https://www.datagalaxy.com/en/?lasource_term=embedding): An embedding is a numerical representation of a word, sentence, or document that captures its meaning and relationships in a multi-dimensional space. Embeddings allow LLMs to perform similarity search, clustering, and semantic reasoning.
- [Hallucination (in AI)](https://www.datagalaxy.com/en/?lasource_term=hallucination-in-ai): Hallucination occurs when an AI system generates information that is syntactically plausible but factually incorrect or entirely made up. It is a major challenge in deploying LLMs for business-critical or regulated use cases.
- [Retrieval-Augmented Generation (RAG)](https://www.datagalaxy.com/en/?lasource_term=retrieval-augmented-generation-rag): RAG is an AI architecture that enhances a language model’s answers by retrieving relevant documents from an external knowledge base and feeding them into the prompt — improving accuracy, grounding, and freshness of the generated response.
- [Foundation Model](https://www.datagalaxy.com/en/?lasource_term=foundation-model): A foundation model is a pre-trained AI model that serves as a base for multiple downstream tasks. Trained on broad, unlabeled datasets at scale, it can be adapted via fine-tuning or prompting for use cases like chatbots, search, and document analysis.
- [Large Language Model (LLM)](https://www.datagalaxy.com/en/?lasource_term=large-language-model-llm): A Large Language Model is an advanced neural network trained on massive text corpora to understand and generate human-like language. LLMs like GPT, Claude, or PaLM can perform a range of tasks, from summarization to code generation, using natural language prompts.
- [Data Labeling](https://www.datagalaxy.com/en/?lasource_term=data-labeling): Data labeling is the process of assigning metadata or categories to raw data — such as tagging images, classifying text, or identifying entities — to make it usable for training supervised ML models.
- [Prompt Engineering](https://www.datagalaxy.com/en/?lasource_term=prompt-engineering): Prompt engineering is the practice of designing, testing, and optimizing inputs (prompts) for large language models (LLMs) to get consistent, relevant, and useful outputs.
- [Data Provenance](https://www.datagalaxy.com/en/?lasource_term=data-provenance): Data provenance tracks the full history of a data asset — where it originated, how it was transformed, and who touched it — offering traceability for audits, compliance, and trust.
- [AI Observability](https://www.datagalaxy.com/en/?lasource_term=ai-observability): AI observability is the ability to monitor, debug, and trace how AI systems behave in real-world environments — across data inputs, model decisions, and user interactions.
- [Data Readiness](https://www.datagalaxy.com/en/?lasource_term=data-readiness-2): Data readiness measures how prepared your data is to support AI and analytics — across completeness, structure, quality, documentation, and business meaning.
- [Knowledge Graphs in AI](https://www.datagalaxy.com/en/?lasource_term=knowledge-graphs-in-ai): Knowledge graphs structure and connect entities (people, places, products, etc.) using relationships — making them highly useful for reasoning, search, and grounding AI/LLMs in enterprise knowledge.
- [Semantic Layer](https://www.datagalaxy.com/en/?lasource_term=semantic-layer-2): The semantic layer sits between raw data and users — translating technical structures into consistent business terms and metrics. It enables clarity, reuse, and self-service analytics.
- [ML Metadata](https://www.datagalaxy.com/en/?lasource_term=ml-metadata-2): ML metadata refers to all the information generated and captured during the ML lifecycle — such as datasets, hyperparameters, environments, code versions, and evaluation metrics. It is critical for reproducibility, governance, and monitoring.
- [Accountability in AI](https://www.datagalaxy.com/en/?lasource_term=accountability-in-ai): Accountability in AI ensures that clear roles, responsibilities, and ownership are defined across the AI lifecycle — from data sourcing to model deployment and audit.
- [AI Risk Management](https://www.datagalaxy.com/en/?lasource_term=ai-risk-management-2): AI risk management involves identifying, assessing, and mitigating risks associated with AI — including model performance, legal exposure, ethical concerns, and operational dependencies.
- [Compliance (AI Act, GDPR for AI)](https://www.datagalaxy.com/en/?lasource_term=compliance-ai-act-gdpr-for-ai): AI compliance refers to adhering to evolving regulations (e.g., EU AI Act, GDPR) that govern how AI systems are developed, validated, documented, and monitored — especially when sensitive data or high-risk use cases are involved.
- [Ethical AI](https://www.datagalaxy.com/en/?lasource_term=ethical-ai): Ethical AI is about aligning AI systems with ethical principles like respect for human rights, dignity, safety, and fairness — going beyond compliance alone.
- [Transparency](https://www.datagalaxy.com/en/?lasource_term=transparency): Transparency in AI involves making the design, intent, data sources, and logic of AI systems visible and understandable to stakeholders — including regulators, users, and developers.
- [Fairness](https://www.datagalaxy.com/en/?lasource_term=fairness): Fairness ensures AI systems do not discriminate or produce unequal outcomes based on protected attributes such as race, gender, or age.
- [Bias in AI](https://www.datagalaxy.com/en/?lasource_term=bias-in-ai): Bias in AI refers to systematic errors that unfairly favor certain groups over others — often introduced through biased training data or flawed assumptions in model design.
- [Explainability (XAI)](https://www.datagalaxy.com/en/?lasource_term=explainability-xai): Explainability is the ability to understand and communicate how an AI system makes decisions — essential for trust, accountability, and debugging complex models.
- [Responsible AI](https://www.datagalaxy.com/en/?lasource_term=responsible-ai-2): Responsible AI refers to the practice of designing, developing, and deploying AI systems in a way that is ethical, fair, accountable, and aligned with human values.
- [AI Literacy](https://www.datagalaxy.com/en/?lasource_term=ai-literacy): AI literacy refers to the ability of individuals across an organization to understand, interpret, and effectively engage with artificial intelligence systems. This includes knowing the capabilities and limitations of AI, ethical implications, and how AI fits into broader data governance and business contexts.
- [AI Governance](https://www.datagalaxy.com/en/?lasource_term=ai-governance-2): AI governance refers to the framework of policies, practices, and regulations that guide the responsible development and use of artificial intelligence. It ensures ethical compliance, data transparency, risk management, and accountability—critical for organizations seeking to scale AI securely and align with evolving regulatory standards.
- [AI Pipeline](https://www.datagalaxy.com/en/?lasource_term=ai-pipeline): An AI pipeline automates the end-to-end workflow of an AI project — from data ingestion to training, evaluation, deployment, and monitoring.
- [Model Versioning](https://www.datagalaxy.com/en/?lasource_term=model-versioning): Model versioning keeps track of iterations and changes to machine learning models — helping teams compare performance and roll back if needed.
- [Model Lineage](https://www.datagalaxy.com/en/?lasource_term=model-lineage-2): Model lineage traces the full history of an ML model — including training datasets, preprocessing steps, code versions, and deployment environments. It ensures transparency and auditability.
- [Model Monitoring](https://www.datagalaxy.com/en/?lasource_term=model-monitoring): Model monitoring tracks the performance, accuracy, and fairness of deployed models — helping teams catch drift, bias, or performance issues in real-time.
- [Model Deployment](https://www.datagalaxy.com/en/?lasource_term=model-deployment): Model drift occurs when a model’s performance degrades over time due to changes in the input data or the real-world environment. It requires continuous monitoring.
- [Model Drift](https://www.datagalaxy.com/en/?lasource_term=model-drift): Model monitoring tracks the performance, accuracy, and fairness of deployed models — helping teams catch drift, bias, or performance issues in real-time.
- [Model Training](https://www.datagalaxy.com/en/?lasource_term=model-training): Model training is the process where an algorithm learns from historical data by adjusting parameters to minimize prediction error.
- [Training Data](https://www.datagalaxy.com/en/?lasource_term=training-data): Training data is the labeled dataset used to teach a machine learning model how to make predictions. Its quality and structure directly impact model performance.
- [Feature Engineering](https://www.datagalaxy.com/en/?lasource_term=feature-engineering): Feature engineering involves selecting, creating, or transforming input variables (features) to improve a model’s ability to learn patterns from data.
- [Machine Learning (ML)](https://www.datagalaxy.com/en/?lasource_term=machine-learning-ml): Machine learning is a subset of AI where algorithms learn from data to make predictions or decisions without being explicitly programmed. It powers applications like fraud detection, recommendations, and forecasting.
- [Data as a product](https://www.datagalaxy.com/en/?lasource_term=data-as-a-product): “Data as a product” is the mindset of managing data with the same rigor as customer-facing products — with clear purpose, usability, trust, and business ownership.
- [Marketplace](https://www.datagalaxy.com/en/?lasource_term=marketplace): In a data context, a marketplace is a centralized hub where users can browse, access, and request certified data products — often integrated with governance workflows, access controls, and business metadata.
- [AI Product Lifecycle](https://www.datagalaxy.com/en/?lasource_term=ai-product-lifecycle): The AI product lifecycle includes problem framing, data collection, model training, deployment, monitoring, and iteration. Managing this lifecycle is key to responsible and successful AI adoption.
- [Data Product Lifecycle](https://www.datagalaxy.com/en/?lasource_term=data-product-lifecycle): The data product lifecycle describes the stages a data product goes through: from design and development to launch, maintenance, and retirement. Governance ensures quality and traceability at every step.
- [AI Product](https://www.datagalaxy.com/en/?lasource_term=ai-product): An AI product is a software solution powered by AI or machine learning — such as a recommendation engine, chatbot, or fraud detection system — that solves specific business problems and evolves over time.
- [Data Product](https://www.datagalaxy.com/en/?lasource_term=data-product-2): A data product is a well-defined data asset — such as a dashboard, dataset, or API — that delivers value to end users. It has clear ownership, SLAs, documentation, and is treated like a product, not a project.
- [Data and Analytics Product Management](https://www.datagalaxy.com/en/?lasource_term=data-and-analytics-product-management): The discipline of applying product management principles to the lifecycle of data and analytics products — including design, governance, value delivery, and continuous iteration across data teams.
- [Value Governance](https://www.datagalaxy.com/en/?lasource_term=value-governance): Value governance is the practice of ensuring data and AI initiatives are aligned with strategic business objectives and deliver measurable outcomes. It connects governance efforts to ROI by prioritizing investments, tracking value realization, and enabling data-driven decision-making at scale.
- [Outcome-Driven Governance](https://www.datagalaxy.com/en/?lasource_term=outcome-driven-governance): Outcome-driven governance focuses governance efforts on measurable business outcomes — rather than just compliance or control — enabling agility and strategic relevance.
- [Data Product Portfolio](https://www.datagalaxy.com/en/?lasource_term=data-product-portfolio): This refers to the full collection of data products (like dashboards, APIs, certified datasets) managed as strategic assets with owners, SLAs, and business goals.
- [Investment Alignment](https://www.datagalaxy.com/en/?lasource_term=investment-alignment): Investment alignment ensures that data spending is targeted at initiatives with measurable and strategic return — rather than isolated, tech-driven projects.
- [Value Tracking](https://www.datagalaxy.com/en/?lasource_term=value-tracking): Value tracking monitors the business outcomes associated with data use — such as revenue growth, operational efficiency, or risk reduction — to demonstrate ROI.
- [Value Management](https://www.datagalaxy.com/en/?lasource_term=value-management): Value management in data refers to systematically planning, measuring, and optimizing the business impact of data initiatives. It shifts the conversation from technical delivery to business outcomes.
- [Data Initiative Prioritization](https://www.datagalaxy.com/en/?lasource_term=data-initiative-prioritization): This involves ranking data projects based on impact, feasibility, and alignment with business goals — to ensure resources are focused on what matters most.
- [Digital Transformation](https://www.datagalaxy.com/en/?lasource_term=digital-transformation): Digital transformation is the broader business shift toward using digital tools and data-driven processes to improve operations, customer experience, and innovation.
- [Data Strategy](https://www.datagalaxy.com/en/?lasource_term=data-strategy): A data strategy defines how an organization will manage and use data to achieve business objectives. It aligns people, processes, and platforms with measurable outcomes.
- [Data Enablement](https://www.datagalaxy.com/en/?lasource_term=data-enablement): Data enablement ensures that users have the right tools, training, and access to use data effectively. It connects data strategy with daily operations.
- [Data Literacy](https://www.datagalaxy.com/en/?lasource_term=data-literacy): Data literacy is the ability to read, understand, question, and communicate with data. It’s essential for creating a data-driven culture across all levels of an organization.
- [Data Democratization](https://www.datagalaxy.com/en/?lasource_term=data-democratization): Data democratization means giving everyone in an organization — not just technical users — access to data they can understand and use. It supports self-service, collaboration, and faster decision-making.
- [Augmented FinOps](https://www.datagalaxy.com/en/?lasource_term=augmented-finops): The use of AI-driven tools and techniques to optimize financial operations in cloud computing environments — including resource allocation, spend forecasting, and performance tracking across data infrastructure.
- [Cloud Data Platform](https://www.datagalaxy.com/en/?lasource_term=cloud-data-platform): A cloud data platform is a suite of cloud-native tools for storing, processing, and analyzing data at scale — often combining storage (e.g., lakehouse), compute, and governance layers.
- [Data Orchestration](https://www.datagalaxy.com/en/?lasource_term=data-orchestration): Data orchestration coordinates the execution of data workflows across different systems, ensuring tasks run in the right order and data dependencies are respected.
- [Data Stack](https://www.datagalaxy.com/en/?lasource_term=data-stack): A data stack is the set of tools and technologies that power data collection, processing, storage, and analysis — from ingestion tools to warehouses and BI platforms.
- [Reference Data](https://www.datagalaxy.com/en/?lasource_term=reference-data): Reference data includes standardized, non-transactional values used across systems — like country codes, currency formats, or product categories. It ensures semantic consistency.
- [Master Data Management (MDM)](https://www.datagalaxy.com/en/?lasource_term=master-data-management-mdm): MDM is the process of defining and managing core business entities (e.g., customers, products) to ensure consistency across systems. It supports data quality, reporting, and operational efficiency.
- [ETL / ELT](https://www.datagalaxy.com/en/?lasource_term=etl-elt): ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are two common approaches to data integration. ETL transforms data before loading, while ELT transforms it within the destination system — often used in modern cloud platforms.
- [Data Warehouse](https://www.datagalaxy.com/en/?lasource_term=data-warehouse): A data warehouse is a centralized, structured repository optimized for querying and reporting. It integrates data from multiple sources to support BI and analytics.
- [Data Product Governance](https://www.datagalaxy.com/en/?lasource_term=data-product-governance): Data product governance ensures that data assets treated as products — with defined owners, SLAs, and quality standards — are discoverable, trusted, and aligned with business outcomes.
- [Data Product](https://www.datagalaxy.com/en/?lasource_term=data-product): A data product is a well-defined data asset — such as a dashboard, dataset, or API — that delivers value to end users. It has clear ownership, SLAs, documentation, and is treated like a product, not a project.
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