data governance

Articles tagged data governance on mistr.AI.

• In 2026, Most Corporate AI Projects Will Fail, and There Is Only One Reason Why — While individuals achieve higher productivity with AI tools, large corporate projects often fail before deployment. Research warns that up to 90 percent of AI initiatives could end in failure in 2026. Surprisingly, the main problem doesn't lie in the technology or choosing the right model, but in something much more mundane. And that's exactly why there's a solution that can save your AI projects.
• Artificial Intelligence and Data Chaos: Companies Face Uncontrollable Data Growth — Implementing generative artificial intelligence brings enormous opportunities for companies, but at the same time revives a long-familiar problem — data sprawl. The rapid growth of data and its unmanageable volume threatens corporate security, as sensitive data is often poorly managed and vulnerable to cyberattacks. What are the roots of this problem and what steps need to be taken to manage the data chaos?
• Data Governance as the Foundation of Trust for Agentic AI Systems — Agentic AI has quickly changed company expectations around data and its management. But the successful deployment of autonomous systems requires a new approach to data governance — not just a catalog, but an active "contract layer" that ensures context, trust, and traceability. Why is this shift necessary and where does it lead? The answers will surprise even seasoned data professionals.
• The Data Product Manager: Key to Effective Use of Company Data — Companies today are drowning in data, but clear answers are often missing. Why do dashboards fail and how can a data product manager bring real value? Let's show why this role is essential in modern companies – and how it will help you get from your data what you truly need.
• How Proper Data Management Determines the Success of Artificial Intelligence in Businesses — Do you want artificial intelligence to truly help your business? The foundation isn't just choosing the right tool – it's the quality and structure of the data that feeds AI. In this article, you'll find out why it matters so much and how to approach it in practice.
• Five Key Strategies That Separate AI Leaders from Companies Stuck in Pilot Mode — Artificial intelligence promises a revolution in business, but most companies are still spinning their wheels on pilot projects without real impact. What do those who manage to scale AI and deliver real value actually do? Discover five key strategies that separate the successful from the rest.
• Artificial Intelligence Is Transforming the Value of Public, Private, and Synthetic Data — Data has long ceased to be just a raw material — it's now the key to success in the digital age. How is artificial intelligence changing the value of public, private, and synthetic data? Read on to find out why some data sources are moving to the forefront and how to extract maximum value from them without compromising security or privacy.
• Unlocking the Full Potential of GenAI: But How? — According to recent IDC reports, investments in artificial intelligence grew significantly last year. According to an IDC report from October 2024, spending on AI is expected to grow to as much as $150 billion by 2027. But this growth raises questions: What key challenges must companies overcome to make their GenAI investments worthwhile?