Search for:
Comparing EU and U.S. State Laws on AI: A Checklist for Proactive Compliance


The global market for artificial intelligence is evolving under two very different legal paradigms. On one side, the European Union has enacted the AI Act, the first comprehensive and enforceable regulatory regime for AI, applicable across all member states and with far-reaching extraterritorial scope. On the other, the United States continues to advance AI oversight primarily at the state level, resulting in a patchwork of rules that vary in focus, definitions, and enforcement…

The post Comparing EU and U.S. State Laws on AI: A Checklist for Proactive Compliance appeared first on DATAVERSITY.


Read More
Author: Fahad Diwan

Ask a Data Ethicist: How Does the Use of AI Impact People’s Perceptions of You?


Last October, I wrote a column about the use of generative AI in producing a professional service. I pondered the question of whether or not others’ knowledge about the use of AI in producing a professional service – such as legal work, consulting, or creative work –  would devalue the service. My hypothesis was that […]

The post Ask a Data Ethicist: How Does the Use of AI Impact People’s Perceptions of You? appeared first on DATAVERSITY.


Read More
Author: Katrina Ingram

Mind the Gap: Agentic AI and the Risks of Autonomy


The ink is barely dry on generative AI and AI agents, and now we have a new next big thing: agentic AI. Sounds impressive. By the time this article comes out, there’s a good chance that agentic AI will be in the rear-view mirror and we’ll all be chasing after the next new big thing. […]

The post Mind the Gap: Agentic AI and the Risks of Autonomy appeared first on DATAVERSITY.


Read More
Author: Mark Cooper

Why Business-Critical AI Needs to Be Domain-Aware


We stand at a pivotal moment. Generative AI, with its large language models (LLMs) and retrieval-augmented generation (RAG) systems, promises to revolutionize how industries operate. We’ve all seen the impressive demos that can summarize articles, write code, or draft marketing copy. But when the stakes are high and an error could lead to a financial […]

The post Why Business-Critical AI Needs to Be Domain-Aware appeared first on DATAVERSITY.


Read More
Author: Andreas Blumauer

Book of the Month: “Rewiring Your Mind for AI” 


This month, we’re reviewing “Rewiring Your Mind for AI” by David Wood. In this book, Dr. Wood shows us how to think differently to leverage the benefits of artificial intelligence (AI).  The book first sets us up to think in terms of growth mindsets instead of limiting mindsets – starting with some anecdotes about how calculators and […]

The post Book of the Month: “Rewiring Your Mind for AI”  appeared first on DATAVERSITY.


Read More
Author: Mark Horseman

How to Overcome Five Key GenAI Deployment Challenges


Generative AI (GenAI) continues to provide significant business value across many use cases and industries. But despite the many successful customer experiences, GenAI is also proving to be challenging for some businesses to get right and deploy across their organizations in full production. As a result, plenty of projects are getting stuck in planning, experimentation, […]

The post How to Overcome Five Key GenAI Deployment Challenges appeared first on DATAVERSITY.


Read More
Author: Jim Johnson

Open Data Fabric: Rethinking Data Architecture for AI at Scale


Enterprise AI agents are moving from proof-of-concept to production at unprecedented speed. From customer service chatbots to financial analysis tools, organizations across various industries are deploying agents to handle critical business functions. Yet a troubling pattern is emerging; agents that perform brilliantly in controlled demos are struggling when deployed against real enterprise data environments. The problem […]

The post Open Data Fabric: Rethinking Data Architecture for AI at Scale appeared first on DATAVERSITY.


Read More
Author: Prat Moghe

Model Context Protocol Demystified: Why MCP is Everywhere

What is Model Context Protocol (MCP) and why is it suddenly being talked about everywhere? How does it support the future of agentic AI? And what happens to businesses that don’t implement it?

The short answer is MCP is the new universal standard connecting AI to trusted business context, fueling the rise of agentic AI. Organizations that ignore it risk being stuck with slow, unreliable insights while competitors gain a decisive edge.

What is Model Context Protocol?

From boardrooms to shop floors, AI is rewriting how businesses uncover insights, solve problems, and chart their futures. Yet even the most advanced AI models face a critical challenge. Without access to precise, contextualized information, their answers can fall short by being generic and lacking critical insights.

That’s where MCP comes in. MCP is a rapidly emerging standard that gives AI-powered applications, like large language models (LLM) assistants, the ability to connect to structured, real-time business context through a knowledge graph.

Think of MCP as a GPS for AI. It guides models directly to the most relevant and reliable information. Instead of building custom integrations for every tool or dataset, businesses can use MCP to give AI applications secure, standardized access to the information they need.

The result? AI systems that move beyond generic responses to deliver answers rooted in a company’s unique and current reality.

Why MCP Matters for Businesses

The rise of AI data analysts, which are LLM-powered assistants that translate natural-language questions into structured data queries, makes MCP mission-critical. Unlike traditional analytics tools that require SQL skills or dashboard expertise, an AI data analyst allows anyone to simply ask questions and get results.

These questions can be business focused, such as:

  • What’s driving our increase in customer churn?
  • How did supply chain delays impact last quarter’s revenue?
  • Are seasonal promotions improving profitability?

Answering these questions requires more than statistics. It demands contextual intelligence pulled from multiple, current data sources.

MCP ensures AI data analysts can:

  • Converse naturally. Users ask questions in plain language.
  • Ground answers in context. MCP optimizes knowledge graphs for context.
  • Be accessible to all users. No coding or data science expertise is needed.
  • Provide action-oriented insights. Deliver answers that leaders can trust.

In short, MCP is the bridge between decision-makers and the technical complexity of enterprise data.

The Business Advantages of MCP

The value of AI isn’t in generating an answer. It’s in generating the right answer. MCP makes that possible by standardizing how AI connects to business context, turning data into precise, actionable, and trusted insights.

Key benefits of MCP include:

  • Improved accuracy. AI reflects current, trusted business data.
  • Scalability across domains. Each business function, such as finance, operations, and marketing, maintains its own tailored context.
  • Reduced integration complexity. A standard framework replaces costly, custom builds.
  • Future-proof flexibility. MCP ensures continuity as new AI models and platforms emerge.
  • Greater decision confidence. Leaders act on insights that reflect real business conditions.

With MCP, organizations move from AI that’s impressive to AI that’s indispensable.

Knowledge Graphs: The Heart of MCP

At the core of MCP are knowledge graphs, which are structured maps of business entities and their relationships. They don’t just store data. They provide context.

For example:

  • A customer isn’t simply a record. They are linked to orders, support tickets, and loyalty status.
  • A product isn’t only an SKU. It’s tied to suppliers, sales channels, and performance metrics.

By tapping into these connections, AI can answer not only what happened but also why it happened and what’s likely to happen next.

Powering Ongoing Success With MCP

Organizations that put MCP into practice and support it with a knowledge graph can create, manage, and export domain-specific knowledge graphs directly to MCP servers.

With the right approach to MCP, organizations gain:

  • Domain-specific context. Each business unit builds its own tailored graph.
  • Instant AI access. MCP provides secure, standardized entry points to data.
  • Dynamic updates. Continuous refreshes keep insights accurate as conditions shift.
  • Enterprise-wide intelligence. Organizations scale not just data, but contextual intelligence across the business.

MCP doesn’t just enhance AI. It transforms AI from a useful tool into a business-critical advantage.

Supporting Real-World Use Cases Using AI-Ready Data

AI-ready data plays an essential role in delivering fast, trusted results. With this data and MCP powered by a knowledge graph, organizations can deliver measurable outcomes to domains such as:

  • Finance. Quickly explain revenue discrepancies by connecting accounting, sales, and market data.
  • Supply chain. Answer questions such as, “Which suppliers pose the highest risk to production goals?” with context-rich insights on performance, timelines, and quality.
  • Customer service. Recommend personalized strategies using data from purchase history, service records, and sentiment analysis.
  • Executive leadership. Provide faster, more reliable insights to act decisively in dynamic markets.

In an era where the right answer at the right time can define market leadership, MCP ensure AI delivers insights that are accurate, actionable, and aligned with the current business reality. From the boardroom to the shop floor, MCP helps organizations optimize AI for decision-making and use cases.

Find out more by watching a short video about MCP for AI applications.

The post Model Context Protocol Demystified: Why MCP is Everywhere appeared first on Actian.


Read More
Author: Dee Radh

No PhD? No Problem: How Accessible AI Is Making Data Science Everyone’s Business


Not long ago, manipulating large datasets, training machine learning models, or visualizing results required advanced programming skills and specialized statistical knowledge.  Today, intuitive AI tools and natural language interfaces are allowing nearly everyone – not just data scientists, engineers, and technical experts – to analyze and act on data. In fact, nearly 8 in 10 organizations now […]

The post No PhD? No Problem: How Accessible AI Is Making Data Science Everyone’s Business appeared first on DATAVERSITY.


Read More
Author: Rosaria Silipo

How an Internal AI Governance Council Drives Responsible Innovation


AI has rapidly evolved from a futuristic concept to a foundational technology, deeply embedded in the fabric of contemporary organizational processes across industries. Companies leverage AI to enhance efficiency, personalize customer interactions, and drive operational innovation. However, as AI permeates deeper into organizational structures, it brings substantial risks related to data privacy, intellectual property, compliance […]

The post How an Internal AI Governance Council Drives Responsible Innovation appeared first on DATAVERSITY.


Read More
Author: Nichole Windholz

The Data Danger of Agentic AI


Agentic AI represents a significant evolution beyond traditional rule-based AI systems and generative AI, offering unprecedented autonomy and transformative potential across various sectors. These sophisticated systems can plan, decide, and act independently, promising remarkable advances in efficiency and decision-making.  However, this high degree of autonomy, when combined with poorly governed or flawed data, can lead […]

The post The Data Danger of Agentic AI appeared first on DATAVERSITY.


Read More
Author: Samuel Bocetta

How to Future-Proof Your Data and AI Strategy


With AI systems reshaping enterprises and regulatory frameworks continuously evolving, organizations face a critical challenge: designing AI governance that protects business value without stifling innovation. But how do you future-proof your enterprise for a technology that is evolving at such an incredible pace? The answer lies in building robust data foundations that can adapt to whatever comes […]

The post How to Future-Proof Your Data and AI Strategy appeared first on DATAVERSITY.


Read More
Author: Ojas Rege

Why Data Governance Still Matters in the Age of AI
At a recent conference, I witnessed something that’s become far too common in data leadership circles: genuine surprise that chief data officers consistently cite culture — not technology — as their greatest challenge. Despite a decade of research and experience pointing to the same root cause, conversations still tend to focus on tools rather than […]


Read More
Author: Christine Haskell

Data Speaks for Itself: Is Your Data Quality Management Practice Ready for AI?
While everyone is asking if their data is ready for AI, I want to ask a somewhat different question: Is your data quality management (DQM) program ready for AI?  In my opinion, you need to be able to answer yes to the following four questions before you can have any assurance you are ready to […]


Read More
Author: Dr. John Talburt

Everything You Need to Know About Synthetic Data


Synthetic data sounds like something out of science fiction, but it’s fast becoming the backbone of modern machine learning and data privacy initiatives. It enables faster development, stronger security, and fewer ethical headaches – and it’s evolving quickly.  So if you’ve ever wondered what synthetic data really is, how it’s made, and why it’s taking center […]

The post Everything You Need to Know About Synthetic Data appeared first on DATAVERSITY.


Read More
Author: Nahla Davies

Beyond Pilots: Reinventing Enterprise Operating Models with AI


The enterprise AI landscape has reached an inflection point. After years of pilots and proof-of-concepts, organizations are now committing unprecedented resources to AI, with double-digit budget increases expected across industries in 2025. This isn’t merely about technological adoption. It reflects a deep rethinking of how businesses operate at scale. The urgency is clear: 70% of the software used […]

The post Beyond Pilots: Reinventing Enterprise Operating Models with AI appeared first on DATAVERSITY.


Read More
Author: Gautam Singh

A Leadership Blueprint for Driving Trusted, AI-Ready Data Ecosystems
As AI adoption accelerates across industries, the competitive edge no longer lies in building better models; it lies in governing data more effectively.  Enterprises are realizing that the success of their AI and analytics ambitions hinges not on tools or algorithms, but on the quality, trustworthiness, and accountability of the data that fuels them.  Yet, […]


Read More
Author: Gopi Maren

The Book Look: Rewiring Your Mind for AI
I collect baseball and non-sport cards. I started collecting when I was a kid, stopped for about 40 years, and returned to collecting again, maybe as part of a mid-life crisis. I don’t have the patience today though, that I had when I was 12. For example, yesterday I wanted to find out the most […]


Read More
Author: Steve Hoberman

Reimagining Data Architecture for Agentic AI


As agentic AI and autonomous systems transform the enterprise landscape, organizations face a new imperative: Fundamentally reimagining data architecture is no longer optional; it’s required for AI success. Many enterprises are coming to the realization that traditional data architectures, which are built for structured data and deterministic workloads, are ill-equipped to support agentic AI’s demands […]

The post Reimagining Data Architecture for Agentic AI appeared first on DATAVERSITY.


Read More
Author: Tami Fertig

Why and How to Enhance DevOps with AIOps


AIOps, the practice of enhancing IT and DevOps with help from artificial intelligence and machine learning, is not an especially new idea. It has been nearly a decade since Gartner coined the term in 2016. Yet, the growing sophistication of AI technology is making AIOps much more powerful. Gone are the days when AIOps was mostly a […]

The post Why and How to Enhance DevOps with AIOps appeared first on DATAVERSITY.


Read More
Author: Derek Ashmore

Deploying AI Models in Clinical Workflows: Challenges and Best Practices


The global healthcare AI market is projected to grow from $32.34 billion in 2024 to $431 billion by 2032. It is evident that artificial intelligence (AI) is transforming the healthcare sector, one workflow at a time. Even so, hospitals and clinics struggle to successfully integrate the technology into their workflows, as real-world deployment is fraught […]

The post Deploying AI Models in Clinical Workflows: Challenges and Best Practices appeared first on DATAVERSITY.


Read More
Author: Gaurav Belani

Improving Data Quality Using AI and ML


In our fast-paced, interconnected digital world, data is truly the heartbeat of how organizations make decisions. However, the rapid explosion of data in terms of volume, speed, and diversity has brought about significant challenges in keeping that data reliable and high-quality. Relying on traditional manual methods for data governance just doesn’t cut it anymore; in […]

The post Improving Data Quality Using AI and ML appeared first on DATAVERSITY.


Read More
Author: Udaya Veeramreddygari

The Role of AI in Mitigating Next-Generation Cyber Threats
The digital age has witnessed an exponential increase in data creation and interconnectivity, resulting in unprecedented challenges in cybersecurity. Businesses, governments, and individuals are perpetually at risk of cyber-attacks ranging from data breaches and financial theft to espionage and infrastructure sabotage. While necessary, traditional cybersecurity measures are often reactive rather than proactive, struggling to adapt […]


Read More
Author: Srinivasa Bogireddy

AI and Business Transformation: Balancing Innovation and Control
AI is no longer just a concept or a futuristic tool. It’s here and it’s likely already integrated into many aspects of your business, potentially in ways you might not even realize. AI’s potential to transform how we operate, deliver services, and optimize workflows offers significant benefits, but it also comes with responsibilities — and […]


Read More
Author: Ben Hunter III

Mind the Gap: AI-Driven Data and Analytics Disruption


We are at the threshold of the most significant changes in information management, data governance, and analytics since the inventions of the relational database and SQL. Most advances over the past 30 years have been the result of Moore’s Law: faster processing, denser storage, and greater bandwidth. At the core, though, little has changed. The basic […]

The post Mind the Gap: AI-Driven Data and Analytics Disruption appeared first on DATAVERSITY.


Read More
Author: Mark Cooper