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Why Everyone’s Talking About MCPs (And What It Means for AI)

Author

Ravit Jain
June 17, 2025

What if every piece of enterprise data you needed came pre-packaged, governed, fresh and ready for use—just like a product?

That’s the promise behind MCP: Modern Data Products. And it’s quietly becoming one of the most important shifts in how enterprises manage and activate data today.

While the term “data product” isn’t new, MCP takes the concept further. It’s not about dashboards or datasets. It’s about delivering real-time, trusted, and contextual data—built around the business entities that actually power your operations, like customers, products, orders, or assets.

So why is everyone suddenly talking about MCPs?

Let’s unpack that.

Why Modern Data Products and Model Context Protocol (MCP) Matter Now

Most enterprise data strategies still rely on stitching together pipelines, warehouses, and APIs. It works—but barely. The systems don’t talk to each other, latency is high, and governance becomes guesswork.

For example, while a Salesforce environment manages much of the customer relationship and sales process, most organizations have additional essential data about customers distributed across other applications, databases, and cloud platforms. This fragmentation means that creating a clean, comprehensive, and compliant picture of a customer is not easy. 

MCPs change this dynamic.

As explained in this K2View blog, a modern data product is a composable, re-usable, governed, and real-time representation of a business entity—ready to be served to any consuming application, system, or AI model.

Think of it like this:

  • Instead of pulling raw data from multiple sources, you query a single, up-to-date product.

  • Instead of rebuilding complex integrations, you publish and consume MCPs via APIs.

  • Instead of dealing with messy, siloed data — you streamline the processby cleaning, aligning, and harmonizing everything into a unified, trusted view.

It’s fast. It’s governed. And it’s scalable.

A New Operating Model for Enterprise Data

MCPs aren’t a feature. They’re a new way to operate.

But MCPs on their own aren’t enough — without a modern data product architecture, they can’t deliver their full value.

Instead of trying to force-fit siloed data into rigid pipelines, you build products that reflect your real-world business entities. These products come with:

  • Unified schema and metadata

  • Governance baked in

  • Lifecycle management

  • Real-time sync across systems

  • APIs for easy consumption

The best part? They can serve both analytical and operational use cases. One product, many applications.

Where MCP Meets GenAI

Let’s talk about the AI angle.

Large Language Models (LLMs) are only as good as the data they can access. And most enterprise data is locked away—hard to reach, delayed, or unstructured. That’s where MCPs shine.

With MCPs, you can feed LLMs real-time, governed data about a customer, an order, a claim, or a device—with full lineage and context. This changes everything.

  • No more hallucinations from stale info

  • No more security risks from ungoverned data scraping

  • No more delays in retrieving context for decisions

If you’re exploring use cases like contextual assistant for employees, customer chatbot, Retrieval-Augmented Generation (RAG), or agent-based automation, MCPs give your AI what it needs: live data with structure and purpose.

Final Take

The shift to Modern Data Products isn’t just about speed or scale—it’s about changing your relationship with data.

From reducing complexity to enabling AI, from improving customer experiences to democratizing access—MCPs lay the groundwork for a smarter, more agile enterprise.

💡 Want to see how K2View uses MCP to connect LLMs to live enterprise data?
Visit the solution page or try the product tour to explore what’s possible.

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Best,

Ravit Jain

Founder & Host of The Ravit Show