For all the talk about an “AI revolution,” selling data is still incredibly hard.

It’s not hard because the data isn’t valuable (it is), or because enterprises don’t need it (they do); rather, it’s hard because demonstrating the value of data to a buyer is hard, because, on its own, data doesn’t do anything.

It’s the bottom level of the DIKW pyramid: Data → Information → Knowledge → Wisdom. If you’re not familiar with DIKW, it represents the shift in value of intelligence as it gets refined from raw data and enriched with context, ultimately providing insights and truths that a person or business could act on. 

Raw data doesn’t tell a story. It doesn’t answer questions. It doesn’t solve a problem until it somehow moves up that DIKW pyramid.

I’ve seen how data providers have been stuck in this trap for years. Your product is a solution in search of a problem, and your customers have to find you before they can even begin to understand the value you offer. Even then, the process breaks down fast. Across the industry, sellers are still relying on workflows that were built for analysts – not for business stakeholders who expect instant insight.

‍

The Real Challenge: Selling Data Today Is Too Hard

Selling external data still feels like selling a house: expensive, opaque, slow, and far more complicated than it should be. Not because the data isn’t valuable, but because discovering, evaluating, buying, and integrating it is fundamentally broken.

‍

1. Discovery is opaque.

McKinsey reports that about 40% of companies have begun monetizing data, yet only a minority see substantial returns. A key barrier is that buyers have no efficient way to discover or compare datasets. Discovery still happens through networks, PDFs, and cold outreach, which is like searching for an unlisted house rather than shopping in a marketplace.

On the other hand, per McKinsey, successful sellers overcome this by pairing effective discovery with strong data strategy and real productization, often generating 20% or more of company revenue.

‍

2. Data is IP – and it’s priced like it.

External data isn’t sold like software; it’s sold like intellectual property. Across the market, the pricing reality is stark:

  • Six- to seven-figure pricing is normal. According to a Neudata study, pricing commonly runs $25k-$500k+ per dataset, and an Eagle Alpha report reveals that some consumer-transaction datasets may cost $500k-$2M+ annually.
  • Neudata finds 92% of buyers increased or maintained spend last year, and 89% expect to do so again.
  • Dataset volume multiplies costs. Buyers juggle 20-50 data feeds, each with its own formats, granularity, and onboarding burden.
  • Price negotiations often kill deals. Per Neudata, 58% of buyers cite this as a main barrier to onboarding a new dataset.

Buyers are expected to make six-figure commitments before they can meaningfully evaluate value. It’s like being asked to buy the house before stepping inside.

‍

3. Evaluation drains resources.

Two of the most expensive stages of any AI initiative are data sourcing and data preparation. More than 50% of an AI project’s time and budget is consumed before a model ever sees the data. Evaluating a dataset means provisioning access, wrangling terabytes, and pulling in engineers and BI teams just to answer basic questions.

‍

4. Integration kills momentum.

IDC reports that nearly 80% of data-team time is spent on data discovery, preparation, and protection, rather than analysis or insight. And with data engineering and data science salaries comfortably in six-figures, this means significant budget is consumed before a single model is trained or insight is produced. Every schema mismatch, permissions request, or pipeline fix compounds that cost.

It’s no surprise that many external data evaluations never make it to production. Demand for domain-specific data keeps rising (RAG, vertical AI, and enterprise agents depend on it), but the buying and onboarding experience remains stuck in a pre-AI workflow.

Until evaluation becomes instant, conversational, and low-risk, selling data will continue to feel like real estate without an MLS: buyers want answers and context, not contracts and CSVs.

‍

The New Risk: Conversational AI

(in other words: Your Prospects Are Already Using ChatGPT — with or without Your Blessing)

Every data CEO I speak with sees the same behavior: Prospects (and even current customers) are pasting tables into ChatGPT and asking it questions about their business.

They’re doing it because conversational AI is the interface people want. But for the most part, this just exposes your business to risk and doesn’t actually provide real or dependable analysis. Why?

  • ChatGPT can’t handle large datasets
  • It invents answers when it lacks context
  • And most critically, it can train on whatever they upload

Not ideal when it’s your data powering their questions.

That said, this behavior shows exactly what the market wants: A simple, conversational way to explore data without added costs, friction, or delay.

The demand is real, but the tooling hasn’t been safe or reliable – until now.

‍

Let’s Fix Every Part of the Data Sales Problem

Alkemi’s DataLab is designed to solve all of these problems at once.

It lets data providers showcase their datasets as interactive, AI-native products, without exposing raw files or requiring engineering support.

Here’s how it works:

  1. Add a sample of your data: Connect Snowflake, BigQuery, Databricks, or upload a CSV — all through a simple interface.
  2. DataLab instantly packages it into a secure, conversational demo. Your data becomes something prospects can chat with, explore, and test in plain English.
  3. You get a link you can share immediately. Your sales team can finally say: “No need to wait for engineering. Here’s your demo, right now!”

Buyers can explore the sample securely, share it internally, and understand its value instantly.

No waiting. No setup. No bottlenecks. No loss of control.

‍

‍

‍

Why It Works: DataLab Gives You the Best of Both Worlds

DataLab is built for the reality of how buyers – and now AI systems – want to evaluate data. It bridges the gap between traditional data sales workflows and the new, conversational ways people expect to interact with information.

‍

1. Conversational evaluation without exposure

Buyers get the natural-language experience they already expect, but your data never leaves your environment. DataLab retrieves answers securely at inference time, with no file transfers, no training risk, and no long-tail exposure.

‍

2. Accuracy grounded in your real schema

Generic LLMs guess. DataLab doesn’t. It understands your tables, relationships, and metadata so queries resolve with precision and prospects get trustworthy results.

‍

3. MCP-ready distribution

Your sample becomes instantly usable inside the tools and agents buyers already rely on: ChatGPT, Claude, enterprise copilots, and any MCP-compatible system. Your data becomes AI-native by default.

‍

4. Modern monetization built in

DataLab isn’t just a better demo. It’s a full monetization engine featuring: 

  • Branded storefronts
  • Programmatic paywalls
  • Usage or consumption-based pricing
  • Subscriptions and hybrid plans
  • Analytics showing who is evaluating your sample and how

You can experiment with pricing, understand buyer intent, and package new products without engineering support.

‍

5. A demo that actually proves value

‍For the first time, prospects can talk directly to your data and experience its value immediately. You move them up the DIKW pyramid (remember: data → information → knowledge → wisdom) in minutes, not months.

‍

Bonus: Your TAM Expands Instantly

Most data providers think about total addressable marketing, or TAM, in terms of industries and use cases.

But conversational AI fundamentally changes who can evaluate your data, how quickly they can do it, and which systems can consume it.

This shift expands your total addressable market in three powerful ways:

‍

1. You eliminate engineering bottlenecks — and you move fast.

When you no longer need engineers to extract, anonymize, package, or provision a sample, you can create and share demos immediately. The result:

  • More samples shared
  • More opportunities opened
  • More prospects moving into evaluation
  • And dramatically shorter cycles across every account

Speed becomes a force multiplier. The faster you can put data in front of people, the larger your practical TAM becomes.

‍

2. Anyone inside the buyer’s organization can evaluate the data.

DataLab removes the traditional dependency on analysts or IT. Decision-makers, operators, product teams, and executives can all explore your data directly through natural language.

This expands your reach within every account. When the people who make buying decisions can evaluate your data as easily as anyone else, your TAM grows horizontally inside organizations, not just across them.

‍

3. You gain an entirely new buyer class: AI agents and enterprise LLMs.

‍With MCP-ready distribution, your data becomes instantly consumable by ChatGPT, Claude, enterprise copilots, internal agents, and emerging LLM-driven workflows.

This is the next frontier of data demand.

By 2030, AI applications and agents will create $4.4 trillion in enterprise value — and every one of them will depend on reliable external data.

With DataLab, your data becomes something both humans and AI can evaluate, integrate, and rely on, dramatically expanding your total addressable market in ways that were never possible before.

‍

The Market Is Shifting — And It Favors Data Providers Who Act Now

Selling data is still hard. But it doesn’t have to be.

DataLab gives providers a way to:

  • Showcase value instantly
  • Protect IP rigorously
  • Sell faster and more confidently
  • Break out of engineering bottlenecks
  • And reach a much larger market — including AI systems

‍

Bring your data to life.

👉 Publish your free interactive sample today: https://alkemi.ai/providers

‍

‍

FAQ

What is Alkemi's DataLab for Data Providers?

A secure platform that lets data providers publish interactive, AI-ready samples buyers can explore in plain English without exposing raw data.

Does DataLab train on my data?

No. DataLab never trains a model on your data, and your IP stays protected at all times.

Can data sales prospects use DataLab without engineers or SQL?

Yes. Prospects explore your dataset using conversational AI instantly.

Can DataLab connect to my existing data sources?

Yes — Snowflake, BigQuery, Databricks, CSVs, and more.

Can I sell my data through DataLab?

Yes. You can set pricing, enable consumption models, and access storefront and analytics features.

Can AI systems access my data through DataLab?

Yes. Your sample is MCP-ready, meaning secure inference-only access for agents, copilots, and enterprise LLMs.

‍