Over the past few years, many organizations have invested in data platforms such as Databricks, Snowflake, or Microsoft Fabric. These platforms enable them to manage, combine, and make data available for analytics and AI from a central location. As AI is increasingly being used in day-to-day business processes, attention is shifting to the content itself: does the data also contain the context needed to provide useful answers?
This is where AI-ready data comes into play. It complements your data platform and helps connect the data you have available to the business reality.
Your data platform is in place. What’s the next step?
A modern data lake or lakehouse provides scalability, governance, and capabilities for machine learning and generative AI. This creates the technical foundation for using data at scale. However, the platform itself does not automatically determine whether the data is complete and up to date, or how companies are connected to one another.
This difference becomes apparent as soon as you ask AI questions that go beyond an individual record. For example:
- Which customers belong to the same corporate group?
- Which suppliers are part of the same parent company?
- Where are the shared risks within your supplier network?
- What commercial opportunities exist within a broader corporate structure?
Answering these questions requires relationships between organizations. Without this context an application can retrieve information but still miss relevant connections.
AI-ready data adds meaning
AI-ready data is information that is sufficiently reliable, up to date, and rich in context to support an AI application. For business data, the most important characteristics are:
- Unambiguous identification: each record refers to the correct organization.
- Documented relationships: corporate structures and ownership relationships are transparent.
- Current and validated data: information is checked and maintained.
- Transparent data provenance: you can trace where the data comes from.
- Consistent definitions and governance: meaning, usage, and responsibilities are clearly defined.
Suppose you ask an AI assistant how much revenue you generate from a corporate group. Your data platform contains the transactions, but to get the full picture, the application also needs to know which customers belong to that corporate group. It is precisely this combination of internal data and business context that enables a more complete analysis.
Interesting read: AI experiment shows why reliable business data makes the difference
From data lake to a business context layer
By enriching internal data with external business information, you add a business context layer to your data platform. This can include organizational structures, ownership relationships, market information, and risk data. This allows individual customer and supplier records to be connected to a broader view of the business.
A unique identifier, such as the D-U-N-S® Number, helps bring together data relating to the same organization. Corporate information then makes the relationships with other companies visible. Altares Dun & Bradstreet provides business information that can be used to add and maintain this context.
This is particularly relevant for AI agents performing tasks related to customers, suppliers, risks, and opportunities. In addition to the records and transactions in your own environment, they need access to the right entities and relationships.
Interesting read: From AI-ready to agent-ready: Is your data ready for Agentic AI?
Two building blocks that reinforce each other
A scalable data platform and AI-ready data each serve a distinct purpose. Databricks, Snowflake, and similar platforms make it possible to process and make data available. Reliable business data adds the context needed to interpret that information more effectively.
The next step, therefore, is to strengthen your existing platform with the information your applications need. This allows you to build on your investment while giving employees and AI systems a more complete view of the companies you do business with.
Your data platform is in place. Ready to get more out of it with AI? Our experts would be happy to discuss the business data and context your AI agents need.
