What is the right
data for AI?

The Meaning behind your Data

Give even the best data analyst a stack of company files and ask for analysis and they will struggle to make sense of them without the relevant business context to understand what the figures represent.

The same principle applies to AI: without providing all the necessary context, the AI might, at best, not be able to complete the task correctly, and at worst, it could make incorrect assumptions and deliver inaccurate results.

Understanding data context is critical in AI because it ensures that the data is interpreted correctly and that the AI models produce accurate and meaningful results.

Verodat helps you define and structure your data and metadata and provides the critical context so it can be easily understood by AI.

A Steady Flow of the Right Data

Supply status refers to the availability and accessibility of data. It involves assessing whether the necessary data is available, of high quality, and accessible in a timely manner. 

If a human is working off old information or doesn’t have all the relevant facts on time, we are likely to reach wrong or incomplete conclusions. Your AI tool is no different.

AI needs data supply status to ensure it has reliable, accurate, and timely data for effective learning, decision-making, and performance.

Verodat offers your AI solution the steady flow of accurate and complete data it needs.

1. Automated Supplier Workflows

With Verodat, suppliers and data systems can safely connect to your data through automated requests or API calls, providing a seamless supply of the relevant and appropriate data. With defined data requirements established, guardrails around quality, format and completeness of your data are inbuilt into the workflow.

2. In-app Error Resolution

These guardrails result in the automated flagging and ability for suppliers to perform in app resolution of any errors, discrepancies or gaps. This removes the need for back and forth with suppliers, reduces data roadblocks and provides the steady and complete data supply AI needs.

3. Establishing Supply Timelines

As you establish your Data Demands with your suppliers, you specify a schedule of expectation. This is a timeline for when you expect this data to be updated. This scheduling is critical as it ensures your data supply chain operates on a known cadence. This provides key information to the AI on the supply status of the data, ensuring its scope of reasoning can be properly adjusted.

The Enriched Data Layer

As data flows through the data supply chain platform, the intuitive workflow ensures completeness that AI requires to support analytics, resulting in more trusted outcomes. With data that has now been filtered, mapped, validated and transformed as well as any error handling along the process logged and tracked, your data layer is now fully enriched with context, supply status and finally, attribution.

The attribution accounts for the data from the point of origin up to when it lands in your analytical database. This attribution and full data provenance is the final critical factor in creating a data layer designed for AI. Not only can AI now fully interpret and learn from the data layer, it can also provide explanation and reasoning for outcomes and repeat its actions for validation.

A User Familiar Environment

Verodat is designed to sit alongside the existing tools that organisations are familiar with. The Verodat platform is intuitive and user friendly, turning difficult data ops projects into a more manageable task.