Data Fit Check

Why are AI projects failing at an 85% rate? Bad Data.

Bad data costs the US $3 trillion a year. 45% of AI project are being scrapped according to executive surveys. Why? Because implementation teams focus on getting data from one system to another, missing key components of the accuracy, consistency and statistical appropriateness required to sustain a production AI system. You can’t wait to implement, but you can’t afford to start with bad data.

Our data fit assessment occurs at the early stages of an implementation project and works alongside any technology team or platform.

We identify what is needed to properly prepare your data for the specific model you are building, starting with the current state of your data. So there are no surprises throughout your implementation.

I've seen the same roadblocks sabotage good projects and tech, time and again.

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The Culprit of Every Problem:

Leaving data requirements to the end of the project.
Assuming data quality is only about moving it from one system to another.
Assuming data readiness can take an MVP or iterative development approach

The Cost:

Months - sometimes years - digging out of technical debt caused by going live before the data requirements were fully satisfied or understood
Projects that don’t deliver ROI and languish with ongoing ‘data issues’

What is it?

A 6-week assessment that evaluates your data's using diagnostics designed by a data scientist designed specifically to ensure the data you are using is ready for use in the model you want to build. We reveal exactly what condition your data is in and what needs to be fixed.

How does it work?

Data Scientists spend 80% of their time preparing and assessing data on every modeling project. The way we build production systems eliminates this essential step in data preparation.

We run your production data through various tests and dummy models to identify areas of incompatibility and waste. We identify issues and missed data requirements before they are hard coded into your system.

What will change?

No more surprises from bad data. Instead of going into a project blind, you will start with a clear understanding of the readiness of your data for the model you are building - and clear instructions to properly prep your data for your model along every stage of the project.

When will it happen?

The assessment can occur at any time during your project but will eliminate the most risk and technical debt if performed early in the discovery phase.

A simple, 6-week engagement does not disrupt or interfere with other teams and the ongoing project timeline.

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