AI Readiness and Data Quality Assessment
Before you build AI: check whether your data is suitable for it.
Many AI initiatives fail not because of the method, but because of the data foundation. This assessment checks before the investment whether data, target variable, granularity, use case and data quality are genuinely viable for forecasting, machine learning or reporting, and delivers a clear roadmap with a risk traffic light. The analysis takes place preferably in your own infrastructure.
Focus
Forecasting, machine learning, business intelligence, reporting, demand planning, returns, quality data and customer analysis. We assess not only formats, but suitability in the intended business context.
Use-case readiness matrix
For each use case a comprehensible rating: suitable, suitable with limitations, or not yet suitable.
What you receive
Four packages, clearly delimited.
For forecasting, machine learning, reporting and AI projects.
Suitable for
- up to 1 million rows
- up to 500 columns
- up to 5 tables
- one defined use case
- up to approx. 500 million data points, depending on the format
In addition to the Data Quality Report
- use-case relevance
- feature suitability
- target availability, if ML is planned
- leakage risks
- granularity check
- data gaps in time series
- forecasting suitability
- ML and BI risk traffic light
- roadmap before an AI project
- PDF report, approx. 25 to 40 pages
- 90-minute results workshop
Let us talk about your data
Free 30-minute initial call. Briefly describe your dataset and your plan; optionally you can state a preferred date.