Data readiness assessments
Take your data to the next level
We assess the capacity of data to meet new and emerging requirements for applying advanced data analytics to improve processes and outcomes. The assessment entails identifying incomplete data coverage, comprehensive review of datasets and metadata, assessment of existing data-sharing portals, and other factors that may hinder future innovative applications.
We follow this project plan to conduct the assessment:
- Project initiation: we meet with stakeholders to discuss the project plan.
- Comprehensive data review: we review the datasets you provide through a data science lens and with a focus on the suitability of the data for AI workflows.
- Data assessment: we evaluate the “usefulness” and “readiness” of each dataset including data quality concepts such as dataset formats, file types, field types, and field formats. We also identify any gaps and opportunities for improving the available data.
- Metadata assessment: we evaluate any available metadata from a human- and machine-readability standpoint. The evaluation covers metadata fields, completeness, schema, and quality.
- Data delivery assessment: we perform an evaluation of existing data portals from the perspective of the FAIR data guiding principles (Findable, Accessible, Interoperable, Reusable).
- Recommendations: we formulate recommendations to improve data readiness for advanced data analytics and AI. Recommendations include methods for improving data fields and formats, data coverage, metadata schema adoption, and recommendations for new datasets to augment the data catalogue to enhance the successful application of AI.
- Project wrap-up: we prepare a comprehensive report outlining the results from the research, the analysis, and the findings, as well as a path forward in the form of a prioritized set of recommendations. We present the report to the project team and provide ample time to discuss the outcomes.