Data Assessment


Data Assessment

When an organization has decided to explore utilizing AI technologies in the near future, they often need external help, or at least confirmation, that their data assets and environment are in a good enough condition. This assessment is recommended regardless of how much emphasis the organization has put on its data infrastructure, data collection and quality processes, and governance.

Assessment Content

The data assessment project should include at a minimum the following tasks, having a main emphasis on the envisioned AI utilization plans:

  • Exploring the data platform and infrastructure
  • Studying the relevant databases and data assets
  • Analysing the data quality and comprehensiveness
  • Identifying the gaps between the data assets currently available and the data required for a potential AI utilization
  • Making recommendations for e.g. data annotation and data capturing approaches

If these topics and data foundation are well-governed and documented, this assessment is fairly straightforward, and the focus can be shifted towards AI development. If the key finding is that certain mandatory steps need to be taken before starting AI utilization, the investment made in conducting the assessment will have already paid off.

Our experience

The Top Data Science team has extensive and in-depth knowledge to carry out data assessments with emphasis on the successful utilization of AI technologies. Our expertise includes e.g. (but is not limited to)

  • Image and video data
  • Time series data
  • Sensor and sensor fusion data

and the usage of these in a wide range of industries and use cases. We also provide data evaluations and trials against envisioned AI technologies to validate the data feasibility and to make recommendations for the most beneficial next steps toward AI-supported way-of-working.

Want to know more?



Receiving an in-depth analysis of the data infrastructure, data assets and any gaps from an AI development perspective


Gaining practical recommendations on how to further develop the data foundation


Receiving a list of key success factors on how to proceed towards AI-supported way-of-working from a data perspective