Data preparation is one of the biggest challenges in the start-up phase of AI projects. For many companies, several steps are necessary until the required data set is “AI-ready”.
Often, tasks such as data cleansing, data merging and standardization must be performed to meet the high demands of AI algorithms. Data analysts and statisticians are needed to successfully master these tasks. This process must be well accompanied and requires a great deal of close coordination and project management. At the end of this process, there must be a data set that fulfils the FAIR data principles: Findable, Accessible, Interoperable, Reusable.
In some cases, the preparation of the different data sources is the most extensive part of an AI project. In such cases, the consolidation of different databases and sources can be bundled in a special project which builds the foundation for further steps that need to be taken.