What is a significant challenge when preparing historical data for AI?

When preparing historical data for AI, consolidating data from disparate systems is indeed a significant challenge. This is because organizations often have data stored across various platforms, formats, and databases, making it difficult to gather a unified dataset for analysis. Each system may have its own structures, terminologies, and data formats, which can complicate integration efforts.

To effectively train an AI model, clean and consistent historical data is crucial. If the data is fragmented, it can lead to incomplete analysis, biased predictions, and ultimately poor performance of the AI model. Therefore, a significant portion of the data preparation process is dedicated to ensuring that data from different sources is harmonized, which can be resource-intensive and time-consuming.

Other challenges like data visualization requirements, choosing the right AI model, and establishing a user permission system are important considerations, but they tend to follow after the foundational challenge of data consolidation has been addressed. Without well-integrated data, it becomes extremely challenging to visualize insights accurately, select appropriate models, or even devise a permissions system governing access to the data.