AI Fails Without Data Understanding

Many institutions invest millions of dollars to artificial intelligence solutions, but the main problem is ignored: the lack of the understanding of veri. Even the most sophisticated AI models can not work with dirty, irregular or incorrectly interpreted data. This is one of the main reasons why corporate transformation projects fail.

What happens if the data problem is observed:

• AI systems produce incorrect results when data quality is low, and decision makers develop false strategies
• Teams that do not know how data resources are collected can not manage models and measure performance
• The data architecture is incompatible with different departments and the integration fails
• Every AI project starts from scratch when internal data ancyclopedia is not created, and costs are increased
• It reveals surveillance and security issues unless the data is owned, where it comes and how it can be used

• Corporate success primarily passes through the data strategy.* Simplified AI technologies, not clean databases and clear business identifications move a business forward. AI systems that they thought to build their company data without understanding, cataloging and managing, will be expensive error. The institutions investing before the data infrastructure can then produce real value with artificial intelligence.