AI requires enormous and expensive leaps of knowledge about data, which itself is often under-governed.
Questions about where to deploy ML plus concerns over costs, security, compliance, and ethics can stop the journey before it even begins.
But what if these use cases don’t apply to your business or aren’t considered in need of solving? This puts the onus on businesses to creatively self-identify their own use cases, which can be hard to do when technology is complex, new, and multi-faceted.
Many business leaders think data analytics is enough. Patterns of data causation or correlation you didn’t know about previously will be infinitely more valuable than the answer to any specific question.
Yes, models need enough data to understand the question and return results with confidence and accuracy, but the amount required depends on the purpose and complexity of the project.
Many use cases need less data than you think. Another concern is whether the data involved is structured or unstructured.
Since 80% of business data is unstructured, many ML projects are abandoned as structuring enough data is often considered too labor-intensive.
It’s easier to make excuses than to make progress. If ML projects are approached correctly with the right partners, businesses can tackle the obstacles and reap the benefits. It is a data world after all. We might as well get to know it better.