Most data-related problems, or extraction of knowledge from data, simply doesn't benefit from Deep Learning.
In my experience, what many organizations lack is simple but high-quality "Business Analytics": Reporting & dashboards are developed that look good but jam too much information together. It is often the wrong information:
Something is requested, and the developer develops exactly what was asked. The problem is that it wasn't what was needed because the person making the request couldn't articulate the question in the same terms the developer would understand. The request will say "Give me X & Y" when the real question is "I want to understand the impact of Y on X". The person gets X & Y, looks at it every day in their dashboard, and never sees much that is useful. The initial request should always be the start of a conversation, but that often doesn't happen. A common result are people in departments spending tons of time in Excel sorting, counting, making pivot tables, etc., when all of that could be automated.
This is part of the reason why companies often go looking for some new "silver bullet" to solve their data problems. They don't have the basics down, and don't understand the data problems well enough to seek out a solution.
I think we’re starting to see peak managerialism. The latest wave in stats has shown more than anything that a significant shortfall in basic statistics knowledge makes it almost impossible to make good decisions with vast amounts of data.
Without the skillsets to work with and then understand that data, they are forced into this long process of asking for data to be put into reporting and dashboards and then once they finally get them, either fixating on the limited metrics it provides while being oblivious to other context not in front of them, or to instead forced to start another long iteration to adjust that reporting and dashboards.
We’ve gone almost 30 years believing management was the sole skill required to manage teams and companies, but dealing with the new era of data is starting to show the limits
In my experience, what many organizations lack is simple but high-quality "Business Analytics": Reporting & dashboards are developed that look good but jam too much information together. It is often the wrong information:
Something is requested, and the developer develops exactly what was asked. The problem is that it wasn't what was needed because the person making the request couldn't articulate the question in the same terms the developer would understand. The request will say "Give me X & Y" when the real question is "I want to understand the impact of Y on X". The person gets X & Y, looks at it every day in their dashboard, and never sees much that is useful. The initial request should always be the start of a conversation, but that often doesn't happen. A common result are people in departments spending tons of time in Excel sorting, counting, making pivot tables, etc., when all of that could be automated.
This is part of the reason why companies often go looking for some new "silver bullet" to solve their data problems. They don't have the basics down, and don't understand the data problems well enough to seek out a solution.