Industry leaders estimate that nearly 80 percent of organizational data is dark – siloed in individual storage locations and buried under outdated or redundant content. As a result, teams often miss critical information needed to drive innovation, improve services and stay ahead of the competition.
This scenario has caused organizations across industries to struggle to make use of their rapidly growing data sets. For example, a hospital creates an average of 50 petabytes of data a year. Of that data, nearly 97 percent of it goes unused. Yet, these organizations still invest significantly in storage and analytics that only get to a fraction of their data. For example, cloud storage spending alone is expected to reach 137.3B by 2025.
When considering deep learning and artificial intelligence, most think of future-looking capabilities like fully autonomous “hands-off” operations. While these capabilities show promise, they are often only implementable for organizations with a very mature machine learning department. However, there are semi-autonomous solutions available where deep learning is making an impact for companies of all stages – particularly in enabling individuals and organizations to unlock and operationalize challenging data sets.