Data Management Falls Short for Unstructured Data

Although data-centric enterprises have adequate strategies in place for managing their structured data, current tools are not sufficient for managing the recent wave of unstructured data growth, according to a new report.

The report, which was commissioned by Igneous and is titled “The State of Unstructured Data Management,” surveyed 200 IT leaders across a broad set of industries, assessing data management challenges and priorities for structured vs unstructured data. The report is in its first year.

This year, the report focused on what’s driving the rapid growth of unstructured data, the pain points that organizations face managing this data, and how IT teams can improve their unstructured data management strategy.

The survey revealed that while unstructured data volumes and  business relevance are on the rise, data-centric organizations are currently struggling to achieve their unstructured data management (UDM) goals.

Unstructured datasets are growing quickly. The typical organization reports its unstructured data growing 23% annually, which means it will double every 40 months. Roughly one-fourth (24%) cite growth rates in excess of 40%, where total unstructured data doubles every 24 months.

“Our data is much more valuable than we’ve ever thought,” says Jeff DiNisco, CTO of P1 Technologies, an IT consultancy based near Los Angeles, California. “That’s why we save so much of it.”

Unstructured data is growing rapidly due to the increase in machine-generated data and machine learning initiatives. Respondents of the survey reported that 55% of their data is generated by machines.

Importantly, organizations highly value and rely on their data, making data management a key business priority.

The typical respondent reports that 40% of their organization’s value comes directly from their data. They rank the value of their data ahead of every other asset they were asked about, with the exception of their customers and employees. That makes data more valuable than their brand, physical assets, and inventory.

One reason that UDM falls short is because managing unstructured data requires different approaches, priorities, and capabilities than the traditional approaches to managing structured data.