unstructured data

A new report from Komprise shows that 85 percent of IT and data storage leaders are projecting an increase in data storage spend in 2026, while 74 percent are storing more than 5PB of unstructured data, a 57 percent increase over 2024.

To cope with these rising data volumes and outsized spending, enterprise IT infrastructure teams are looking to implement unstructured data classification. Survey respondents rank this as the top strategy to discreetly understand data for storage optimization, data governance, ransomware defense, security and AI curation needs. In parallel, classifying and tagging unstructured data is the top challenge in preparing unstructured data for AI.

A new report finds 78 percent of organizations report being challenged by administering and maintaining existing data loss prevention technology solutions and policies, and 94 percent report using at least two tools and, on average, more than three tools with DLP capabilities, resulting in significant man-hours to administer and maintain multiple solutions.

The study from DLP specialist MIND and Enterprise Strategy Group also shows 91 percent of organizations say it's important to reduce alert noise produced by their current DLP controls due to simple, poor and outdated classification schemes.

In DevOps and IT circles, the word 'observability' has been bandied about for the past few years. Observability is one of those hot and trendy terms which also means different things to different people.

Yet the goal is generally the same: how can we observe our environment and then proactively and even automatically make fixes to things that aren't working, are anomalous, suspicious and/or could potentially cause a disastrous outcome? Such outcomes could include a network failure, a security breach, a server reaching capacity, or in the unstructured data management world -- something else entirely.