The unstructured data risk is real, and it’s creeping into every part of your organization

Organizations have always struggled to manage their unstructured data, but the advent of generative AI (GenAI) has drastically increased the urgency. A February 2025 report from Gartner reported a strong increase of ~150% in inquiries on “unstructured data management” in the previous 12 months.

The reason? GenAI. Organizations who wish to leverage the technology by deploying retrieval-augmented generation (RAG) pipelines need access to AI-ready data – accurate, reliable, transparent, and with sensitive customer data masked or redacted. This means they need the ability to understand and manage their unstructured data. Otherwise, they risk being left behind.

The scale of the problem

Unlike structured data, such as data residing in a database, which has a pre-defined structure or data model, unstructured data is inherently freeform and has no pre-defined shape or format. The most common examples of unstructured data in the modern business world are documents, emails, and media files. Until you open a Word document, for example, you can’t tell whether it is going to be a single-page, eight-paragraph blog post draft or a 100-page dissertation featuring embedded videos, graphics, and tables. Or – more relevant for regulated industries – a document containing customer personally identifiable information (PII) or protected health information (PHI).

Unstructured data is the fastest type of data growing within enterprise orgs, and 90% of enterprise data is unstructured, so it’s no minor consideration. Yet 60% of the average organization’s technology spend is allocated toward structured data initiatives.

What makes unstructured data management difficult?

Most organizations lack an accurate picture of the state of unstructured data in their systems. Structured data is easier to manage because it’s easier to see – unstructured data has to be identified before it can be managed at all. One reason for the proliferation of unstructured data is that team members don’t know what they have. A recent survey by analyst firm IDC saw 22% of IT decision-makers polled say unstructured data is unnecessarily replicated because organizations simply don’t know what they have or how to find it. Another revealing statistic: just 58% of unstructured data is reused after initial use/creation.

What are the risks?

So, if 90% of enterprise data is unstructured, and the average company struggles to manage their unstructured data, and the scale of the problem is only increasing, this leads to significant and growing risks.

• You can’t manage what you don’t know is there. If you’re in a regulated industry such as financial services or healthcare, you can’t be sure you are managing PII, PHI, or payment card information (PCI) correctly, leaving you vulnerable to a data breach or a failed audit.

• With unstructured data, it is far too easy to over-retain data, which again can worsen the effects of a breach and put you at risk of compliance violations. Data you don’t know exists will be stored forever.  

• Without consistent formatting/metadata, unstructured data often requires manual management. This is inefficient, time-consuming, and introduces the potential for human error factor. Or, more commonly, the work does not happen at all.  

• All of this creates costs: would you prefer to use your budget or your personnel to manage useless, redundant data, or to grow your business? Managing unstructured data will hamstring you long-term.

Data governance leads to AI governance

Data concerns are among the most cited challenges related to GenAI adoption. A Gartner survey found that 46% of survey respondents across eight corporate functions cited data accuracy, reliability and transparency as their topmost concerns. In our own research, we found 84% of respondents lacked governance over data, privacy, and security.

What can you do?

It’s time to take the unstructured data challenge seriously, rather than hoping it gets better. Don’t treat unstructured data as an afterthought.

Bridge the divide by managing structured and unstructured data in one place. Implement data governance best practices, and your unstructured data will follow. The most important parts: accurate, consistent classification/tagging, a strong metadata approach, and – of course – proactive retention/disposal policies.

The RecordPoint solution

Adequate management of unstructured data can be found through solid data governance, and RecordPoint makes achieving this much easier. The RecordPoint platform puts end-to-end governance of structured and unstructured data in easy mode.

• With automated classification and intelligent signaling driven by AI, manual record management has become a thing of the past.  

• Embrace a trusted data platform infused with AI and ML to help you intelligently classify and dispose of sensitive data, faster

• Ensure compliance with regional and industry-specific data governance and privacy regulations like GDPR, PIPEDA, and CCPA; easily adapt retention policies in your file plan to meet changing laws, regulations, or internal policies

• Metadata enrichment automatically enhances metadata by incorporating relevant information from external or third-party systems. Automatically add metadata from external systems, such as data size, type, location, author, and other custom fields, to help streamline data minimization. Enhance typically metadata-poor sources, such as Exchange Online, Teams, or Dropbox  

• Enrich your records with privacy signals, such as PII and PCI data, for better risk assessment

• Prepare for AI by consistently scanning for PII and PCI data, ensuring data does not contain confidential or sensitive information. Accelerate AI adoption with a risk-based, data-centric approach to identifying fit for purpose data for model training

Learn more about how RecordPoint can help you gain control over your unstructured data with a tour of the platform or by booking a demo.