When we talk about managing data, it’s crucial to not just know where it is, but also to understand what it is. As regulations tighten, businesses are increasingly focused on data sovereignty, especially for cloud-stored information. However, the real challenge is knowing precisely what data they possess.
Data classification plays a key role here. It’s not a new idea, but with the surge in unstructured data, having a clear view of all data assets is more critical than ever. Many companies are now turning to AI tools to help.
So, what is data classification? Companies have traditionally organized data by function—like HR files or sales records—and then sorted them by sensitivity. They also look at context, such as when and where the data was created, along with other technical details like file types. Thanks to cheaper cloud storage, businesses can keep more data for longer. This abundance allows firms to leverage data for insights, particularly to train AI models. But if the data isn’t organized, it’s tough to use effectively. Good governance and stewardship hinge on solid classification, and storage efficiency suffers without it.
Manual classification can work, but it’s often clumsy, prone to errors, and not scalable. Sure, creating policies for users to label their data sounds good, but that usually only helps with broad categories and new files. As companies pull in more data from various external sources, it becomes clear that they need automated classification. It’s vital for managing the data lifecycle and securing information.
Analysts like those at Gartner warn that manual classification leads to errors and fails to capture important context. Labels are often one-dimensional and don’t adapt as data changes. Automation improves this situation by providing context and analyzing data content, location, and related documents. Standard tools do well with properly structured data, but they struggle with unstructured data.
Many vendors are harnessing machine learning to dig into datasets. They identify, record, and track relevant elements, although they face limitations when dealing with proprietary data. The market has a mix of classification tools, from standalone applications to those embedded in enterprise systems, often part of business intelligence setups. Some vendors bundle these tools with broader governance and compliance applications, increasingly integrating AI to enhance accuracy and reduce reliance on manual input.
AI fits naturally into data classification. While machine learning has been part of data cataloging for some time, newer tools are experimenting with generative AI and large language models. Companies utilize algorithms like neural networks to detect patterns, especially in unstructured data, ultimately applying automated tagging.
One of the significant benefits is that customers can adjust AI models before using them. Different datasets require tailored approaches; a generic tool might miss the specifics of a company’s data. When effective, an AI model can boost the metadata linked to files, leading to better data control and management.
Also, these AI systems are adaptable. When data needs reclassification—due to regulatory changes, for instance—the tool can update automatically. This dynamic approach is especially crucial for managing unstructured data effectively. Well-organized metadata and catalogs support data retention and enhance security measures, meeting rules for where data resides.
Some big players are leading the way in data classification tools. Microsoft offers AI-based classifiers through its Purview product, which benefits from a blend of business data and domain knowledge. IBM has its Knowledge Catalog, which employs AI and ML for classification in its Cloud Pak for Data. After retiring its Document Classification tool, SAP launched a new service centered on generative AI for document processing.
On the cloud side, Oracle provides metadata harvesting and data catalog features. Google Cloud includes options like Data Catalog, creating inventories from various sources. AWS features the Glue Data Catalog for automated data discovery.
Beyond the giants, many specialized platforms are making waves in data classification and management. Companies like Alation, Ataccama, Collibra, and Informatica offer sophisticated solutions that blend classification with business intelligence needs.