Data Management Glossary

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Unstructured Data Classification

Unstructured data classification involves the process of categorizing and organizing unstructured data based on its content, context, or other characteristics. Unstructured data typically refers to information that does not have a predefined data model or is not organized in a structured manner, such as text documents, images, audio files and videos. Classifying unstructured data is increasingly recognized as essential for efficient unstructured data management, search, and analysis.

Unstructured Data Classification: A Top Enterprise Data Storage Trend

According to Gartner’s Top Trends in Enterprise Data Storage 2023 (subscription required):

By 2027, at least 40% of organizations will deploy data storage management solutions for classification, insights and optimization, up from 15% in early 2023.

The report goes on to note that:
Data classification or categorization helps improve IT and business outcomes such as storage optimization, data life cycle enforcement, security risk reduction and faster data workflows. Data classification and insights solutions are typically vendor storage agnostic, and work on any data that can be accessed over a file or object access protocols like NFS, SMB or S3.

Why unstructured data classification matters

Classification adds structure to unstructured data – which makes it easier to find and leverage across the organization. Classification starts with the metadata that’s automatically generated by data storage technology.

System-generated metadata includes information about when the data was created, who created it, its type, its size, when it was last accessed and when it was last modified. This helps IT managers classify data by the department it belongs to and identify rarely accessed data as ready for archiving and tiering to lower-cost storage destinations. IT professionals can also search based on data types, such as video or medical imaging files, which may be consuming too much storage (and budget) and require action such as migration. Enriching metadata adds additional classification, such as to identify project data, demographic data, sensitive data or other content based on keywords.

Use Cases for Data Classification

Security and Privacy: Data classification is critical to discover personally identifiable information, IP and other sensitive data that may be hidden or has been copied and stored in noncompliant locations. An organization can apply levels of security classification too, such as low, medium or high risk.

Audits and E-discovery
Some organizations have regular audits, such as for proper management of financial or personal health information data, which requires IT to work with auditors and demonstrate compliance. Without classification and segmentation of audited data, an organization may face heavy manual work to locate audited data. For e-discovery, which happens out of the blue, a company may need to quickly locate and copy security video footage to facilitate an investigation, for instance.

Data Retention
Industry or corporate rules may dictate the retention of files for a period. Searching metadata for file type, such as medical images, and time of creation, IT can find files that are prime for deletion. This also saves money by avoiding the endless storage of data that is no longer needed or required. Komprise Smart Data Workflows can allow IT to create workflows that discover and confine or delete files by policy.

Cost Savings
Data classification by age and time of last access is a smart way to find data that is rarely accessed, or “cold,” and move it to archival storage where it can be retained for as long as necessary — at a fraction of the cost. Metadata indicating file type, such as instrument or research data, further informs long-term storage strategies. Learn more about Komprise Analysis here.

Search and AI
Deep classification of unstructured data sets, such as by keyword or project name, helps employees can find what they need without bugging IT. They can then feed it to analytics tools or other applications as needed. For instance, healthcare analysts may want to run a study of breast cancer images from a certain demographic and with a particular diagnosis code. Enriching metadata with these tags in a policy-driven, automated way means that the required data sets are always updated and easy to locate by researchers.

Data Governance for AI
IT and security teams can tag and segment proprietary data sets which are banned from ingestion by AI tools, as well. This is an important consideration when using GenAI tools in the public domain, since sensitive and protected data can be easily and unwittingly leaked into training models. Read more about Komprise Sensitive Data Management.

What are some approaches and techniques for unstructured data classification?

Text-Based Classification

Natural Language Processing (NLP): NLP techniques, including text tokenization, sentiment analysis, and named entity recognition, can be used to analyze the content of textual data.
Keyword Matching: Classifying documents based on the presence of specific keywords or key phrases related to predefined categories.

Image-Based Classification

Computer Vision: Utilizing computer vision techniques, such as image recognition and object detection, to classify and categorize images based on their visual content.
Feature Extraction: Extracting features from images, such as color histograms or texture patterns, and using machine learning models for classification.

Audio and Speech-Based Classification

Speech Recognition: Converting spoken language into text for further analysis and classification.
Audio Analysis: Extracting features from audio files, such as pitch or frequency, and using machine learning algorithms for classification.

Metadata-Based Classification

File Metadata: Utilizing metadata associated with files, such as creation date, author, or file type, for classification purposes.
Exif Data: For images, extracting metadata embedded in the file, such as camera settings and location information. Exchangeable image file format (EXIF).

Pattern Recognition

Machine Learning Algorithms: Training machine learning models, including supervised or unsupervised learning algorithms, to recognize patterns and classify unstructured data based on historical examples.
Clustering: Grouping similar data points together using clustering algorithms to discover natural groupings within unstructured data.

Rule-Based Classification

Predefined Rules: Establishing rules and criteria for classifying data based on certain characteristics or conditions.
Expert Systems: Using expert systems that encode human expertise and rules for classification.

Content Analysis

Topic Modeling: Identifying topics or themes within unstructured text data using techniques like Latent Dirichlet Allocation (LDA).
Sentiment Analysis: Determining the sentiment expressed in textual content, such as positive, negative, or neutral sentiments.

Combination of Techniques

Hybrid Approaches: Combining multiple techniques, such as text analysis, image recognition, and metadata examination, for a more comprehensive and accurate classification.

Deep Learning

Neural Networks: Leveraging deep learning models, such as convolutional neural networks (CNNs) for images or recurrent neural networks (RNNs) for sequential data, to automatically learn features and patterns for classification.

Feedback Loop and Continuous Improvement

Establishing a feedback loop where the classification system continuously learns and improves based on user feedback, corrections, and updates to the training data.

Unstructured data classification is a challenging task, but advancements in machine learning, deep learning, and natural language processing have significantly improved the accuracy and efficiency of these classification methods and modern unstructured data management software solutions have emerged to address elements of data classification and ongoing data lifecycle management.

Depending on the specific requirements and characteristics of the unstructured data, different techniques or a combination of approaches may be suitable for effective unstructured data classification.