Data Management Glossary
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.
Komprise Deep Analytics allows you to find the right data that fits specific criteria across all your data storage silos to answer questions, such what file types the top data owners are storing. Once you connect Komprise to your file and object storage, Komprise indexes the data and creates a Global File Index of all your data. You do not have to move the data anywhere; but you now have a single way to query and search across all file and object stores. For instance, say you have some NetApp, some Isilon, some Windows servers, some Pure Storage at different sites and you have some cloud file storage on Amazon, Azure, and Google. You get a single index via Komprise of all the data across all these environments you can search and find exactly the data you need across all these environments with a single console and API. Once you find the data you want to operate on, you can systematically move it using Komprise Intelligent Data Management. For example, if you want to tier files generated by certain instruments to the cloud, you can create a policy so that as new files are generated, they are continuously and automatically moved. This makes it easy to systematically leverage analytics to move and operate on unstructured data.
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.
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.
Read the article: How to Control Unstructured Data
Komprise Use Case: Data Classification
