What is data classification?
When it comes to data annotation and labeling, data classification is the practice of organizing data into categories to improve the efficiency of working with that data. Data classification can, for example, make it easier to retrieve data by allowing users to specify a subset of data categories to search.
Data classification is also an important part of upping your organization's security. With properly categorized data, organizations can apply automated and manually executed access controls, effectively limiting access to data to authorized employees and customers. For example, by applying security classification levels, organizations can determine the sensitivity of the data and the damage that might occur in the event of an unauthorized disclosure.
Beyond security, classification efforts are useful for reining in massive amounts of data and making it useful for running machine learning (ML) models. Through data classification, it becomes possible to determine which datasets should be archived or deleted after a period of time.
Keep reading for the types, benefits, best practices and applications of data classification.
Types of data classification
There are a number of criteria that can be used for classifying data. Some of the most common types include context, content and user-based classification.
- Context-based classification (also known as semantic annotation) is the process of attaching additional information to various concepts relevant to it. This type is dependent on attributes of the data, such as where it is located or with whom it is associated. This is important for some compliance requirements; for example, personal data about citizens of the European Union are subject to the General Data Protection Regulation (GDPR), making context-based classification important when working with data subject to GDPR.
- Content-based classification uses the data itself for determining classification categories. The content of information is used by machine learning algorithms to determine which categories apply to the data. When working with text or speech data, linguistic annotation is central to classification. Examples of use cases include chatbots and virtual assistants, search engines, spam filters and machine translation.
- User-based classification is used when domain experts with knowledge of multiple aspects of data, including context and content, determine appropriate classifications for data.
Benefits of data classification
Systematic classification of data helps organizations manipulate, track and analyze individual pieces of data. Depending on the specific ways data are used, organizations will have different approaches to classifying data and will likely use different categories in their classification schemes.
Three fundamental benefits of data classification are cleaner data, improved accessibility and increased productivity.
- Data classification allows ML experts to maintain a clean and efficient data environment: Data classification is a crucial first step on the road to collecting and cleansing data for machine learning projects. All data needs to be clean before it can be used and analyzed, especially when it will be fed through a machine learning algorithm. However, without data cleansing practices in place, scientists may run analyses and reach the wrong conclusions.
- Data classification improves accessibility: When working on machine learning projects, data scientists come across a lot of data that, at best, has no value and, at worst, introduces errors and skews analysis. Much of that data can be categorized as redundant, obsolete or trivial (ROT) and it can be filtered automatically from the dataset to improve the accessibility of high-quality data. Data accessibility is important for the legitimacy and democratization of science, a core tenet of the field.
- Data classification boosts the productivity of ML teams: Data scientists spend a lot of their time preparing data. Without data classification, ML teams need to spend more time cleansing and sorting through irrelevant data. Correctly classified data is necessary for a streamlined data pipeline.
Quality, quantity, and responsibility: Three key data pillars for successful AI implementations
In this IDC Info Snapshot, learn about the three key pillars for any successful AI implementation and the advantages of partnering with a trusted advisor to create and deliver your AI vision.
Data classification best practices
The first step in data classification is gathering information about the types of data generated, collected and managed for a project. This can be a significant amount of work, but machine learning can be used to automatically cluster or group similar kinds of data. For instance, machine learning techniques can be applied to unstructured data (such as documents) to identify groups of documents with similar characteristics, which would likely have similar data classification requirements.
After gathering information about the data, the second step involves defining labels and related metadata tags for describing data. Labels could include, for example, a broad category such as public, confidential, sensitive or private data. Metadata tags could be used to describe the context of how the data is used, such as by labeling the location or adding a date by which the data should be archived or deleted.
Collecting and labeling data may be easier for some teams than it is for others. To guide the process, the creation of a data classification matrix, sometimes called a "confusion matrix," is helpful when dealing with the high volume of data, as well as the different data types. The classification matrix sorts data from a model into categories, and determines whether the predicted values match the actual values. By using a classification matrix, data scientists can better explain and account for the impact of errors when evaluating predictions.
With a classification framework defined, it is then time to apply categories and metadata tags. This can be a substantial amount of work, so automation is crucial. Machine learning is particularly adept at categorizing data, but requires specially trained models to apply the specific classification framework developed in the previous step. Training a classification model will require training using labeled examples. There should be sufficient examples of all categories so the model can learn how to accurately identify each category.
Whether using manual or automated methods, the third stage of the data classification process is to apply the framework and standards to data within the organization. This will require some type of high-level inventory of data storage locations and a plan for applying classification rules to data in all those locations. Note that after the initial classification step, a continual process will need to be in place to classify new and changed data.
Finally, the data classification results will need to be evaluated. This is an ongoing data quality protection step meant to ensure the classification framework is applied as expected. This process should also identify new categories that might emerge or detect changes that are not captured in machine learning models used for data classification. This is known as "data drift" in machine learning, and is addressed by standard machine learning best practices.
Applications for data classification
Data classification has an array of applications across organizations and industries. Some of the most common are text classification, image classification and document classification.
- Text classification can be applied to any free-form, natural language text. This includes short descriptions in product catalogs, customer comments and online reviews. Text classification can be used to help identify high priority messages, such as a customer complaint or a request with a near-term deadline.
- Image classification is used in a variety of applications. In manufacturing, image analysis is used to help identify quality problems in the manufacturing process. Consider a machine learning model that can classify images of newly manufactured parts that meet quality control criteria. With such a model, parts that don't meet quality control standards can be automatically identified and removed from the production process.
- Document classification is similar to text classification, but is applied to longer texts. Once again, machine learning models can be used to apply classification labels to documents for the purposes of routing documents to different departments or individuals for processing. These models can also be used to block the transfer of sensitive or confidential documents outside of the organization without proper authorization.
- Audio classification (also known as sound classification) is the process of listening to and analyzing audio recordings. By starting with annotated audio data, machines learn how to 'hear' and what to listen for. This then helps them learn to differentiate between sounds to accomplish specific tasks. A popular example of this is the classification of music into genres, based on its style and the instruments played.
Data classification and labeling with TELUS International
With decades of domain expertise supporting global machine learning teams, TELUS International's cutting-edge artificial intelligence (AI) training platform and diverse workforce help organizations overcome common data annotation and classification hurdles. Using our best-in-class community management and sophisticated technology, we help organizations deliver accurate results on time and at scale.
To maximize the benefits of data classification, partner with our team of AI Data Solutions experts to start your data classification project or optimize an existing one. Today's data driven universe demands a systematic approach — and that starts with classifying all the various types of data being collected.