Data is growing incredibly fast, especially unstructured data, which makes it really hard for companies to keep up.
When it comes to managing data effectively, data classification is essential.
It allows businesses to organize data in the most efficient way possible. By sorting data into topics, sensitive information, importance, and more, you can find your data in an instant, secure it (if necessary), and even use it to discover insights.
Read on to find out what data classification is, and how your business can use AI tools to automate data classification:
Data classification is the process of organizing data by relevant categories, to make it easy to find, store, and analyze. Automated data classification consists of using machine learning algorithms to classify unseen data using predefined tags.
Having a data classification strategy in place helps businesses:
A data classification system follows certain criteria for organizing structured and unstructured data. For instance, you may classify data according to its type, value, or sensitivity level. It all depends on how you want to organize your data, what you want to do with it, and the insights you’re hoping to gain.
Businesses collect and generate a lot of data. But not all data is equal. Some of it is critical for everyday operations and business decisions, while other data is irrelevant.
Data classification helps companies:
Organize and structure relevant data. Storing unnecessary or duplicate data is expensive and it can also harm your business by skewing lead metrics and your overall performance. Through data classification, you can discover what’s relevant and discard the outliers.
Make data accessible. Data classification ensures the right people get reliable and timely access to data. Also, tagging your data facilitates data discovery and increases productivity. With a clear structure of your data, teams can find what they need faster.
Ensure data security. Classification is key to identify the types of data you have and protect sensitive information properly. Data classification policies authorize who can access critical data. Securing your data and limiting its access makes your business less vulnerable to cyber-attacks and mitigates the impact of data breaches.
Meet regulatory compliance. Business data is often tied to industry-specific regulations that require them to protect sensitive data, such as personal data, credit card information, and health records. Data classification is essential to meet compliance standards and pass audits successfully, by ensuring private data is stored in secured locations.
Perform data analytics. Classifying data enables businesses to detect trends and gain insights to answer questions and make smart decisions. Through data analysis, companies can understand the causes of particular events, predict future outcomes, or measure the effectiveness of a given action.
One of the key steps in data classification is to sort data based on its sensitivity level. This helps companies determine how to secure data based on the impact it may have if it is disclosed, stolen, or damaged.
Creating guidelines for data classification makes it easy for everyone to understand how to handle confidential data on a daily basis and reduces risks.
A simple data classification framework usually includes three confidentiality or sensitivity levels: low, medium, or high.
Each level involves different types of control in terms of who is authorized to access the data, where it should be stored, and what are the requirements to access it.
Low sensitivity: public data with no restrictions of access. This includes content from websites, blogs, catalogs, and social media data.
Medium sensitivity: internal data that is not meant to be shared publicly, such as contracts, student education records, or marketing reports. Since this data doesn’t contain any confidential information, it has low-security requirements. In the event of a data breach, this data won’t harm the business.
High sensitivity: restricted data that requires high-security protection. For example, credit card numbers, social security numbers, health records, authentication data (like passwords). The disclosure of any of this data could have a negative impact on a company, whether legally or financially. High sensitive data often falls under data protection regulations, such as HIPAA (patient data), GDPR (EU residents’ personal data), or PCI DSS (credit card data).
Data classification consists of assigning one or more tags to a piece of information based on certain parameters. There are three standard types of data classification that businesses can use to define tags:
Content-based classification. This approach examines what’s inside documents and looks for sensitive information.
Context-based classification. This type of classification observes all sorts of additional information (such as creator, application, or location) that may suggest the data’s sensitivity level.
User-based classification. In this case, data classification is done manually. An agent is in charge of labeling data based on their personal judgement.
Content and context-based classification can be automated, using AI tools that apply a predefined criteria to data. Text classification, for example, allows companies to tag large amounts of data in an instant, 24/7.
AI data classification tools enable companies to perform data classification at scale. While you’ll need to outline a data classification process and define categories, the task of data tagging can be fully automated with machine learning.
Machine learning models are fed tagged datasets or training data, from which they learn how to understand and automatically classify data. They will always use the same criteria (and keep improving as they classify new data), making them more accurate than human classifiers.
There is no right or wrong when it comes to data classification. But you will need to outline a data classification process that fits your business needs. Here are three basic steps you can follow to implement automated data classification:
Why do you want to classify your data? Define the main purpose and the outcome of data classification. Identify how data is going to be used to make business decisions.
For example, if your main interest is confidentiality, your focus will be to identify where your sensitive data resides and how to secure it. Likewise, if your objective is data availability, your strategy will be to ensure quick and easy access to all your data.
Establish the different sensitivity levels for data categorization and decide which tags and labels to use.
Create a data classification policy that contains your goals and straightforward criteria to apply in different scenarios.
Define roles and responsibilities to access, change, and delete data. Describe how you are going to store and secure your data. For example, are you going to encrypt data, assign user permissions, or implement loss prevention software?
SaaS tools like MonkeyLearn provide intuitive no-code solutions for data classification.
Instead of building software from scratch, you can start performing complex tasks in next to no time, without having to worry about huge investments or losing time.
To get an idea of how you can sort your data by confidentiality, try out this sentiment analyzer, which identifies positive, negative, and neutral responses:
For specific use cases, like detecting confidentiality levels in data, you’ll need to build a customized model.
You can train a custom classifier adapted to your needs using your own data and criteria, and define the tags that you want to apply: High Confidentiality, Medium Confidentiality and Low Confidentiality. Follow this tutorial to build a custom data classification model with machine learning.
Data classification is essential to keep track of your business data. It helps you secure sensitive information, identifies relevant data, and makes data accessible to everyone that needs it.
Classifying your data effectively is a step towards making better business decisions. Discover other text analysis tools and techniques that you can use to classify your data and gain insights.
For example, use sentiment analysis to classify data as positive, negative, or neutral, and better understand your customers’ needs.
October 23rd, 2020