What is data classification: best practices and types

November 16, 2022
5 minute read

Working with uncategorized, disorganized data is challenging. Imagine getting inside a library to find a book without knowing where it might be. The books aren’t organized by any system familiar to you - not alphabetically or by genre.

Not only will it annoy you, but it will also make it very hard to find what you are looking for. The same applies to data. Disorganized data can’t be easily retrieved, managed, or even stored.

This is why businesses need to use data classification -- to keep everything organized and enable proper data usage. Research shows that 95% of businesses deal with unstructured data, preventing them from operating efficiently. When data is unstructured, it can go against compliance requirements and bring forward risks for the business. Classification is often linked to risk management, which is why companies must handle data properly.

What is Data Classification Best Practices And Types


What is data classification?

Data classification is a process where data is categorized into subgroups, making it easier to retrieve, find, and use.

On the one hand, you have properly structured data and, therefore, easy to search and analyze. On the other hand, you have data that needs to be organized in a predefined manner and, consequently, is hard to explore and analyze.

Still, this is not a final differentiation. Data can be semi-structured, which means it’s been organized at a certain level. Levity’s analysis of "What is data classification?" shows this in detail:

What is data classification?


At a time when almost everything is digitized, most businesses still struggle to organize and structure their data. In the past, businesses utilized complicated analog processes, including handling and organizing piles of paperwork. But today, with technology at our fingertips, this is much simpler.

In data science, data classification is a process that categorizes data through tagging systems. This makes it easy for users to find, understand, and analyze the data.

Why is data classification important?

Certain things at a company are supposed to be shared with the public, such as the company’s mission, for example. Other things are supposed to be kept solely to the people inside the organization, such as the budget and strategies.

There’s data that should be confidential and shared only with selected employees and data that is extremely sensitive and intellectual property.

Clients who share their data with a company expect it to be kept private. Take, for example, a bank collecting data from its clients. They handle personal client data that is supposed to be kept confidential and used solely for the purposes related to the bank’s services.

If these are not classified properly, data can leak, causing tremendous problems for an organization. This can even lead to legal woes.

A data classification system is implemented for everything from legal discovery to risk management to law compliance. Data classification can help tag the most sensitive data, ensuring it’s properly handled alongside less sensitive data that can be more easily shared. Since companies must comply with external regulations, data classification is also used to keep the company out of trouble.

Here are the 4 main reasons why data classification is important:

  • Compliance. When data is classified properly, it is easier to comply with regulatory frameworks such as the GDPR and the CCPA. It also proves the compliance of the company with regulations.
  • Security. Almost all business have some sensitive data. When the data is properly identified and organized, the business can protect it and be aware of its sensitivity.
  • Access. Data is collected and stored to be accessed when needed. Even so, if it’s not properly classified, it’s very hard to find data. Classification serves to provide easy access to the information a company needs, as well as do some data cleaning to optimize the storage.
  • Governance. Finally, data classification makes it simpler for the organization to find, track, control, and use data when needed. In some cases, without data classification, elements like data governance are nearly impossible.

Five best practices for data classification

Now that you know how important data classification is, it’s time to learn the 5 best practices for it. Let’s go through the 5 best practices for classifying data:

  1. Organize and classify your data with AI
  2. Create an inventory
  3. Conduct a risk assessment for your data
  4. Set data security controls
  5. Maintain and monitor the data

Businesses handle an incredible amount of data, which can be an equally incredible challenge. For many companies, organizing data is impossible to perform manually. That’s why the modern best practice lets AI handle this process.

Where do you store your data? Do you keep it all in one place, or is it scattered across many locations? If you want it to be classified and organized, you need to know where to find a piece of data in the first place.

To achieve this, it’s best to use data discovery tools and techniques to locate data. Before you categorize it, create a detailed inventory.

You can still store your data in different places but create distinct inventories for different data segments to make the process more effective. You can allocate the data based on sensitivity, purpose, type, etc.

To classify data properly, you’ll need some data requirements. After all, you can’t structure your information unless you know which data belongs in each category.

If you spend time assessing the risks and their negative impact, you can use this to identify relevant security controls. This will also pinpoint the risks you need to protect against from an awareness point of view.

Does your company even have a data classification policy?

To ensure that your data is protected appropriately now and in the future, set security measures for it. For each classification label you create, define the policy-based control and update those controls whenever necessary.

Start by defining what type of protection each label needs. Use the information you gathered on the regulations and company requirements to create a safety policy for each label. This is not a one-off task. Instead, think of this practice as a way of working.

Keep your data classification process up to date

You can’t organize data once and expect your system to work forever. Regulations change, so you’ll need to tweak your strategies and classification methods to meet new requirements.

Data is dynamic and can be created, copied, modified, moved, and deleted. It can undergo endless changes, and because of it, you need to maintain and monitor the data you have – especially sensitive data.

Data classification challenges

Just like any other process, data classification has its challenges.

Some of the challenges that organizations face in their data classification process are:

    • Big data
      Let’s face it, data is ever-growing and poses bigger and bigger challenges as time passes. Non-continuous tools for classification can struggle when applied to data lakes and warehouses with huge amounts of data.
  • False positives
    When the same bit of data appears in different contexts and formats, it’s a false positive. If your classification algorithms don’t consider the context and format, you will likely have false positives.
  • False negatives
    On the other hand, you can have false negatives. Data can be sensitive in one context but not in another context. Since regulations differ, a piece of data can be considered sensitive in one state and not sensitive in another. This can often lead to incorrect classification.

The three classification levels of data explained

If you’re trying to classify data for your organization, you should be aware of the 3 data classification levels described below:

  • Integrity - data mustn’t be changed in transit, which means that you must take steps to ensure it is not altered by people who are unauthorized to do so. Integrity equals maintaining the accuracy and consistency of data over its lifecycle.
  • Confidentiality - confidential data is information that can only be shared with a limited number of people or teams because it contains sensitive information.
  • Availability - information should be readily and consistently accessible to authorized parties. This means that the technical infrastructure, hardware, and systems that hold the information must be properly maintained.

How well-classified is your organization’s data?

Is your classification system keeping your organization’s data safe? Have you made sure that you are compliant with the current regulations? Can you find the information you need with ease?

If the answer is no to any of these questions, you should roll up your sleeves and start working on data classification as soon as possible.