What Is Classification?
Experts predict 120 zettabytes of data will be created, copied, captured and consumed worldwide in 2023—up from 2 zettabytes in 2010. That’s a lot of data! With so many data sets at their fingertips, data scientists need to organize this information into relevant categories, making it easier to retrieve and manage in the future. This sounds like a mammoth task, but it’s no problem for well-trained scientists who love to solve data-related problems.
Data Classification Meaning
Data classification is the process of categorizing all the data in an organization. Data scientists use this process to identify different data types and manage and secure vast data sets.
Data classification helps scientists answer these questions:
- Where does an organization’s data come from?
- What types of data does an organization have?
- How can data be categorized to improve security and meet compliance requirements?
Although data classification’s origins are unknown, this method has become more critical as organizations adhere to data governance legislation. Data classification systems support scientists when finding, retrieving and categorizing data for this purpose.
Data Classification Types
Data scientists typically classify data into categories such as:
The government maintains public information and can disclose it in certain circumstances. Birth, death and marriage records are examples of public data.
Confidential data might have legal restrictions regarding how it can be shared or managed. Government data that might pose a national security risk if revealed to the public is an example of confidential data.
Sensitive data doesn’t have as many legal restrictions as confidential data. However, authorization requirements and rules for its use will limit how this information is shared and managed. Healthcare records are examples of sensitive information.
Personal information (or PII) needs to be expertly handled by organizations as various data security laws govern its use. Customer login details and credit card numbers are examples of personal information.
Benefits of This Method
Here are some advantages of data classification:
- Categorizing data into different groups can help organizations adhere to data governance frameworks like GDPR, CCPA and HIPAA. That can avoid expensive fines from the government for non-compliance.
- Data classification lets an organization identify what data they can share with team members and what data should remain confidential.
- This process also lets organizations know what data sets and types need specific security measures, such as user access controls.
Data Classification Drawbacks
The disadvantage of data classification include:
- Classifying data can be a long process that involves advanced data science skills. Although software can help, organizations need to hire a qualified and experienced scientist to categorize data and ensure it meets security and compliance requirements.
- Data classification isn’t always possible if an organization has data sets that are unreliable or full of inaccuracies and inconsistencies.
Here is an example of the data classification process:
A data scientist working for a large company identifies data for classification. She learns how many data sets exist in the company and which team members can access this information. Then she develops a data classification framework and organizes the data sets into different categories using the latest software tools. The scientist applies standards to the data to ensure sensitive and personal data adheres to data governance legislation. After securing and managing the data, she processes the data as usual.
This glossary term answers the question “What is classification?” so you can better understand this data science process. Data classification involves scientists categorizing data sets to identify, manage and secure all the data that flows through an organization. Although this process can be long and complicated, it can improve security and compliance and help organizations identify what data they can share with others.
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