How can data classification help you better manage your data?

1 What is data classification?

Data classification is the act of assigning labels or tags to data sets or individual data elements based on predefined criteria. These criteria can range from the type and nature of the data, such as personal, financial, operational, or analytical, to the level of confidentiality, integrity, and availability of the data. Additionally, legal and regulatory requirements and obligations related to the data must be taken into account, such as GDPR, HIPAA, or PCI-DSS. Furthermore, the business value and impact of the data must be considered, whether it is strategic, tactical, or operational. Lastly, the data lifecycle and retention period should be determined as well; whether it is active, archived, or deleted. Data classification can either be done manually or automatically with tools and techniques such as metadata, data dictionaries, data catalogs, data quality rules, machine learning, or artificial intelligence.

2 Why is data classification important?

Data classification is an important practice that helps you identify and prioritize your most critical and sensitive data assets, enabling you to allocate appropriate resources and attention to them. It also enables you to implement and enforce data security and access control policies and procedures based on the data classification levels and roles of the data users and owners. Additionally, data classification can help you comply with relevant laws and regulations, demonstrate accountability and transparency to stakeholders and customers, optimize data storage and backup costs, enhance data quality and accuracy, as well as facilitate data discovery and analysis through consistent metadata.

3 How to implement data classification?

To implement data classification effectively, you need to follow a systematic and iterative process. This includes defining your objectives and scope, establishing criteria and levels, assigning roles and responsibilities, conducting a data inventory and assessment, applying and documenting classifications, and monitoring and reviewing classifications. Defining objectives and scope involves deciding which data sources, domains, and categories to classify, as well as why. Establishing criteria and levels involves determining which factors and metrics to use for classifying data, as well as the number of levels. Assigning roles involves determining who will classify, review, approve, and update data classifications. Conducting a data inventory involves deciding which tools and methods to use for identifying and evaluating data elements. Applying and documenting classifications involves choosing labels or tags to mark data, as well as where to store information. Lastly, monitoring and reviewing classifications requires deciding how often to check and update data classifications in light of any changes in the data or business environment.

4 What are the challenges and best practices of data classification?

Data classification is not a one-time or easy task and requires ongoing commitment and collaboration from various stakeholders and functions across the organization. Challenges such as subjectivity and inconsistency can be addressed by establishing clear and objective criteria and guidelines, as well as providing adequate training and communication to the data classifiers. Time-consuming and resource-intensive processes can be addressed by leveraging automation and technology to assist with the data classification process. Outdated or inaccurate data classification can be addressed by implementing regular audits and reviews, as well as incorporating feedback and improvement mechanisms.