How can you classify data for multiple departments?

Understand Departmental Data Needs: Identify the types of data each department handles. For instance, HR may deal with personal employee data, while Finance handles financial information. Develop Broad Data Categories: Establish general categories applicable across departments, such as confidential, internal use, and public data. Set Department-Specific Subcategories: Within each broad category, create subcategories tailored to departmental needs. For example, within 'confidential' data, HR might have 'employee health records' and Finance might have 'investment strategies'. Establish Access Controls: Define who can access each data category and subcategory. Access should be based on the employee's role and necessity.

Another classification on sensitivity should be on the laws, regulations and legal structure of the company this can drive access and usability (data rights) requirements. Third party data rights and access must also consider contractual permissions for acceptable use.

Data classification can be based on business data or function categorization and classification based sensitivity of the data. Classification of data based on sensitivity helps protect data based on data’s Confidentiality, Integrity, Availability, that’s CIA. To do this firstly the organization needs to define and agree on the classification scheme based on the organization data protection requirements and compliance mandates. There are baseline compliance and also tools available to help achieve this

Involving all the business stakeholders is important. If you focus on a single department, you get that single department's siloed view. If you established data governance first, you've identified the people you need to work on this task. It is important to remember though why you're doing this. Too often, people can find themselves in a classification spiral where they want to classify everything in no particular order and forget about the people who need to use the data. The classifications can be very valuable, but if your high-priority project needs data that isn't going to be classified until next year, it's not useful to you.

Why should you involve Business data stakeholders??? Understand the data is all about business and its implication. Considering the Generative AI trends, it expected that you have a cross functional governance team. The involvement of business data stakeholders is key because... 1. Know the data better 2. Consume the data to derive insights 3. Identify the right set of business rules, glossary for Data Entities 4. Monetize the data

Involving data stakeholders in the classification process is crucial for aligning with the needs of various departments. This includes engaging data owners, custodians, users, and consumers, who offer vital insights on data management and utilization. By facilitating stakeholder meetings, organizations can gather feedback on classification criteria, ensuring the system reflects real business needs. This engagement not only aids in identifying gaps and inconsistencies but also fosters inter-departmental collaboration and communication on data issues. Achieving stakeholder buy-in through this process is key to a successful, widely-supported data classification initiative.

Implementing data classification tools is the third crucial step. These tools scan and inventory data assets across platforms, applying and enforcing classification criteria. They monitor and audit compliance, reporting on classification results and metrics. Types of tools include data catalogs for centralized metadata repositories, data tagging for labeling assets, data masking to anonymize sensitive data, and data encryption for security. These technologies streamline and automate the classification process, enhancing efficiency and accurac

Reviewing and updating data classification regularly is vital to maintain relevance and accuracy. This ongoing process adapts to changes in business context, data sources, types, sensitivity, and usage. It's also crucial for complying with evolving legal and regulatory standards. Engage stakeholders for feedback on improvements or modifications. Establish a transparent review process with clear roles and responsibilities, set review schedules, and criteria for evaluating classification performance. Document and communicate changes to stakeholders, ensuring data classification aligns with dynamic departmental needs and governance standards.