regulatory requirements and communicated across all departments. The principles of data classification apply equally to existing data and new data acquisition, although the approaches and challenges might differ for each. For existing data, the primary challenge is assessing and categorizing data that has already been collected and stored, often under various formats, standards and sensitivity levels. This process involves: • Auditing and inventory: Conducting comprehensive audits to identify and catalog existing data assets. This step is crucial for understanding the scope of data that needs to be classified. • Cleansing and organizing: Existing data might be outdated, duplicated or stored in inconsistent formats. Cleansing and organizing this data is a preparatory step for effective classification. • Retroactive classification: Implementing classification schemes on existing data can be time-consuming and require substantial manual effort, especially if automated classification tools are not readily available or cannot be easily retrofitted to legacy systems. New data acquisition, by contrast, allows for the opportunity to embed data classification processes at the point of entry, making the process more seamless and integrated. This involves: • Pre-defined classification schemes: Establishing and integrating classification protocols into the data collection process ensures that all new data is classified upon acquisition. • Automation and AI tools: Leveraging advanced technologies to automate the classification of incoming data can significantly reduce manual labor and improve accuracy. • Data governance policies: Implementing strict data governance policies from the outset can ensure that all newly acquired data is handled OPENING L INES 9 CIVIL AND MUNICIPAL VOLUME 5, ISSUE 03
RkJQdWJsaXNoZXIy MTI5MjAx