How do you handle data conversion security and privacy issues?

If the enterprise has a data catalog or glossary then it helps. Further more if the data has been classified as sensitive, being PII, PCI or SPI data then one can infer that access to data will be restricted. In the absence of such a catalog or classification, understanding the domain and the business context of the data in collaboration with the business stakeholders will help to understand the data access requirements. As a general rule, only those with a need to access the data need to be given access. Role based access combined with data encryption and obfuscation measures are a good way to restrict access to data to only personnel that need to.

In my overall journey, I'd like to say that: 1. **Understand Data Flow**: Knowing data sources and destinations is key. ?? 2. **Assess Sensitivity**: Determine data sensitivity and risks early on. ??? 3. **Compliance Check**: Always comply with legal and regulatory requirements. ?? 4. **Privacy Matters**: Respect privacy laws and consent forms. ?? 5. **Quality Assurance**: Ensure data integrity and availability throughout. ??

On top of data cataloging & tagging, understanding lineage and the flow of data, as well as what processes, decisions or tasks the data supports. Understanding the WHY behind a specific data set will help tremendously in defining the conversion scope. Often, companies want to bring everything and the kitchen sink over during a conversion, but if the purpose of the data is unclear, or it is unusable due to staleness or bad source data, leave it behind. This reduces security, privacy and operational risk during and after the conversion.

1. Classify Data Sensitivity: Determine the sensitivity and confidentiality levels of the data being converted, identifying potential security risks and privacy concerns based on data type and origin. 2. Secure Data Transfer: Implement robust encryption and secure transfer protocols to protect data during the conversion process, ensuring compliance with relevant legal and regulatory requirements. 3. Monitor and Audit: Continuously monitor the data conversion process for security breaches and regularly audit procedures to maintain data integrity and privacy throughout the transfer.

The level of scrutiny depends on the data being converted, as well as the level of standardization & complexity within the various data sources. In general, it is often wise to clean up data sets prior to conversion, not just because this can now be done more programmatically with modern data management tools, but also because it often does not happen after conversion as planned, because operational priorities take over. If the data contains sensitive PII, additional security controls might be needed, especially if migrations consolidate data sets that have to be collected in temporary locations before being processed (a common occurrence during large enterprise tech stack migrations).

Data Conversion usually involves a data transformation as the source data is changed to target data. A lot of data conversion involves validating, enriching, transforming as well as removing errors in the data. A data mapping sheet is essential which specifies how the source data will be transformed into the target data. This mapping sheet will need to be validated by business and technical experts. Then the data conversion scripts ensure that the data is indeed converted as per the specifications in the mapping sheet. The Extract, Transform and Load tools and scripts should extract the source data, transform and load into target as required. Ease of use and functionality and workflow of data matters play into tool selection.

In my overall journey, I'd like to say that: 1. Prioritize understanding your data's nature, format, and end goals before selecting conversion tools/methods. ??? 2. Assess tools for features, performance, security, and privacy. Encryption and validation can enhance data integrity. ?? 3. Validate and verify data post-conversion for accuracy and completeness. Quality control is key. ?

In my experience, it is best to start small. Start with a small set of sample data, which needs to be converted from source to target in the Conversion process. Here it is important to have the full set of data meeting the business requirements that is required in the transformation. For example if you are performing the data Conversion for the Customer Master data, then it is essential to include all the data elements that are mandatory for the Customer Master record. This may include Customer Name, Customer Address, Zip code, Customer Contact Person, Customer Phone Number etc. This is done at a per record level, and the entire conversion process is tested, irrespective of data volume. Once tested, increase the data volume in batches.

In my overall journey, I'd like to say that: 1. Implementing data conversion is crucial; ensure it follows best practices like secure protocols, channels, and devices ???. 2. Maintain clear workflow and documentation to avoid confusion and streamline the process ??. 3. Testing and debugging are non-negotiable for smooth data conversion; catch errors early on ??. 4. Monitor diligently; track progress, status, and errors to ensure everything runs smoothly ??. 5. Logging activities and events, plus auditing outcomes, adds transparency and accountability to the process ??.

Perform the data conversion testing in batches. Do not go to the second batch of testing without resolving the errors of the previous batch. This is because the errors will repeat as the conversion script has not been modified to handle the data error. Business Involvement and setting up a Data Governance platform to resolve data errors in conversion is critical for success.

On of the challenges in data conversion is handling the characters in Non-English languages. To allow for accuracy and fidelity in data conversion, one must ensure that the database settings is set to unicode. Also to handle double byte character set, database definitions are often set to Vargraphic, and ETL tool settings to varchar. Extensive testing and validation is advisable to ensure that data is transformed correctly from source to target in the conversion process.

Speaking from experience migrating enterprise clients onto a new data platform, it is wise to get sample data and refine the scope and purpose/use cases of the incoming data as early as during the sales process. The quality, quantity, standardization level and complexity of the incoming data sets can widely vary client by client, and affect timelines, cost, required resources and the conversion process itself. At the end of a data migration (often a feared and costly endeavor) is usually a client (or end user) expecting a better experience, more effective use of their data, better insights, greater security, governance controls, etc., so making sure expectations are set early on will drive later adoption and migration success.