What is High Fidelity Data Visual Integrity and why it matters.
High Fidelity Data: Visual Integrity means the transformed data should comply with the original data in several ways: - Length of Words and Phrases: Transformations should maintain the original length of the data. For instance, Base64 or AES encrypted names would make them 15-30% longer, which is undesirable. - Data Types: Data should retain its type (e.g., phone numbers should remain as dashed digital characters). Extracting only the last four digits as integers would break validation rules throughout the pipeline. - Data Format: The format of the data should remain consistent with the original. - Internal Structure of Composite Data: Complex data types, like addresses, should maintain their internal structure. Although Visual Integrity might not seem significant at first glance, it profoundly impacts how analysts use the data and how trained LLMs predict outcomes. - Example 1: If geographic data is transformed by merging smaller regions into larger ones, important local variations can be masked. This change might hinder analysts from identifying localized disease outbreaks, and LLMs trained on such data might struggle to make accurate predictions for specific areas. - Example 2: The Safe Harbor guidelines recommend keeping the last four digits of phone numbers or SSNs. Altering these formats can disrupt data validation and usability. As illustrated: | Trait | Original | Transformed | SSN | 372-46-1176 | 447-21-8841 | DOB | 1983-03-02 | 1970-10-11 | Email | [email protected] | [email protected] | Phone | 301-369-7653 | 042-347-7255 - Transformed birthdates still appear as dates. - Transformed phone numbers or SSNs still resemble phone numbers or SSNs, rather than random strings. - Transformed emails look like valid email addresses but cannot be looked up. Popular domains like "gmail" doesn't need encoding, but for less common domains, the domain itself will be encoded as well. In modern complex software ecosystems, especially in production environments, Visual Integrity is critical. Changes in data type and length could necessitate database schema changes, which are labor-intensive, time-consuming, error prone. Validation failures during QA could restart development sprints as worst case, and may even trigger configuration changes in firewalls and security monitoring systems. For instance, invalid email addresses or phone numbers might trigger security alerts. Preserving the "Look & Feel" of data or to say maintaining Visual Integrity is essential for data engineers and analysts, leading to less error-prone insights. #HiFidelityData #HiFiData #PrivacyProtection #WhoOwnsPrivacy