From the course: Practical Database Design: Implementing Responsible Data Solutions with SQL Querying
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The data you're creating: Pros and cons - SQL Tutorial
From the course: Practical Database Design: Implementing Responsible Data Solutions with SQL Querying
The data you're creating: Pros and cons
- [Instructor] Some data environments elect to create synthetic data sets themselves or license them from other organizations. The banking and credit lending industry use synthetic financial data to improve fraud detection. Manufacturing companies use synthetic data for better quality control testing and predictive maintenance processes. AI-powered companies use synthetic data to test the robustness of their facial feature recognition and natural language processing systems. In doing so, there are expected pros and cons. Let's discuss the pros first. Top benefits include, number one, your ability to generate data sets that are diverse and realistic. You can mimic characteristics found in the real world and test different models or scenarios relatively easily. Number two, you have the opportunity to save costs. Once data generation is complete, an organization isn't spending a bunch of money repeatedly on real dataset licenses, and you're avoiding the real world data collection…
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