A data derivative is essentially new data created by combining or aggregating existing datasets.
Based on the provided reference, Derivative Data means any and all data that result from the commingling or other aggregation of the Customer Data and/or the Provider Data. This means when data from different sources are brought together and processed, the output is considered derivative data.
Understanding Data Derivatives
At its core, a data derivative isn't raw information. Instead, it's derived from primary datasets through various processes. Think of it like financial derivatives – their value comes from an underlying asset. Similarly, data derivatives gain their value from the underlying Customer Data and Provider Data (or other initial data sources) they are built upon.
Key Concepts:
- Commingling: This involves mixing data from different sources. For instance, combining customer purchase history with their website browsing behavior.
- Aggregation: This refers to summarizing or rolling up data. Examples include calculating average sales per region, total website visitors per hour, or the most popular product category across all users.
- Customer Data: Information provided by or collected about customers.
- Provider Data: Information held or generated by the service provider or data handler.
When Customer Data and Provider Data are commingled or aggregated, the resulting output is the data derivative.
Why Create Data Derivatives?
Organizations create data derivatives to extract deeper insights, identify trends, and generate new forms of valuable information that aren't immediately apparent in the raw data.
- Analytics and Insights: By combining different data points, businesses can perform more sophisticated analysis, revealing patterns, customer segments, or performance metrics.
- Performance Monitoring: Aggregated data helps track overall system health, usage patterns, or service performance across a user base.
- Developing New Services or Products: Aggregated or anonymized derivative data can inform the development of benchmarks, industry reports, or features that benefit all users without exposing individual raw data.
- Benchmarking: Comparing performance metrics derived from aggregated data against industry standards or other groups.
Practical Examples
| Original Data Sources | Process | Resulting Data Derivative |
|---|---|---|
| Customer Purchase History + Website Visit Data | Commingling & Aggregation | Average order value per visit source |
| User Location Data + Time Stamps | Aggregation | Peak hours of activity in a specific city |
| Provider System Logs + Customer Usage Metrics | Commingling & Aggregation | Average load time experienced by users during peak hours |
| Customer Demographic Data + Product Ratings | Commingling & Aggregation | Most preferred product category for a specific age group |
| Provider Server Performance Data + Aggregate Usage | Commingling & Aggregation | Correlation between server load and user error rates |
These examples show how combining and summarizing raw data creates new, informative datasets – the data derivatives.
Important Considerations
Understanding data derivatives is crucial, especially in data-sharing agreements, privacy policies, and terms of service. Contracts often specify who owns or has rights to use the raw data versus the data derivatives.
- Ownership and Usage Rights: Clarifying who owns the derivative data (Customer, Provider, or shared) is vital in data partnerships.
- Privacy: While derivative data often involves aggregation or anonymization to protect individual privacy, the process and resulting data still require careful handling in compliance with regulations like GDPR, CCPA, etc.
- Value Creation: Derivative data often holds significant value as it represents processed insights ready for analysis or commercialization.
In summary, a data derivative is the transformed outcome when disparate data sources, specifically Customer Data and Provider Data according to the reference, are combined or summarized, leading to new, potentially more valuable, datasets for analysis and use.
