Database Server Reach Potential Analysis
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Solution Overview
Problem
Conventional database systems fail to capture full reach potential for target audiences due to limited analysis beyond selected attributes, often missing insights from external data sets and being susceptible to human biases in attribute selection.
Innovation Solution
The system performs reach analysis by incorporating external data sets from data providers, comparing the similarity between the organization's population and the data provider's feature population, and calculating reach indexes using Jaccard indices and binary entropy functions to identify potential user devices outside the organization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If analysts select which attributes or features of a data set should be observed, then the analysis can be focused and manageable, but useful information may be missed due to human biases
Solution Approach 1:
The system automatically performs reach analysis by comparing organization population attributes with data provider feature population attributes without requiring manual attribute selection by analysts. The database server autonomously identifies correlations and calculates reach indexes, eliminating human bias while maintaining operational simplicity.
Solution Approach 2:
The system changes the analysis parameters by using Jaccard indices and binary entropy functions to automatically determine which attributes are most relevant for reach analysis. This mathematical approach dynamically selects parameters based on data characteristics rather than human judgment, preventing information loss due to bias.
2Loss of information
If the system performs comprehensive analysis across all data sets, then more useful information can be captured, but the complexity and computational resources required increase
Solution Approach 1:
The system extracts only the most relevant attributes and features for reach analysis using automated correlation methods. By pulling out only the necessary data elements rather than analyzing everything, the system maintains information completeness while reducing computational complexity and resource requirements.
Solution Approach 2:
The analysis is segmented into manageable components: the database server separately processes organization population data, data provider feature population data, and reach index calculations. This segmentation allows comprehensive analysis to be performed in discrete, efficient steps rather than as an overwhelming monolithic process.
3Reliability
If the system uses only internal organization data, then data privacy and security are maintained, but reach potential analysis is limited
Solution Approach 1:
The database server acts as an intermediary that receives and processes data from external data providers without requiring the organization to directly handle or store external data. The server performs reach analysis by comparing internal organization population attributes with external feature population attributes through controlled data exchange, maintaining security while enabling comprehensive analysis.
Data Source
AI summary
A database server may perform reach potential analysis for a local segment, or a target audience, of a data set. The local segment may include user devices which share a specific, common attribute. The database server may calculate similarities and correlations between a first data set for a user and a second data set from a data provider. The database server may calculate a reach index using the second data set from the data provider to determine whether user devices are likely to join the local segment by taking on the specific attribute which defines the local segment. Using the data set from the data provider, the database server may determine a reach potential within the first data set, outside of the first data set, or both.