Correlating experience data and operations data without compromising anonymity

1. A computer-implemented method for preserving user anonymity in correlating experience data (X-data) and operational data (O-data) in analytics of computer-implemented services, the method being executed by one or more processors and comprising:

determining, by an anonymity-preserving data analysis platform, an initial distribution based on a set of survey boundaries and historical O-data, the historical O-data being generated through execution of a computer-implemented service, the historical distribution comprising a first set of parameter ranges and a second set of parameter ranges defining a set of range cells;

adjusting, by the anonymity-preserving data analysis platform, one or more parameters ranges of at least one of the first set of parameter ranges and the second set of parameter ranges;

determining, by the anonymity-preserving data analysis platform, an adjusted distribution defining a range space for a computer-based survey;

receiving, by the anonymity-preserving data analysis platform, X-data from a set of users participating in the computer-based survey, the X-data comprising responses of users in the set of users provided to the computer-based survey;

providing a set of X-data to O-data range vector correlations; and

executing analytics of the computer-implemented service based on the set of X-data to O-data range vector correlations.

2. The method of claim 1 , wherein each X-data to O-data range vector correlation is provided by:

determining an O-data parameter vector for a user in the set of users, the O-data parameter vector corresponding to X-data provided by the user,

determining an O-data range vector by mapping the O-data parameter vector to the range space of the adjusted distribution, and

correlating the X-data to the O-data range vector.

3. The method of claim 1 , wherein the adjusting one or more parameters ranges of the initial distribution is executed in response to determining that each of the one or more parameter ranges exceeds an anonymity limit level.

4. The method of claim 1 , wherein adjusting the one or more parameter ranges comprises merging at least two parameter ranges.

5. The method of claim 1 , wherein adjusting the one or more parameter ranges comprises splitting a parameter range.

6. The method of claim 1 , wherein the user is identifiable from the O-data parameter vector and is unidentifiable from the O-data range vector and X-data.

7. The method of claim 1 , wherein executing analytics comprises querying an O-data database for additional O-data at least partially based on the O-data range vector.

8. A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for preserving user anonymity in correlating experience data (X-data) and operational data (O-data) in analytics of computer-implemented services, the operations comprising:

determining, by an anonymity-preserving data analysis platform, an initial distribution based on a set of survey boundaries and historical O-data, the historical O-data being generated through execution of a computer-implemented service, the historical distribution comprising a first set of parameter ranges and a second set of parameter ranges defining a set of range cells;

adjusting, by the anonymity-preserving data analysis platform, one or more parameters ranges of at least one of the first set of parameter ranges and the second set of parameter ranges;

determining, by the anonymity-preserving data analysis platform, an adjusted distribution defining a range space for a computer-based survey;

receiving, by the anonymity-preserving data analysis platform, X-data from a set of users participating in the computer-based survey, the X-data comprising responses of users in the set of users provided to the computer-based survey;

providing a set of X-data to O-data range vector correlations; and

executing analytics of the computer-implemented service based on the set of X-data to O-data range vector correlations.

9. The computer-readable storage medium of claim 8 , wherein each X-data to O-data range vector correlation is provided by:

determining an O-data parameter vector for a user in the set of users, the O-data parameter vector corresponding to X-data provided by the user,

determining an O-data range vector by mapping the O-data parameter vector to the range space of the adjusted distribution, and

correlating the X-data to the O-data range vector.

10. The computer-readable storage medium of claim 8 , wherein the adjusting one or more parameters ranges of the initial distribution is executed in response to determining that each of the one or more parameter ranges exceeds an anonymity limit level.

11. The computer-readable storage medium of claim 8 , wherein adjusting the one or more parameter ranges comprises merging at least two parameter ranges.

12. The computer-readable storage medium of claim 8 , wherein adjusting the one or more parameter ranges comprises splitting a parameter range.

13. The computer-readable storage medium of claim 8 , wherein the user is identifiable from the O-data parameter vector and is unidentifiable from the O-data range vector and X-data.

14. The computer-readable storage medium of claim 8 , wherein executing analytics comprises querying an O-data database for additional O-data at least partially based on the O-data range vector.

15. A system, comprising:

a computing device; and

a computer-readable storage device coupled to the computing device and having instructions stored thereon which, when executed by the computing device, cause the computing device to perform operations for preserving user anonymity in correlating experience data (X-data) and operational data (O-data) in analytics of computer-implemented services, the operations comprising:

determining, by an anonymity-preserving data analysis platform, an initial distribution based on a set of survey boundaries and historical O-data, the historical O-data being generated through execution of a computer-implemented service, the historical distribution comprising a first set of parameter ranges and a second set of parameter ranges defining a set of range cells;

adjusting, by the anonymity-preserving data analysis platform, one or more parameters ranges of at least one of the first set of parameter ranges and the second set of parameter ranges;

determining, by the anonymity-preserving data analysis platform, an adjusted distribution defining a range space for a computer-based survey;

receiving, by the anonymity-preserving data analysis platform, X-data from a set of users participating in the computer-based survey, the X-data comprising responses of users in the set of users provided to the computer-based survey;

providing a set of X-data to O-data range vector correlations; and

executing analytics of the computer-implemented service based on the set of X-data to O-data range vector correlations.

16. The system of claim 15 , wherein each X-data to O-data range vector correlation is provided by:

determining an O-data parameter vector for a user in the set of users, the O-data parameter vector corresponding to X-data provided by the user,

determining an O-data range vector by mapping the O-data parameter vector to the range space of the adjusted distribution, and

correlating the X-data to the O-data range vector.

17. The system of claim 15 , wherein the adjusting one or more parameters ranges of the initial distribution is executed in response to determining that each of the one or more parameter ranges exceeds an anonymity limit level.

18. The system of claim 15 , wherein adjusting the one or more parameter ranges comprises merging at least two parameter ranges.

19. The system of claim 15 , wherein adjusting the one or more parameter ranges comprises splitting a parameter range.

20. The system of claim 15 , wherein the user is identifiable from the O-data parameter vector and is unidentifiable from the O-data range vector and X-data.