Data integration method

• US 20040162742A1
• Filed: 02/18/2003
• Published: 08/19/2004
• Est. Priority Date: 02/18/2003
• Status: Active Grant

• Current Assignee: Dun & Bradstreet Corp., DUN & Bradstreet Incorporated (Dun & Bradstreet Corp.), Hoover's Incorporated (Dun & Bradstreet Holdings, Inc.)
• Original Assignee: DUN & Bradstreet Incorporated (Dun & Bradstreet Corp.)
• Inventors: Lucas, Christopher John, Duckworth, Alan, Sharif, Ahmad Tariq, Prevoznak, Michael E., Seckler, Maria P., Stoker, Sandra L., Benke, Charles R.
• Granted Patent: US 7,822,757 B2
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

A data integration method involves a unique method of collecting raw business data and processing it to produce highly useful and highly accurate information to enable business decisions. This process includes collecting global data, entity matching, applying an identification number, performing corporate linkage, and providing predictive indicators. These process steps work in series to filter and organize the raw business data and provide quality information to customers in a report. In addition, the information is enhanced by quality assurance at each step in this process to ensure the high quality of the resulting report.

Claims

1. A method of data integration, comprising:

collecting information comprising primary data;

testing said primary data for accuracy;

processing said primary data to produce secondary data; and

providing enhanced information comprising said primary data and said secondary data.

2. The method according to claim 1, further comprising:

sampling said primary and/or secondary data periodically, thereby generating sample data;

evaluating said sample data against at least one predetermined condition; and

adjusting said testing and/or processing based upon said evaluation.

3. The method according to claim 1, wherein said testing comprises at least one step selected from the group consisting of:

(a) determining if said primary data matches stored data; and

(b) assigning an identification number to said primary data.

4. The method according to claim 3, further comprising the step of:

determining if said primary data meets a first threshold condition before assigning an identification number in step (b) if said primary data does not match said stored data in step (a).

5. The method according to claim 4, wherein said first threshold condition is at least two sources confirm that a business associated with said primary data exists.

6. The method according to claim 3, wherein said identification number is an entity identifier.

7. The method according to claim 3, further comprising the step of:

storing said primary data if said primary data does not meet said first threshold condition.

8. The method according to claim 7, further comprises:

receiving additional primary data;

determining if said primary data and said additional primary data meet said first threshold condition; and

assigning an identification number in step (b) if said primary data and said additional data meet said first threshold condition.

9. The method according to claim 1, wherein said processing of said primary data comprises at least one step selected from the group consisting of:

determining corporate linkage; and

determining at least one predictive indicator.

10. The method according to claim 9, wherein said determining said corporate linkage comprises:

determining if said primary data meets a second threshold condition; and

processing said primary data to generate said secondary data.

11. The method according to claim 10, wherein said second threshold condition is a predetermined sales volume.

12. The method according to claim 10, wherein said processing of said primary data comprises associating at least one affiliated entity with said primary data.

13. The method according to claim 9, wherein said determining at least one predictive indicator comprises:

determining if said primary data meets a third threshold condition;

processing said primary data to generate said secondary data.

14. The method according to claim 13, wherein said third threshold condition comprises a predetermined level of customer inquiry.

15. A system for data integration comprising:

a data generator which is capable of gathering primary data from at least one data source;

a testing unit which is capable of testing said primary data for accuracy; and

a first processing unit which is capable of analyzing said primary data and generating secondary data from the result of said analysis; and

a second processing unit which is capable of merging said primary data and said secondary data to form enhanced information, wherein said testing unit, first processing unit and second processing unit may be the same or independent of one another.

16. The system according to claim 15, wherein said testing unit comprises at least one selected from the group consisting of:

a data matching unit and an entity identifier unit.

17. The system according to claim 15, wherein said first processing unit comprises at least one selected from the group consisting of:

a corporate linkage unit and predictive indicator unit.

18. A machine-readable medium storing executable instructions for data integration, the instructions comprising:

collecting information comprising primary data;

testing said primary data for accuracy;

processing said primary data to produce secondary data; and

providing enhanced information comprising said primary data and said secondary data.

19. The machine-readable medium according to claim 18, further comprising:

sampling said primary and/or secondary data periodically, thereby generating sample data;

evaluating said sample data against at least one predetermined condition; and

adjusting said testing and/or processing based upon said evaluation.

20. The machine-readable medium according to claim 18, wherein said testing comprises at least one step selected from the group consisting of:

(a) determining if said primary data matches stored data; and

(b) assigning an identification number to said primary data.

21. The machine-readable medium according to claim 20, further comprising the step of:

determining if said primary data meets a first threshold condition before assigning an identification number in step (b) if said primary data does not match said stored data in step (a).

22. The machine-readable medium according to claim 21, wherein said first threshold condition is at least two sources confirm that a business associated with said primary data exists.

23. The machine-readable medium according to claim 20, wherein said identification number is an entity identifier.

24. The machine-readable medium according to claim 20, further comprising the step of:

storing said primary data if said primary data does not meet said first threshold condition.

25. The machine-readable medium according to claim 24, further comprises:

receiving additional primary data;

determining if said primary data and said additional primary data meet said first threshold condition; and

assigning an identification number in step (b) if said primary data and said additional data meet said first threshold condition.

26. The machine-readable medium according to claim 18, wherein said processing of said primary data comprises at least one step selected from the group consisting of:

determining corporate linkage; and

determining at least one predictive indicator.

27. The machine-readable medium according to claim 26, wherein said determining said corporate linkage comprises:

determining if said primary data meets a second threshold condition; and

processing said primary data to generate said secondary data.

28. The machine-readable medium according to claim 27, wherein said second threshold condition is a predetermined sales volume.

29. The machine-readable medium according to claim 27, wherein said processing of said primary data comprises associating at least one affiliated entity with said primary data.

30. The machine-readable medium according to claim 26, wherein said determining at least one predictive indicator comprises:

determining if said primary data meets a third threshold condition;

processing said primary data to generate said secondary data.

31. The machine-readable medium according to claim 30, wherein said third threshold condition comprises a predetermined level of customer inquiry.