ATTRIBUTE SEGMENTS AND DATA TABLE BIAS REDUCTION

• US 20090006490A1
• Filed: 01/29/2008
• Published: 01/01/2009
• Est. Priority Date: 02/20/2004
• Status: Active Grant

• Current Assignee: Information Resources Incorporated
• Original Assignee: Symphonyiri Group Incorporated (New Mountain Capital Group LLC)
• Inventors: Johnson, Arvid C., Bergeon, Cheryl G., Hudson, Gregory David Neil, Basilico, Andrea, Gibbs, Marshall Ashby, Chapa, Craig Joseph, Yusko, Jay Alan, Griglione, Bradley Michael, Mason, Trevor, Hunt, Herbert Dennis, Agostinelli, Alberto, West, John Randall
• Granted Patent: US 7,949,639 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

The present invention provides a method for updating data sources. The method may include identifying a plurality of data sources, identifying a plurality of overlapping attribute segments to use for comparing the data sources, calculating a factor as a function of each of the plurality of overlapping attribute segments, and using the factors to update a first group of values in the second data source to reduce bias. Further, at least a first data source is more accurate than a second data source.

Claims

1. A method comprising:

identifying a plurality of data sources having data segments of varying accuracy;

identifying a plurality of overlapping data segments among the plurality of data source to use for comparing the data sources;

calculating a factor as a function of the comparison of the overlapping data segments; and

applying the factor to update at least one of the data sources.

2. The method of claim 1, wherein the factor is used to reduce bias.

3. The method of claim 1, wherein the data source is a fact data source.

4. The method of claim 3, wherein the fact data source is a retail sales dataset.

5. The method of claim 3, wherein the fact data source is a syndicated sales dataset.

6. The method of claim 5, wherein the syndicated sales dataset is a scanner dataset.

7. The method of claim 5, wherein the syndicated sales dataset is an audit dataset.

8. The method of claim 5, wherein the syndicated sales dataset is a combined scanner-audit dataset.

9. The method of claim 3, wherein the fact data source is point-of-sale data.

10. The method of claim 3, wherein the fact data source is a syndicated causal dataset.

11. The method of claim 3, wherein the fact data source is an internal shipment dataset.

12. The method of claim 3, wherein the fact data source is an internal financials dataset.

13. The method of claim 1, wherein the plurality of overlapping data segments includes a product attribute.

15. The method of claim 13, wherein the product attribute is a brand.

16. The method of claim 13, wherein the product attribute is product category.

18. The method of claim 13, wherein the product attribute is physical attribute.

24. The method of claim 1, wherein the plurality of overlapping data segments includes a consumer attribute.

26. The method of claim 24, wherein the consumer attribute is a consumer category.

30. The method of claim 24, wherein the consumer attribute is a consumer demographic.

50. The method of claim 24, wherein the consumer attribute is a user-created custom consumer attribute.

14. (canceled)

17. (canceled)

19-23. -23. (canceled)

25. (canceled)

27-29. -29. (canceled)

31-49. -49. (canceled)