Operating plan data aggregation system with real-time updates

• US 7,558,784 B2
• Filed: 04/26/2005
• Issued: 07/07/2009
• Est. Priority Date: 04/26/2004
• Status: Active Grant

• Current Assignee: Ignite Enterprise Software Solutions, Inc. (Infonow Corporation)
• Original Assignee: Right90, Inc. (ESW Capital, LLC)
• Inventors: Skelton, Dean, Petiot, David, Orumchian, Kim, Stabenow, Art
• Primary Examiner(s): Rones; Charles
• Assistant Examiner(s): Khoshnoodi; Fariborz
• Application Number:
  US11/115,970
• Publication Number:
  US 20060074741A1
• Time in Patent Office: 1,533 Days
• Field of Search: 707/3, 707/100, 707/101, 707/102, 705/10
• US Class Current: 707/713
• CPC Class Codes: G06F 16/2228 Indexing structures
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06F 16/90335 Query processing
• CPC Class Codes: G06F 2216/03 Data mining
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99956 File allocation

Abstract

System, method and apparatus for forecasting data with real-time updates. The system includes an analysis server including an OLAP cube. System also includes information database coupled to analysis server to support the OLAP cube. System may include a first client and an analysis server coupled to first client. System may further include first customer information database coupled to analysis server. Method of maintaining information may include receiving set of forecast data, incorporating the forecast data into information database through OLAP cube, extracting a baseline forecast from the database, and receiving updates to database. Method may include propagating updates throughout the information through the OLAP cube.

Claims

1. A real-time collection and analysis computer implemented system configured for use with a first client and a second client, the system comprising:

an analysis server coupled to the first client, the analysis server including a centralized OLAP cube that provides collection and analysis of forecast data in real-time and the committing of changes and collaborative communication in real-time; and

a first customer database of information coupled to the analysis server and to the OLAP cube to receive essentially real-time updates of forecast data from said first client, the first customer database is to embody forecast data and to receive essentially real-time updates to the forecast data, wherein the forecast data includes at least one estimated forecast value;

the first customer database is dedicated to use by a first set of selected identified users belonging to the first customer of the system;

an identification database and identification server coupled to the first client and coupled to the first customer database, the identification database and identification server configured to identify and authenticate the first set of selected identified users and the customer to which they belong, the identification and authentication facilitating an understanding of data contributed by and entering the system from the first customer wherein updates from a first set of selected identified users is directed to the first customer database;

the system is configured to support a multi-tenant environment including the first client and the first customer database of information and the second client and a second customer database of information;

the second client coupled to the identification server and identification database and to the analysis server; and

the second customer database of information coupled to the identification server and identification database and the analysis server, the second customer database dedicated to use by a second set of users of the system, the identification server and identification database further configured to authenticate the second set of users and the second customer to which they belong, the identification and authentication facilitating an understanding of data contributed by and entering the system from the second customer wherein updates from a second set of users is directed to the second customer database.

2. The system of claim 1, wherein:

the first client is a smart client, and the first client includes an OLAP cube that permits real-time capture of the data and for communicating the real-time changes in aggregate through the OLAP cube to the system.

3. The system of claim 1, wherein:

the information is forward looking financial data comprising a financial forecast information.

4. The system of claim 1, wherein:

the first customer database includes watches of data, wherein a watch is a substantially real-time user notification triggered when a selected value reaches a predetermined threshold, and wherein a user creates and saves one or collections of watches with corresponding messages including messages that are converted to customized reports.

5. The system of claim 1, wherein:

the first customer database includes sales forecast prediction data.

6. The system of claim 1, wherein:

the first customer database includes sales forecast data with changes from non-member personnel.

7. The system of claim 1, wherein:

the different customer data is segregated into different logical and/or physical data repositories, and a first user associated with the first customer uses a first client to access the system and access a first dedicated data repository using a first OLAP cube or a first instantiation of an OLAP cube, and a second user associated with the second customer uses a second client to access the system and access a second dedicated data repository using a second OLAP cube or a second instantiation of the OLAP cube; and

each first and second dedicated repository has associated with different first and second respective customizations for the particular first or second customer.

8. The system of claim 7, wherein:

the identification database and identification server configured to identify and authenticate the users and the customer to which they belong, the identification facilitating an understanding of data contributed by and entering the system from the customer wherein updates from users is directed to the customer database.

9. The system of claim 1, wherein:

the system acquires and analyzes financial forecast data including forecast predicted financial information and the information database stores the forecast predicted financial information.

10. The system in claim 1, wherein the update to the forecast data includes an update to the at least one estimated financial forecast value.

11. The system of claim 1, wherein:

the first client supports a first group of users associated with the first customer and the second client supports a second group of users associated with the second customer in the multi-tenant environment; and

the system further stores and analyzes changes that enter the system in isolation from the first and second groups of users.

12. The system in claim 1, wherein:

the information database coupled to the analysis server is an element of the analysis server.

13. A real-time collection and analysis computer implemented system configured for use with a first client and a second client, the system comprising:

an analysis server coupled to the first client, the analysis server including a centralized OLAP cube that provides collection and analysis of forecast data in real-time and the committing of changes and collaborative communication in real-time; and

a first customer database of information coupled to the analysis server and to the OLAP cube to receive essentially real-time updates of forecast data from said first client, the first customer database is to embody forecast data and to receive essentially real-time updates to the forecast data, wherein the forecast data includes at least one estimated forecast value;

the analysis server incorporating an OLAP cube therein, the OLAP cube to capture data in real-time from the first client and to analyze and update the information of the first customer database including to update the forecast data and to propagate the updates in real time through the OLAP cube;

the first customer database is dedicated to use by a first set of selected identified users belonging to the first customer and coupled to the analysis server to receive essentially real-time updates of forecast data from the first client, the first customer database is to embody first forecast data and to receive essentially real-time updates to the first forecast data, wherein the first forecast data includes at least one first estimated forecast value; and

the system further includes;

a second customer database dedicated to use by a second set of selected identified users belonging to the second customer and coupled to the analysis server to receive essentially real-time updates of forecast data from a second client, the second customer database is to embody second forecast data and to receive essentially real-time updates to the second forecast data, wherein the second forecast data includes at least one second estimated forecast value, the second customer database is dedicated to use by a second set of selected identified users of the system; and

an identification server and identification database coupled to the first client and the second client and coupled to the first customer database and the second customer database, the identification server and identification database configured to identify and authenticate the first set of selected identified users and the second set of selected identified users, the identification and authentication facilitating an understanding of data contributed by and entering the system from the first customer and from the second customer wherein updates from a first set of selected identified users and from a first set of selected identified users is directed to the first customer database.

14. The system of claim 13, wherein:

the first customer database is dedicated to use by a first set of selected identified users belonging to the first customer of the system; and

further comprising;

an identification database and identification server coupled to the first client and coupled to the first customer database, the identification database and identification server configured to identify and authenticate the first set of selected identified users and the customer to which they belong, the identification and authentication facilitating an understanding of data contributed by and entering the system from the first customer wherein updates from a first set of selected identified users is directed to the first customer database.

15. A computer implemented method of maintaining information on a computer system for use with a first client and first customer and a second client and second customer, the method comprising:

receiving a set of forecast data by an analysis server coupled to the first client, the analysis server including a centralized OLAP cube that provides collection and analysis of forecast data in real-time and the committing of changes and collaborative communication in real-time;

maintaining a first customer database of information dedicated to use by a first set of selected identified users belonging to the first customer and coupled to the analysis server and to the OLAP cube to receive essentially real-time updates of forecast data from said first client, the first customer database is to embody forecast data and to receive essentially real-time updates to the forecast data, wherein the forecast data includes at least one estimated forecast value, the first forecast data includes at least one first estimated forecast value;

maintaining a second customer database dedicated to use by a second set of selected identified users belonging to the second customer and coupled to the analysis server and to the OLAP cube to receive essentially real-time updates of forecast data from a second client, the second customer database is to embody second forecast data and to receive essentially real-time updates to the second forecast data, wherein the second forecast data includes at least one second estimated forecast value;

capturing data in real-rime by the OLAP cube from at least one of the first client the second client and analyzing and updating the information of the first customer database or the second customer database depending upon the identity of client and customer, including to update the forecast data and to propagate the updates in real time through the OLAP cube;

maintaining an identification server and an identification database coupled to the first client and the second client and coupled to the first customer database and the second customer database; and

identifying and authenticating the first set of selected identified users and the second set of selected identified users, the identification and authentication facilitating an understanding of data contributed by and entering the computer system from the first customer and from the second customer wherein updates from a first set of selected identified users and from a first set of selected identified users is directed to the first customer database.

16. The method of claim 15, further comprising:

watching a set of user-selected watched data points of the information, a user-selected watch data point being a substantially real-time user notification triggered when a selected value for the data point reaches a predetermined threshold set by the user; and

notifying a user of chances in the set of user-selected watched data points responsive to the propagating and receiving updates.

17. The method of claim 15, further comprising:

providing information to a user;

receiving expected changes data of the information from the user at the server that includes the OLAP cube; and

propagating the received expected changes data as updates almost immediately in essentially real-time throughout the information through the server and the OLAP cube.

18. The method of claim 15, further comprising:

reviewing updates received and propagated through the information; and

providing user-readable updates of information responsive to the reviewing.

19. The method of claim 15, wherein:

the forecast data comprises forecast predicted financial information.

20. The method of claim 15, wherein:

the method is executed by a processor in response to instructions, the instructions embodied in a tangible machine-readable medium.

21. The method of claim 15, further comprising:

receiving actual historical data corresponding to the same subject as the forecast predicted information;

comparing the actual historical data to the forecast predicted information; and

providing user-readable comparisons of the forecast predicted information and the corresponding actual historical data.

22. The method of claim 18, wherein the user-readable updates provide an ability for a user to review the change data in the system and provide a comment for a forecast for consideration by other users.