US20120150905A1

US20120150905A1 - Concerted Coordination of Multidimensional Scorecards

Info

Publication number: US20120150905A1
Application number: US13/404,032
Authority: US
Inventors: Ian Tien; Corey Hulen; Chen-I Lim
Original assignee: Microsoft Corp
Current assignee: Microsoft Technology Licensing LLC
Priority date: 2006-04-27
Filing date: 2012-02-24
Publication date: 2012-06-14
Also published as: US20070254740A1; US8126750B2

Abstract

Data retrieval from data sources associated with elements of a scorecard is optimized by combining the queries for the same data sources into a single query. The querying process is further optimized based on an attribute and/or condition of a data source. Query parameters and execution schedule may be adjusted based on the elements, data sources, or subscriber identity. Retrieved data is cached at element, combination of elements, data source, or scorecard level for rapid and efficient building and maintenance of scorecards.

Description

RELATED APPLICATION

This application is a Continuation of co-pending U.S. application Ser. No. 11/412,458 entitled “Consolidating Data Source Queries for Multidimensional Scorecards” filed Apr. 27, 2006, which is incorporated herein by reference.

BACKGROUND

Key Performance Indicators, also known as KPI or Key Success Indicators (KSI), help an organization define and measure progress toward organizational goals. Once an organization has analyzed its mission, identified all its stakeholders, and defined its goals, it needs a way to measure progress toward those goals. Key Performance Indicators are used to provide those measurements.
Scorecards are used to provide detailed and summary analysis of KPIs and aggregated KPIs such as KPI groups, objectives, and the like. Scorecard calculations are typically specific to a defined hierarchy of the above mentioned elements, selected targets, and status indicator schemes. Business logic applications that generate, author, and analyze scorecards are typically enterprise applications with multiple users (subscribers), designers, and administrators. It is not uncommon, for organizations to provide their raw performance data to a third party and receive scorecard representations, analysis results, and similar reports.
Scorecards are composed of business metrics from a variety of sources and summarize business performance for decision makers so the performance can be monitored. However, bringing together multiple data sources may introduce scalability problems that limit a frequency of generating reports, a size of the audience they can attend to, and a number of metrics that can be included. A naïve implementation of scorecards can lead to significantly hampered productivity.
It is with respect to these and other considerations that the present invention has been made.

SUMMARY

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended as an aid in determining the scope of the claimed subject matter.
Embodiments are directed to determining data sources associated with elements of a scorecard and dynamically generating queries that optimize data retrieval for the elements that are associated with the same data source(s). According to other embodiments, query optimization may be based on an attribute and/or a condition of a data source, a subscriber identity, and the like. Retrieved data may be cached at element level, at combination of elements level, or at scorecard level for further optimization of scorecard building process.
These and other features and advantages will be apparent from a reading of the following detailed description and a review of the associated drawings. It is to be understood that both the foregoing general description and the following detailed description are explanatory only and are not restrictive of aspects as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example computing operating environment;

FIG. 2 illustrates a system where example embodiments may be implemented;

FIG. 3 illustrates an example scorecard architecture according to embodiments;

FIG. 4 illustrates a screenshot of an example scorecard;

FIG. 5 illustrates a conceptual diagram of an example scorecard and its elements' association with multiple data sources according to embodiments; and

FIG. 6 illustrates a logic flow diagram for a process of optimizing scorecard building by optimizing queries.

DETAILED DESCRIPTION

As briefly described above, a scorecard building and/or update process may be enhanced by generating queries that are optimized by combining data retrieval for elements associated with the same data source. The queries may be further optimized based on an attribute and/or a condition of a data source. In the following detailed description, references are made to the accompanying drawings that form a part hereof, and in which are shown by way of illustrations specific embodiments or examples. These aspects may be combined, other aspects may be utilized, and structural changes may be made without departing from the spirit or scope of the present disclosure. The following detailed description is therefore not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims and their equivalents.
Referring now to the drawings, aspects and an exemplary operating environment will be described. FIG. 1 and the following discussion are intended to provide a brief, general description of a suitable computing environment in which the invention may be implemented. While the embodiments will be described in the general context of program modules that execute in conjunction with an application program that runs on an operating system on a personal computer, those skilled in the art will recognize that aspects may also be implemented in combination with other program modules.
Generally, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that embodiments may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. Embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.
Embodiments may be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media. The computer program product may be a computer storage media readable by a computer system and encoding a computer program of instructions for executing a computer process. The computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process.
With reference to FIG. 1, one example system for implementing the embodiments includes a computing device, such as computing device 100. In a basic configuration, the computing device 100 typically includes at least one processing unit 102 and system memory 104. Depending on the exact configuration and type of computing device, the system memory 104 may be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.) or some combination of the two. System memory 104 typically includes an operating system 105 suitable for controlling the operation of a networked personal computer, such as the WINDOWS® operating systems from MICROSOFT CORPORATION of Redmond, Wash. The system memory 104 may also include one or more software applications such as program modules 106, scorecard application 120, and query optimization module 122. Scorecard application 120 manages business evaluation methods, computes KPIs, and provides scorecard data to reporting applications. In some embodiments, scorecard application 120 may itself generate reports based on metric data.
Query optimization module 122 is configured to optimize queries for retrieving data associated with the elements of a scorecard based on the data sources from which the data is retrieved. Query optimization module 122 may reduce a number of total queries based on the data sources shared by some of the elements. The queries may be further adjusted and optimized based on attributes and conditions of the data sources or even identitys of subscribers requesting the scorecard information. Query optimization module 122 may be an integrated part of scorecard application 120 or a separate application. Scorecard application 120 and query optimization module 122 may communicate between themselves and with other applications running on computing device 100 or on other devices. Furthermore, either one of scorecard application 120 and query optimization module 122 may be executed in an operating system other than operating system 105. This basic configuration is illustrated in FIG. 1 by those components within dashed line 108.
The computing device 100 may have additional features or functionality. For example, the computing device 100 may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 1 by removable storage 109 and non-removable storage 110. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. System memory 104, removable storage 109 and non-removable storage 110 are all examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computing device 100. Any such computer storage media may be part of device 100. Computing device 100 may also have input device(s) 112 such as keyboard, mouse, pen, voice input device, touch input device, etc. Output device(s) 114 such as a display, speakers, printer, etc. may also be included. These devices are well known in the art and need not be discussed at length here.
The computing device 100 may also contain communication connections 116 that allow the device to communicate with other computing devices 118, such as over a network in a distributed computing environment, for example, an intranet or the Internet. Communication connection 116 is one example of communication media. Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. The term computer readable media as used herein includes both storage media and communication media.
Referring to FIG. 2, a system where example embodiments may be implemented, is illustrated. System 200 may comprise any topology of servers, clients, Internet service providers, and communication media. Also, system 200 may have a static or dynamic topology. The term “client” may refer to a client application or a client device employed by a user to perform business logic operations. Scorecard service 202, database server 204, and report server 206 may also be one or more programs or a server machine executing programs associated with the server tasks. Both clients and application servers may be embodied as single device (or program) or a number of devices (programs). Similarly, data sources may include one or more data stores, input devices, and the like.
A business logic application may be run centrally on scorecard service 202 or in a distributed manner over several servers and/or client devices. Scorecard service 202 may include implementation of a number of information systems such as performance measures, business scorecards, and exception reporting. A number of organization-specific applications including, but not limited to, financial reporting, analysis, marketing analysis, customer service, and manufacturing planning applications may also be configured, deployed, and shared in system 200. In addition, the business logic application may also be run in one or more client devices and information exchanged over network(s) 210.
Data sources 212-214, 216, and 218 are examples of a number of data sources that may provide input to scorecard service 202 directly or through database server 204. Additional data sources may include SQL servers, databases, non multi-dimensional data sources such as text files or EXCEL® sheets, multi-dimensional data source such as data cubes, and the like. Database server 204 may manage the data sources, optimize queries, and the like. Elements of a scorecard built and maintained by scorecard service 202 may be associated with one or more of the data sources.
Users may interact with scorecard service 202 running the business logic application from client devices 222, 224, and 226 over network(s) 210. In one embodiment, additional applications that consume scorecard-based data may reside on scorecard service 202 or client devices 222, 224, and 226. Examples of such applications and their relation to the scorecard application are provided below in conjunction with FIG. 3.
Report server 206 may include reporting applications, such as charting applications, alerting applications, analysis applications, and the like. These applications may receive scorecard data from scorecard service 202 and provide reports directly or through scorecard service 202 to clients.
Network(s) 210 may include a secure network such as an enterprise network, or an unsecure network such as a wireless open network. Network(s) 210 provide communication between the nodes described above. By way of example, and not limitation, network(s) 210 may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.
Many other configurations of computing devices, applications, data sources, data distribution and analysis systems may be employed to implement a business logic application automatically generating dashboards with scorecard metrics and subordinate reporting.
Now referring to FIG. 3, example scorecard architecture 300 is illustrated. Scorecard architecture 300 may comprise any topology of processing systems, storage systems, source systems, and configuration systems. Scorecard architecture 300 may also have a static or dynamic topology.
Scorecards are a simple method of evaluating organizational performance. The performance measures may vary from financial data such as sales growth to service information such as customer complaints. In a non-business environment, student performances and teacher assessments may be another example of performance measures that can employ scorecards for evaluating organizational performance. In the exemplary scorecard architecture 300, a core of the system is scorecard engine 308. Scorecard engine 308 may be an application that is arranged to evaluate performance metrics. Scorecard engine 308 may be loaded into a server, executed over a distributed network, executed in a client device, and the like.
In addition to performing scorecard calculation, scorecard engine may also provide report parameters associated with a scorecard to other applications 318. The report parameters may be determined based on a subscriber request or a user interface configuration. The user interface configuration may include a subscriber identity or a subscriber permission attribute. The report parameter may include a scorecard identifier, a scorecard view identifier, a row identifier, a column identifier, a page filter, a performance measure group identifier, or a performance measure identifier. The performance measure may be a KPI, a KPI group, or an objective. The page filter determines a period and an organizational unit for application of the scorecard calculations.
Data for evaluating various measures may be provided by a data source. The data source may include source systems 312, which provide data to a scorecard cube 314. Source systems 312 may include multi-dimensional databases such as an Online Analytical Processing (OLAP) database, other databases, individual files, and the like, that provide raw data for generation of scorecards. Scorecard cube 314 is a multi-dimensional database for storing data to be used in determining Key Performance Indicators (KPIs) as well as generated scorecards themselves. As discussed above, the multi-dimensional nature of scorecard cube 314 enables storage, use, and presentation of data over multiple dimensions such as compound performance indicators for different geographic areas, organizational groups, or even for different time intervals. Scorecard cube 314 has a bi-directional interaction with scorecard engine 308 providing and receiving raw data as well as generated scorecards.
Scorecard database 316 is arranged to operate in a similar manner to scorecard cube 314. In one embodiment, scorecard database 316 may be an external database providing redundant back-up database service. Data from scorecard cube 314, scorecard database 316, or other data sources may be provided to individual elements of a scorecard such as actuals, targets, and the like associated with individual metrics (KPIs, Objectives, and the like).
Scorecard builder 302 may be a separate application, a part of the performance evaluation application, and the like. Scorecard builder 302 is employed to configure various parameters of scorecard engine 308 such as scorecard elements, default values for actuals, targets, and the like. Scorecard builder 302 may include a user interface such as a web service, a Graphical User Interface (GUI), and the like.
Strategy map builder 304 is employed for a later stage in scorecard generation process. As explained below, scores for KPIs and parent nodes such as Objective and Perspective may be presented to a user in form of a strategy map. Strategy map builder 304 may include a user interface for selecting graphical formats, indicator elements, and other graphical parameters of the presentation.
Data Sources 306 may be another source for providing raw data to scorecard engine 308. Data sources may be comprised of a mix of several multi-dimensional and relational databases or other Open Database Connectivity (ODBC)—accessible data source systems (e.g. Excel, text files, etc.). Data sources 306 may also define KPI mappings and other associated data.
Scorecard architecture 300 may include scorecard presentation 310. This may be an application to deploy scorecards, customize views, coordinate distribution of scorecard data, and process web-specific applications associated with the performance evaluation process. For example, scorecard presentation 310 may include a web-based printing system, an email distribution system, and the like. A user interface for scorecard presentation 310 may also include an overview of available scorecards for a subscriber to select from. Scorecard presentation 310 may further include a matrix or a list presentation of the scorecard data. The scorecard presentation and one or more zones for other applications may be displayed in an integrated manner.
Query optimization module 320 is arranged to reduce a total number of queries and further optimize the queries for overall enhancement of the scorecard building and maintenance process. Query optimization module 320 may also be involved in caching of a portion or all of the data for a particular scorecard. Data retrieved from remote data sources may be cached for rapid and efficient building and updating of scorecards. The data may be cached as a single block for the entire scorecard, for individual elements, or for combinations of selected elements. This way, multiple scorecards may be built using the same cached data with increased efficiency.
Other applications 318 may include any application that receives data associated with a report parameter and consumes the data to provide a report, perform analysis, provide alerts, perform further calculations, and the like. The data associated with the report parameter includes content data and metadata. Other applications may be selected based on the report parameter, a subscriber request, or a user interface configuration. The user interface configuration may include a subscriber identity or a subscriber permission attribute. Other applications 318 may include a graphical representation application, a database application, a data analysis application, a communications application, an alerting application, or a word processing application.
FIG. 4 illustrates a screenshot of an example scorecard. As explained before, Key Performance Indicators (KPIs) are specific indicators of organizational performance that measure a current state in relation to meeting the targeted objectives. Decision makers may utilize these indicators to manage the organization more effectively.
When creating a KPI, the KPI definition may be used across several scorecards. This is useful when different scorecard managers might have a shared KPI in common. The shared use of KPI definition may ensure a standard definition is used for that KPI. Despite the shared definition, each individual scorecard may utilize a different data source and data mappings for the actual KPI.
Each KPI may include a number of attributes. Some of these attributes include frequency of data, unit of measure, trend type, weight, and other attributes. The frequency of data identifies how often the data is updated in the source database (cube). The frequency of data may include: Daily, Weekly, Monthly, Quarterly, and Annually.
The unit of measure provides an interpretation for the KPI. Some of the units of measure are: Integer, Decimal, Percent, Days, and Currency. These examples are not exhaustive, and other elements may be added without departing from the scope of the invention.
A trend type may be set according to whether an increasing trend is desirable or not. For example, increasing profit is a desirable trend, while increasing defect rates is not. The trend type may be used in determining the KPI status to display and in setting and interpreting the KPI banding boundary values. The trend arrows displayed in scorecard 400 indicate how the numbers are moving this period compared to last. If in this period the number is greater than last period, the trend is up regardless of the trend type. Possible trend types may include: Increasing Is Better, Decreasing Is Better, and On-Target Is Better.
Weight is a positive integer used to qualify the relative value of a KPI in relation to other KPIs. It is used to calculate the aggregated scorecard value. For example, if an Objective in a scorecard has two KPIs, the first KPI has a weight of 1, and the second has a weight of 3 the second KPI is essentially three times more important than the first, and this weighted relationship is part of the calculation when the KPIs' values are rolled up to derive the values of their parent Objective.
Other attributes may contain pointers to custom attributes that may be created for documentation purposes or used for various other aspects of the scorecard system such as creating different views in different graphical representations of the finished scorecard. Custom attributes may be created for any scorecard element and may be extended or customized by application developers or users for use in their own applications. They may be any of a number of types including text, numbers, percentages, dates, and hyperlinks.
One of the benefits of defining a scorecard is the ability to easily quantify and visualize performance in meeting organizational strategy. By providing a status at an overall scorecard level, and for each perspective, each objective or each KPI rollup, one may quickly identify where one might be off target. By utilizing the hierarchical scorecard definition along with KPI weightings, a status value is calculated at each level of the scorecard.
First column of scorecard 400 shows example elements perspective 420 “Manufacturing” with objectives 422 and 424 “Inventory” and “Assembly” (respectively) reporting to it. Second column 402 in scorecard 400 shows results for each measure from a previous measurement period. Third column 404 shows results for the same measures for the current measurement period. In one embodiment, the measurement period may include a month, a quarter, a tax year, a calendar year, and the like.
Fourth column 406 includes target values for specified KPIs on scorecard 400. Target values may be retrieved from a database, entered by a user, and the like. Column 408 of scorecard 400 shows status indicators.
Status indicators 430 convey the state of the KPI. An indicator may have a predetermined number of levels. A traffic light is one of the most commonly used indicators. It represents a KPI with three-levels of results—Good, Neutral, and Bad. Traffic light indicators may be colored red, yellow, or green. In addition, each colored indicator may have its own unique shape. A KPI may have one stoplight indicator visible at any given time. Indicators with more than three levels may appear as a bar divided into sections, or bands. Column 416 includes trend type arrows as explained above under KPI attributes. Column 418 shows another KPI attribute, frequency.
FIG. 5 illustrates a conceptual diagram of an example scorecard and its elements' association with multiple data sources according to embodiments. The diagram includes example scorecard 510 with hierarchically structured KPIs (512) and a set of actuals (514) and four example sets of targets (516) for each KPI. The diagram also includes representative data sources 520.
Scorecards can be composed of multiple metrics (KPI₁−KPI₅) from different data sources (520). Each metric may be composed of an actual (data values DV₁−DV₅) and up to N targets (data values DV₆−DV₂₅). Each target may be associated with up to two data sources depending on what the actual is to be compared against (e.g. displaying a comparison against one data value and calculating banding logic based on another). According to one scenario, one might compare the actual number of product defects to the number of product defects in the previous week, but determine a green, yellow, or red status based on the percentage of products with defects comparing this week from last.
An upper boundary for a query that needs to take place for each scorecard is: (1*Actual+(Number of Targets*2))*Number of elements. According to some embodiments, a list of elements (KPIs) in a scorecard is scanned at runtime and those associated with the same data sources are determined. The total number of queries is then optimized by rewriting the appropriate queries.
According to other embodiments, the queries may be further optimized based on an attribute or condition of a data source. For example, time-outs and other query parameters may be adjusted depending on an accessibility or retrieval speed associated with a data source. Even a time of execution for the queries may be set based on the data source(s). In another example, a scorecard may include data from a number of data sources in different countries. To accommodate server and communication channel capacity, the queries for some of those data sources may be scheduled to be executed in off-peak times.
Page filters for scorecards that define time, product, geography, and similar filtering of scorecard views may also be considered in rewriting the queries. In one embodiment, the queries, both multidimensional and relational, may be rewritten (and combined) based on multiple scorecards, such that data for elements of different scorecards that are associated with the same data source can be retrieved in a single process. In another embodiment, the query rewriting process may take into account a subscriber's identitys and/or permissions since those may affect access to data sources.
Frequency of update may be different for data associated with various elements. Hence, not all data has to be retrieved at the same interval. Accordingly, some or all of the retrieved data may be cached for the whole scorecard, for a combination of elements, or even for individual elements. When a scorecard is to be built or updated, cached data may be used reducing the retrieval process to only the portion of the data that needs to be updated based on its renewal frequency.
According to yet other embodiments, appropriate new timeouts may be set for combined queries to reflect the modified data retrieval or timeouts of individual queries adjusted to reflect attributes and/or conditions of data sources. For example, a timeout value for a query that is used to retrieve data for KPI₂may be set at 15 seconds. If KPI₁, KPI₂, and KPI₃share the same data source, the new query would retrieve data for all three KPIs. Hence, the timeout for the new query may be slightly increased to accommodate the increased data retrieval for that particular query.
The scorecards, metrics, and data sources discussed in FIGS. 2-5 are for illustration purposes only. Embodiments are not limited to the example scorecards, metrics, systems, and data retrieval methods for optimizing a scorecard building and maintenance process. Concerted coordination of multiple scorecards may be provided in many other ways using the principles described herein.
FIG. 6 illustrates a logic flow diagram for a process of optimizing scorecard building by optimizing queries. Process 600 may be implemented in a business logic application such as a scorecard application as described in FIGS. 1 and 2.
Process 600 begins with operation 602, where data sources associated with individual metrics (e.g. KPIs, Objectives, and the like) are determined. As mentioned previously, each metric may be associated with one or more data source that may be virtually or physically separated. Processing advances from operation 602 to optional operation 604.
At optional operation 604, an attribute or a condition of the data sources associated with the metrics of the scorecard(s) is determined. Access parameters for each data source may be different. Furthermore, a condition of each data source such as physical location, access time, communication channel quality, and the like, is also likely to vary among the data sources associated with the scorecard(s). Processing moves from optional operation 604 to operation 606.
At operation 606, one or more queries are dynamically rewritten. Rewriting the queries may involve combining multiple queries associated with the same data source into a single query, optimizing the queries based on access conditions of each data source, user identitys, and the like. The queries may be further optimized by adjusting timeouts and other query parameters. The new queries may be associated with a group of scorecard elements or with a group of scorecards. Processing proceeds from operation 606 to operation 608.
At operation 608, the data is retrieved from the data sources based on the rewritten queries. As mentioned previously, new timeouts may be defined for the retrieval of data based on a scope of the new queries and/or data source conditions. Moreover, the data may be retrieved such that the result set is optimized for a scorecard matrix. Processing moves from operation 608 to optional operation 610.
At optional operation 610, retrieved data may be cached for subsequent use in building or maintaining one or more scorecards. Some data may be updated more frequently than other data. In such cases, the less frequently updated data may be cached at element, group of elements, or scorecard levels. Caching can also occur at the data source level. For example, a target called “monthly forecast” may be associated with five KPIs that draw their values from the same data source. That data source may be cached and the results for that particular target may be returned without a query, while other values in the scorecard may require additional queries. The cached data can be used in building or updating one or more scorecards without a need to retrieve the same data repeatedly saving system resources. Processing advances from optional operation 610 to operation 612.
At operation 612, a scorecard is built or updated using the retrieved (or cached) data according to the predefined metric hierarchy. Other operations may follow operation 612 such as generating or presenting reports, issuing alerts, providing notifications, and the like. After operation 612, processing moves to a calling process for further actions.
The operations included in process 600 are for illustration purposes. Optimizing data retrieval in a scorecard application by rewriting queries may be implemented by similar processes with fewer or additional steps, as well as in different order of operations using the principles described herein.
The above specification, examples and data provide a complete description of the manufacture and use of the composition of the embodiments. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims and embodiments.

Claims

1. A method to be executed at least in part in a computing device for optimizing data retrieval in a scorecard service, the method comprising:

determining a plurality of scorecard elements that share data sources;

determining an attribute associated with a shared data source;

generating a query to retrieve data associated with the plurality of elements, the query being optimized based the attribute associated with the shared data source.

2. The method of claim 1, further comprising retrieving the data by executing the generated query.

3. The method of claim 1, further comprising determining a condition associated with the shared data source.

4. The method of claim 3, wherein generating the query further comprises generating the query that is optimized based on the condition associated with the shared data source.

5. The method of claim 1, further comprising adjusting the generated query based on a page filter parameter.

6. The method of claim 1, wherein determining the attribute comprises determining the attribute comprising one of: a data source type and a security protocol associated with the data source.

7. The method of claim 1, further comprising:

adjusting at least one timeout associated with the generated combined query based on the attribute.

8. The method of claim 1, further comprising:

assigning at least one timeout associated with the generated query based on the attribute.

9. The method of claim 2, further comprising:

caching the retrieved data at one of: an element level, a combination of elements level, a data source level, and a scorecard level.

10. The method of claim 9, wherein caching the retrieved data comprises caching the retrieved data to update an existing scorecard.

11. The method of claim 1, wherein generating the query further comprises generating the query to provide an optimized result set for a scorecard matrix based on an attribute associated with one of the first data source and the second data source.

12. The method of claim 1, wherein determining the plurality of scorecard elements that share the data source comprises determining a first scorecard element that is associated with a data source and a second score card element that is associated with the data source.

13. The method of claim 1, wherein generating the query comprises:

receiving a first scorecard element query for the first scorecard element; receiving a second scorecard element query for the second scorecard element; and combining the first scorecard element query and the second scorecard element query into a combined query.

14. The method of claim 13, wherein generating the query comprises accommodating a third scorecard element that is associated with the shared data source.

15. A computer-readable medium having computer executable instructions for optimizing queries in a scorecard system, the instructions comprising:

determining a plurality of data sources associated with a plurality of elements of a scorecard;

determining shared data sources of the plurality of data sources, the shared data sources comprising data sources that are shared by at least one scorecard element of the plurality of element of the scorecard;

determining an attribute and a condition for each of the shared data sources;

generating at least one query to retrieve data associated with the at least one scorecard element, the query being optimized based on the shared data sources, the attribute of each shared data source, and the condition of each shared data source; and

retrieving the data by executing the generated at least one query.

16. The computer-readable medium of claim 15, wherein the instructions further comprise caching a portion of the retrieved data based on its association with one of an element, a group of elements, and the scorecard.

17. The computer-readable medium of claim 15, wherein the instructions further comprise adjusting at least one of a timeout and an execution time for the at least one query based on the condition of each data source.

18. A system for managing data retrieval from a plurality of data sources in a scorecard system, the system comprising:

a memory storage;

a processing unit coupled to the memory storage, the processing unit being configured to:

determine a data source associated with a plurality of scorecard metrics;

determine an attribute and a condition of the data source;

generate a query configured to cause a retrieval of each of the scorecard metrics together; and

optimize the query based on the attribute and the condition of the data source.

19. The system of claim 18, wherein the processing unit is further configured to adjust the generated query based on a page filter parameter.

20. The system of claim 18, wherein the processing unit is further configured to retrieve each of the scorecard metrics.