Patent Application
20060036475
The present invention relates generally to business dashboards and, more particularly, to the improve performance of a business dashboard.
Over recent years, business dashboards have become more commonplace. Business dashboards are essentially visual representations of business events. Through the use of middleware, such as computer networks, dashboards are able to collect and harvest data about a business. For example, tracking sales for a particular business sector, like energy, are commonly measured. The varying measurements are then refined using a variety of statistical analyses, like regression analysis.
The varying analyses, though, are of little use unless the information is provided to the correct persons. Hence, graphical and other user interfaces have been employed to provide information to employees. These employees, then, can use the information to make predictions and business judgments, which can lead to successes or failures, such as increase or decrease sales.
However, it is not entirely clear as to how successes or failures are predicted or what may cause the successes or failures. Currently, such measurements or correlation techniques to determine the characteristics of both predictions and successes do not exist.
Therefore, a need exists for a method and/or apparatus for determining the characteristics of predictions in a business dashboard environment that addresses at least some of the problems associated with conventional business dashboards.
The present invention provides an apparatus, method, and computer program for debugging business activities. A plurality of metrics and a plurality of source databases are provided. Each metric is configured to choose and process data from the source databases according to predefined criteria to produce refined data. Also, the metrics are coupled to a user interface. Included in the middleware is a processor, wherein the processor utilizes statistically analysis with the refined data at or after a user labeled event. The statistical analysis determines if there is an association between the user defined event and the refined data.
For a more complete understanding of the present invention and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
In the following discussion, numerous specific details are set forth to provide a thorough understanding of the present invention. However, those skilled in the art will appreciate that the present invention may be practiced without such specific details. In other instances, well-known elements have been illustrated in schematic or block diagram form in order not to obscure the present invention in unnecessary detail. Additionally, for the most part, details concerning network communications, electro-magnetic signaling techniques, and the like, have been omitted inasmuch as such details are not considered necessary to obtain a complete understanding of the present invention, and are considered to be within the understanding of persons of ordinary skill in the relevant art.
It is further noted that, unless indicated otherwise, all functions described herein may be performed in either hardware or software, or some combination thereof. In a preferred embodiment, however, the functions are performed by a processor such as a computer or an electronic data processor in accordance with code such as computer program code, software, and/or integrated circuits that are coded to perform such functions, unless indicated otherwise.
Referring to
In order for the business dashboard to operate and effectively measure the performance of a company, middleware 102 is utilized. The middleware 102 comprises a processor with an incorporated predictive algorithm 112, a plurality of metrics 108, and storage 114. The plurality of metrics 108 is essentially the receiver portion of the middleware 102. Each of the plurality of metrics 108 defines a manner in which a measurement can be taken. Typically, metrics 108 are subroutines that collect and mine data, wherein the data is derived from a specific source or type of source, such as financial databases. Metrics, too, can be tired to a user interface. By coupling a metric to a user interface, a user can easily manipulate the measurements for the middleware 102 and user labeled events. For example, if a metric is utilized to determine the state of a business that operates in multiple sectors, such as the energy industry, a metric can collect financial data, sales data, and so forth of a company concerning the energy industry. The metrics can collect data from employee input 106, such as hourly time invested by an employee or employees, and can collect from other input 104, such as from financial databases. Each of the employee inputs 106 and the other inputs 104 are coupled to the middleware 102 through a first communication channel 120 and a second communication channel 118 to provide the means of communication between the middleware 102 and the inputs 104 and 106.
Once the data has been collected, the data is then processed. A processor 112 is utilized to process the input data from the employee inputs 106 and the other inputs 104 that has been refined by the metrics 108. Typically, the processor 112 employs a predictive or pattern recognition algorithm to determine possible patterns of behaviors that may have caused a particular event, be it a relatively good or bad event. The data from the plurality of metrics 108 is provided to the processor 112 through a third communication channel 126. Once the predictive algorithm utilized by the processor 112 has processed the data refined by a metric 108, a resultant, recognized pattern, if any, is communicated. The processor 112 can then store the resultant, recognized pattern, or lack thereof, in storage 114. The processor 112 communicates the resultant, recognized pattern, or lack thereof, to storage through a fourth communication channel 130.
Once the data has been processed and stored, the recognized pattern can be displayed to a user. The recognized pattern is output from the middleware 102 to an output device 116 through a fifth communication channel 124. The business dashboard 100, though, does not appear to be different from conventional dashboards, except that the pattern recognition used by the processor 112 is substantially different.
The predictive algorithm used by the processor 112 operates by effectively taking snap shot of the state of a company or division of a company. The processor 112 determines when a user labeled event, such as a good event or a bad event, has occurred. The good or bad event can be the improved sales or some recognized trend. For example, a recognized trend could be specific growth of a portion of an industrial sector. The processor 112 could be then prompted to take such a snap shot of the state of a company or division of a company. Once the snap shot of the state of the company or division of a company has been taken, then the predictive algorithm used by the processor 112 employs statistical analysis, such as regression analysis, to determine why the a user labeled event, such as a good event or a bad event, occurred. The event or prediction could be as a result of the type of information gathered by a specific employee, or the event could be as a result of chance when there appears to be no statistical significance.
Referring to
The entire process of measuring the state of a company begins with data entry. In step 202, data is entered into the business dashboard of
The data that is entered, though, is meaningless unless the data is organized. Organization of data is accomplished through data mining. Data mining is the sorting and the selecting of data based on specified criteria, which is accomplished through the use of metrics. In step 204, metric measurements are taken. Metrics are the specific manner in which data is measured. For example, return on investments, churn rates, revenues, and so forth are all metrics. The metrics are usually deployed in algorithms that mine the input data.
Once the data has been mined, a determination of whether a user labeled event, such as a good or bad event, has occurred in step 206. The user labeled event, such as good or bad events, can be characterized in many ways. For example, exceeding expected earnings for a particular sector and an employee prediction of a small market depression could be characterized as a good and bad event respectively. The characterization of user labeled events can be preprogrammed into the business dashboard 100 of
However, if a user labeled event, such as a good event or a bad event, occurs or a request for a periodic measurement occurs, then the data is properly processed. For a user labeled event, such as a good event or a bad event, a snap shot of all of the input data is taken. Effectively, the state of the company or division of the company is captured. The data gathered by the specific metric is stored in step 210. Then, an algorithm begins to analyze the data gathered by the metric in step 214. Many types of statistical analyses, such as linear regression analysis, multiple regression analysis, nonlinear least square fitting, and so forth, can be employed to determine trends in step 216. Once the business dashboard 100 of
The benefit of utilizing such a dashboard system, such as the business dashboard 100 of
It is understood that the present invention can take many forms and embodiments. Accordingly, several variations may be made in the foregoing without departing from the spirit or the scope of the invention. The capabilities outlined herein allow for the possibility of a variety of programming models. This disclosure should not be read as preferring any particular programming model, but is instead directed to the underlying mechanisms on which these programming models can be built.
Having thus described the present invention by reference to certain of its preferred embodiments, it is noted that the embodiments disclosed are illustrative rather than limiting in nature and that a wide range of variations, modifications, changes, and substitutions are contemplated in the foregoing disclosure and, in some instances, some features of the present invention may be employed without a corresponding use of the other features. Many such variations and modifications may be considered desirable by those skilled in the art based upon a review of the foregoing description of preferred embodiments. Accordingly, it is appropriate that the appended claims be construed broadly and in a manner consistent with the scope of the invention.
