Abstract:
A system, process and method for automatically collecting, collating and transforming data into useful formats and displaying or otherwise outputting the transformed data into useable information. The system provides outputs that are useful in optimizing the enterprise performance of a business. The system, process and method are grounded in an established logical framework for systematically classifying areas of business concerns.

Description:
BACKGROUND 
     1. Field of Invention 
     The present invention relates to a system and method for identifying and qualifying sources of data, collecting, filtering and analyzing data and transforming the data into visual images associated with a selected framework that is useful as tool for managers to optimize enterprise performance. 
     2. Background of the Invention 
     Since the mid-20th century, the field of business and management has revolutionized business and industry. Beginning with consultants such as Peter Drucker and W. Edwards Deming, numerous authors and consultants have suggested frameworks for analyzing an enterprise to give management insight and provide tools to improve performance through, for example, enhanced clarity of roles, responsibilities, and expectations. Numerous “frameworks” have been suggested since that time, but the lack of systematic approach to gaining insight into all areas of an organization diminishes the usefulness of these frameworks. 
     Two distinct methodologies are commonly used to measure organizational effectiveness and collect information on the functional performance of business processes: outside business consultants and internal review processes. This approach to gaining insight into an enterprise is disconnected and disorganized. 
     When performance is measured based on external perspectives from consultants, individuals or a team conduct interviews of key executives in a company, review financials, and compare results to their established methodology. Expertise generated by consultancies is based on soft variables and subjective information provided by the consultancy. The actual methodology of consulting varies widely due to different established practices, varied strategic differences between internal opinions provided within the business community, and different institutional philosophies constructed on a variety of experiences uniquely shaped by the circumstances the individual consultancy encounters during their operation as a functional business. Consultancies produce results based on their varied methodologies and then provide executive recommendations based on their private findings. Typical consulting fees are quite expensive. This makes consultation an unattractive option for business managers unless they are forced into unfortunate circumstances that inhibit their operations or their strategic position is compromised in their own space within their operational market. In addition, the “learning” and recursive benefit that comes from in depth analysis of different organizations inures almost entirely to the outside consultants. Stated differently, the more engagements a consultant takes on the wider their knowledge base becomes. Learning form the best practices of one organization allows a consultant to better advise another organization with regard to benchmarking or best practices. However, the ability to share and benefit from this increased insight is controlled by the outside consultant. Moreover, protection of know how relating to best practices depends on the outside consultant. Organizations would plainly benefit from being able to capture for themselves some of these side benefits of in depth organizational analysis. Likewise, systemized benchmarking (as opposed to human benchmarking) improves the ability to control the dissemination of know how. 
     Business management might also choose to investigate optimization options by establishing internal review processes. Internal processes that businesses use to conduct performance reviews tend to be broad and disparate. An individual business might use performance reviews ranging from strategic off site based internal executive team evaluations to internal employee surveys. The variance among separate business entities is not of itself problematic. However, it is often the case that an individual business utilizes completely separate methods for collecting information. Initiatives typically target separated issues based on entirely different points of strategy. Data collection and management can also vary greatly. These methods are all disconnected from an overall perspective and lack organized means of comparing the performance of each method. Since these different methods cannot be universalized, it is difficult to examine the strategic importance of the information. 
     The absence of a systemized approach to data collection limits the ability to use the data to gain enterprise wide insight. Most data collected through consultants and internal review, assuming it is even translated into useful strategic insights for the business, is eventually neglected as of little value beyond the narrow case for collection. The disparate nature of the information means further limits the value of the data gathered in conventional internal review processes. Since there is no existing framework for organizing all of the information, none of the respective pieces of data have any larger meaning for the business. There is no methodology for universalizing the information to the broader implications of the business itself. Generated connect insights is difficult, if not impossible, with disconnected data. 
     Thus, there remains a need for a system and method for identifying, gathering and transforming useful data into a desired framework. 
     SUMMARY OF THE INVENTION 
     The present invention provides a system and systematic approach (method) for identifying and qualifying sources of data, collecting, filtering and analyzing data and transforming the data into useful output (e.g., visual images and print outs) associated with a selected framework that is useful as tool for managers to optimize enterprise performance. As used here, a “framework” is an analytical structure for organized presentation of data that encompasses the assets, processes and structures that drive business success. Embodiments described herein refer to Edwin Miller&#39;s 9Lenses framework, but the invention may be applied to other frameworks as well. An embodiment of the present invention provides a system, methods, processes, software, and standards designed to collect and collate information pertaining to the condition particular to the company that concern the successful operation of the company evaluated. 
     A challenge encountered by business leaders seeking to utilize a framework, (e.g., 9Lenses) is that data within and available to the organization is not directly applicable to the framework. Moreover, data that may be relevant or necessarily is not being collected. The invention provides a system and systematic approach for identifying, collecting and transforming available data into framework data. The system takes input from a wide variety of data sources, transforms the data by processing the input as necessary and mapping the input to a MAIN SCHEMA using a mapping engine. A transformation engine (analytics engine) may be used to transform or assist in transforming the MAIN SCHEMA data into a selected output framework (Business Context). The presentation format may be a “preset” format related to known or established business context or customized to meet a particular need. 
     The input data sources used may include both people providing input in response to surveys or data pulled from existing internal or external data sources. The people from whom data is obtained can be anyone connected with the enterprise: employees, managers, customers, vendors and any other stake holder. Existing internal data sources could include, for example, Enterprise resource planning (ERP) systems, human resource (HR) systems and operational systems. External data sources could include, for example, market intelligence and competitive rankings. 
     The output of the system, methods, processes, and software can be displayed (presented) in a format tailored to address specified problems based on criteria of assessing (1) immediate business pains (2) specific areas of concern (3) scope of the problem (4) potential returns for solutions to the problem. Information is then classified according to business complexity and immediate needs. Selections of the specific systems utilized under the framework are based on company preference, but recommendations are provided based on the inputs provided by the company. The raw data is persevered in association with the transformed data and the presentation of the data is hierarchically structured so that a user may see all available data at the highest transformed data level and then “drill down” into progressively lower levels so that raw data is at the lowest level of the hierarchy. 
     The output of the system, methods, processes, and software may include presentations of data transformed and applied according to a selected schema and may include the output of one or more software engines that provide useful business tools. For example, a recommendation engine may be provided to make recommendations based on the data and the selected schema. Likewise, a prediction engine may be provided to make predications based on the data and the selected schema. A comparison engine may be used to take system output and compare the output to a standard for that industry using a database that stores ideal metrics of that industry, i.e., compare actual to ideal. Based on signals from the comparison engine, the system may provide a visual signal [e.g., “red” “yellow” “green” display] to identify where the data presented lies on the spectrum of comparable organizations. Additionally, the comparison engine may provide recommended action-steps for using the data in strategic plans. A valuation engine may be used to generate a valuation of the enterprise based on the data. 
     The system also includes data filters that, for example, allow a user to turn off selected segments of data from the inputs. The segmentation of the data is based on preset organization of the data. This functionality allows the system to display outputs based on different combinations of segmented data from the inputs. 
     The present invention is applicable to a wide variety of business problems. Utilization can theoretically apply to any company operating with a multiplicity of employees, operations, functions, and systems. Meaningful insight is derived from the collection, development, and transformation of data based on the inputs, data aggregation, and systems. Outputs regarding the aforementioned problems functionally operate under the mechanism of the system logic (schema) in regard to how data is transformed into useful insight driving materials. 
     The tools provided by the invention may be applied to all business problems that can be articulated in a known context for procedural evaluation. Areas of application broadly concern market potential, market behaviors, competitor interests, human resource solutions, organizational design, financial resource management, business planning strategy, marketing planning, sales strategies, operational considerations, infrastructure planning, operational assessment, potential returns for investments, measures for assessment, performance assessments, stakeholder investigations, governance practices, and legal concerns. These issues all fit into the 9Lenses framework, called the schema. The invention develops solutions based on this framework. 
     Business problems under the framework function as points of evaluation. Points of evaluation are deployed in the system based on the working methods established. The specific systems, methods, processes, software, and standards utilized break down based on the workflow of the issue classification. Aggregated data functionally overrides strategic evaluation difficulties by automating data collection and transforming the simple data points into meaningful information with direct application to immediate concerns as well as applications to future problems. Additionally, by providing contextual understanding of comprehensive organizational structure, the data functions as a conceptual insight engine. Data aggregation reduces the operational and opportunity costs of strategic assessments while maximizing the valuation and visibility of potential solutions. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a schematic diagram illustrating the architecture of the system. 
         FIG. 2  is a schematic diagram illustrating the functional overview of the system. 
         FIG. 3  is a process flow for how data in the system is transformed into actionable enterprise intelligence. 
         FIG. 3A  is an illustration of a main schema and the accompanying output of from the system dashboard. 
         FIG. 3B  is an illustration of an alternative output from the system dashboard. 
         FIG. 3C  is a illustration of the description of the main components of a main schema. 
         FIG. 4A  is a schematic diagram of an exemplary system for transforming various inputs of raw data into useable information within the schema. 
         FIG. 4B  shows an aspect of the invention that allows the user the ability to control data outputs. 
         FIG. 4C  is a schematic diagram of the system used to measure various inputs and determine comparative analysis of the entire business operation 
         FIG. 5  is a schematic diagram of an exemplary system used in a process for evaluating and confirming business data inputs based on interpretative logic grounded in dynamic feedback loops. 
         FIG. 6  is a schematic diagram of an exemplary system for collection, organization, schematization, storage and use of queries designed to elicit data pertaining to business problems into a universal database. 
         FIG. 7  is a schematic diagram of an exemplary system and process for selection of individuals to participate in a business initiative based on a process using a/b testing to determine expertise on information for the purpose of planning and segmenting participants. 
         FIG. 8  is a schematic diagram of the prediction engine used in predicting outcomes from separate business problems. 
         FIG. 9  is a schematic diagram illustrating the system used for aggregating business data and automatically publishing content to specified users. 
         FIG. 10  is a schematic diagram of an exemplary system used based on a decision engine for automatically generating diagnostic queries for business problems and then refining the automatically generated apps. 
         FIG. 11  a schematic diagram of an exemplary system for collecting systems data into the interpretation and scoring logic system and aggregating the data and building it into the schema. 
         FIG. 12  is a schematic diagram of the system used in a process for collecting assorted public (external) data and systematizing the information based on an interpretative scoring process and sequencing it into a logical framework. 
         FIG. 13  is a schematic diagram of the system for generating specific population lists to be queried based on predetermined inputs that in turn generate an automatically selected population for sessions based on determinant algorithms and comparisons. 
         FIG. 14  is a schematic diagram illustrating an exemplary system used for creating business solutioning ideas within the organization. 
         FIG. 15A  is a schematic diagram of an exemplary system for presenting output according to an alternative schema format based on a master schema. 
         FIG. 15B  is a schematic diagram illustrating an example of a system from presenting translated content from alternative schemas into a format based on a master schema. 
         FIG. 16  is a schematic diagram of an exemplary system for automatically recommending business conversations based on the data obtained from the holistic business diagnostics. 
         FIG. 17  is a schematic diagram of an exemplary system that automates meetings. 
         FIG. 18  is a schematic diagram of an exemplary system that monitors inputs to generate recommendations and report on changes. 
         FIG. 19  is a schematic diagram illustrating an exemplary system used for matching consultant-generated solutions concerning specific enterprise related issues. 
         FIG. 20  is a schematic diagram illustrating an exemplary system for using data inputs vetted through established protocols to determine bid decisions for contracts. 
         FIG. 21  is a schematic diagram illustrating an exemplary system for measuring the financial model of an enterprise. 
         FIG. 22  is a schematic diagram illustrating an exemplary system for automatically calibrating the predictive success from automatic interviews based on successes of previous candidates. 
         FIG. 23  is a schematic diagram illustrating a process of automatically calibrating the automatic hiring determinant system based on successes of previous candidates. 
         FIG. 24  is a schematic diagram the system used for process responses from automated interviews. 
     
    
    
     DETAILED DESCRIPTION 
     An embodiment of the present invention provides a broad system for business optimization by presenting data according to a selected schema. The data presented according to the schema is generated by transforming data received into schema data according to a selected schema. 
     I. Core System Logic 
       FIG. 1  is an overview of the system architecture. As shown, the system includes a communications system  100  for network communication with a plurality of data sources. A query engine  110  is connected to the communication system for requesting data from the data sources. The data sources include external data sources that communicate with the system through the Global Information Network (GIN) and internal data sources in direct communication with the system. Examples of external sources include data feeds  122  that provide market intelligence or other news and inputs from social media or outside sources made through communication devices such as mobile phones  124 , tablet computers  126  and other computers  126 . Examples of internal sources include the CRM system  132 , HR Database  133 , CRP System  134  as well as user inputs through computers such as tablets  136  and other computers  138 . A database  140  stores data received from the data sources and a schematic interpretation engine  150  engine transforms data in the database  140  to master schema data according to a selected schema. The system may also include an engine for transforming the master schema data into data for alternative schemas and allows customized schemas. Various displays  170  may be used to display data in a format dictated by the selected schema. A user interface  160  allows a user to control the display. The hardware used to implement the system preferably includes at least one CPU with on board RAM; an input/output system bus (including control bus, address bus and data bus functionality); system memory; system storage (flash or hard drive); communications hardware for TCP/IP (or other protocol) based end-to-end connectivity and a wireless communication processor for enabling Wi-Fi, Bluetooth and/or other wireless data exchange over a local or global information network. 
       FIG. 2  shows a functional overview of the system. As shown, the data from External  120  and Internal  130  sources is aggregated  210  and passed to an interpretation engine  230  for transformation into schema data according to the selected schema  310 S (in this example the 9Lenses schema). The data is then selectively displayed as system output  310 D. The function of the invention is divided by two categories, (1) the fundamental logic of the system that drives the data collection, storage, transformation, and dissemination and (2) the extended uses of the systems logic in a plurality of subsystems. 
       FIG. 3  shows the process flow for data transformation according to the invention. As shown, the process beginning at step  300  includes the step  310  of selecting a schema, which is described in greater detail below. At step  315 , the components and sub-components of the selected schema are defined. The defined components (and their sub-components) are the characteristics of the enterprise that are to be evaluated according to the schema. A challenge arises in that there is rarely (if ever) a single data source within an enterprise that provides a complete measure of a component used according to an established schema. Thus, it becomes necessary to transform available data into data that provides the desired evaluation of a component according to the selected schema. At step  320 , an available data source that is relevant to one or more of the schema components is identified. At step  325 , a strategy for collecting the relevant data is designed and implemented and the relevant data is collected and stored at step  330 . The process is repeated (step  327 ) so long as there are relevant data sources. At step  333 , a determination is made as to which of the components or sub-components each data source is relevant to and at step  335  the importance of the data to a component/sub-component is defined by a weighting factor assigned to each data source. At step  340 , a weighting factor is assigned to each subcomponent to reflect the relative importance of that sub-component to the component being measured. The weighting factors associated with data sources are preferable dynamically adjusted based on previous users responses from a particular participant and past performance. For example, the input of a particularly insightful data source (participant/respondent) may be given more weight, while a less insightful data source may be given less weight. A dynamic data weighing engine may be used for this purpose. At step  350 , the system displays the component level results (as shown, for example, in  FIGS. 3A and 3B ) and the user is provided with the option (though user interface  160 ) to display the underlying constituent data, i.e., drill down to see the subcomponents and data that resulted in the overall result. At step  360  users are provided the option for considering specified sub-sets of the data (from step  333 ) apart from the aggregate data provided by the system. Users can select specific data from specified sources. At step  363 , in response to the users&#39; selections, the system removes one or more data sources from the calculation and reweights the remaining data sources  365 . The system also provides the user with the option of outputting data from the system (at step  370 ) and allows the user to select an output format (step  375 ). 
     The step  310  of selecting a schema involves selecting an analytical structure for organized presentation of data that encompasses the assets, processes and structures that drive business success. By way of example,  FIG. 3A  shows the 9Lenses framework  310 S and one example of an output display  310 D of transformed data. In the 9Lenses schema, the components defined (step  315 ) are the 9Lenses (strategy, execution, operations, expectation, governance, entity, market, people and finance). The sub components are the “sub lenses” of the 9Lenses schema.  FIG. 3B  shows an alternative output that provides a more through overview of the data at the component level. 
     As shown and explained in  FIG. 3C , the 9Lenses components provide insight into the assets, processes and structure within an enterprise. In this regard, the market, people and finance lenses may be grouped under the category “assets.” The strategy, operations and execution lenses may be grouped under the category “processes.” The expectation, governance and entity lenses may be grouped under the category “structures.” Other schemas typically use different labels for the different components and sub components used to provide insight into an enterprise. However, in accordance with an aspect of the invention, the component data for one schema (e.g., 9Lenses) may be transformed into and presented as component/sub-component data for another schema using a schema conversion process, one example of which is described in  FIG. 15  below. 
       FIG. 4A  shows the system used for transforming various inputs from raw data into usable information within the schema. Although  FIG. 1  depicts the process at a high level as occurring in a schematic interpretation engine  150  that is in communication with other system components and the user interface  160 , the process may occur at various locations based on various inputs. The process steps employed in the transformation of raw data into schema data comprise: collection of raw data; classification of raw data; assignment of data that has been classified; weighting of data and application of data to the schema components/sub-components. 
     As shown in  FIG. 4A , the raw data that has been collected is classified (step  410 ) according to, for example, data type: active  412 ; passive  414 ; binary  415 ; scaled  416  and user generated  418 . At step  420 , the data is then assigned to one or more components/subcomponents of the schema and a weighting factor is determined for the data with respect to each component/subcomponent. The previous classification ( 412 - 418 ) is preferably a factor in determining the weighting assigned to data (step  420 ). At step  430 , the transformed data is then applied to the selected schema. Preferably, the transformed data sources are each assigned to a subcomponent with a respective weighting factor and the subcomponents are given a weighting factor for their respective component. Once transformed data is applied to the schema and appropriately weighted, the system can output schema data in various forms according to user preference at step  440 . For example, the data may be displayed in the “dashboard” format depicted in  FIG. 3A or 3B  or output to another program or application or a printable format. 
     As shown at  470  in  FIG. 4A , the system may also use transformed schema data to generate and output action step guide outputs such as recommendations  473 ; industry benchmark comparisons  474 ; red flags  475  and people analysis  476 . In this way, the system leverages the transformed data to provide additional tools in the form of reports and indicators based on more accurate and up to date data than would otherwise be available. For example, the industry benchmark feature allows comparison of an enterprise&#39;s performance to other enterprises in the industry. Importantly, the system allows such comparisons even among companies that select different schemas because of the ability to interpret data from other schemas. 
       FIG. 4B  shows an aspect of the invention which allows the user to control, through the user interface  160 , data input and weighting to permit segmentation and analysis of the degree of impact of departments or sectors and analysis according to one&#39;s own view as to the significance of particular business relevant data to business issues. As shown at  480 , the system includes control switches to allow the user to enable and disable inputs used to generate the system output along the lines shown at  360  in  FIG. 3 . As shown in  FIG. 3 , when data inputs are disabled, the system reweights remaining data sources  365  and generates revised output. The system further includes a weighting control feature  482  that allows the user to override the default weighting in defining the weighting for a data source (step  335 ). The system generates revised output based on the new weighting so that the user can see the impact of the change in weighting. 
       FIG. 4C  shows the system used to measure various inputs and determine comparative analysis of the entire business operation. As shown, the system is similar to that of  FIG. 4A  and system exclusive data is depicted as distinct from public and or enterprise data that is used for purposes other than the system per se. System exclusive data is data that is, in the first instance, generated or collected expressly for the purpose of inputting into the system, e.g., responses to system queries. As shown, the system includes an interpretation and comparison engine  478  performs comparisons across data sets to provide additional views and recommendations based on the transformed data. An example, described below in connection with  FIG. 8 , is the predictive analysis of predicted outcomes of business problems. 
       FIG. 5  shows the system used in a process for evaluating and confirming business data inputs based on interpretative logic grounded in dynamic feedback loops. By way of example, when data input is based on human input (e.g., response to a system query), the interpretation logic engine  520  evaluates the response against previous responses  522 , public data  523  and systems data  524  to identify a possible inconsistency, incongruity or anything else that might indicate erroneous input or enterprise inconsistency. When a possible error is identified, the dynamic confirmation engine  525  seeks confirmation of the data input by, for example, sending a query to the data source. Information from the interpretation logic engine may be viewed as a single instance (static view) or as a dynamic view and the system generates recommendations to remedy the detected error or inconsistency in data input. This aspect of the invention is especially important in detecting instances where a single input source may have relevant information that is unknown to others and separating such instances from mere errors in input. 
       FIG. 6  shows the system used for systematic collection, organization, schematization, storage and use of queries designed to elicit data pertaining to business problems into a universal database. The system includes a diagnostic input  610  for receiving a new diagnostic query from a user or agent. The diagnostic is then schematized  620 , i.e., a record is created as to which components/subcomponents of the schema the query is relevant to. In addition, a record may be created as to whether the query is enterprise (client) specific or generally applicable. If the query is enterprise specific, it is passed to a diagnostic creation interface where it is processed as an enterprise diagnostic for use in an enterprise app. The query is then evaluated (at step  640 ) for general applicability and if found applicable (perhaps with modification) for general use, the query is passed to the central repository  670 . If the query is not generally applicable, it is stored in the Enterprise Repository  650 . Queries stored in the Central Repository  670  may be displayed by the diagnostic library display  680  and also used to create apps using the app creation interface  690 . In this way, the system permits intake of individual diagnostics that are then transformed into queries that elicit interrelated information based on a logical framework for compilation into business diagnostics. The individual diagnostics may be transformed into apps (using the app creation interface  690 ) for the purpose of assessing business problems. 
     By virtue of the transformation and organization of data according to a schema, stored data may be used for other purposes. For example,  FIG. 7  is a schematic diagram of an exemplary system and process for selection of individuals to participate in a business initiative based on a process using a/b testing to determine expertise on information for the purpose of planning and segmenting participants. A shown, a system query  701  initiates the A/B test process  710 . The A/B test process takes into both performance assessment  720  (based on desired resource commitment  721  and probability of success  723  given the desired resource commitment) and influencing factors  725  regarding the proposed app. A logic module  730  processes the inputs and outputs segmentation  750  and resource planning data  770 . Segmentation  750  defines the role, organization, tenure or other characteristics of personnel suited for the task. Resource planning  770  outputs the availability of personnel and the enterprise impact of assigning available personnel. 
       FIG. 8  shows the prediction engine used in predicting outcomes from separate business problems. Predictive analysis begins with aggregated responses from schematized responses from participants  810 . Based on predetermined connections, the prediction engine takes actual responses around specific components and sub-components  822  and predicts responses to other schema queries  824  that have established connections to the queries  822  for which actual responses have been received. As shown, a cross comparison engine  830  uses the actual responses  822  together with Historical Response Data  840  to provide inputs to a predictive estimation engine  850  that generates a prediction of the response to schema queries  824  that are known to have a predetermined relationship to the actual responses  822 . Once the predicted responses to queries  824  have been generated, the system will prompt the user at  860  to validate the predicted response, e.g., confirm the predicted responses or provide new input. The results of the prediction are stored in the predictive database  870  and used as an input to refine future predictions by the predictive estimation engine  850 . Preferably, the validation step  860  occurs as a separate user session to allow a more comprehensive response to specified business problems. In other words, the validation step is more than just a data input validation, but provides an opportunity to elicit important data used within the schema in a systematic way that is more efficient and focused because it is based on information already known to the system. The predictive analysis system of  FIG. 8  thus acts as an intelligent agent to improve user input queries (at the validation step  860 ) though the use of predictive estimation. 
     II. Functional Extension of Core Logic 
       FIG. 9  shows the system used for aggregating business data and automatically publishing content to specified users, in this case enterprise board members. At step  910  a determination is made as to which subset of data will be provided to the user. The selection is input to a data-filtering engine  920 , which flags the relevant data fields. The automated data selection engine  930  generates an automated Relevant Data report  940  periodically or whenever a threshold of new data in the flagged fields has been received. 
       FIG. 10  shows the system used based on a decision engine for automatically generating diagnostic queries for business problems and then refining the automatically generated apps. As shown, the system includes a decision engine  1010  that allows priorities to be set according to enterprise organizational profile  1012  (industry, size, growth, inflection points) and preferences  1014  with respect to features such as time to completion, expertise required, source providing resources and area of focus (e.g. operations, execution etc.). The output of the decision engine  1010  together with the diagnostic library  650  and/or  670  and optionally the output of the automatic population engine of  FIG. 13  are aggregated  1020  as inputs to an automated app generation engine  1030  that generates an automatically generated app  140  composed of diagnostic queries selected from the repositories  650 ,  670  based on the output of the decision engine  1010 . The automatically generated app may then be evaluated by the user at the diagnostic rating step  150  preferably though a diagnostic-by-diagnostic assessment that results in a refined app  170 . The refined app  170  is then subject to active monitoring (according to  FIG. 18 ) to continuously refine the app  170 . 
       FIG. 11  is a schematic diagram of an exemplary system for collecting systems data into the interpretation and scoring logic system and aggregating the data and building it into the schema. As shown, internal data  130  that is not system exclusive is transformed into schema useable data by assigning a schema useable score to the data. The score is assigned by an interpretation scoring engine  1110  pursuant to the selected schema (e.g., a score of 1-9) based on predetermined conversion algorithms or tables. The scores are then input into schema specific locations at step  1120  and applied as diagnostics input  1115  to diagnostics from the enterprise repository  650  for use in system output  1130  such as data interpretation, company reports and data feedback. 
       FIG. 12  shows the system used in a process for collecting assorted public (external) data  120  and systematizing the information based on an interpretative scoring process and sequencing it into a logical framework. As shown, external data  120  is transformed into schema useable data by assigning a schema useable score to the data. The score is assigned by an interpretation scoring engine  1210  pursuant to the selected schema (e.g., a score of 1-9) based on predetermined conversion algorithms or tables. The scores are then input into schema specific locations at step  1220  and applied as diagnostics input  1215  to diagnostics from the enterprise repository  650  for use in system output  1230  such as data interpretation, company reports and data feedback. 
       FIG. 13  shows the system for generating specific population lists to be queried based on predetermined inputs that in turn generate an automatically selected population for sessions based on determinant algorithms and comparisons. As shown, a parameter selection interface  1310  allows the user to set parameters based on factors such as segmentation, previous participation (and performance) and weighting of criteria. Based on the parameters set and data drawn from a HR database  1320 , an automated selection engine  1330  generates a population selection report  1340  for user review at step  1350 . If the report  1350  is approved, it is used in an app session at step  1360  and eventually results in a statistical report  1370 . If the report is not approved at step  1350 , the user selections participants to be removed and the process returns to the automated selection engine  1330 . 
     The system shown in  FIG. 13  may thus be used for automatically calculating a statistically significant population for addressing specific business problems. Likewise, the system may be used to invite the statistically significant population to an application, and determine their representative perspective based on relative calculations of the deviation of initial population participants. The system acts as a decision engine that uses relative A/B testing preferences to determine significant issues and workflows for determining which populations are expert in which topics. 
       FIG. 14  shows the system used for creating business solutioning ideas within the organization. The system solicits uncollected ideas from employees  1405  and includes a repository  1410  for storing and processing the ideas. The data is schematized at step  1420  and at step  1430  the idea is approved or rejected (presumably by a manager). If approved, the idea may be reformatted and rated as an output proposition  1440  for further consideration and rating. A logic module  1450  includes algorithms for selecting best comments/ideas, thumbs up/down rating for manual rating, algorithm for aggregating responses; use of the best data to determine consistent performance. Output from the logic module  1450  may include, for example, benchmarking reports, top comment reports and idea comparisons. The system further includes feedback loops for identifying and relating top solvers and best ideas to predictive solutions. As shown, ideas are associated with the individuals submitting them in an Individual Report  1460  and validated (or not) through future data and reports are generated on an entire session  1470 . User data is also stored in an enterprise repository  1480  and used to identify top performers based on submissions over time. Process steps may be performed by software engines, agents or a combination of both. 
       FIG. 15A  shows the system for presenting output according to an alternative schema format based on a master schema. In the example shown, the master schema is the 9Lenses schema. As shown, the user selects an alternative schema at step  1510 . An analysis agent defines the components and sub-components of the alternative schema at step  1515 . The agent then maps the components and subcomponents of the alternative schema to the master schema (step  1520 ). In addition, at step  1525  the agent identifies externalities, i.e., inputs required by the alternative schema that cannot be mapped from the master schema. To the extent externalities exist, it becomes necessary to define and implement a data collection strategy to satisfy the externalities. At step  1530 , an available data source that is relevant to one or more of the schema components is identified and a strategy for collecting the relevant data is designed and implemented. The relevant data is collected and stored at step  1540 . The process is repeated (step  1550 ) so long as there are relevant data sources. It will be appreciated that the agent described above maybe an automated software agent, a human agent or a combination of both. Once externalities are fully satisfied, the proposed mapping and internal systems information are presented for review and approval at step  1560 . If approved, mapped content is output at step  1570 . If not approved, a reason for rejection is obtained and the system revalidates the proposal (at step  1580 ) and the process resumes at  1525 . 
     Similarly,  FIG. 15B  shows an example of a system for extracting and presenting translated content from alternative schemes into a format based on a master schema. In this example, the analyst agent translates content from a relevant book on business expertise  1505  according to the collection of external research  1513  and previous information on the development of business procedures  1515 . The resultant diagnostic  610  is translated into a master schema  1520 . At step  1530 , the agent generates a refinement of the diagnostic according to pre-established criteria on comparison to known business problems  1533 , quality of the language used as it relates to traditionally accepted terminology  1535  and investigative strength of the diagnostic according to the likelihood of eliciting useful responses. The refinement is presented for review and approval  1540 . If approved, the diagnostic content is output at step  1560  and then stored in the diagnostic repository  670 . If not approved, a reason for rejection is obtained and the system revalidates the diagnostic (at step  1550 ) and the process resumes at  1530 . 
       FIG. 16  shows a system for automatically recommending business conversations based on the data obtained from the holistic business diagnostics (as shown in  FIG. 4C ). The system extracts data from responses  1610  to determine the statistically significant misalignment of scores between executives  1620  and specified needs identified by leadership  1630 . The system then compares this data with identified areas of concern from previous data  1640 . The resulting comparisons of data are output as a proposal for which business problems should be evaluated  1650 . Ideally, the system further includes a display with specific data within the schema from which the proposal was generated  1660 . 
       FIG. 17  shows the system that automates meetings. As shown, users create draft agendas  1710  in the system. The agendas are validated  1720  through manual confirmation from other participants and system-generated preferences from system specific data. At step  1730 , the system filters the human responses, these responses are then schematized  1740 . At step  1750 , the system creates areas of importance according to the schema components and sub-components. The system may use active monitoring  1760 , further described in  FIG. 18 , as a feedback loop to confirm the accuracy of the system-generated preferences. The system generates an actions and recommendation report  1770 , which users and participants may then validate according to their own preferences. The system may then provide outputs on the validated actions  1780 . 
     As the system collects data from a plurality of sources, the user may monitor the general trends in the usefulness of information that the system collects from different systems. As shown in  FIG. 18 , the system monitors individual inputs using decision logic modules to generate recommendations on how sources should be weighted. Data from a plurality of sources,  120 ,  410 , and  130  for example, is aggregated  1810 , similar to the system in  FIG. 2 , and passed to an interpretation engine  1820  for transformation into schema data according to a master schema  1830 . At step  1840 , the user may select criteria for preferences regarding data sources according to the decision logic engine. The system consistently tracks the inputs from the schematized data and the selections made in the engine. The engine outputs recommendations for data weighting  1850  according to the resultant information from the output feedback and the decision logic engine. 
       FIG. 19  shows the system used for matching consultant-generated solutions concerning specific enterprise related issues. As shown in step  1910 , users input solution data based on established criteria (preferably solution implemented, relevant characteristics of consultant, and experience in field). Solutions data may be stored in a database  1920 . The system then schematizes the data pursuant the main schema  1930 . Step  1940  shows the users generate data on specific enterprise problems. The data is input into a problem-matching engine that associated the specific problem with the main schema  1930  and generates matching recommendations for the solutioning the enterprise problems  1950 . The recommendations may then be evaluated by the respective executives managing the enterprise issue  1960 . The system uses the feedback generated by the executives to further improve the problem matching engine suggestions  1970 . The system then generates a proposed solution report  1980 . 
     The present embodiment of the invention has multiple systems for automating enterprise processes. For example,  FIG. 20  shows the system for automating bid/no bid decisions on contracts. Data from existing workflows, ratings from participants, and corporate resource management  2010  is aggregated  2015  and passed into an interpretation engine  2020  for transformation into the main schema  2030 . A logic engine  2040  processes the schematized data. The logic engine may then output a bid/no-bid report detailing predictive success from the data. Step  2060  validates the actual decision. Users indicate the wins and losses on specific bid and input reasons for the outcome. The system generates comparative reviews of these reasons for improving the accuracy of future predictions. 
       FIG. 21  shows a system for automatically determining the financial model of an enterprise. The system pulls data generated from automated interviews  2110 , further illustrated in  FIG. 24 , and system data  2120  targeted around financial information. The system displays an output model of the aggregated information  2130  according to three criteria (touch, volume, and margin). The system compares data from the output model to available industry data within the system and publically available data  2140 . The system generates an automated value estimate  2150  that, preferably, provides a “best in class” comparison financial models. The system also displays a benchmarking report  2160  that provides information for strategic improvement of the financial model. The system generates a KPI report  2177  and displays particular action steps  2173  for recommended actions for altering an enterprise financial model. 
     The system may provide a system that automatically interviews candidates for employment. As shown in  FIG. 22 , the system queries the user based on pre-determined characteristics  2210 . The system then feeds those inputs into the automatic interview engine  2220 , further illustrated in  FIG. 24 . The system is further comprised of a ranking integration for classified data  410 , as illustrated in  FIG. 4C . The system pulls the resultant data from automated interviews to generate a predictive probability for successful performance of the candidate within the enterprise role according to the predictive success indicator engine  2240  and outputs a display of the results accordingly  2250 . The system is further comprised of a self-correcting feedback loop that pulls information from the auto-tuned feature  2260 , further illustrated in  FIG. 23 . The system creates a corrective formulation for comparisons  2270  that feeds into the calculations provided by the predictive success indicator engine. 
       FIG. 23  shows the system for automatically calibrating the predictive success of job applicants from automatic interviews based on successes of previous candidates. The system pulls user inputs from the pre-determined characteristics  2210 . The outcomes measuring engine  2310  provides an approximated value for current user responses by assigning a numerical value to their responses. The system generates a deviation score for estimating how much the respondent differs from the predictive model for a successful candidate  2320 . The system compares the deviation score with the results of the success measurement engine  2330 , which uses standardized measurements from a plurality of inputs (for example, performance review, training costs, established enterprise performance metrics, and employee engagement). The system outputs the data of successful candidates and stores them in a repository  2340 . The data from the repository is further used to adjust  2350  the estimated weighting of scores provided by the outcomes measuring engine. 
       FIG. 24  shows the system used for process responses from automated interviews. The system displays the M13 criteria  2210  in a standardized user interface  2410 . Through the interface, users input responses  2420  to targeted queries. The responses are preferably input into a response database  2430 . The system generates a success criteria  2440  preference that marks the data according to the pre-established success measurement categories, as described in  FIG. 23 . The raw responses and annotated responses and compared at step  2450  and the system outputs the resultant responses for use within the system  2460 . 
     As described above, the system uses various engines and agents to perform specified functions. The engines are preferably implemented as general purpose computing devices controlled by software to perform as special purpose engines. The computing device(s) on which the system is implemented communicate with other system components and external system systems and users through conventional communications, protocols and interfaces. The agents used or interacting with the system may be automated agents or human agents or combinations of both. 
     The embodiments described herein are exemplary and not intended to be exhaustive of the applications of the systems and methods of the invention.