Patent Publication Number: US-10311394-B2

Title: System and method analyzing business intelligence applied to physical assets and environment factors

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to, which is a Continuation in Part of patent application Ser. No. 14/310,759, filed Jun. 20, 2014, which is a Continuation in Part of patent application Ser. No. 14/310,785, filed Jun. 20, 2014, which is a Continuation in Part of Provisional Patent Application No. 61/974,035, filed Apr. 2, 2014, which is a Continuation in Part of U.S. patent application Ser. No. 14/053,927, filed Oct. 15, 2013, which is a Continuation in Part of U.S. patent application Ser. No. 13/586,520, filed Aug. 15, 2012, which is a Continuation of U.S. patent application Ser. No. 13/586,529, filed Aug. 15, 2012, now U.S. Pat. No. 8,571,909, issued Oct. 29, 2013, which is a Continuation in Part of U.S. Provisional Patent Application No. 61/524,422, filed Aug. 17, 2011. 
    
    
     BACKGROUND 
     Field of the Invention 
     The invention relates generally to energy information and analytic tools and more particularly to a data system for applying business intelligent to physical assets and environmental factors. 
     Description of Related Art 
     Currently, business leaders managing “build asset”/facilities portfolios often make mission-critical decisions using: 1) no data, 2) the wrong data, or 3) inaccurate data. Vendors nominally in this space include: business intelligence developers, consultants, integrated workplace management system vendors, computer-aided facilities management systems providers and others. Clients can use technology and services to optimize efficiency around a wide variety of facilities related business problems, from project management to lease administration to space utilization and occupancy. These offerings, however, are standardized and afford the client only limited ability to customize them. Moreover, they are designed for and constrained by the organizational “silo” in which they reside. 
     Existing systems specify the data requirements based on the data&#39;s expected relationship to a type of outcome or discrete task (i.e., $$energy efficiency, lease administration, etc.). Where the outcomes are multi-dimensional, the data points within those dimensions are ill defined. The client “value” set is often predefined and solving for non-standard or multi-dimensional definitions of value is not supported. For example, available tools fail to provide built asset portfolio planning tools that allow an education client to solve for their own definition of value, i.e., maximum teacher retention against declining CapEx and contracting building inventory. In short, no existing analytics engine correlates asset-related (A), resources/environment-related (E) and culture-related (C) data over time (T) to illustrate current performance, optimum performance and/or benchmark performance. 
     Accordingly, there exists a need for modern, on-demand technology to extract, classify, validate, qualify, analyze, store, enhance and display data related to multi-dimensional enterprise decision making with adjustable value definitions. There is a further need to provide systems and methods that take an actuarial approach to predictive modeling related to human performance, resource utilization/environmental factors and architectural data. Optimal performance is dependent on hundreds (if not thousands) of factors, many of which are E/C/A/T dependent. The complexity of these interactions and correlations calls for powerful methodologies and technology to provide insight and the basis for action. 
     Accordingly, there is a need for improved data systems, and their methods of use, for qualifying and analyzing data for at least one business intelligence. There is a further need for data systems, and their methods of use, for qualifying and analyzing data for at least one business intelligence that uses multi-dimensional analysis relative to a scale for at least one business intelligence. 
     SUMMARY 
     An object of the present invention is to provide systems and methods for analyzing business intelligence applied to at least one of physical assets and environment factors. 
     An object of the present invention is to provide systems and methods for analyzing business intelligence applied to at least one of physical assets and environment factors using a computing system that includes one or more processors of a data management system for at least one stream of data selected from at least one of physical assets and environment factors. 
     An object of the present invention is to provide systems and methods for analyzing business intelligence applied to at least one of physical assets and environment factors using one or more processors to provide multi-dimensional analysis of the at least one stream of data for at least one of, environmental resources, the impacts of environmental resources and the impacts on physical assets. 
     An object of the present invention is to provide systems and methods for analyzing business intelligence applied to at least one of physical assets and environment factors using one or more processors to correlate data from the multi-dimensional analysis. 
     These and other objects of the present invention are achieved in a method for analyzing business intelligence applied to at least one of physical assets and environment factors. A computing system is used that includes one or more processors of a data management system for at least one stream of data selected from at least one of physical assets and environment factors. A data transformer is used to transform data from the at least one stream of data to their item and attribute characterizations. One or more analytic engines are used to receive the items with their attributes from the data management system and provide multi-dimensional analysis relative to a scale of study for at least one physical asset and environmental factor. Multi-dimensional analysis is defined as analysis that compares, calculates, correlates, or operates on items and/or attributes from at least two distinct dimensions. The one or more analytic engines are used to provide one or more outputs of processed and correlated data that is sent to an analysis qualifier to determine a quality of analysis. A data analysis evaluator is used to determine a quality score for information generated by the analytic engine. 
     In another embodiment a system includes at least one processor and a memory storing instructions that, when executed by the at least one processor, cause the system to perform: selecting a set of signal inputs relating to a plurality of energy usage conditions; determining input values for at least a portion of the plurality of sensors; and applying multi-dimensional analysis to the input values to identify energy usage conditions associated with non-technical loss. 
     In another embodiment of the present invention a method qualifies and analyzes business intelligence applied to physical assets and environment factors. One or more processors of a data management system are used with a plurality of stream of source data and to receive first and second streams of source data. The first stream of data is client provided energy management physical assets and the second stream is data directed to environmental resources relating to energy management or usage. One or more processors are used to organize the first and second streams of data according to their associated items and attributes. The one or more processors are used to provide multi-dimensional analysis of the at least one stream of data for environmental resources, or the impacts of environmental resources on physical assets for at least one business intelligence that is defined as contributing insight into the impacts of physical assets on energy management or usage, the one or more processors taking a progression of complexity of analysis and findings as inputs of a first order to produce an output and is tags by scale and attribute. The first order of analysis is available as inputs for second order analysis that become outputs which are summarized. The one or more processors are used to correlate data from the multi-dimensional analysis using user-inputted priorities. The one or more processors are used for a data analysis evaluator of the analytic engine to communicate with the client to determine relevance of the analyzed data to question business intelligence. The one or more processors use business intelligence as improved knowledge about a business problem or a study criteria to make an informed decision. A feedback loop is applied to compare the analyzed data to peer or public benchmarks or trends and includes an intelligence logic that receives updated data and associates this updated data with data in the data warehouse for use by the analytic. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is an embodiment of the present invention illustrating the positioning of the business intelligence system relative to incoming data and client services. 
         FIG. 2  is an embodiment of the present invention illustrating the primary stages of the system. 
         FIG. 3  is an embodiment of the present invention illustrating the system components and the flow of data and information through the platform. 
         FIG. 4  is an embodiment of the present invention illustrating the types of data being used by the system. 
         FIG. 5  is an embodiment of the present invention illustrating a component view of the system&#39;s extract-transform-loader (ETL). 
         FIG. 5( a )  is an embodiment of the present invention illustrating the internal processes of the system&#39;s ETL. 
         FIG. 6  is an embodiment of the present invention illustrating an internal view of the system&#39;s data management system database. 
         FIG. 7  is an embodiment of the present invention illustrating a component view of the system&#39;s analytic engine. 
         FIG. 7( a )  is an embodiment of the present invention illustrating the internal processes of the analytic engine. 
         FIG. 7( b )  is an embodiment of the present invention illustrating a chart representing the system&#39;s analysis logic by dimension and scale. 
         FIG. 7( c )  is an embodiment of the present invention illustrating a Venn diagram illustrating 1.sup.st order analysis logic. 
         FIG. 7( d )  is an embodiment of the present invention illustrating a Venn diagram illustrating 2.sup.nd order analysis logic. 
         FIG. 7( e )  is an embodiment of the present invention illustrating a Venn diagram illustrating 3.sup.rd order analysis logic. 
         FIG. 7( f )  is an embodiment of the present invention illustrating a Venn diagram with respect to time and value. 
         FIG. 8  is an embodiment of the present invention illustrating a component view of the data analysis evaluator. 
         FIG. 9  is an embodiment of the present invention illustrating a component view of the report builder. 
         FIG. 10  is an embodiment of the present invention illustrating a component view of the user interface. 
         FIG. 11  is an embodiment of the present invention illustrating a component view of the peer &amp; public network. 
         FIG. 12  is an embodiment of the present invention illustrating a component view of the adaptive intelligence system. 
     
    
    
     DETAILED DESCRIPTION 
     Referring to  FIGS. 1 and 2 , in one embodiment of the present invention, a data management system  10  and analysis and report preparation system  18  are provided for qualifying and analyzing data for at least one business intelligence. As a non-limiting example, the business intelligence includes but is not limited to occupancy and utilization optimization at an academic facility looking to understand the financial impact of an expansion in its enrollment. A platform is provided that receives source data. In one embodiment, the source data can be three streams of data. The first, second and third streams of data can be client source data, public source data and data acquired by the data management system  10 . The data management system  10  transforms raw data and stores it. 
     The analysis and report preparation system  18  includes an analytic engine. In operation, the data management system  10  receives first, second, and third streams of source data, the first stream of data being client source data, the second stream of data being public source data and the third stream being data acquired by the data management system. The data management system  10  organizes the first, second and third streams of data into items and their attributes. Examples of item and attribute types include but are not limited to, items: classroom, building, individual person, department, window, temperature sensor, site, city, and the like; attributes: size/quantity, orientation/location, identity characteristics, material composition, construction/implementation process, maintenance characteristics, measurement values, and the like. 
     Furthermore, the data management system  10  classifies the items and attributes as belonging to at least one of the following dimensions: asset, culture, environment, time, and value. The analytic engine receives the items with their attributes from the data management system and applies logic to provide single and multi-dimensional analysis relative to a scale of study for at least one business intelligence. Multi-dimensional analysis is defined as analysis that compares, calculates, correlates, or otherwise operates on items and/or attributes from at least two distinct dimensions, while uni-dimensional analysis refers to the same operations within a single dimension. Scale is defined as the level of detail of the particular analysis relative to the dimension under study. As a non-limiting example of scale, a high-detail study for a school may be at the room level, while a less-detail study may be at the building level. Dimensions can have unlimited levels of scale. 
       FIG. 1  is a block diagram illustrating an overview of the data management system  10  functional stages relative to source data  12 , external source data and the client  14 . The external source data  12  can be inputted into the data management system  10  manually or automatically. The external source data  12  can be imported to a data warehouse  16 , assigned, associated, classified, scaled, validated, and archived for future use. The source data  12  is then combined with internal source data (data from previous projects) and received at an analysis and report preparation function  18  for analytical interpretation, modeling, and report prep. This information continues to a user interface  19  for graphic display, query navigation, and report output. Additionally, client peer related information can be provided to the system through a peer network system  20  and used to provide report context by a peer network advisor. Feedback from the platform circulates back into the data management system  10  through an adaptive intelligence  21 . This information is formalized and used to refine the data management system  10  operations. 
       FIG. 2  is a diagram of the overall system showing how it uses cloud-based servers to both store data and conduct analysis operations. In one embodiment, the client  14 , or a service team member, uploads data from various sources to a data management system cloud server farm  15  using a data management system user portal, via user interface  19 , accessible on any compatible device and it can be connected to a network, including but not limited to a wide area network (WAN). The cloud farm  15  transforms raw data into a format usable by the data management system  12  and passes it to the data management system warehouse  14  via a WAN/LAN (local area network). Data from pre-determined sources may be directly accessed and used by the data management system  10  and platform via developed application programming interfaces (APIs). 
     The overall system also includes: analysis and report preparation logic  18 ; a user interface  19  that a user uses to interact with the data management system  10 , a peer network  20  and adaptive intelligence  21 . 
     As illustrated in  FIG. 3 , the client source data  22  is data provided by a client, or secured from the client by others but is owned by the client, and is specific to that client. The client source data  22  can be related to at least one: facility assets; human factors; cultural factors; user data; environmental resource data; scheduling data; financial data; custom data, and the like. 
     The public source data  24  is data accessed from public resources such as public federal government records, local city planning databases, public utility records, census records, university sponsored research, publicly released business indices, and the like. The public source data can be data related to at least one: facility assets; human factors; cultural factors; user data; environmental resource data; scheduling data; financial data; custom data and the like. 
     The data management system proprietary data  26  is collected by the data management system  10  based on the needs of the specific project. Data management proprietary data  26  can be collected by a designated team or technology using sensors, written notes, data-basing software, photo/video-capture, surveys, and the like. 
     Referring to  FIGS. 4 and 5 , data management system  10  transforms the raw source data into a format and logic usable by the platform. The data management system  10  includes an extract-transform-loader (ETL)  28  and the data warehouse  16 . 
     The ETL  28  includes one or more of, a data transformer  30  and a data validator  32 . The data transformer  30  includes at least one of; logic for assigning raw source data to predefined data management system database fields; association logic for associating raw data through item/attribute relationships; classification logic for classifying raw data as asset-related, environment-related, culture-related dimensions, time-related, or value-related; logic for relating items to each other based on scale; and qualifying logic for qualifying raw data based on data-related collection methods. The data validator  32  includes validation logic for determining if the raw data is valid for input into the data management system database. This logic includes a set of rules governing valid data format and valid data value for the item or attribute type. 
     The Data Classifier  52  and Data Qualifier  56  can further include logic for assigning metadata to the raw data during classification and qualification. The metadata is used for analysis, evaluation, and reporting. The data warehouse  16  includes a plurality of databases, selected from at least one of, data management system database, distinct client databases; qualifier metadata; peer network metadata; and data management system historical data. 
     Source data  12  enters the data management system  10  and is first processed by the ETL  28 . The ETL  28  transforms and validates the data before storage. After the data is processed by the ETL  28 , it can be sent to the data warehouse&#39;s client database and archived in a unique database prior to analysis. The data remains archived until a user creates an executable demand to analyze the data. The data is then passed to the analysis and report preparation  18  where it is analyzed by the analytic engine  36 . The analytic engine  36  outputs tables of processed and correlated data that is sent to an analysis qualifier  38  to determine an overall “quality” of analysis based on the accuracy of the data collection methodology, calibration of collection equipment, quantity of data, and so forth. From there the data  12  is sent to a report builder  42  for modeling and scenario building or sent directly to the user interface  19  for reporting. 
     The data  12  that is sent to a report builder  72  and can be correlated with user-inputted priorities and divided into short-term (recurring) and long-term (future and singular) impact reports and recommendations. These reports are then sent to the user interface  19 . 
     The user interface  19  outputs the reports sent to it by the analytic engine  36  and the report builder  42 . Additionally, data and information is sent back into the data management system  10  as it is uploaded and configured by client and administration users. 
     Peer and public network  20  creates report context by showing client data and information in relation to at least one of large-scale industry trends, peer trends, local benchmarks, and so forth. This data can be collected manually and uploaded through the user interface  19 ; it may be collected automatically by the ETL; or it can be calculated internally from the historical data database  26 . 
     Adaptive intelligence  21  makes the data management system  10  and analysis and report preparation system smarter by both increasing information context and calibrating their rules and engines. The single component of this function, the feedback Interpreter  46 , collects data and information from within the platform, from user interaction patterns and from uploaded data, without limitation to a particular data stream. This information is processed within the feedback interpreter  46 , and updates and refinements are made to at least one of data validation logic, association logic, the analytic engine logics, the report builder logic, and the query building and interpretation logic. 
     Referring now to  FIG. 4 , all of the source data  12  relates to six types of data: facility assets data, human resources (HR)/user Characteristic data, Environmental data, schedule and other User Operational data, financial and data, and other data types as determined by the specific project as well as potential additional data determined through proprietary methods. The client source data  22 , comes from a facility manager, COO, or whoever represents the interests of a client company and has the data available in some format. The public source data  24  comes from public archives such as federal government records, local city planning databases, publicly available indices, public utility records, census records, and so forth. Types of data collected include historic retrofit costs, real estate valuation, salary costs, absentee rates, education level, historic energy use, rates, types, and the like. The data management system proprietary data  26  comes from data collected by the data management system  10  using custom installed sensors, researcher perceived observation, privately secured and maintained rates and indices, privately secured client information, and other data management system  10  data collection methods and the like. 
       FIG. 5  illustrates the ETL  28  that prepares incoming raw data for storage and analysis within the data management system  10 . The ETL data transformer  30  receives source data  12  from user-uploaded data in the form of comma separated values, or other standard format understood by the data management system  10 . These values may be mapped by the user to the appropriate data management system data categories (Parent, Alias and the like.). Once the source data  12  is mapped to the data management system  10  by the data field assigner  48  the data is sent as a “batch” to a data associator  50 . In the data associator  50 , a batch of data is accepted comprising individual data “objects”. The data associator  50  uses an association logic to sort objects into Items and attributes, and relate them to each other. Items are real world “things” and attributes are descriptors of the items, or “adjectives” related to the items. For example, a classroom is an item and “transparency coefficient of the room walls” is an attribute related to the room. 
     The associated data is then sent to a data dimension classifier  52  for classification into one of the five dimensions: asset, culture, environment, value, and/or time. Asset-related data may refer to items such as rooms, mechanical and lighting systems, windows, buildings, and the like. Environment-related data may refer to units of energy, amount of solar energy collected, available water resources, weight of a client&#39;s waste, consumable organizational resources, statistics associated with contextual environmental variables (interior surface temperature, exterior air temperature, humidity, rainfall, acoustics, CO2 Levels, etc.) and the like. Culture-related data may refer to characteristics associated with individual people, departments, and organizations such as health, ethnicity, education level, morale, technology adoption rates, productivity and the like. Value data may refer to balance sheet information, income statement information, unit costs, financial indices, as well as non-financial value data such as sustainability, renewability, brand awareness, happiness, and crime rate. Time-related data may refer to durations of study, frequency of data collection, milestone for projections, impact on schedule, historic and projected trends patterns, and the like. The data dimension classifier  52  tags all pass through data with metadata classifying it as one of the above dimensions. 
     The classified data is then sent to the data scale relater  54  to be tagged with metadata relating all of the dimensional items to each other by scale. Scale may be defined as a level of study ranging from a close-up, more detailed view to a less detailed, high-level view. Scale may relate across dimensions using non-dimensional metadata similar to scale using an architectural ruler, or it may be unrelated across dimensions depending on the specifics of the analysis. 
     The scaled data is then sent to a data qualifier  56  where it is again tagged with metadata related to data collection methods and accuracy. These metadata may include but not be limited to whether or not the data was collected by a client or specialized team, whether or not the data was double-checked, how recent the data was collected, the accuracy of the sensors if applicable, and so forth. Once the data is qualified it sent to the data validator  32 . 
     The data validator  32  runs an algorithm matching at least one of the actual data formats, types and values to the expected value types, value thresholds, and so forth for the mapped categories. In other words, if a data table is mapped to the data management system “data” protocol, then the data validator  32  expects all of the data values to be formatted as dates. If a batch does not conform to the expected data types, then the batch is flagged for review and sent to a data resolver  58 . Additionally, if a data value is outside of the expected range for a particular data type, then it will be flagged for review as well. 
     Data sent to the data resolver  58  may be accepted or rejected. Data that is transformed and validated successfully is then sent to the data warehouse  16  for storage in one of several possible databases. 
     Referring to  FIG. 5( a ) , operation of the ETL  28  is illustrated. Source data is acquired and uploaded and the data transforming process begins. The data transformer  30  assigns data columns to data management system database fields. Data columns are associated using item/attribute characterization. The data associator model  60  is coupled to the data transformer  30  and receives the associated data columns. The data associator model  60  takes object A and produces item A with associated attribute C, attribute B and attribute A related to the item by time. Items are classified as asset (A), culture (C) or environment (E) using metadata tags. Classified items are transformed into scaled relationships with an A/C/E dimension. Data is qualified based on source, collection methodologies and the like. The data validator determines if the data meets expected data format and value thresholds. If yes, the transformed and validated data is sent to the data warehouse. If not, the data is flagged for resolution by an admin user. If the data is valid, or made valid, it is sent to the data warehouse  16 . If not, it goes to trash. 
       FIG. 6  illustrates that the data warehouse  16  represents the location where data is archived after being processed by the ETL  28 . 
     Referring to  FIG. 7 , analytic engine  36  runs correlations, calculations, comparisons, and other data management system analysis per user query settings. As a non-limiting example of a building, the calculations can be: occupancy calculation, utilization calculation, monthly energy use per square foot calculation, and the like. As a non-limiting example, comparisons can include: energy use compared to occupancy over one month, building maintenance frequency versus monthly sky conditions and the like. As a non-limiting example, the correlations can be: high occupancy correlated with high energy use, high student performance correlated with the quantity of natural daylight in a classroom, employee productivity per building between specified dates, absenteeism rate of a specific function type per floor during a specified weather condition, and the like. 
       FIG. 7( a )  is a flow chart that illustrates operation of the analytic engine  36 . The analytic engine  36  is associated with the analytic operation, data aggregator  62 , data analyzer  64  and the data aggregator model  66 . An analytic operator  68  receives an analysis query from the user interface  19  and interprets the query to determine data needed to produce a query answer. Required data is called from the data warehouse  16 . If the attributes need to be scaled to a less detail view then the data aggregator  62  runs a data aggregator model  66  to aggregate attributes of a more detailed item into attributes of a less detailed item. The data aggregator model  66  then receives the various items and associates attributes to the item to create a new item with a new attribute. When the query requires a mathematical operation before being sent to the user interface  19  and the data analyzer  64  runs the mathematical operation and stores the resultant data in a new table. If the query does not require a mathematical operation then a determination is made to determine if the query is part of a report request. If not, then a data table is sent to the user interface  19 . If the query is part of a report request then a data table is sent to the advisor for inclusion in the specified report. 
     The analysis and report preparation system  18  runs multi-dimensional analysis on data in the data warehouse  16 , and prepares reports relating the analysis to short-term (recurring) and long-term (future and singular) value information related to client priorities. In one embodiment, the analysis and report preparation system  18  includes the analytic engine  36 , data analysis evaluator  70  ( FIG. 8 ), and report builder  72  ( FIG. 9 ). In one embodiment, the analytic engine  36  includes, the analytic operator  68  that interprets user queries; the data aggregator  62  that aggregates items and attributes at different scales; the data analyzer  64  that executes multi-dimensional calculations, comparisons, correlations, and other operations on selected data sets. 
     In one embodiment, the analytic operator  68  includes: query logic to interpret user queries related to a specific client assignment; and retrieval logic to determine which data sets are required from the data warehouse  16  to execute the requested analysis. 
     The data aggregator  62  has item/attribute aggregation logic to aggregate attributes from a more detailed scale into an attribute(s) for an item in a less detailed scale. For example, a room may have multiple temperature sensors in multiple zone locations within the room. A user may want to see the room as an item with a single temperature attribute and see the building with a single temperature attribute. To do so, all or a selection of the temperature measurements may be aggregated into a single item and attribute association at the room level. In this case, the temperatures are collected by unique sensors, each classified as an item in the data management system platform. To attribute a single temperature value to a single item, an average of the selected temperatures can be taken and associated with an existing room item, or a new item representing the room may be created. Then the attributes may be combined again for all of the rooms in a building to form a single temperature attribute for the entire building. This example shows at least two scale jumps, one from the zone level to the room level, and another from the room level to the building level. 
     The data analyzer  64  runs logic selected from at least one of: logic to compare data sets within a single dimension or across dimensions, over time, and related to value (comparison logic); logic to calculate averages, minimums, maximums, ranges, and other mathematical functions on a data set within a single dimension or across dimensions (math logic); and logic to correlate attributes to outcomes expressed as value, data set calculations, or other within a single dimension or across dimensions (correlation logic). Operation of one of these logics may involve an input that is the result of logic. For example, comparing occupancy to energy use using comparison logic may first require calculating occupancy (math logic) and using it as an input in the comparison. 
     In one embodiment, the previous logics (comparison, math, and correlation logic) may be applied in conjunction with dimensional analysis logic which includes a single dimension or multiple dimensions of data leading to different orders of data management system analysis. These orders consisting of: first order data management system analysis with data from a single dimension of asset, environment, or culture; second order data management system analysis with data from at least two dimensions of asset, environment, and/or culture; and third order data management system analysis with data from all three dimensions of asset, environment, and/or culture. 
       FIG. 7( b )  is a flowchart illustrating data management system analysis with increasing dimensionality and increasing or decreasing scale. First, second and third order analysis is performed at different scales as referenced on the vertical axis of the flow chart. 
       FIGS. 7( c )-7( f )  are Venn diagrams illustrating the dimensional logic of the analytic engine  36 .  FIG. 7( c )  shows the 1.sup.st order of analysis represented by data coming from within individual dimensions and among individual dimensions without cross-operations.  FIG. 7( d )  shows the 2.sup.nd order of analysis with data coming from the areas of intersection between two dimensions. As a non-limiting example, occupancy would be considered a 2.sup.nd order calculation because it requires culture-related data (number of people) and asset-related data (capacity of room based on square footage, or other criteria).  FIG. 7( e )  shows the 3.sup.rd order of analysis where data is taken from the intersection of all three dimensions. As a non-limiting example, a chart showing the energy use (E) by occupancy (C) associated with a specific sector a building (A) would be considered a 3.sup.rd order analysis because it uses data from all three dimensions. Finally,  FIG. 7( f )  shows the three-dimensional Venn diagram with respect to time and value. This indicates that time and value can be related to the three physical dimensions to understand how the metrics change over historic or projected time and how that affects financial and non-financial value. 
     The data analysis evaluator has evaluator logic for calculating a quality score for the information generated by the analytic engine, with the score being based on qualifier metadata referenced in the ETL&#39;s data qualifier  56 . The evaluation logic uses the qualifier metadata, possibly with a weighting system, to determine an analysis ranking that may be indexed across all reports, against other indices, or against an ideal standard. The ranking allows users of the data to quickly understand the quality and relative value of the data analysis. 
     The report builder  72  communicates with the analytic engine  36  and the data analysis evaluator  64  to generate reports related to specific client assignments. 
     Reports generated by the report builder  72  are packaged for presentation to the user in terms of capital (Long-term and singular) and operational (Short-term and recurring) allocations determined by client priorities. 
     A diagram of the data analysis evaluator  64  is illustrated in  FIG. 8  which assigns a relative rating or handicap based on the data&#39;s qualifier data, to the information created by the analytic engine. 
     As illustrated in  FIG. 9  the report builder  72  receives formalized data from the analytic engine  36  and user-prioritized lenses (obtained from a master set list of lenses) from the user interface  19 . A strategic operator  74  receives the data from both components and feeds the data into a scenario builder  76 . The scenario builder  76  uses defined relationships between the user-prioritized lenses and the incoming data to determine possible short-term and long-term responses, scenarios, to client facilities and operations. The short-term responses are developed by the recurring response developer  78  and are passed to the operation decisions report generator  80  for “packaging”, in that the data and models are ordered, contextualized, and made ready for visualization by the user interface  19 . These responses affect the everyday operations of the facility, and therefore relate to non-fixed assets. The long-range response developer  82  produces models that predict outcomes based on capital responses including but not limited to changes such as sale or renovation of fixed assets, relocation of personnel, and infusion of new funds. Operation decisions report generator  83  and long-range response developer  82  send their reports to the user interface&#39;s  19  sub-component graphic display  84  for presentation to the user. 
       FIG. 10  illustrates an internal view of the user interface  19 . The user interface  19  receives input from the data management system  10  user through a sub-component user input portal  86 . The user interface  19  allows users to perform a variety of different activities including at least one of: setting organizational priorities; inputting data; building queries to define specific assignments and viewing reports. 
     The user input portal  86  can include up of three subcomponents that the user interacts with: a data uploader  88 , priorities builder  90  queries builder  92 . Additionally, the user input portal  86  provides a range of services that support the use of these components, not-limited to default options (i.e. pre-set selections for both queries and priorities), query recommendations, and other user-centric features. 
     The data uploader  88  provides data uploading services to platform users. These services provide both automatic upload options, such as for a non-limiting example, downloadable API&#39;s that communicate with user computer systems and manual upload options (e.g. templates for data input). The user enters the user Input portal  86 , selects the data uploader  88 , and chooses how to upload the data. The data uploader  88  can receive a pre-set list of file types including but not limited to .xls, .csv, .xlm, .kml, .rvt, .jpg, .doc, and the like, determined by the capabilities of a translator  94  and the application program interfaces (API&#39;s) available for communication through the data uploader  88 . 
     Data from the data uploader  88  is sent to the translator  94  if the data is from a project that is new to the platform. If the data is coming from a project that has already been analyzed by the platform, then the data is sent to the platform refiner  610  for model calibration. 
     The priorities builder  90  allows the platform user to choose the type and relative importance of the ‘values’ to optimize their institution related to a variety of objectives, including but not limited to, sustainability, financial annual bottom-line, employee health and wellness, long-term productivity improvements, brand awareness and the like. These ‘values’ can be thought of as lenses that an advisor  96  wears when making short-term and long-term reports. The platform user prioritizes these lenses relative to each other using a numeric scoring reference with variable scores which can be adjusted by the user but will sum to preset total. As a non-limiting example, if there are ten lenses with a preset total of 100 points, then each will have a numeric score attached to it ranging from 1-91 with the other nine lenses having an allocation set by the user for the remainder of the points for a total of 100 points. 
     Each lens may be made up of sub-lenses that deconstruct the primary lenses into simpler value judgments for the user. As a non-limiting example, a lens of sustainability may be constructed of the sub-lenses water use, energy use, building materials, site location, and the like where the user rates the importance of these sub-lenses as a sub-total of the value of the master lens: in this case, sustainability. 
     The scenario builder  76  uses the prioritizations from the priorities builder  90  to determine which primary lenses will most influence the model development. Once a primary lens is selected, the sub-lenses, which relate to measurable building operations or capital decisions, are used to evaluate long-term and short-term responses. For instance, if sustainability is selected as a top priority, and water usage is selected as a top-priority sub-lens, then the scenario-builder  76  can solve for optimized water usage above other primary and sub-lenses in both its short-term and long-term responses. 
     Weightings and correlations of the lenses are numerous and can be reoriented in several different ways. The design allows for flexibility in how these lenses are prioritized, how they relate to the data being fed into the Analysis and Report preparation system  18  of the data management system  10 , and how they are presented in the final reports. 
     The queries builder  92 , also illustrated in  FIG. 14 , further develops the user input portal  520  by adding a command search functionality to the interface. By default, the queries builder  92  may already be set-up to request a particular type of output, in a particular sequence, related to a particular time horizon, and so forth. This default request may be project-specific, that is based on the type of data entered into the platform tagged to a specific project, related to global conditions, including but not limited to, types of output most educational organizations, government agencies, and healthcare institutions are interested in and the like, related to a “linked-network”, that is based on the type of output that most of these ‘peer’ institutions are interested in. 
     In the above case, “linked-network” refers to an opt-in network of peer institutions determined by several criteria and available in a multitude of varieties. For instance, top ten US Graduate Schools of Business may want to opt-in to a “linked-network” of these ten peer institutions that give a varying degree of context to the data output received from the platforms reporting function. For instance, members of the “linked-network” may use averages from the network as benchmarks for performance. Additionally, the “linked-network” may have query defaults based on user patterns, or other smart platform features based on network trends. Thus, a query default may be based on the user patterns of these institutions or related to some other platform determined criteria. Users may decide to search for a specific type of dimensional information, or construct a different order of reports, or in some way alter the report outputs. To do so the user can change the settings in the queries builder  92 . Changing the settings can be made by, but not limited to, altering the fields for search, typing in a text-string question, or selecting from a pre-set list of report outputs. 
     Queries generated by the queries builder  92  are sent to the analytic engine  36  or the advisor  96  depending on the type of query. Queries that require “sense-making” (i.e. an explanation), or user-defined prioritization, are sent to the advisor  96  and eventually outputted in the form of short-term impact and long-term impact reports. Whereas queries that require only a “noisy”, uncorrelated data output may be sent directly to the analytic engine  36 , and from the analytic engine&#39;s  36  output to the graphics display  84 , bypassing the advisor  96 . 
     As illustrated in  FIG. 11 , the reports display  84  is configured to visualize data tables sent to it by the analytic engine  36  and the advisor  40 . The visualization tool may be a proprietary, in-house tool or an off-the-self product, including but not limited to, Data Graph, Wonder Graph and the like, and customized to receive these data tables and graphically visualize their relationships as directed by the analytic engine  36  and advisor  40 , which are preset to output relationships in default ways. These visualizations may include, but are not limited to: histograms, bar charts, heat maps, bubble maps, pie charts, and architecture drawing overlays, time-lapsed animation and the like. All charts can have default settings but may be customized by the platform user based on project specifications, including but not limited to, levels of access which limited functionality for some users and the like. 
     In one embodiment, the graphics display  84  includes three sub-components: the operations decisions display  98 , the capital decisions display  100 , and the snap-shot display  102 . The operations decisions display  98  receives reports from the operations decisions report generator  80  and visually presents those reports to the platform using a plurality of techniques: static display, interactive display, downloadable documents and the like. The visuals may be singular or exist in relation to several visual and text that make up the report. The capital decisions display  100  receives reports from the long-range response developer  82  and presents similar visuals as stated above except that they relate directly to long-term, fixed asset decisions. These reports, both operations and capital can be designed for a range of general executive functions in an organization such as the CEO, COO, CFO and the like, as well as for specialized job functions such as Facilities Director, Operations Director, Human Resources Director, and the like, for their interpretation and implementation. 
     The snap-shot display  102  visually displays output from the analytic engine  36 . Data tables coming from the analytic engine  36  may not be correlated with user-defined priorities or be contextualized. In general, these reports are a “snap-shot” of current conditions as analyzed by the analytic engine  36 . As a non-limiting example, they may display a specific sub-set of room occupancy at a point in time for a specific building, or over a set period of time and the like. 
       FIG. 12  illustrates an internal view of the feedback Interpreter  104 , a component within the system&#39;s adaptive intelligence. The feedback Interpreter  104  collects data from within the system, including but not limited to user interface patterns, non-first-time project data and the like, as well as outside of the system, including but not limited to large-scale economic trends and externally published indices, and the like, as well as from within “linked-networks” such as peer trends and the like. This data is then interpreter by several sub-components within the feedback Interpreter  104  and used to increase the intelligence of the system&#39;s advising, recommending, and analyzing—also known within the system as ‘adaptive intelligence’. 
     The feedback interpreter  108  handles the internal data collection pulling data from the user-interface  19 . The noise-canceling refiner  110  analyzes all source data coming into the system, categorizes it, and creates “like” relationships between data sets (i.e. two academic institutions with similar characteristics will be related to each other). These “like” relationships will be used to refine the validation logic used by the data validator  32 . Using this methodology, the data validator&#39;s logic will become more accurate in identifying erroneous and invalid data. 
     The model calibrator  114  pulls data from the data uploader  88  if the data is tagged as a previously uploaded data set, including but not limited to the same data such as a non-limiting example facilities asset data, from the same project was uploaded to the data management system database  34  at an earlier time. As a non-limiting example, this can be termed ‘data set  2 ’. This data is then compared to the previously stored data set ‘data set  1 ’. Patterns and anomalies are analyzed and differences in the two time-stepped data sets are analyzed. If enough contextual data is available, other data sets uploaded for the second time, the model calibrator  114  will automatically use multi-variant statistics to determine causalities among data sets and use its findings to refine model prediction in the scenario-builder  76 . If there is not enough contextual data to automatically determine causalities, then the differences in singular data sets will be compared and conclusions regarding causality will be determined by service technicians working on the projects. As a non-limiting example, conclusions regarding a data set about water usage differences from January compared to the same water usage data set from March without any other relevant data provided during analysis can be made manually by a technician who can investigate various causes. 
     A user Interaction trend analyzer  116  pulls data from the user interface  19  while the user is interacting with the interface. This data may come from user query patterns, user priority building patterns and the like. 
     A public contextualizer  118  pulls data from public source data  24  and analyzes it in relation to platform projects. This data then provides ‘context’ to platform data, helping platform users understand how their data measures up or relates to large-scale public trends and other facilities with information in the public sphere. Within the public contextualizer  118 , the large-scale trend analyzer  120  pulls macro-data that is trending, including but not limited to financial market building indices, building cost indices, human resource trends, occupancy trends, labor trends and the like. As a non-limiting example, this component may grab US News and World Report 2012&#39;s Top 10 Business school index and search it for characteristics relevant to the platform&#39;s analysis. The public comparator  122  pulls relevant data that is publicly available and “like” a platform user&#39;s project (“like” refers to a variety of similar characteristics between projects that make comparison relevant). This data is used to place the platform user&#39;s project performance in context with other project performances. 
     A peer contextualizer  124  acts in a similar way to the public contextualizer  118  by providing data that contextualizes performance by platform users&#39; Assets, Environment, and Culture. However, the peer contextualizer  124  collects data solely from “linked-network” members. This data can contain more relevant and more specific information than the public contextualizer  118  because it is shared within a closed-network of peer institutions that have opted to share with each other. A peer benchmarker  126  provides sample data from “linked-network” members to the advisor  40  to contextualize the reports delivered to other “linked-network” members from the same network. A peer query analyzer  128  provides query suggestions to the user-interface  19  to help guide the querying by other “linked-network” members, including but not limited to the queries builder  92  that adjust query defaults to match querying patterns by “linked-network” members. 
     The adaptive intelligence performs at least one of: recognizing patterns in user interaction; measuring predicted verses actual outcomes; calibrating data management system  10  proprietary data; and adjusting the rules used by the data validator in the data management system  10 . Calibration refers to refining the accuracy of the proprietary data. 
     The peer network advisor provides a comparison of a first client specific report to one or more different second client specific reports. 
     The foregoing description of various embodiments of the claimed subject matter has been provided for the purposes of illustration and description. It is not intended to be exhaustive or to limit the claimed subject matter to the precise forms disclosed. Many modifications and variations will be apparent to the practitioner skilled in the art. Particularly, while the concept “component” is used in the embodiments of the systems and methods described above, it will be evident that such concept can be interchangeably used with equivalent concepts such as, class, method, type, interface, module, object model, and other suitable concepts. Embodiments were chosen and described in order to best describe the principles of the invention and its practical application, thereby enabling others skilled in the relevant art to understand the claimed subject matter, the various embodiments and with various modifications that are suited to the particular use contemplated.