Briefly described, embodiments of the present invention pertains to a key performance indicator (KPI)-driven digital genome system or framework as well as various systems and methods of use and interaction therewith. Unlike conventional stand-alone KPI applications or pure-play centralized KPI solutions, embodiments of the present invention provide an automated way to codify the organizational objectives, goals, behavior, and motivations by continuously measuring, correlating, and discovering hidden relationships among various metrics, attributes, causal relationships, and networks display genomic findings via business applications without a priori knowledge of machine learning or statistical techniques.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for generating an enterprise data genome for an enterprise, the method comprising: identifying key attributes, measures, and thresholds of enterprise entities that influence the financial, operational, and innovational outcomes of the enterprise using the statistical and artificial intelligence methods; extracting and contextualizing these attributes and measures from measured enterprise data; mapping the entities as nodes and relationships between entities as edges ; creating contextual relevance scores to entities and relationships as labels; connecting and automatically extracting and creating ontologies and semantic maps of enterprise entities. 2. The method of claim 1, wherein the method is to executed continuously against enterprise data sources. 3. The method of claim 1, wherein extracting the attributes and measures and creating scores is performed using the machine learning to learn from historical data and to codify the knowledge into human understandable data. 4. The method of claim 1, wherein the measures comprise key performance indicators, and further comprising aggregating key performance indicators based on the identified causal relationships and contextual intelligence. 5. The method of claim 1 wherein identifying key attributes, measures, and thresholds of an enterprise is performed using the statistical and artificial intelligence methods. 6. The method of claim 1 wherein the data from which attributes and measures are extracted and contextualized comprises historical data and real-time data. 7. An Enterprise Data Genome Engine (EDGE) system comprising: a contextual analysis engine to mine an enterprise data set to identify patterns based on current and historical enterprise KPIs and extract contextual information from the identified patterns and data mined information; a statistical modeling engine to create contextual performance indicators based on the context and pattern information including the temporal ordering of events, measurements, and performance measurement data; a real-time recommendation engine to create contextual performance scores and measure key attributes and evaluate the key attributes against business measures; an augmented intelligence system to solicit and process feedback to update the markers via labels, properties, and re-computed scores; and a real-time reality engine to deliver information to an end user. 8. The system defined in claim 7 wherein the measures are defined by one or more business users and a recommendation and learning engine. 9. The system of claim 7 further comprising a software defined beacon system to measure, and report metrics and performance data associated with the enterprise. 10. The system of claim 9, wherein the software defined beacon system is operable to continuously monitor enterprise data sources. 11. The system of claim 10, wherein the software defined beacon system automatically scans the enterprise data sources to identify the relevant attributes, metrics, properties, key indicators (KIs) and key measures (KMs) components utilizing the machine learning augmented with subject matter experts (SMEs) intelligence. 12. The system of claim 7, wherein the recommendation engine is operable with the augmented intelligence system to continuously measure, process feedbacks, and record newly discovered markers and scores. 13. The system of claim 7, wherein the augmented intelligence system is operable to generate predictive models and receive feedback from subject matter experts or business users to further refine the predictive models. 14. The system of claim 7, wherein the recommendation engine is operable to automatically learn and adjust one or more target variables associated with KI based on the machine learning. 15. The system of claim 7, further comprising a remediation engine operable to generate a next best action to remedy the affected KIs. 16. A method comprising: receiving a search query; performing a search to traverse a data genome semantic network, at least in part, on the search query to identify a group of search result KIs and dependent attributes, properties, and key measures; determining trends and anomalies of a first set of KIs in a set of the search result KIs in the group based on date information; generating a score for one or more search result KIs in the set of search result KIs, at least in part, on a difference between the target values of the search result KIs and the actual values of the search result KIs in the set of search result KIs; and ranking the search result KIs with regard to at least one other KI of the search result KIs based, at least in part, on the score. 17. The method defined in claim 16 further comprising: determining a measure of how sentiments and mood of enterprise data associated with a KI changes over time; generating a score for the KI based, at least in part, on the measure of how the attributes, measures, and trends associated with the KI changes over time; and ranking the KI with regard to at least one other KI based, at least in part, on the score. 18. A computer system comprising: a data fusion and enrichment component that processes one or more data sources that contains plurality of entities, relationships, dimensional data, facts, associations, and attributes being part of metadata that describes data from which the data genome is generated, wherein the one or more data genome components are located in a data store that also stores the data from which the data genome generated, wherein each data genome includes a plurality relationships and functions for processing and serving the data genome component data; a contextual intelligence (CI) component that creates interest graphs based on the plurality of datasets and data generated by the data fusion and enrichment service; a data access and serving component operable to enable communication with the data genome to facilitate retrieval and query of data genome component data; an enterprise data genome engine component to generate the data genome; a learning, enrichment, analytics and prediction component to automatically predict, recommend, or infer possible attributes to be included in data genome component and perform data enrichment to support predicted KIs.