4. A method comprising: creating an innovation strategy at an innovation strategy creation engine; aggregating outcomes associated with a market, using a processor of an outcome aggregation engine; aggregating jobs associated with the market using the processor of the outcome aggregation engine; identifying opportunities associated with the market, the identifying the opportunities including automating calculation, ranking, and reporting of opportunity scores with associated metadata, using a processor of an opportunity identification engine; segmenting the market, including providing tools to interact with statistical analysis and modeling packages and import additional metadata tags into a job/outcome data schema comprising a set of data entities, each of the set of data entities incorporating a unified jobs framework that provides a quantifiable metric of an interaction a customer has with products or services, the segmenting using a processor of a market segmentation engine; defining a target strategy using a processor of a target strategy definition engine, the defining including: tagging each of the set of data entities with one or more job identifiers to provide a numerical value associated with each data entity of a set of data entities, the numerical value correlating the quantifiable metric of customer interaction with the one or more of the opportunities; assigning the numerical value of the each data entity a normalization score that has a particular market meaning independent of data used to gather the aggregated outcomes and the aggregated jobs, but has a scale reflecting a significance of the quantifiable metric and the one or more job identifiers in market terms; assigning a priority of the each data entity with respect to another data entity of the data entities, the priority based on an ODI based model of the market, including a measure of one of the aggregated jobs and the normalization score of the each data entity; providing a tool to meta-tag jobs and outcome statements in respective data entities with thematic identifiers, workflow or business process dependencies, links to other data entities declared in the system, and correlation values; optimizing a portfolio containing the each data entity using a processor of a portfolio optimization engine; and conceptualizing a breakthrough with the optimized portfolio using a processor of a breakthrough conceptualization engine.
4. A method comprising: creating an innovation strategy at an innovation strategy creation engine; aggregating outcomes associated with a market, using a processor of an outcome aggregation engine; aggregating jobs associated with the market using the processor of the outcome aggregation engine; identifying opportunities associated with the market, the identifying the opportunities including automating calculation, ranking, and reporting of opportunity scores with associated metadata, using a processor of an opportunity identification engine; segmenting the market, including providing tools to interact with statistical analysis and modeling packages and import additional metadata tags into a job/outcome data schema comprising a set of data entities, each of the set of data entities incorporating a unified jobs framework that provides a quantifiable metric of an interaction a customer has with products or services, the segmenting using a processor of a market segmentation engine; defining a target strategy using a processor of a target strategy definition engine, the defining including: tagging each of the set of data entities with one or more job identifiers to provide a numerical value associated with each data entity of a set of data entities, the numerical value correlating the quantifiable metric of customer interaction with the one or more of the opportunities; assigning the numerical value of the each data entity a normalization score that has a particular market meaning independent of data used to gather the aggregated outcomes and the aggregated jobs, but has a scale reflecting a significance of the quantifiable metric and the one or more job identifiers in market terms; assigning a priority of the each data entity with respect to another data entity of the data entities, the priority based on an ODI based model of the market, including a measure of one of the aggregated jobs and the normalization score of the each data entity; providing a tool to meta-tag jobs and outcome statements in respective data entities with thematic identifiers, workflow or business process dependencies, links to other data entities declared in the system, and correlation values; optimizing a portfolio containing the each data entity using a processor of a portfolio optimization engine; and conceptualizing a breakthrough with the optimized portfolio using a processor of a breakthrough conceptualization engine. 7. The method of claim 4 , wherein aggregating outcomes comprises: extracting job and outcome statements directly into web survey tools; facilitating procurement of customer lists for direct quantitative research; providing rule-based utilities to assign survey participants to segments or groups for later analytical purposes; providing tools to deploy surveys directly to customers, screen out known survey abusers, and randomize data collection for reliability; assessing customer response data validity through co-variance assessments on like outcomes; automating collection of price sensitivity input during initial quantitative research; providing tools to import survey response data directly into a data model; providing tools to optionally distribute data with population averages back to survey participants to encourage customer engagement; providing search tools to find other jobs and outcomes from other company or benchmark research reports and a utility for affinity tagging.