Description: OBJECTIVE: Develop an architecture, technology roadmap, and working prototype that operationalizes Logistics Systems Data Error Handling, Analytics, and Corrective Action Management Activities. DESCRIPTION: Air Force IT system modernization efforts continue to highlight the growing need for robust, standards-based, open service-oriented architectures. There is a critical need to provide a foundational approach to complex data error analysis, exception handling, and corrective action behaviors while providing reliable and consistent analytics, integration, workflow, and collaboration to support those behaviors. Multiple initiatives and programs require this type of solution within the Logistics Enterprise and by allowing each to develop their own approach to error handling and resolution, they run the risk of developing redundant, divergent, and potentially incompatible architectures that are costly to sustain. Solution should include an extensible methodology, architecture and tool suite for identifying, resolving, reporting, and managing data exceptions and errors that can be used across the Logistics Enterprise. Solution should assume a common view of data and provide tailored information to specific user groups, including differentiation between master data and transaction layer services necessary for daily operations. Solution should include analytics that facilitate root cause analysis of data anomalies. Strategic, operational and tactical points of view must be considered in the proposed model. This product must address the following objectives: Support creation of a global, enterprise-wide framework sourced from non-standard, heterogeneous, multi-organizational, distributed, centralized, and federated IT systems. Production of a strategic, operational, and tactical product roadmap, to include the integration with, and to, DoD and Air Force data and metadata repositories. A software solution that addresses, at a tactical level, prioritization of transactions, transaction definition changes, and corrective actions based on changing mission needs. The solution and commercially viable prototype must address the following technical challenges: Account for structured and unstructured data. Support scalable"product configuration"options and incremental fielding based on customized sizing, throughput, partitioning, reliability, data model inputs and options. Address the operational challenges of data synchronization, sequencing, drill downs, periodicity and timeliness. Address merging standards and best practices related to data collection and profiling methodologies, data mining, data categorization, data correlation, and data error resolution, to include reporting, measurement, and threshold monitoring. Extend emergent technologies that support features such as rule-based engines; data Extract, Transform and Load (ETL) principles; data hub/master data management; segregation of duties; workflow and notification management. Integrate with existing"help desk"structures, command specific escalation and release management activities, Business Intelligence (BI) infrastructure and Root Cause Analysis tools, and applicable Information Assurance and Certification/Accreditation laws, rules, and regulations. Address data problem identification, analysis/corrective actions and the use of predictive analysis to uncover and mitigate data anomalies. Propose extensible best-of-breed standards, topologies, and architectures to fulfill and field the recommended solution Air Force wide. PHASE I: Submit a proof-of-concept architecture with concept paper, technology design, software prototype, and Phase II build plan. Include solution scenarios, realistic data sets, and technical issues/risks. Prototype software demonstrates use of configurable business rules/innovative data quality-related algorithms. PHASE II: Mature the prototype, incorporate operational (transaction level) layered services, integrate Master Data Management, extend predictive analysis, and demonstrate full solution capabilities using multidimensional data and complex business scenarios. Deliver revisions to the conceptual framework, technology design, and technical issues. Deliver solution-based training, maintenance materials and an implementation plan based on operational data migration recommendations and considerations. PHASE III DUAL USE APPLICATIONS: Extend and transition prototype to operational use across the Logistics Enterprise domain.