A comprehensive model for management and validation of federal big data analytical systems

Background: In this era of data science, many software vendors are rushing towards providing better solutions for data management, analytics, validation and security. The government, being one of the most important customers, is riding the wave of data and business intelligence. However, federal agencies have certain requirements and bureaucracies for data-related processes, certain rules and specific regulations that would entail special models for building and managing data analytical systems. In this paper, and based on work done at the US government, a model for data management and validation is introduced: Federal Model for Data Management and Validation (FedDMV). FedDMV is 4-step model that has a set of best practices, databases, software tools and analytics. Automated procedures are used to develop the system and maintain it, and association rules are used for improving its quality.