Our Insights

Analytica’s engineering solutions group implemented a rapid open source application modernization for our client, the Centers for Disease Control and Prevention (CDC) Office of Science Quality (OSQ). Through leveraging open source technologies and an Agile SAFE project management framework we designed, developed, and implemented an enterprise publishing platform that supported clearing and publishing CDC’s Research and Non-research projects on the web.

Analytica is supporting the Centers for Medicare and Medicaid Services (CMS) Office of Information Technology (OIT) to design and implement a federal data strategy and governance approach to support enterprise cloud environments. As part of work we are helping adapt data architecture, design standards and governance frameworks to support innovations around cloud based data management and analytics.

Analytica leads the modernizing of federal applications and web platforms that incorporate a strong human centered design and focus on customer experience and citizen engagement as part of building modern data-centric, federal enterprise applications. Our client, the Internal Revenue Service, had an mission critical web application that was data-centric and had significant potential, but had low citizen and stakeholder engagement.

The Patient Protection and Affordable Care Act (ACA) created new health insurance exchanges that began processing applications for coverage in 2014, creating large volumes of heterogenous data across Federal and State data sources. Our client, the Internal Revenue Service, is accountable for calculating and applying premium tax credits (PTC) to individuals and small businesses based on taxable income and insurance throughout the coverage period. They also use this data for matching and analysis to produce accurate measures of tax reporting, filing and payment compliance as well as decision-making and reporting to such external stakeholders as the Treasury, Congress, and the Government Accountability Office.

Analytica is supporting the development of complex artificial intelligence and machine learning model development for the Securities and Exchange Commission (SEC) to combat insider trading and financial fraud. Our work applying artificial intelligence / machine learning for regulatory enforcement provides the SEC with the ability to reduce costs for regulatory enforcement while providing more accurate, actionable intelligence for enforcement.