Data Management & Integration
Effectively managing and integrating various data types such as structured and unstructured data has become essential in making better informed business decisions. This data consolidation provides organizations with a more comprehensive view of information related to multiple Business Units that often work in silos. We work with our clients to provide a host of services that include business intelligence and data warehousing for critical data such as chemical, biological, radiological, and nuclear (CBRN) incidents.
Visualizing data in order to identify patterns and generate meaningful reports is challenging in today’s world of big data. Our end-to-end Enterprise Data Warehouse and Business Intelligence solutions provide an integrated, enterprise-wide view of data stored in numerous, disparate data sources. Our solutions provide a consolidated, clear dashboard view of essential information allowing organizations to make data-driven decisions.
Transforming and processing Big Data into usable data can further assist organizations with achieving their strategic goals and objectives. Our team provides support in integrating enterprise-wide Big Data solutions as well as visualizing this data through customizable reporting and dashboarding tools built on Microsoft .NET, SharePoint, SQL Server 2008/2012 tools and platforms.
Artificial intelligence (AI) helps solve daunting challenges involving big data and analytics, to put advanced computing to work so that humans can do what they do best, which is creatively solve problems. Our experts utilize open source platforms to enable users to gain valuable insights from massive amounts of data derived from disparate sources. We are continually reviewing and testing the advances in machine learning (ML), Deep Learning and natural language processing (NLP) technologies to determine how they can alleviate the burden on humans to understand their increasingly complex environments and make better decisions.
Our proven capabilities include:
- Transformation Analytics and Automation (aggregation, enrichment, processing);
- Learning Analytics and Automation (Regression, Clustering, Classification and Recommendation);
- Predictive Analytics and Automation (Simulation and Optimization);
- Learning Models (Reinforced, Supervised, Semi-Supervised and Unsupervised);
- Training Style (offline, reinforcement and online); and,
- Execution models such as batch and streaming from scheduling perspective, serial and parallel from sequencing perspectives