Focused on turning data into information that drives innovation and fuels competitive advance.
The Data Sciences Group (DSG) is a discipline developed to bring our clients a data-focused experience. Data Sciences encompasses what we would typically consider traditional Business Intelligence and Data Integration; but much more. Our mission is to take our client’s data and turn it into information that leads to knowledge, innovation and competitive advantage. We do this through the addition of “data sciences” or, more succinctly, statistics, biostatistics, predictive models and predictive analytics. The focus is strategic data initiatives lead by Architects and Data Scientists as opposed to traditional (historical) tool based focuses. Data visualization, another key component of our data sciences offering, renders mathematical models and predictive studies into visual representations that non-data scientists can understand.
To accomplish our mission, DSG offers complete data integration strategies and solutions; the combination of technical and business processes used to combine data from disparate sources into meaningful and valuable information. Data integration strategies include common structures, such as operational data stores, and also extends into the big data space and data lakes utilizing common approaches such as Hadoop, Casandra and MongoDB.
DSG also embraces dimensional data warehousing, data marts and analytic sandboxes for the purpose of contextually binding data, deriving measures and facts and integrating third party data for analysis. Through dimensional structures and sandboxes, data visualization and exploration is greatly simplified and opens the door to self-service business intelligence strategies. Also, dimensional and sandbox structures provide a perfect platform for visually interacting with data, performing descriptive statistics, discovery and situational analytics.
Analytic sandboxes provide an avenue for data scientists to perform advanced analytics including statistics and biostatistics, data mining, and predictive analysis. Such advanced techniques may be accomplished via common statistical tools, such as SAS and SPSS, as well as specialty tools that also visually represent mathematical models.