Charlotte has worked with many SAS customers in the government, financial, retail and education sectors as well as with system integrators and business partners. Her areas of expertise include information management, data quality and integration, data management methodology/architecture, data governance, SAS architecture, business analytics, and SAS programming. She also has experience with energy demand statistical modeling, time series analysis and forecasting, credit risk modeling and applications development in the areas of web applications/interfaces and automation. She holds an M.S. in mathematics with an emphasis in numerical analysis, linear and non-linear statistical data modeling.
Data warehouse (DWH) environments have typically been the standard when it comes to supporting analytical environments. There can be many systems supporting a particular modeling or analytical group, and because these groups have varying requirements for data, the replicated data is maintained because the transition to new storage and computing
When you hear the term “business rules,” what comes to mind? Some examples would be constraints in a query, a data mapping, a data quality constraint, a data transformation, a model or an algebraic equation. It can also be a policy that must be enforced as part of a business
When I first thought about writing a blog post about data management, picking a topic was an obvious, if daunting, first step. The challenges facing both IT and business are increasing in the era of bigger, more complex and more immediate data. My experience working with organizations across a variety
