We provide business and intelligence to many industries with clients in the governmental, medical, oil and gas, fraud detection, and disaster recovery spaces to name a few. We always start with the data. The old saying “garbage in garbage out” is simply that, bad data yields poor results. Thus, we start with a data requirements specification for the data needed in order to effectively report on all critical phases of a project, process, or analysis. This begins with an audit of the data currently available, metadata, data quality testing, data environment, and transformation requirements. Where data sets are missing, Plexos builds the necessary databases and enterprise applications and / or uses its existing “off the shelf” Data Grove® technology platform. In complex labor and expense projects we typically add mechanisms to track assigned workflow steps with timestamps by each labor resource. This allows us to track efficiency and bottlenecks at a granular level. In large data mining projects, we combine additional structured and unstructured data from licensed data sets or data available on the web. All said, we begin with the data needed for making critical decisions.
With data sets established and, in many cases, updated in real-time, Plexos performs real-time data analysis to establish trends, correlation, variances, and other analytics necessary for Business Intelligence (BI). Plexos also employs Artificial Intelligence (AI) with machine learning on large data mining projects including medical industry projects. AI plays a greater role in BI, where intelligent systems pour over more data than any human could reasonably examine and regression and other techniques yield improved predictive analytics.
To display the results of data aggregation and analytics, we publish real-time informative dashboard and drill-down reports. Access to reports are based on role level security. Depending on the engagement, we utilize our own reporting packages, Tableau ® or Microsoft Power BI ®. The end result allows for targeted reporting at all levels of a project or analysis. Thus, a manager can see all aspects of a project including bottlenecks and poor performers and high performers. Our models have been used for