DRAFT DOCUMENT REV 2.0 – 031017 Department of Medical Assistance Services (DMAS) – Medicaid Enterprise VITA Discussion Document System (MES) Environment EDWS Solutions Stack

EDWS Production ; Visualization Data Sources Extract – Transform – Load (ETL) Data Marts Tools; Reporting (MARS, SURS, FADS) Extract Data Transform Data Load Data

ETL & Data Technical Quality Metadata Metadata Executive Business Data Staging Rules SOW p374 SOW p374 Users Users Analysts DB Engine Data Acquisition Operational Security Metadata Metadata SOW p374 SOW p374

End-user Business Tableau Dashboards Metadata Metadata SOW p374 SOW p375 Excel Dashboards Application Servers

Teradata Cloud Data Warehouse Metadata Manager Appliance 2800 56-core / two- SOW p108/p370/ Monitoring node Big Data Analytics SAS Accelerators p373/p375/p376 Rule Engine Data Aggregation and Access layer of the data Scoring and Analytics SOW Scheduled BI Extracts SOW p165 of 521 Consolidation SOW p189 of 521 warehouse environment used to get data out to p337 the users. It is a subset of the data SDLC warehouse and is usually SOW p230 oriented to a specific business line, group, or Teradata includes EBM Connect team within DMAS such Virtualization support for running SQL in SOW p240 as Finance. parallel. SOW p240 While transactional Business Architecture are designed to be updated, data Analytic Foundation Suite warehouses or marts are FADS and MARS read only. of Tools data accessed via SOW p107 Cognos for analysis Ad hoc BI Extract Capability SOW p205 Data Enrichment Data Loading Utility that SOW p16/255/299/300/ uses Teradata SQL 372 SOW p517 Document Management

Geospatial Analytics Enterprise Metadata SQL Multimedia and Advanced Security Repository Data delivery. Application Packages Informatica for Data (SQL/MM) SOW p329 Acquisition. SOW p15 of 521 Data warehouse/vault. Sandboxes Single Source of Truth for Symmetry Analytics. Central repository for all Episode Treatment Group (ETG) . Data DMAS data. Replicating databases for Risk Assessment and enrichment. Clean, Apply Loads data into specified developers, testing, etc. Predictive Modeling (ERG) business rules, Data target systems and/or file Quality Measurement integrity checks, Aggregate formats. (EBM Connect) Data Acquisition. Data Discovery. Extract data from one or or disaggregate, Enriched data loaded more DMAS sources… Validate data. Raw data loaded weekly. Standardize, etc. monthly. Analytics User Access Layer | User Presentation | Data Views Ingestion Tier Data Storage / Repository Data Feeds Distillation Tier Insights / Consumption Tier Data Analysis / Action Tier / Publish Layer Real-time / Batch / Scheduled Processing Tier

ETL Process (SOW p15) Data acquisition (real-time and scheduled), business and quality rules, and data enrichment.

Oracle Audit Vault and Database Firewall SOW p321/p322

System Monitoring TASM

Documentation Management and Metadata Tagging and Repository SOW p126/p174

Auditing

Quality Assurance and Testing

Source Code and Version Control Change Control and Help Desk Reporting

Change Management Application Lifecycle Management

Committee for Quality Assurance (CQA) Data Monitoring Fraud and Abuse Detection System (FADS) Management and Administrative Reporting System (MARS) Surveillance and Utilization Review (SURS) Enterprise Performance for the EDW, ETL, relational data marts, metadata, and document management Teradata Active System Management (TASM) Optum™ Triple Aim Analytic Services (OTAAS)

PURPOSE: To identify the EDWS from an Optum SOW perspective. In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place and are used for creating analytical reports for knowledge workers throughout the enterprise. The data stored in the warehouse is uploaded from the operational systems (such as PBMS, OPSS, PEMS, etc.). The data may pass through an operational data store and may require for additional operations to ensure data quality before it is used in the DW for reporting. The EDW maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to: 1) Integrate data from multiple sources into a single database and data model. 2) Mitigate the problem of database isolation level lock contention in transaction processing systems caused by attempts to run large, long running, analysis queries in transaction processing databases. 3) Maintain data history, even if the source transaction systems do not. 4) Integrate data from multiple source systems, enabling a central view across the enterprise. 5) Improve data quality, by providing consistent codes and

descriptions, flagging or even fixing bad data. 6) Present the organization's information consistently. 7) Provide a single common data model for all data of interest regardless of the data's source. 8) Restructure the data so that it makes sense to the business users. 9) Restructure the data so that it Robert Kowalke ~ Enterprise Architecture ~ [email protected] Relationship Management & Governance (RM&G) @ Virginia Information Technologies Agency (VITA) delivers excellent query performance, even for complex analytic queries, without impacting the operational systems. https://en.wikipedia.org/wiki/Data_warehouse Commonwealth Enterprise Solutions Center (CESC)