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Oracle Audit Vault and Database FirewallSOW p321/p322
Enterprise Performance for the EDW, ETL, relational data marts, metadata, and document management
ETL Process (SOW p15)Data acquisition (real-time and scheduled), business and quality rules, and data enrichment.
Data Monitoring
Change ManagementApplication Lifecycle Management
Source Code and Version ControlChange Control and Help Desk Reporting
System Monitoring
Documentation Management andMetadata Tagging and Repository
SOW p126/p174
Auditing
Quality Assurance and Testing
Robert Kowalke ~ Enterprise Architecture ~ [email protected] Management & Governance (RM&G) @ Virginia Information Technologies Agency (VITA)
Commonwealth Enterprise Solutions Center (CESC)
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 data cleansing 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 itdelivers excellent query performance, even for complex analytic queries, without impacting the operational systems. https://en.wikipedia.org/wiki/Data_warehouse
Department of Medical Assistance Services (DMAS) – Medicaid Enterprise
System (MES) Environment EDWS Solutions Stack
DRAFT DOCUMENTREV 2.0 – 031017
VITA Discussion Document
Data integration. Data enrichment. Clean, Apply business rules, Data integrity checks, Aggregate or disaggregate, Standardize, etc.
Extract Data
Data Sources
Transform Data Load Data
MetadataExtract – Transform – Load (ETL)
Data Warehouse
Data Acquisition. Data Discovery. Extract data from one or more DMAS sources… Validate data. Raw data loaded weekly.
Data warehouse/vault. Single Source of Truth for Analytics.Central repository for all DMAS data.Loads data into specified target systems and/or file formats.Enriched data loaded monthly.
Data Storage / RepositoryProcessing Tier
Ingestion TierReal-time / Batch / Scheduled
Distillation Tier
Data Marts
Sandboxes
Insights / Consumption Tier Data Analysis / Action Tier / Publish Layer
Business Intelligence; Visualization Tools; Reporting (MARS, SURS, FADS)
User Access Layer | User Presentation | Data Views
Data delivery.
Analytics
Tableau Dashboards
Excel Dashboards
SAS AcceleratorsScoring and Analytics SOW
p337
Committee for Quality Assurance (CQA)Fraud and Abuse Detection System (FADS)Management and Administrative Reporting System (MARS) Surveillance and Utilization Review (SURS)Teradata Active System Management (TASM)Optum™ Triple Aim Analytic Services (OTAAS)
FADS and MARS data accessed via
Cognos for analysis
Access layer of the data warehouse environment used to get data out to the users. It is a subset of the data warehouse and is usually oriented to a specific business line, group, or team within DMAS such as Finance.
While transactional databases are designed to be updated, data warehouses or marts are read only.
Document Management
Data Acquisition
Business Architecture Analytic Foundation Suite
of ToolsSOW p107
Data EnrichmentSOW p16/255/299/300/
372
Replicating databases for developers, testing, etc.
Executive Users
Business Users
Data Analysts
Virtualization
Application Servers
Monitoring
SDLC
Advanced Security
Rules Engine
Staging DB
Rule EngineSOW p165 of 521
Teradata Cloud Data Warehouse Appliance 2800 56-core / two-
node Big Data AnalyticsData Aggregation and
Consolidation SOW p189 of 521
Informatica for Data Acquisition. SOW p15 of 521
TASM
Ad hoc BI Extract Capability SOW p205
Scheduled BI Extracts
Teradata database includes support for running SQL in
parallel. SOW p240
Geospatial AnalyticsSQL Multimedia and Application Packages (SQL/MM) SOW p329
Data Loading Utility that uses Teradata SQL
SOW p517
Metadata ManagerSOW p108/p370/p373/p375/p376
Enterprise Metadata Repository
ETL & Technical Metadata
SOW p374
Data Quality
MetadataSOW p374
Operational Metadata
SOW p374
Security Metadata
SOW p374
End-user Metadata
SOW p374
Business Metadata
SOW p375
Data Feeds
EDWS Production
SOW p230EBM Connect
SOW p240
Symmetry
Episode Treatment Group (ETG)Risk Assessment and Predictive Modeling (ERG)Quality Measurement (EBM Connect)