尊敬的 微信汇率：1円 ≈ 0.046089 元 支付宝汇率：1円 ≈ 0.04618元 [退出登录]
SlideShare a Scribd company logo

Healthcare Enterprise Data Model: The Buy vs. Build Debate

•
•
Perficient, Inc.
Perficient, Inc.

Every mid-to-large-sized healthcare organization will at some point need an enterprise data warehouse to consolidate data from its many source systems. The first question most will ask is, “Do I build it from scratch or buy a model?” For some organizations, the answer to this question is simple and obvious. For others, it may be the source of internal debate on whether someone else can create a model that will address their nuances. Some will argue that a pre-built model will save time and money and should be explored as an option, while others may curb discussions due to the belief that their electronic medical records vendor already has it figured out. In this webinar, we explored the pros and cons of building your own data model vs. buying one and looked at real customer use cases to help weigh the pros and cons of this critical enterprise decision. Topics included: -How experience plays into the equation -Which solution delivers value more quickly -Which solution helps reduce the risk to the organization -How easy is it to integrate other solutions -How the decision to build vs. buy can impact your internal team

Technology
1 of 24
Download to read offline
Thank you for your time and attention today. Please visit us at Perficient.com Healthcare Enterprise Data Model: The Buy vs. Build DebateNovember 18, 2014 facebook.com/perficient twitter.com/Perficient_HC linkedin.com/company/perficient
Perficient is a leading information technology consulting firm serving clients throughoutNorth America. We help clients implement business-driven technology solutions that integrate business processes, improve worker productivity, increase customer loyalty and create a more agile enterprise to better respond to new business opportunities. About Perficient
3 •Founded in 1997 •Public, NASDAQ: PRFT •2013 revenue $373 million •Major market locations: •Allentown, Atlanta, Boston, Charlotte, Chicago, Cincinnati, Columbus, Dallas, Denver, Detroit, Fairfax, Houston, Indianapolis, Minneapolis, New York City, Northern California, Oxford (UK), Philadelphia, Southern California, St. Louis, Toronto, Washington, D.C. •Global delivery centers in China and India •>2,200 colleagues •Dedicated solution practices •~90% repeat business rate •Alliance partnerships with major technology vendors •Multiple vendor/industry technology and growth awards Perficient Profile
BUSINESSSOLUTIONS Business Intelligence Business Process Management Customer Experience and CRM Enterprise Performance Management Enterprise Resource Planning Experience Design (XD) Management Consulting TECHNOLOGYSOLUTIONS Business Integration/SOA Cloud Services Commerce Content Management Custom Application Development Education Information Management Mobile Platforms Platform Integration Portal & Social Our Solutions Expertise
Healthcare Practice Strategic Partners Experts in Consumer-Driven Healthcare Technology HEALTH PLAN PROVIDER Connected Health Business Intelligence and Analytics Interoperability & Integration Information Exchange Regulatory Compliance Solutions & Services CONSUMERS Select Clients Global Delivery Centers/Offshore Delivery Domestic Delivery Center
David MeintelDirector of Healthcare, Perficient David has 20 years of experience in data warehousing and analytics in a wide range of industries. Focusing this experience in the last 4 years exclusively on the healthcare industry, David has worked with a large number of provider and health plan organizations to assist them in better leveraging their data assets. Our Speaker
Agenda •Brief look at the challenge in healthcare causing the need for a data warehouse •Evolution of the solution to the challenge •Discuss key factors in the decision to buy or build your data model •Reality!
The Challenge Regulatory Compliance Population Health Management ReduceRe-Admissions Service-Line Management Claims Analysis Other…
Evolution of the Solution Organizations move from siloedsolutions to consolidated operational data stores to enterprise warehouse… Operational
Enterprise Data Model Data structures to hold all of the key decision making information for the enterprise integrated across subject areas such as: Clinical Claims Financial Operational
Enterprise Data Model •Architected to be extensible •Phased implementations •The “single version of the truth” for the organization •Have evolved greatly over the years
Enterprise Data Model (example) Core Provider Content Payer Content Supply Chain Content Core Master Data Provider Patient Data Payer Patient Data Supply Chain Data •Person •Patient •Organization •Facility •Distributors •Product Pricing Tier •Price Activation •General Ledger •Accounts Payable •Accounts Receivable •Contract (Supplier) •Member (Insured) •Subscriber •Member Enrollment •Member Enrolled PCP •ADT Event (Hospitalization) •Biospecimen •Encounter •Finding •Lab Result •Payer •Regulatory Agencies •Contact •Groupings/Hierarchies •Unified Standard Codes •Supplier •Wholesaler •Distributor •Unified Standard Codes •Practitioner •Unified Standard Codes •Groupings/Hierarchies •Reference Data •Allergy •Diagnosis •Discharge Summary and Instruction •Imaging •Procedure •Surgery •Survey •Appointment •Bill •Bill Encounter •Care Team & Role •Contract (Patient) •Contracts •Disease Management •Encounter •Episode •EOB •COB •Provider Network •Campaign •Premium Payments •Location •Order •Patient Life Event •Patient Medications; Includes Order, Dispense, Administration, Formulary •Problem (chief complaint, etc.) •Event (General) •Exposure & Lifestyle •General Ledger •Location •Patient Satisfaction •Payroll •Immunization •Product Master •Policy •Coverage •Benefit •Claims (all types) •Claims (Adjustments) •Purchase Order •Invoice •Product Item Master •Purchase History •Contracts •Suppliers •Wholesalers •Unified Standard Codes •Groupings/ Hierarchies •Ambulatory Encounter (date, time, type) •Ambulatory procedure •Ambulatory Test order •Ambulatory Result •Ambulatory Diagnoses •Ambulatory Billing •Wellness Targets •HEDIS Measures •PQRS Measures •ACO Measures •Quest Measures •Agreement •Money Item •Financial Transaction •Claim Recovery
Buy vs. Build Key areas to consider: –How experienceplays into the equation –How is time to value impacted –How is riskfactored in with the decision –How easy is it to integrateother solutions –How the decision to build vs. buy can impact your internal team –How will costbe impacted
Experience Key points to consider –Buy •Experience comes bundled in •Top sellers of EDM have years of experience both technical and healthcare •Clinicians, technologists and business analysts to bridge the two –Build •Does your current staff have the requisite experience? •If not, you will likely need to hire or contract someone who does •In addition to IT resources with technical skills, this will require plenty of support from your line of business resources (clinicians, etc.) Many of our customers have existing data warehouses that we end up replacing and most were built internally.
Time to Value Key points to consider –Buy •Large portion of design is already done for you •Customizations willneed to be done regardless of what your vendor tells you •Often times you can implement in a phased approach to reduce time to value even more –Build •The more experience your team has the faster this will go •Often time subject area driven to reduce time to value •Only build what you need, can reduce time as well Implementing something that is already designed, will be faster than designing and building it yourself.
Risk Key points to consider –Buy •Lower typically due to –Level of experience and resources that went into it –Refined over time (customer experiences incorporated) –Repeatable so potential issues are known •Sometimes monolithic in nature can cause scope to grow larger than needed –Build •Higher typically due to –Organizations typically don’t do this lots of times, so no experience –Common mistakes are likely to be encountered due to lack of experience Manage scope and leverage experience to mitigate risk.
Integration / Accelerators Key points to consider –Buy •Standardized models encourage accelerators •Integration points need to be standard and often times take a lowest common denominator approach, which sometimes causes slight increase in effort –Build •If you build it yourself, you have the ultimate in flexibility to design interface the way you want them •Integrating with outside solutions will likely take more effort due to no standard approach for vendors to build to When standards exist, so does common integration points and pre-built accelerators…don’t reinvent the wheel.
18 Integration / Accelerators
Staff Impact Key points to consider –Buy •Many key decisions on design are made for you •Staff doesn’t have to be experts in designing model which lessens the pressure in early phases •Learning curve for potential new technologies –Modeling tool, ETL tool, Metadata management etc •Consulting assistance for peeks in need often more effective than hiring a large team –Build •Every decision is yours to make, increases pressure •Analysis paralysis is a common issue, trying to make perfect •Leverage strengths of team in choosing technologies Make sure staff is setup for success, not failure.
Cost Key points to consider –Buy •Initial purchase price may seem high •Total cost of ownership generally lower •Other accelerators also help to reduce total cost of ownership –Build •Pay as you go •Paying to reinvent the wheel Buying might cost more up front, but will save money over time.
Summary Experience–Leverage experience of people who have done what you are trying to do Time to Value–Implementing a model that is already designed will be faster Risk-Managing scope and leveraging experience will mitigate risk Integration / Accelerators–Don’t reinvent the wheel Staff Impact–Know your staff and make sure they are set up for success Cost–Buying a model might have higher initial costs, but building will cost more over time
Reality, Buy and Build Buy –Data model •Leverage the experience of many •Don’t reinvent the wheel –High value initial use case •Physicalize required portions of the data model •Connectivity to your source systems to populate model •Gain quick value for investment made in model Build –Custom analytics for your use case •Leverage existing investments in tools •This is what your end user with interact with the most –Additional use cases •Follow patterns from initial use case •Leverage experience and iterate
Questions?
Questions? Connect with Perficient -Upcoming events & webinars -On-demand webinars -White papers & perspectives-Thought leadership David Meintel David.Meintel@Perficient.com | Phone: 646.457.4865

Recommended

Data Lakehouse, Data Mesh, and Data Fabric (r1)
Data Lakehouse, Data Mesh, and Data Fabric (r1)Data Lakehouse, Data Mesh, and Data Fabric (r1)
Data Lakehouse, Data Mesh, and Data Fabric (r1)
James Serra
 
Strategic imperative the enterprise data model
Strategic imperative the enterprise data modelStrategic imperative the enterprise data model
Strategic imperative the enterprise data model
DATAVERSITY
 
2019 07 Bizbok with Archimate 3 v3 [UPDATED !]
2019 07 Bizbok with Archimate 3 v3 [UPDATED !] 2019 07 Bizbok with Archimate 3 v3 [UPDATED !]
2019 07 Bizbok with Archimate 3 v3 [UPDATED !]
COMPETENSIS
 
Liberating data with Talend Data Catalog
Liberating data with Talend Data CatalogLiberating data with Talend Data Catalog
Liberating data with Talend Data Catalog
Jean-Michel Franco
 
Big Data Analytics Strategy and Roadmap
Big Data Analytics Strategy and RoadmapBig Data Analytics Strategy and Roadmap
Big Data Analytics Strategy and Roadmap
Srinath Perera
 
Free Training: How to Build a Lakehouse
Free Training: How to Build a LakehouseFree Training: How to Build a Lakehouse
Free Training: How to Build a Lakehouse
Databricks
 
Operational Data Vault
Operational Data VaultOperational Data Vault
Operational Data Vault
Empowered Holdings, LLC
 
Essential Reference and Master Data Management
Essential Reference and Master Data ManagementEssential Reference and Master Data Management
Essential Reference and Master Data Management
DATAVERSITY
 

Recommended

Data Lakehouse, Data Mesh, and Data Fabric (r1)
Data Lakehouse, Data Mesh, and Data Fabric (r1)Data Lakehouse, Data Mesh, and Data Fabric (r1)
Data Lakehouse, Data Mesh, and Data Fabric (r1)
James Serra
 
Strategic imperative the enterprise data model
Strategic imperative the enterprise data modelStrategic imperative the enterprise data model
Strategic imperative the enterprise data model
DATAVERSITY
 
2019 07 Bizbok with Archimate 3 v3 [UPDATED !]
2019 07 Bizbok with Archimate 3 v3 [UPDATED !] 2019 07 Bizbok with Archimate 3 v3 [UPDATED !]
2019 07 Bizbok with Archimate 3 v3 [UPDATED !]
COMPETENSIS
 
Liberating data with Talend Data Catalog
Liberating data with Talend Data CatalogLiberating data with Talend Data Catalog
Liberating data with Talend Data Catalog
Jean-Michel Franco
 
Big Data Analytics Strategy and Roadmap
Big Data Analytics Strategy and RoadmapBig Data Analytics Strategy and Roadmap
Big Data Analytics Strategy and Roadmap
Srinath Perera
 
Free Training: How to Build a Lakehouse
Free Training: How to Build a LakehouseFree Training: How to Build a Lakehouse
Free Training: How to Build a Lakehouse
Databricks
 
Operational Data Vault
Operational Data VaultOperational Data Vault
Operational Data Vault
Empowered Holdings, LLC
 
Essential Reference and Master Data Management
Essential Reference and Master Data ManagementEssential Reference and Master Data Management
Essential Reference and Master Data Management
DATAVERSITY
 
Enterprise Data Architecture Deliverables
Enterprise Data Architecture DeliverablesEnterprise Data Architecture Deliverables
Enterprise Data Architecture Deliverables
Lars E Martinsson
 
Data Modelling is NOT just for RDBMS's
Data Modelling is NOT just for RDBMS'sData Modelling is NOT just for RDBMS's
Data Modelling is NOT just for RDBMS's
Christopher Bradley
 
Bi Strategy Roadmap
Bi Strategy RoadmapBi Strategy Roadmap
Bi Strategy Roadmap
Salil Mehendale
 
Requirements Gathering And Management
Requirements Gathering And ManagementRequirements Gathering And Management
Requirements Gathering And Management
Alan McSweeney
 
Process Oriented Architecture
Process Oriented ArchitectureProcess Oriented Architecture
Process Oriented Architecture
Alan McSweeney
 
Enterprise Architecture Implementation And The Open Group Architecture Framew...
Enterprise Architecture Implementation And The Open Group Architecture Framew...Enterprise Architecture Implementation And The Open Group Architecture Framew...
Enterprise Architecture Implementation And The Open Group Architecture Framew...
Alan McSweeney
 
Designing An Enterprise Data Fabric
Designing An Enterprise Data FabricDesigning An Enterprise Data Fabric
Designing An Enterprise Data Fabric
Alan McSweeney
 
Enterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data ArchitectureEnterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data Architecture
DATAVERSITY
 
Data Warehouse Basics
Data Warehouse BasicsData Warehouse Basics
Data Warehouse Basics
Ram Kedem
 
Data Mesh
Data MeshData Mesh
Data Mesh
Piethein Strengholt
 
Event driven architecture
Event driven architectureEvent driven architecture
Event driven architecture
Shadrach Jabonir
 
Big Data Management: What's New, What's Different, and What You Need To Know
Big Data Management: What's New, What's Different, and What You Need To KnowBig Data Management: What's New, What's Different, and What You Need To Know
Big Data Management: What's New, What's Different, and What You Need To Know
SnapLogic
 
Data modeling for the business 09282010
Data modeling for the business 09282010Data modeling for the business 09282010
Data modeling for the business 09282010
ERwin Modeling
 
Modeling Big Data with the ArchiMate 3.0 Language
Modeling Big Data with the ArchiMate 3.0 LanguageModeling Big Data with the ArchiMate 3.0 Language
Modeling Big Data with the ArchiMate 3.0 Language
Iver Band
 
Anatomy of a data driven architecture - Tamir Dresher
Anatomy of a data driven architecture - Tamir Dresher Anatomy of a data driven architecture - Tamir Dresher
Anatomy of a data driven architecture - Tamir Dresher
Tamir Dresher
 
Strategic Enterprise Architecture Roadmap
Strategic Enterprise Architecture RoadmapStrategic Enterprise Architecture Roadmap
Strategic Enterprise Architecture Roadmap
Joaquin Marques
 
Putting the Ops in DataOps: Orchestrate the Flow of Data Across Data Pipelines
Putting the Ops in DataOps: Orchestrate the Flow of Data Across Data PipelinesPutting the Ops in DataOps: Orchestrate the Flow of Data Across Data Pipelines
Putting the Ops in DataOps: Orchestrate the Flow of Data Across Data Pipelines
DATAVERSITY
 
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...
Denodo
 
Data Audit Approach To Developing An Enterprise Data Strategy
Data Audit Approach To Developing An Enterprise Data StrategyData Audit Approach To Developing An Enterprise Data Strategy
Data Audit Approach To Developing An Enterprise Data Strategy
Alan McSweeney
 
Design Guidelines for Data Mesh and Decentralized Data Organizations
Design Guidelines for Data Mesh and Decentralized Data OrganizationsDesign Guidelines for Data Mesh and Decentralized Data Organizations
Design Guidelines for Data Mesh and Decentralized Data Organizations
Denodo
 
Going Beyond the EMR for Data-driven Insights in Healthcare
Going Beyond the EMR for Data-driven Insights in HealthcareGoing Beyond the EMR for Data-driven Insights in Healthcare
Going Beyond the EMR for Data-driven Insights in Healthcare
Perficient, Inc.
 
Europeana Creative. EDM Endpoint. Custom Views
Europeana Creative. EDM Endpoint. Custom ViewsEuropeana Creative. EDM Endpoint. Custom Views
Europeana Creative. EDM Endpoint. Custom Views
Vladimir Alexiev, PhD, PMP
 

More Related Content

What's hot

Enterprise Data Architecture Deliverables
Enterprise Data Architecture DeliverablesEnterprise Data Architecture Deliverables
Enterprise Data Architecture Deliverables
Lars E Martinsson
 
Data Modelling is NOT just for RDBMS's
Data Modelling is NOT just for RDBMS'sData Modelling is NOT just for RDBMS's
Data Modelling is NOT just for RDBMS's
Christopher Bradley
 
Bi Strategy Roadmap
Bi Strategy RoadmapBi Strategy Roadmap
Bi Strategy Roadmap
Salil Mehendale
 
Requirements Gathering And Management
Requirements Gathering And ManagementRequirements Gathering And Management
Requirements Gathering And Management
Alan McSweeney
 
Process Oriented Architecture
Process Oriented ArchitectureProcess Oriented Architecture
Process Oriented Architecture
Alan McSweeney
 
Enterprise Architecture Implementation And The Open Group Architecture Framew...
Enterprise Architecture Implementation And The Open Group Architecture Framew...Enterprise Architecture Implementation And The Open Group Architecture Framew...
Enterprise Architecture Implementation And The Open Group Architecture Framew...
Alan McSweeney
 
Designing An Enterprise Data Fabric
Designing An Enterprise Data FabricDesigning An Enterprise Data Fabric
Designing An Enterprise Data Fabric
Alan McSweeney
 
Enterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data ArchitectureEnterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data Architecture
DATAVERSITY
 
Data Warehouse Basics
Data Warehouse BasicsData Warehouse Basics
Data Warehouse Basics
Ram Kedem
 
Data Mesh
Data MeshData Mesh
Data Mesh
Piethein Strengholt
 
Event driven architecture
Event driven architectureEvent driven architecture
Event driven architecture
Shadrach Jabonir
 
Big Data Management: What's New, What's Different, and What You Need To Know
Big Data Management: What's New, What's Different, and What You Need To KnowBig Data Management: What's New, What's Different, and What You Need To Know
Big Data Management: What's New, What's Different, and What You Need To Know
SnapLogic
 
Data modeling for the business 09282010
Data modeling for the business 09282010Data modeling for the business 09282010
Data modeling for the business 09282010
ERwin Modeling
 
Modeling Big Data with the ArchiMate 3.0 Language
Modeling Big Data with the ArchiMate 3.0 LanguageModeling Big Data with the ArchiMate 3.0 Language
Modeling Big Data with the ArchiMate 3.0 Language
Iver Band
 
Anatomy of a data driven architecture - Tamir Dresher
Anatomy of a data driven architecture - Tamir Dresher Anatomy of a data driven architecture - Tamir Dresher
Anatomy of a data driven architecture - Tamir Dresher
Tamir Dresher
 
Strategic Enterprise Architecture Roadmap
Strategic Enterprise Architecture RoadmapStrategic Enterprise Architecture Roadmap
Strategic Enterprise Architecture Roadmap
Joaquin Marques
 
Putting the Ops in DataOps: Orchestrate the Flow of Data Across Data Pipelines
Putting the Ops in DataOps: Orchestrate the Flow of Data Across Data PipelinesPutting the Ops in DataOps: Orchestrate the Flow of Data Across Data Pipelines
Putting the Ops in DataOps: Orchestrate the Flow of Data Across Data Pipelines
DATAVERSITY
 
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...
Denodo
 
Data Audit Approach To Developing An Enterprise Data Strategy
Data Audit Approach To Developing An Enterprise Data StrategyData Audit Approach To Developing An Enterprise Data Strategy
Data Audit Approach To Developing An Enterprise Data Strategy
Alan McSweeney
 
Design Guidelines for Data Mesh and Decentralized Data Organizations
Design Guidelines for Data Mesh and Decentralized Data OrganizationsDesign Guidelines for Data Mesh and Decentralized Data Organizations
Design Guidelines for Data Mesh and Decentralized Data Organizations
Denodo
 

What's hot (20)

Enterprise Data Architecture Deliverables
Enterprise Data Architecture DeliverablesEnterprise Data Architecture Deliverables
Enterprise Data Architecture Deliverables
 
Data Modelling is NOT just for RDBMS's
Data Modelling is NOT just for RDBMS'sData Modelling is NOT just for RDBMS's
Data Modelling is NOT just for RDBMS's
 
Bi Strategy Roadmap
Bi Strategy RoadmapBi Strategy Roadmap
Bi Strategy Roadmap
 
Requirements Gathering And Management
Requirements Gathering And ManagementRequirements Gathering And Management
Requirements Gathering And Management
 
Process Oriented Architecture
Process Oriented ArchitectureProcess Oriented Architecture
Process Oriented Architecture
 
Enterprise Architecture Implementation And The Open Group Architecture Framew...
Enterprise Architecture Implementation And The Open Group Architecture Framew...Enterprise Architecture Implementation And The Open Group Architecture Framew...
Enterprise Architecture Implementation And The Open Group Architecture Framew...
 
Designing An Enterprise Data Fabric
Designing An Enterprise Data FabricDesigning An Enterprise Data Fabric
Designing An Enterprise Data Fabric
 
Enterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data ArchitectureEnterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data Architecture
 
Data Warehouse Basics
Data Warehouse BasicsData Warehouse Basics
Data Warehouse Basics
 
Data Mesh
Data MeshData Mesh
Data Mesh
 
Event driven architecture
Event driven architectureEvent driven architecture
Event driven architecture
 
Big Data Management: What's New, What's Different, and What You Need To Know
Big Data Management: What's New, What's Different, and What You Need To KnowBig Data Management: What's New, What's Different, and What You Need To Know
Big Data Management: What's New, What's Different, and What You Need To Know
 
Data modeling for the business 09282010
Data modeling for the business 09282010Data modeling for the business 09282010
Data modeling for the business 09282010
 
Modeling Big Data with the ArchiMate 3.0 Language
Modeling Big Data with the ArchiMate 3.0 LanguageModeling Big Data with the ArchiMate 3.0 Language
Modeling Big Data with the ArchiMate 3.0 Language
 
Anatomy of a data driven architecture - Tamir Dresher
Anatomy of a data driven architecture - Tamir Dresher Anatomy of a data driven architecture - Tamir Dresher
Anatomy of a data driven architecture - Tamir Dresher
 
Strategic Enterprise Architecture Roadmap
Strategic Enterprise Architecture RoadmapStrategic Enterprise Architecture Roadmap
Strategic Enterprise Architecture Roadmap
 
Putting the Ops in DataOps: Orchestrate the Flow of Data Across Data Pipelines
Putting the Ops in DataOps: Orchestrate the Flow of Data Across Data PipelinesPutting the Ops in DataOps: Orchestrate the Flow of Data Across Data Pipelines
Putting the Ops in DataOps: Orchestrate the Flow of Data Across Data Pipelines
 
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...
 
Data Audit Approach To Developing An Enterprise Data Strategy
Data Audit Approach To Developing An Enterprise Data StrategyData Audit Approach To Developing An Enterprise Data Strategy
Data Audit Approach To Developing An Enterprise Data Strategy
 
Design Guidelines for Data Mesh and Decentralized Data Organizations
Design Guidelines for Data Mesh and Decentralized Data OrganizationsDesign Guidelines for Data Mesh and Decentralized Data Organizations
Design Guidelines for Data Mesh and Decentralized Data Organizations
 

Viewers also liked

Going Beyond the EMR for Data-driven Insights in Healthcare
Going Beyond the EMR for Data-driven Insights in HealthcareGoing Beyond the EMR for Data-driven Insights in Healthcare
Going Beyond the EMR for Data-driven Insights in Healthcare
Perficient, Inc.
 
Europeana Creative. EDM Endpoint. Custom Views
Europeana Creative. EDM Endpoint. Custom ViewsEuropeana Creative. EDM Endpoint. Custom Views
Europeana Creative. EDM Endpoint. Custom Views
Vladimir Alexiev, PhD, PMP
 
Hybrid Analytics in Healthcare: Leveraging Power BI and Office 365 to Make Sm...
Hybrid Analytics in Healthcare: Leveraging Power BI and Office 365 to Make Sm...Hybrid Analytics in Healthcare: Leveraging Power BI and Office 365 to Make Sm...
Hybrid Analytics in Healthcare: Leveraging Power BI and Office 365 to Make Sm...
Perficient, Inc.
 
Information management and enterprise architecture
Information management and enterprise architectureInformation management and enterprise architecture
Information management and enterprise architecture
nvvrajesh
 
Data Architecture for Data Governance
Data Architecture for Data GovernanceData Architecture for Data Governance
Data Architecture for Data Governance
DATAVERSITY
 
Data Governance Best Practices
Data Governance Best PracticesData Governance Best Practices
Data Governance Best Practices
Boris Otto
 
Data modeling for the business
Data modeling for the businessData modeling for the business
Data modeling for the business
Christopher Bradley
 
Implementing Effective Data Governance
Implementing Effective Data GovernanceImplementing Effective Data Governance
Implementing Effective Data Governance
Christopher Bradley
 
What is the best Healthcare Data Warehouse Model for Your Organization?
What is the best Healthcare Data Warehouse Model for Your Organization?What is the best Healthcare Data Warehouse Model for Your Organization?
What is the best Healthcare Data Warehouse Model for Your Organization?
Health Catalyst
 

Viewers also liked (9)

Going Beyond the EMR for Data-driven Insights in Healthcare
Going Beyond the EMR for Data-driven Insights in HealthcareGoing Beyond the EMR for Data-driven Insights in Healthcare
Going Beyond the EMR for Data-driven Insights in Healthcare
 
Europeana Creative. EDM Endpoint. Custom Views
Europeana Creative. EDM Endpoint. Custom ViewsEuropeana Creative. EDM Endpoint. Custom Views
Europeana Creative. EDM Endpoint. Custom Views
 
Hybrid Analytics in Healthcare: Leveraging Power BI and Office 365 to Make Sm...
Hybrid Analytics in Healthcare: Leveraging Power BI and Office 365 to Make Sm...Hybrid Analytics in Healthcare: Leveraging Power BI and Office 365 to Make Sm...
Hybrid Analytics in Healthcare: Leveraging Power BI and Office 365 to Make Sm...
 
Information management and enterprise architecture
Information management and enterprise architectureInformation management and enterprise architecture
Information management and enterprise architecture
 
Data Architecture for Data Governance
Data Architecture for Data GovernanceData Architecture for Data Governance
Data Architecture for Data Governance
 
Data Governance Best Practices
Data Governance Best PracticesData Governance Best Practices
Data Governance Best Practices
 
Data modeling for the business
Data modeling for the businessData modeling for the business
Data modeling for the business
 
Implementing Effective Data Governance
Implementing Effective Data GovernanceImplementing Effective Data Governance
Implementing Effective Data Governance
 
What is the best Healthcare Data Warehouse Model for Your Organization?
What is the best Healthcare Data Warehouse Model for Your Organization?What is the best Healthcare Data Warehouse Model for Your Organization?
What is the best Healthcare Data Warehouse Model for Your Organization?
 

Similar to Healthcare Enterprise Data Model: The Buy vs. Build Debate

SQL Saturday STL 2016 Presentation
SQL Saturday STL 2016 PresentationSQL Saturday STL 2016 Presentation
SQL Saturday STL 2016 Presentation
Matthew W. Bowers
 
Best Practices in Software Vendor Selection
Best Practices in Software Vendor SelectionBest Practices in Software Vendor Selection
Best Practices in Software Vendor Selection
Advantiv Solutions, LLC
 
Gain Faster Closure, Lower Risk and Better Results with IBM Emptoris
Gain Faster Closure, Lower Risk and Better Results with IBM EmptorisGain Faster Closure, Lower Risk and Better Results with IBM Emptoris
Gain Faster Closure, Lower Risk and Better Results with IBM Emptoris
Perficient, Inc.
 
Engage Patients, Reduce Manual Processes and Drive Key Insights with Interope...
Engage Patients, Reduce Manual Processes and Drive Key Insights with Interope...Engage Patients, Reduce Manual Processes and Drive Key Insights with Interope...
Engage Patients, Reduce Manual Processes and Drive Key Insights with Interope...
Perficient, Inc.
 
Fundamentals Of Organization Best Practices And Templates Powerpoint Presenta...
Fundamentals Of Organization Best Practices And Templates Powerpoint Presenta...Fundamentals Of Organization Best Practices And Templates Powerpoint Presenta...
Fundamentals Of Organization Best Practices And Templates Powerpoint Presenta...
SlideTeam
 
Lecture 8 Resources and Costs.pptx
Lecture 8 Resources and Costs.pptxLecture 8 Resources and Costs.pptx
Lecture 8 Resources and Costs.pptx
GelmelinaLeaLepitenH
 
Agile metrics - Agile KC Meeting 9/26/13
Agile metrics - Agile KC Meeting 9/26/13Agile metrics - Agile KC Meeting 9/26/13
Agile metrics - Agile KC Meeting 9/26/13
molsonkc
 
Synergis60: 6 Critical Steps to Implementing Data Managment
Synergis60: 6 Critical Steps to Implementing Data ManagmentSynergis60: 6 Critical Steps to Implementing Data Managment
Synergis60: 6 Critical Steps to Implementing Data Managment
Synergis Engineering Design Solutions
 
Stop the madness - Never doubt the quality of BI again using Data Governance
Stop the madness - Never doubt the quality of BI again using Data GovernanceStop the madness - Never doubt the quality of BI again using Data Governance
Stop the madness - Never doubt the quality of BI again using Data Governance
Mary Levins, PMP
 
PrADS Introduction & offerings 2017
PrADS Introduction & offerings 2017 PrADS Introduction & offerings 2017
PrADS Introduction & offerings 2017
Kiran Kumar Muthyala
 
Business case 290819 finalfinal
Business case 290819 finalfinalBusiness case 290819 finalfinal
Business case 290819 finalfinal
KAYODE ADEBIYI
 
Chap002
Chap002Chap002
Chap002
Arshian Siddiqui
 
ITM business barriers.pptx
ITM business barriers.pptxITM business barriers.pptx
ITM business barriers.pptx
rajalakshmi5921
 
Selecting The Right Business Technology Solution and Business Partners
Selecting The Right Business Technology Solution and Business PartnersSelecting The Right Business Technology Solution and Business Partners
Selecting The Right Business Technology Solution and Business Partners
Proformative, Inc.
 
R a ci & innovation
R a ci & innovationR a ci & innovation
R a ci & innovation
Alan Culler
 
Integrating AI - Business Applications
Integrating AI - Business ApplicationsIntegrating AI - Business Applications
Integrating AI - Business Applications
Hal Kalechofsky
 
Selling Solutions Using a Compelling Value Proposition
Selling Solutions Using a Compelling Value PropositionSelling Solutions Using a Compelling Value Proposition
Selling Solutions Using a Compelling Value Proposition
CompTIA
 
Business Centric IT: Data Management
Business Centric IT: Data ManagementBusiness Centric IT: Data Management
Business Centric IT: Data Management
David A. Chapa
 
Engineering Perspectives on Business
Engineering Perspectives on Business Engineering Perspectives on Business
Engineering Perspectives on Business
Michael Zargham
 
Five Attributes to a Successful Big Data Strategy
Five Attributes to a Successful Big Data StrategyFive Attributes to a Successful Big Data Strategy
Five Attributes to a Successful Big Data Strategy
Perficient, Inc.
 

Similar to Healthcare Enterprise Data Model: The Buy vs. Build Debate (20)

SQL Saturday STL 2016 Presentation
SQL Saturday STL 2016 PresentationSQL Saturday STL 2016 Presentation
SQL Saturday STL 2016 Presentation
 
Best Practices in Software Vendor Selection
Best Practices in Software Vendor SelectionBest Practices in Software Vendor Selection
Best Practices in Software Vendor Selection
 
Gain Faster Closure, Lower Risk and Better Results with IBM Emptoris
Gain Faster Closure, Lower Risk and Better Results with IBM EmptorisGain Faster Closure, Lower Risk and Better Results with IBM Emptoris
Gain Faster Closure, Lower Risk and Better Results with IBM Emptoris
 
Engage Patients, Reduce Manual Processes and Drive Key Insights with Interope...
Engage Patients, Reduce Manual Processes and Drive Key Insights with Interope...Engage Patients, Reduce Manual Processes and Drive Key Insights with Interope...
Engage Patients, Reduce Manual Processes and Drive Key Insights with Interope...
 
Fundamentals Of Organization Best Practices And Templates Powerpoint Presenta...
Fundamentals Of Organization Best Practices And Templates Powerpoint Presenta...Fundamentals Of Organization Best Practices And Templates Powerpoint Presenta...
Fundamentals Of Organization Best Practices And Templates Powerpoint Presenta...
 
Lecture 8 Resources and Costs.pptx
Lecture 8 Resources and Costs.pptxLecture 8 Resources and Costs.pptx
Lecture 8 Resources and Costs.pptx
 
Agile metrics - Agile KC Meeting 9/26/13
Agile metrics - Agile KC Meeting 9/26/13Agile metrics - Agile KC Meeting 9/26/13
Agile metrics - Agile KC Meeting 9/26/13
 
Synergis60: 6 Critical Steps to Implementing Data Managment
Synergis60: 6 Critical Steps to Implementing Data ManagmentSynergis60: 6 Critical Steps to Implementing Data Managment
Synergis60: 6 Critical Steps to Implementing Data Managment
 
Stop the madness - Never doubt the quality of BI again using Data Governance
Stop the madness - Never doubt the quality of BI again using Data GovernanceStop the madness - Never doubt the quality of BI again using Data Governance
Stop the madness - Never doubt the quality of BI again using Data Governance
 
PrADS Introduction & offerings 2017
PrADS Introduction & offerings 2017 PrADS Introduction & offerings 2017
PrADS Introduction & offerings 2017
 
Business case 290819 finalfinal
Business case 290819 finalfinalBusiness case 290819 finalfinal
Business case 290819 finalfinal
 
Chap002
Chap002Chap002
Chap002
 
ITM business barriers.pptx
ITM business barriers.pptxITM business barriers.pptx
ITM business barriers.pptx
 
Selecting The Right Business Technology Solution and Business Partners
Selecting The Right Business Technology Solution and Business PartnersSelecting The Right Business Technology Solution and Business Partners
Selecting The Right Business Technology Solution and Business Partners
 
R a ci & innovation
R a ci & innovationR a ci & innovation
R a ci & innovation
 
Integrating AI - Business Applications
Integrating AI - Business ApplicationsIntegrating AI - Business Applications
Integrating AI - Business Applications
 
Selling Solutions Using a Compelling Value Proposition
Selling Solutions Using a Compelling Value PropositionSelling Solutions Using a Compelling Value Proposition
Selling Solutions Using a Compelling Value Proposition
 
Business Centric IT: Data Management
Business Centric IT: Data ManagementBusiness Centric IT: Data Management
Business Centric IT: Data Management
 
Engineering Perspectives on Business
Engineering Perspectives on Business Engineering Perspectives on Business
Engineering Perspectives on Business
 
Five Attributes to a Successful Big Data Strategy
Five Attributes to a Successful Big Data StrategyFive Attributes to a Successful Big Data Strategy
Five Attributes to a Successful Big Data Strategy
 

More from Perficient, Inc.

Driving Strong 2020 Holiday Season Results
Driving Strong 2020 Holiday Season ResultsDriving Strong 2020 Holiday Season Results
Driving Strong 2020 Holiday Season Results
Perficient, Inc.
 
Transforming Pharmacovigilance Workflows with AI & Automation
Transforming Pharmacovigilance Workflows with AI & Automation Transforming Pharmacovigilance Workflows with AI & Automation
Transforming Pharmacovigilance Workflows with AI & Automation
Perficient, Inc.
 
The Secret to Acquiring and Retaining Customers in Financial Services
The Secret to Acquiring and Retaining Customers in Financial ServicesThe Secret to Acquiring and Retaining Customers in Financial Services
The Secret to Acquiring and Retaining Customers in Financial Services
Perficient, Inc.
 
Oracle Strategic Modeling Live: Defined. Discussed. Demonstrated.
Oracle Strategic Modeling Live: Defined. Discussed. Demonstrated.Oracle Strategic Modeling Live: Defined. Discussed. Demonstrated.
Oracle Strategic Modeling Live: Defined. Discussed. Demonstrated.
Perficient, Inc.
 
Content, Commerce, and... COVID
Content, Commerce, and... COVIDContent, Commerce, and... COVID
Content, Commerce, and... COVID
Perficient, Inc.
 
Centene's Financial Transformation Journey: A OneStream Success Story
Centene's Financial Transformation Journey: A OneStream Success StoryCentene's Financial Transformation Journey: A OneStream Success Story
Centene's Financial Transformation Journey: A OneStream Success Story
Perficient, Inc.
 
Automate Medical Coding With WHODrug Koda
Automate Medical Coding With WHODrug KodaAutomate Medical Coding With WHODrug Koda
Automate Medical Coding With WHODrug Koda
Perficient, Inc.
 
Preparing for Your Oracle, Medidata, and Veeva CTMS Migration Project
Preparing for Your Oracle, Medidata, and Veeva CTMS Migration ProjectPreparing for Your Oracle, Medidata, and Veeva CTMS Migration Project
Preparing for Your Oracle, Medidata, and Veeva CTMS Migration Project
Perficient, Inc.
 
Accelerating Partner Management: How Manufacturers Can Navigate Covid-19
Accelerating Partner Management: How Manufacturers Can Navigate Covid-19Accelerating Partner Management: How Manufacturers Can Navigate Covid-19
Accelerating Partner Management: How Manufacturers Can Navigate Covid-19
Perficient, Inc.
 
The Critical Role of Audience Intelligence with Eric Enge and Rand Fishkin
The Critical Role of Audience Intelligence with Eric Enge and Rand FishkinThe Critical Role of Audience Intelligence with Eric Enge and Rand Fishkin
The Critical Role of Audience Intelligence with Eric Enge and Rand Fishkin
Perficient, Inc.
 
Cardtronics Future Ready with Oracle EPM Cloud
Cardtronics Future Ready with Oracle EPM CloudCardtronics Future Ready with Oracle EPM Cloud
Cardtronics Future Ready with Oracle EPM Cloud
Perficient, Inc.
 
Teams Summit - What is New and Coming
Teams Summit - What is New and ComingTeams Summit - What is New and Coming
Teams Summit - What is New and Coming
Perficient, Inc.
 
Empower Your Organization with Teams & Remote Work Crisis Management
Empower Your Organization with Teams & Remote Work Crisis ManagementEmpower Your Organization with Teams & Remote Work Crisis Management
Empower Your Organization with Teams & Remote Work Crisis Management
Perficient, Inc.
 
Adoption & Change Management Overview
Adoption & Change Management OverviewAdoption & Change Management Overview
Adoption & Change Management Overview
Perficient, Inc.
 
Microsoft Teams: Measuring Activity of Employees Working from Home
Microsoft Teams: Measuring Activity of Employees Working from HomeMicrosoft Teams: Measuring Activity of Employees Working from Home
Microsoft Teams: Measuring Activity of Employees Working from Home
Perficient, Inc.
 
Securing Teams with Microsoft 365 Security for Remote Work
Securing Teams with Microsoft 365 Security for Remote WorkSecuring Teams with Microsoft 365 Security for Remote Work
Securing Teams with Microsoft 365 Security for Remote Work
Perficient, Inc.
 
Infrastructure Best Practices for Teams Remote Workers
Infrastructure Best Practices for Teams Remote WorkersInfrastructure Best Practices for Teams Remote Workers
Infrastructure Best Practices for Teams Remote Workers
Perficient, Inc.
 
Accelerate Adoption for Microsoft Teams
Accelerate Adoption for Microsoft TeamsAccelerate Adoption for Microsoft Teams
Accelerate Adoption for Microsoft Teams
Perficient, Inc.
 
Preparing for Project Cortex and the Future of Knowledge Management
Preparing for Project Cortex and the Future of Knowledge ManagementPreparing for Project Cortex and the Future of Knowledge Management
Preparing for Project Cortex and the Future of Knowledge Management
Perficient, Inc.
 
Utilizing Microsoft 365 Security for Remote Work
Utilizing Microsoft 365 Security for Remote Work Utilizing Microsoft 365 Security for Remote Work
Utilizing Microsoft 365 Security for Remote Work
Perficient, Inc.
 

More from Perficient, Inc. (20)

Driving Strong 2020 Holiday Season Results
Driving Strong 2020 Holiday Season ResultsDriving Strong 2020 Holiday Season Results
Driving Strong 2020 Holiday Season Results
 
Transforming Pharmacovigilance Workflows with AI & Automation
Transforming Pharmacovigilance Workflows with AI & Automation Transforming Pharmacovigilance Workflows with AI & Automation
Transforming Pharmacovigilance Workflows with AI & Automation
 
The Secret to Acquiring and Retaining Customers in Financial Services
The Secret to Acquiring and Retaining Customers in Financial ServicesThe Secret to Acquiring and Retaining Customers in Financial Services
The Secret to Acquiring and Retaining Customers in Financial Services
 
Oracle Strategic Modeling Live: Defined. Discussed. Demonstrated.
Oracle Strategic Modeling Live: Defined. Discussed. Demonstrated.Oracle Strategic Modeling Live: Defined. Discussed. Demonstrated.
Oracle Strategic Modeling Live: Defined. Discussed. Demonstrated.
 
Content, Commerce, and... COVID
Content, Commerce, and... COVIDContent, Commerce, and... COVID
Content, Commerce, and... COVID
 
Centene's Financial Transformation Journey: A OneStream Success Story
Centene's Financial Transformation Journey: A OneStream Success StoryCentene's Financial Transformation Journey: A OneStream Success Story
Centene's Financial Transformation Journey: A OneStream Success Story
 
Automate Medical Coding With WHODrug Koda
Automate Medical Coding With WHODrug KodaAutomate Medical Coding With WHODrug Koda
Automate Medical Coding With WHODrug Koda
 
Preparing for Your Oracle, Medidata, and Veeva CTMS Migration Project
Preparing for Your Oracle, Medidata, and Veeva CTMS Migration ProjectPreparing for Your Oracle, Medidata, and Veeva CTMS Migration Project
Preparing for Your Oracle, Medidata, and Veeva CTMS Migration Project
 
Accelerating Partner Management: How Manufacturers Can Navigate Covid-19
Accelerating Partner Management: How Manufacturers Can Navigate Covid-19Accelerating Partner Management: How Manufacturers Can Navigate Covid-19
Accelerating Partner Management: How Manufacturers Can Navigate Covid-19
 
The Critical Role of Audience Intelligence with Eric Enge and Rand Fishkin
The Critical Role of Audience Intelligence with Eric Enge and Rand FishkinThe Critical Role of Audience Intelligence with Eric Enge and Rand Fishkin
The Critical Role of Audience Intelligence with Eric Enge and Rand Fishkin
 
Cardtronics Future Ready with Oracle EPM Cloud
Cardtronics Future Ready with Oracle EPM CloudCardtronics Future Ready with Oracle EPM Cloud
Cardtronics Future Ready with Oracle EPM Cloud
 
Teams Summit - What is New and Coming
Teams Summit - What is New and ComingTeams Summit - What is New and Coming
Teams Summit - What is New and Coming
 
Empower Your Organization with Teams & Remote Work Crisis Management
Empower Your Organization with Teams & Remote Work Crisis ManagementEmpower Your Organization with Teams & Remote Work Crisis Management
Empower Your Organization with Teams & Remote Work Crisis Management
 
Adoption & Change Management Overview
Adoption & Change Management OverviewAdoption & Change Management Overview
Adoption & Change Management Overview
 
Microsoft Teams: Measuring Activity of Employees Working from Home
Microsoft Teams: Measuring Activity of Employees Working from HomeMicrosoft Teams: Measuring Activity of Employees Working from Home
Microsoft Teams: Measuring Activity of Employees Working from Home
 
Securing Teams with Microsoft 365 Security for Remote Work
Securing Teams with Microsoft 365 Security for Remote WorkSecuring Teams with Microsoft 365 Security for Remote Work
Securing Teams with Microsoft 365 Security for Remote Work
 
Infrastructure Best Practices for Teams Remote Workers
Infrastructure Best Practices for Teams Remote WorkersInfrastructure Best Practices for Teams Remote Workers
Infrastructure Best Practices for Teams Remote Workers
 
Accelerate Adoption for Microsoft Teams
Accelerate Adoption for Microsoft TeamsAccelerate Adoption for Microsoft Teams
Accelerate Adoption for Microsoft Teams
 
Preparing for Project Cortex and the Future of Knowledge Management
Preparing for Project Cortex and the Future of Knowledge ManagementPreparing for Project Cortex and the Future of Knowledge Management
Preparing for Project Cortex and the Future of Knowledge Management
 
Utilizing Microsoft 365 Security for Remote Work
Utilizing Microsoft 365 Security for Remote Work Utilizing Microsoft 365 Security for Remote Work
Utilizing Microsoft 365 Security for Remote Work
 

Recently uploaded

Communications Mining Series - Zero to Hero - Session 2
Communications Mining Series - Zero to Hero - Session 2Communications Mining Series - Zero to Hero - Session 2
Communications Mining Series - Zero to Hero - Session 2
DianaGray10
 
Day 2 - Intro to UiPath Studio Fundamentals
Day 2 - Intro to UiPath Studio FundamentalsDay 2 - Intro to UiPath Studio Fundamentals
Day 2 - Intro to UiPath Studio Fundamentals
UiPathCommunity
 
MySQL InnoDB Storage Engine: Deep Dive - Mydbops
MySQL InnoDB Storage Engine: Deep Dive - MydbopsMySQL InnoDB Storage Engine: Deep Dive - Mydbops
MySQL InnoDB Storage Engine: Deep Dive - Mydbops
Mydbops
 
Call Girls Chandigarh🔥7023059433🔥Agency Profile Escorts in Chandigarh Availab...
Call Girls Chandigarh🔥7023059433🔥Agency Profile Escorts in Chandigarh Availab...Call Girls Chandigarh🔥7023059433🔥Agency Profile Escorts in Chandigarh Availab...
Call Girls Chandigarh🔥7023059433🔥Agency Profile Escorts in Chandigarh Availab...
manji sharman06
 
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdfLee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf
leebarnesutopia
 
The Strategy Behind ReversingLabs’ Massive Key-Value Migration
The Strategy Behind ReversingLabs’ Massive Key-Value MigrationThe Strategy Behind ReversingLabs’ Massive Key-Value Migration
The Strategy Behind ReversingLabs’ Massive Key-Value Migration
ScyllaDB
 
Fuxnet [EN] .pdf
Fuxnet [EN] .pdfFuxnet [EN] .pdf
Fuxnet [EN] .pdf
Overkill Security
 
Multivendor cloud production with VSF TR-11 - there and back again
Multivendor cloud production with VSF TR-11 - there and back againMultivendor cloud production with VSF TR-11 - there and back again
Multivendor cloud production with VSF TR-11 - there and back again
Kieran Kunhya
 
ScyllaDB Topology on Raft: An Inside Look
ScyllaDB Topology on Raft: An Inside LookScyllaDB Topology on Raft: An Inside Look
ScyllaDB Topology on Raft: An Inside Look
ScyllaDB
 
From NCSA to the National Research Platform
From NCSA to the National Research PlatformFrom NCSA to the National Research Platform
From NCSA to the National Research Platform
Larry Smarr
 
Guidelines for Effective Data Visualization
Guidelines for Effective Data VisualizationGuidelines for Effective Data Visualization
Guidelines for Effective Data Visualization
UmmeSalmaM1
 
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...
AlexanderRichford
 
Brightwell ILC Futures workshop David Sinclair presentation
Brightwell ILC Futures workshop David Sinclair presentationBrightwell ILC Futures workshop David Sinclair presentation
Brightwell ILC Futures workshop David Sinclair presentation
ILC- UK
 
Introducing BoxLang : A new JVM language for productivity and modularity!
Introducing BoxLang : A new JVM language for productivity and modularity!Introducing BoxLang : A new JVM language for productivity and modularity!
Introducing BoxLang : A new JVM language for productivity and modularity!
Ortus Solutions, Corp
 
TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...
TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...
TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...
TrustArc
 
Leveraging AI for Software Developer Productivity.pptx
Leveraging AI for Software Developer Productivity.pptxLeveraging AI for Software Developer Productivity.pptx
Leveraging AI for Software Developer Productivity.pptx
petabridge
 
CTO Insights: Steering a High-Stakes Database Migration
CTO Insights: Steering a High-Stakes Database MigrationCTO Insights: Steering a High-Stakes Database Migration
CTO Insights: Steering a High-Stakes Database Migration
ScyllaDB
 
QA or the Highway - Component Testing: Bridging the gap between frontend appl...
QA or the Highway - Component Testing: Bridging the gap between frontend appl...QA or the Highway - Component Testing: Bridging the gap between frontend appl...
QA or the Highway - Component Testing: Bridging the gap between frontend appl...
zjhamm304
 
Corporate Open Source Anti-Patterns: A Decade Later
Corporate Open Source Anti-Patterns: A Decade LaterCorporate Open Source Anti-Patterns: A Decade Later
Corporate Open Source Anti-Patterns: A Decade Later
ScyllaDB
 
DynamoDB to ScyllaDB: Technical Comparison and the Path to Success
DynamoDB to ScyllaDB: Technical Comparison and the Path to SuccessDynamoDB to ScyllaDB: Technical Comparison and the Path to Success
DynamoDB to ScyllaDB: Technical Comparison and the Path to Success
ScyllaDB
 

Recently uploaded (20)

Communications Mining Series - Zero to Hero - Session 2
Communications Mining Series - Zero to Hero - Session 2Communications Mining Series - Zero to Hero - Session 2
Communications Mining Series - Zero to Hero - Session 2
 
Day 2 - Intro to UiPath Studio Fundamentals
Day 2 - Intro to UiPath Studio FundamentalsDay 2 - Intro to UiPath Studio Fundamentals
Day 2 - Intro to UiPath Studio Fundamentals
 
MySQL InnoDB Storage Engine: Deep Dive - Mydbops
MySQL InnoDB Storage Engine: Deep Dive - MydbopsMySQL InnoDB Storage Engine: Deep Dive - Mydbops
MySQL InnoDB Storage Engine: Deep Dive - Mydbops
 
Call Girls Chandigarh🔥7023059433🔥Agency Profile Escorts in Chandigarh Availab...
Call Girls Chandigarh🔥7023059433🔥Agency Profile Escorts in Chandigarh Availab...Call Girls Chandigarh🔥7023059433🔥Agency Profile Escorts in Chandigarh Availab...
Call Girls Chandigarh🔥7023059433🔥Agency Profile Escorts in Chandigarh Availab...
 
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdfLee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf
 
The Strategy Behind ReversingLabs’ Massive Key-Value Migration
The Strategy Behind ReversingLabs’ Massive Key-Value MigrationThe Strategy Behind ReversingLabs’ Massive Key-Value Migration
The Strategy Behind ReversingLabs’ Massive Key-Value Migration
 
Fuxnet [EN] .pdf
Fuxnet [EN] .pdfFuxnet [EN] .pdf
Fuxnet [EN] .pdf
 
Multivendor cloud production with VSF TR-11 - there and back again
Multivendor cloud production with VSF TR-11 - there and back againMultivendor cloud production with VSF TR-11 - there and back again
Multivendor cloud production with VSF TR-11 - there and back again
 
ScyllaDB Topology on Raft: An Inside Look
ScyllaDB Topology on Raft: An Inside LookScyllaDB Topology on Raft: An Inside Look
ScyllaDB Topology on Raft: An Inside Look
 
From NCSA to the National Research Platform
From NCSA to the National Research PlatformFrom NCSA to the National Research Platform
From NCSA to the National Research Platform
 
Guidelines for Effective Data Visualization
Guidelines for Effective Data VisualizationGuidelines for Effective Data Visualization
Guidelines for Effective Data Visualization
 
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...
 
Brightwell ILC Futures workshop David Sinclair presentation
Brightwell ILC Futures workshop David Sinclair presentationBrightwell ILC Futures workshop David Sinclair presentation
Brightwell ILC Futures workshop David Sinclair presentation
 
Introducing BoxLang : A new JVM language for productivity and modularity!
Introducing BoxLang : A new JVM language for productivity and modularity!Introducing BoxLang : A new JVM language for productivity and modularity!
Introducing BoxLang : A new JVM language for productivity and modularity!
 
TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...
TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...
TrustArc Webinar - Your Guide for Smooth Cross-Border Data Transfers and Glob...
 
Leveraging AI for Software Developer Productivity.pptx
Leveraging AI for Software Developer Productivity.pptxLeveraging AI for Software Developer Productivity.pptx
Leveraging AI for Software Developer Productivity.pptx
 
CTO Insights: Steering a High-Stakes Database Migration
CTO Insights: Steering a High-Stakes Database MigrationCTO Insights: Steering a High-Stakes Database Migration
CTO Insights: Steering a High-Stakes Database Migration
 
QA or the Highway - Component Testing: Bridging the gap between frontend appl...
QA or the Highway - Component Testing: Bridging the gap between frontend appl...QA or the Highway - Component Testing: Bridging the gap between frontend appl...
QA or the Highway - Component Testing: Bridging the gap between frontend appl...
 
Corporate Open Source Anti-Patterns: A Decade Later
Corporate Open Source Anti-Patterns: A Decade LaterCorporate Open Source Anti-Patterns: A Decade Later
Corporate Open Source Anti-Patterns: A Decade Later
 
DynamoDB to ScyllaDB: Technical Comparison and the Path to Success
DynamoDB to ScyllaDB: Technical Comparison and the Path to SuccessDynamoDB to ScyllaDB: Technical Comparison and the Path to Success
DynamoDB to ScyllaDB: Technical Comparison and the Path to Success
 

Healthcare Enterprise Data Model: The Buy vs. Build Debate

  • 1. Thank you for your time and attention today. Please visit us at Perficient.com Healthcare Enterprise Data Model: The Buy vs. Build DebateNovember 18, 2014 facebook.com/perficient twitter.com/Perficient_HC linkedin.com/company/perficient
  • 2. Perficient is a leading information technology consulting firm serving clients throughoutNorth America. We help clients implement business-driven technology solutions that integrate business processes, improve worker productivity, increase customer loyalty and create a more agile enterprise to better respond to new business opportunities. About Perficient
  • 3. 3 •Founded in 1997 •Public, NASDAQ: PRFT •2013 revenue $373 million •Major market locations: •Allentown, Atlanta, Boston, Charlotte, Chicago, Cincinnati, Columbus, Dallas, Denver, Detroit, Fairfax, Houston, Indianapolis, Minneapolis, New York City, Northern California, Oxford (UK), Philadelphia, Southern California, St. Louis, Toronto, Washington, D.C. •Global delivery centers in China and India •>2,200 colleagues •Dedicated solution practices •~90% repeat business rate •Alliance partnerships with major technology vendors •Multiple vendor/industry technology and growth awards Perficient Profile
  • 4. BUSINESSSOLUTIONS Business Intelligence Business Process Management Customer Experience and CRM Enterprise Performance Management Enterprise Resource Planning Experience Design (XD) Management Consulting TECHNOLOGYSOLUTIONS Business Integration/SOA Cloud Services Commerce Content Management Custom Application Development Education Information Management Mobile Platforms Platform Integration Portal & Social Our Solutions Expertise
  • 5. Healthcare Practice Strategic Partners Experts in Consumer-Driven Healthcare Technology HEALTH PLAN PROVIDER Connected Health Business Intelligence and Analytics Interoperability & Integration Information Exchange Regulatory Compliance Solutions & Services CONSUMERS Select Clients Global Delivery Centers/Offshore Delivery Domestic Delivery Center
  • 6. David MeintelDirector of Healthcare, Perficient David has 20 years of experience in data warehousing and analytics in a wide range of industries. Focusing this experience in the last 4 years exclusively on the healthcare industry, David has worked with a large number of provider and health plan organizations to assist them in better leveraging their data assets. Our Speaker
  • 7. Agenda •Brief look at the challenge in healthcare causing the need for a data warehouse •Evolution of the solution to the challenge •Discuss key factors in the decision to buy or build your data model •Reality!
  • 8. The Challenge Regulatory Compliance Population Health Management ReduceRe-Admissions Service-Line Management Claims Analysis Other…
  • 9. Evolution of the Solution Organizations move from siloedsolutions to consolidated operational data stores to enterprise warehouse… Operational
  • 10. Enterprise Data Model Data structures to hold all of the key decision making information for the enterprise integrated across subject areas such as: Clinical Claims Financial Operational
  • 11. Enterprise Data Model •Architected to be extensible •Phased implementations •The “single version of the truth” for the organization •Have evolved greatly over the years
  • 12. Enterprise Data Model (example) Core Provider Content Payer Content Supply Chain Content Core Master Data Provider Patient Data Payer Patient Data Supply Chain Data •Person •Patient •Organization •Facility •Distributors •Product Pricing Tier •Price Activation •General Ledger •Accounts Payable •Accounts Receivable •Contract (Supplier) •Member (Insured) •Subscriber •Member Enrollment •Member Enrolled PCP •ADT Event (Hospitalization) •Biospecimen •Encounter •Finding •Lab Result •Payer •Regulatory Agencies •Contact •Groupings/Hierarchies •Unified Standard Codes •Supplier •Wholesaler •Distributor •Unified Standard Codes •Practitioner •Unified Standard Codes •Groupings/Hierarchies •Reference Data •Allergy •Diagnosis •Discharge Summary and Instruction •Imaging •Procedure •Surgery •Survey •Appointment •Bill •Bill Encounter •Care Team & Role •Contract (Patient) •Contracts •Disease Management •Encounter •Episode •EOB •COB •Provider Network •Campaign •Premium Payments •Location •Order •Patient Life Event •Patient Medications; Includes Order, Dispense, Administration, Formulary •Problem (chief complaint, etc.) •Event (General) •Exposure & Lifestyle •General Ledger •Location •Patient Satisfaction •Payroll •Immunization •Product Master •Policy •Coverage •Benefit •Claims (all types) •Claims (Adjustments) •Purchase Order •Invoice •Product Item Master •Purchase History •Contracts •Suppliers •Wholesalers •Unified Standard Codes •Groupings/ Hierarchies •Ambulatory Encounter (date, time, type) •Ambulatory procedure •Ambulatory Test order •Ambulatory Result •Ambulatory Diagnoses •Ambulatory Billing •Wellness Targets •HEDIS Measures •PQRS Measures •ACO Measures •Quest Measures •Agreement •Money Item •Financial Transaction •Claim Recovery
  • 13. Buy vs. Build Key areas to consider: –How experienceplays into the equation –How is time to value impacted –How is riskfactored in with the decision –How easy is it to integrateother solutions –How the decision to build vs. buy can impact your internal team –How will costbe impacted
  • 14. Experience Key points to consider –Buy •Experience comes bundled in •Top sellers of EDM have years of experience both technical and healthcare •Clinicians, technologists and business analysts to bridge the two –Build •Does your current staff have the requisite experience? •If not, you will likely need to hire or contract someone who does •In addition to IT resources with technical skills, this will require plenty of support from your line of business resources (clinicians, etc.) Many of our customers have existing data warehouses that we end up replacing and most were built internally.
  • 15. Time to Value Key points to consider –Buy •Large portion of design is already done for you •Customizations willneed to be done regardless of what your vendor tells you •Often times you can implement in a phased approach to reduce time to value even more –Build •The more experience your team has the faster this will go •Often time subject area driven to reduce time to value •Only build what you need, can reduce time as well Implementing something that is already designed, will be faster than designing and building it yourself.
  • 16. Risk Key points to consider –Buy •Lower typically due to –Level of experience and resources that went into it –Refined over time (customer experiences incorporated) –Repeatable so potential issues are known •Sometimes monolithic in nature can cause scope to grow larger than needed –Build •Higher typically due to –Organizations typically don’t do this lots of times, so no experience –Common mistakes are likely to be encountered due to lack of experience Manage scope and leverage experience to mitigate risk.
  • 17. Integration / Accelerators Key points to consider –Buy •Standardized models encourage accelerators •Integration points need to be standard and often times take a lowest common denominator approach, which sometimes causes slight increase in effort –Build •If you build it yourself, you have the ultimate in flexibility to design interface the way you want them •Integrating with outside solutions will likely take more effort due to no standard approach for vendors to build to When standards exist, so does common integration points and pre-built accelerators…don’t reinvent the wheel.
  • 18. 18 Integration / Accelerators
  • 19. Staff Impact Key points to consider –Buy •Many key decisions on design are made for you •Staff doesn’t have to be experts in designing model which lessens the pressure in early phases •Learning curve for potential new technologies –Modeling tool, ETL tool, Metadata management etc •Consulting assistance for peeks in need often more effective than hiring a large team –Build •Every decision is yours to make, increases pressure •Analysis paralysis is a common issue, trying to make perfect •Leverage strengths of team in choosing technologies Make sure staff is setup for success, not failure.
  • 20. Cost Key points to consider –Buy •Initial purchase price may seem high •Total cost of ownership generally lower •Other accelerators also help to reduce total cost of ownership –Build •Pay as you go •Paying to reinvent the wheel Buying might cost more up front, but will save money over time.
  • 21. Summary Experience–Leverage experience of people who have done what you are trying to do Time to Value–Implementing a model that is already designed will be faster Risk-Managing scope and leveraging experience will mitigate risk Integration / Accelerators–Don’t reinvent the wheel Staff Impact–Know your staff and make sure they are set up for success Cost–Buying a model might have higher initial costs, but building will cost more over time
  • 22. Reality, Buy and Build Buy –Data model •Leverage the experience of many •Don’t reinvent the wheel –High value initial use case •Physicalize required portions of the data model •Connectivity to your source systems to populate model •Gain quick value for investment made in model Build –Custom analytics for your use case •Leverage existing investments in tools •This is what your end user with interact with the most –Additional use cases •Follow patterns from initial use case •Leverage experience and iterate
  • 24. Questions? Connect with Perficient -Upcoming events & webinars -On-demand webinars -White papers & perspectives-Thought leadership David Meintel David.Meintel@Perficient.com | Phone: 646.457.4865
  翻译：