How Hadoop and a Modern Data Platform Can Enable Transformation in Healthcare

Briefing for 2016 Hadoop Summit

Beata Puncevic

Puncevic.Beata@Gmail.com

https://www.linkedin.com/in/beatapuncevic

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Big Data and it’s Value Potential in Healthcare

Reduce the cost of healthcare by $450 billion

Improve the quality of care of through precision medicine and targeted programs

Prevent disease and improve quality of life through preventative medicine and mobile health

Improve effectiveness of medicine through improved R&D productivity

Make the world a better place by curing disease, predicting epidemics and improving access to care

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Big Data and Reality in Healthcare

With a few exceptions, established healthcare players are still just experimenting with big data through:• POCs• Pilots• Simple workloads• Ad hoc analytical use cases

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Plan for Today

1. Industry landscape and challenges healthcare companies face when adopting a modern data platform

2. Strategies for overcoming these challenges

3. What we did and what to prepare for

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• Composition of customer base

• Expectations and preferences

• Distribution channels

Customer Expectations

Integrated Health Management

Regulatory Impacts

Evolving Competitive Landscape

• Patient centered, value based coordinated care

• Outcomes based or alternative reimbursements

• Risk optimization across the health care supply chain

• Federal minimum value and affordability standards

• Evolution of new products and networks

• Margins driven by admin costs instead of MLR

• New entrants from other industries are changing the face of care practices

• Industry consolidation

Industry Context

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Challenges Healthcare Organizations Face

A complex and often outdated current state

One size fits all processes and methodologies

Conservative culture

Low margins

Resources and skill sets

Immature enterprise architecture disciplines

These conditions make it especially difficult to implement new capabilities while keeping operations running in what often times is a real time business

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Building a Business Case

Tier 3 – Data Drives the BusinessGenerate revenue

Enable a competitive edge

Tier 2 – Advanced Use CasesPredictive models

Prescriptive analytics

Tier 1 – Essential Use Cases Data FeedsReporting

Running the business

Tier 0 –Foundation• Quality, trusted data• Agile and scalable architecture• Data governance• Efficient data management• Data usage capabilities

Approach

Tactics

Focus on the solving the problems most prominently recognized as a priority:1. Driving cost out of essential data

solutions2. Enabling pursuit of analytics insights3. Building for agility and adaptability

• Obtain centralized funding for initial and subsequent implementations

• Form a team focused on developing an ROI framework

• Measure value using before/after comparisons

• Secure senior business sponsorship

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Planning for Success

Start with Data Governance

An effective data governance discipline is critical for a big data platform. Plan on building support from senior level data owners, business glossary definitions, frameworks and processes for decision making

Architect for the Long Term

Focus not on meeting 1 or 2 use cases, but on enabling use cases through a platform that is agile, scalable, reliable and can flex with business needs. This requires a well formed perspective on your target state and architectural patterns

Adoption includes Transition

Value cannot be fully realized until the current state is migrated and retired to the new platform. Plan on developing a transitional architecture across all domains, partnering with portfolio management, measuring sunk costs and ROI, and focusing on change management

Plan to Pay Up for the Sins of the Past

Are your data quality, master data management, metadata management capabilities mature? If not, those gaps will become problematic when building a big data platform and should be addressed

It is Not Just About Technology

Work with an experienced partner, but establish a training approach to build the skills you need. Reach out to HR early on to adjust policies, grades etc. Hire the right people to drive changeProcesses and roles will need to adjust. Plan on developing a new operating model and processes

Consider The Whole Platform

A big data platform alone is not enough, plan on rounding out the architecture with data management, data integration and data access capabilities. Be aware of current state limitations and any dependencies in the bottom part of the stack

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Case Study: Program Goals

Establish a vision for data as an enterprise asset and guiding principles

Operationalize a governance model for prioritization and data-related decisions

Build a modern, agile data architecture that enables necessary data capabilities

Implement by choosing strategic business use cases to quickly enable value

Outcome• The organization is unified around a

common perspective and provides a set of guardrails for data decisions

• The organization obtains the right skills and adopts the most optimal organizational structure

• Data decisions are made consistently enabling reduction of complexity, re-use and cost reduction

• Technical platforms, processes and architecture are nimble, cost effective and enable advanced analytical capabilities

• Transformation of our information infrastructure begins right away, rapidly creating business value

Recommendations

Refresh the operational model to align skills and clarify roles

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Case Study: Foundational Capabilities

Foundational Capabilities

1. Rapid data ingestion and integration

2. Proactive data quality management

3. Common definitions in a business glossary

4. Person and Member 360 data foundation

5. Trusted source of enterprise data6. Data as a service data exchange7. Event-driven and real-time data

management 8. Search and collaboration9. Self service reporting and

dashboards10. Advanced analytics

Desired Business Outcomes

• Administrative cost reduction resulting from improved data quality and re-use of enterprise data

• Faster and cheaper project implementations

• Rapid data integration

• Fast and cost effective data exchange with third parties

• Agile and adaptive solutions and capabilities

• Enablement of business analysts to significantly reduce their data preparation efforts

• Analytical data insights that enable new business opportunities and processes

• Reduced complexity of our data and technologies

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Case Study: Reference Architecture

Data Ingestion For Purpose Data Zone

Data is processed and published with applied

joins, predefined relationships, and

derivations for purpose

Refined Data ZoneData is cleansed, standardized, and

mastered into books of record

Metadata Layer

ELT/ETLOrchestrate the

transfer of data, and capture lineage

Transactional

Data Analysis

Reporting and BI

Data MasteringRaw Data ZoneData is in the native file format in which it

was received

Secure File

Messaging

SOA ClientsExternal Sources Data Access Layer

HL7

Internal Sources

File Storage

Messages

Data Ingestion Layer

Message StorageStream

Ingestion

SQL Result Ingestion

Mainframe Copybook

File Copy

Relational DBSource

Entity ManagementRelationship Mgmt.

Data Enrichment Layer

Entity Relationship Matching

Entity ResolutionRelation Linking

ODS

Data Services

Data Feeds

Inquiry MicroServices

Scheduler

Advanced Analytics

Analytical and Distribution

For Purpose Data Files

Data Marts

Application Extract

Application API

System/ Event Logs

External Database3rd Party Extract

Device Data/ Data Stream

Social Media

Hosted Application

Technical Metadata

Metadata Management

Business Glossary

Data Catalog

Business

Metadata

Data Profiling

Reference Data

Table Storage

Data Job Scheduling

Books of Record

API Gateway

Event Publish MicroServices

Message Queue

SecurityAPI Catalog

RoutingLoad Balancing

Monitoring

Secure FTP

Analytic/Data Science

User

InternalApplications

Portals

ExternalService/Event Consumers

ExternalVendors

(Batch Files)

Standard Data Feeds

Custom Data Feeds

Governance

Consumers

AnalyticWorkspaces

Cubes In-Memory ReportConsumer

MobileConsumer

Business/Data Analysts

Data Quality

Data Search

Data Sharing and Annotation

Data Governance

Web Logs

Data Validation

Audit, Balance and ControlData Quality

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Data Access Layer

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Capabilities1. Rapid data

ingestion and integration

2. Proactive data quality management

3. Common definitions in a business glossary

4. Person and Member 360 data foundation

5. Trusted source of enterprise data

6. Data as a service data exchange

7. Event-driven and real-time data management

8. Search and collaboration

9. Self service reporting and dashboards

10. Advanced analytics

Q&A

Beata Puncevic

Puncevic.Beata@Gmail.com

https://www.linkedin.com/in/beatapuncevic