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

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

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

  1. 1. 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
  2. 2. 2 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
  3. 3. 3 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
  4. 4. 4 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
  5. 5. 5 • 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
  6. 6. 6 Challenges Healthcare Organizations Face A complex and often outdated current state One size fits all processes and methodologies Conservative culture Low margins Resources and skill sets Immature enterprise architecture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
  7. 7. 7 Building a Business Case Tier 3 – Data Drives the Business Generate revenue Enable a competitive edge Tier 2 – Advanced Use Cases Predictive models Prescriptive analytics Tier 1 – Essential Use Cases Data Feeds Reporting 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 solutions 2. Enabling pursuit of analytics insights 3. 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
  8. 8. 8 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 change Processes 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
  9. 9. 9 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
  10. 10. 10 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 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 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
  11. 11. 11 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 Zone Data is cleansed, standardized, and mastered into books of record Metadata Layer ELT/ETL Orchestrate the transfer of data, and capture lineage Transactional Data Analysis Reporting and BI Data MasteringRaw Data Zone Data is in the native file format in which it was received Secure File Messaging SOA Clients External Sources Data Access Layer HL7 Internal Sources File Storage Messages Data Ingestion Layer Message Storage Stream Ingestion SQL Result Ingestion Mainframe Copybook File Copy Relational DB Source Entity Management Relationship Mgmt. Data Enrichment Layer Entity Relationship Matching Entity Resolution Relation 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 Database 3rd Party Extract Device Data/ Data Stream Social Media Hosted Application Metadata Management Business Glossary Data Catalog Data Profiling Reference Data Table Storage Data Job Scheduling Books of Record API Gateway Event Publish MicroServices Message Queue Security API Catalog Routing Load Balancing Monitoring Secure FTP Analytic/ Data Science User Internal Applications Portals External Service/Event Consumers External Vendors (Batch Files) Standard Data Feeds Custom Data Feeds Governance Consumers Analytic Workspaces Cubes In-Memory Report Consumer Mobile Consumer Business/Data Analysts Data Quality Data Search Data Sharing and Annotation Data Governance Web Logs Data Validation Audit, Balance and Control Data Quality 1 2 3 4 5 6 7 8 Data Access Layer 9 10 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 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
  12. 12. Q&A Beata Puncevic Puncevic.Beata@Gmail.com https://www.linkedin.com/in/beatapuncevic

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