Agile Data and Information Management in the Age of Health Care Reform

Presented by Cisco Perin and Rodger Karl at Empower 2016 on May 5, 2016

Learn how taking a data-centric approach and utilizing big data tools and practices will position your health plan to react more quickly to the changing landscape and accomplish more timely and sophisticated solutions improving member and provider satisfaction. A modern enterprise data strategy is critical in the digital age, where the depth of operational sophistication and management of scale can be an existential problem for growing health plans. Fundamentally, data should serve the strategic imperatives of the business. In the end, you are only as agile as your data platform.

Transcript

1. Cisco Perin, SVP, Technology & Information Strategy, Health Integrated Rodger Karl, Founder and Principal, InfoSense Solutions Hosted By
2. Big Data & Health Care Reform: Creating Agility and Opportunity through New Data Architecture Presented by Cisco Perin & Rodger Karl
3. 01 Strategy Development: How to Enable the 5 Vs Discussion: The Current State of Data Management in Health Care The Thesis: Creating Agility and Opportunity through New Data Architecture 02 03 2 Session Overview
4. The Thesis: Creating Agility and Opportunity through New Data Architecture 3
5. Hypothesis: A Data Centric Approach is the Solution “A modern enterprise data strategy is critical in the digital age, where depth of operational sophistication and management of scale can be existential problems for growing enterprises... Fundamentally, data should serve the strategic imperatives of a business. In the end you are only as agile as your data platform.” 4
6. Health Care CIO’s Challenges Demand Big Data Solutions  Increasing pressure on IT-related budgets, CIOs, and IT departments to help manage costs and deliver value  It takes time and resources to efficiently work with health care data and by embracing modern tool sets, a data democracy can be realized more quickly.  Real Business Problems: – It takes too long to map and ingest data – It takes months to get access to data to do any kind of analysis – You need developer to write code to ingest and map the data – You need developers to model data and build data bases for any use – Data Provenance and Quality “I don’t trust the data” – It takes weeks or months to get Analytics working on data 5 Big Data Varied Volumes Velocity Veracity Value
7. Health Care Big Data – The 5 Vs 6 • the size and depth of data Volume • the complexity of data structures. Variety • The speed at which the data is produced and consumed. Velocity • The quality of the data representation to real events. Veracity • The business impact that data can provide to your business operations and products. Value
8. Discussion: The Current State of Data Management in Health Care
9. Current State – Tools and Skills Data Engineering, Analyst, Scientist 8 * By 2018 the U.S. could face a shortage of 1.5 million people who know how to leverage data analysis to make effective decisions - McKinsey Global Institute
10. A Paradigm Shift in Data Management 9 Big Data Don’t Want Emerging Trends Volume increased costs, barriers to scale, severe limitations to leverage new data sources Less Engineering allows for more volume. Less is More! Variety Increased data engineering, increased pipelines and complexity. No distillation of data. Simplify , Simplify , Simplify! The more adapted data is , the less adaptable it becomes Abstract , Don’t Adapt! Velocity produce untimely response to business operations, reduced to passive or reactive management of real issues It’s not the daily increase but daily decrease. Hack away at the unessential. Delivery data at the right time! Veracity the consequence of unintended use causing slow and costly remediation , ad hoc workarounds, poor or incorrect insights. Analysis is only as good as the data on which it is based. Detect and correct! Value to use statistics as a drunken man uses lamp posts – for support rather than for illumination. Things get done only if the data we gather can inform and inspire those in a position to make a difference. Democratize your Data Value!
11. Current State – Emerging Trends 10 Enable Transition – from traditional Data Engineering to Data Analysis and Data Science Scale – solution where engineering ‘less is more.' Provide an at scale solution to the 5Vs with economies that also scale. Be Prepared. Widen the Stream – Let the machines do the housekeeping. A learning system in which the data janitors are streamlined as much as possible whilst the system is beholden to your data producers and consumers for a wider data flow. Democratize Your Data Ecosystem – Enable solutions where self service and ease of use are the principal drivers to data production and consumption. Allow business use cases to develop organically.
12. The Strategy: How to Enable the 5 Vs
13. First: Develop a Data Strategy That Will Scale Out Data Strategy Method Identify Strategic Imperatives Define Business Objectives Define Data Requirements Identify Gaps In Current Systems & Technology Map Objectives And Requirements To Use Cases Rationalize Use Cases Into Workloads Project Action Plan and Roadmap 12
14. What’s needed The first 3 Vs (Varied, Volumes, and Velocity) 13 The components and capabilities needed achieve the first three Vs. Data Ingestion - Edge Nodes Inbound Data Lake - Staging History– Archive Data Warehouse - Information Hub Data Egress – Analytics , Semantic Tier , Outbound
15. The Other 2 Vs (Veracity & Value) 14 How does your team know? Data Awareness Providing self-service solutions to acquire , analyze, and integrate data from a variety of data sources into a cohesive whole, including modeling content, and establishing context for data quality rules. An information hub fabricated and maintained by use of data steward user driven tools Self Service Metadata Management (The RNA) • Data Source Identification • Data Mapping to the Hub or Data Egress • Data Quality Rules Library • Detection Notification (Anomalies) • Correction (Manual or Auto-Correction) Data Analysts are really just Junior Data Scientists
16. Analytics & Business Intelligence Services Core Data (Star) Analytic Core Core Services SEMANTIC INFO VAULT HUB Master Data User Sandboxes INGEST & ARCHIVE User Sandboxes SUPER MART Operations (Synch) Analytic Insights Metadata Workflow Mapping Data Quality Reference Data Master Data Enrichment Data From Ingest to Applicable Business Case How does it work? 15 Discovery & Insight Operational Analytics Ops Supported Patterns 1) Operations - Client Engagement using Supported Data Exchange Formats 2) Operational Analytics – Aggregate or Analytics based upon known strata 3) Innovation and Discovery – New use cases being discovered and tested on unknown content or contextual data
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