Getting Down to Business DAMA Chicago Schaumburg, IL June 21, 2017 Getting Down to Business Integrating the Data Lake Within an Analytics and Business Intelligence Ecosystem Susan Swanson Health Care Service Corporation (HCSC)

Agenda Introduction and Background Requirements Gathering and Implementation Communications Proving It Out Controlled Self-Service Business Intelligence Summary and Conclusion 6/21/2017

By the Numbers 6/21/2017

Today’s Health Care Insurance Landscape An evolving health insurance environment Focusing on quality, cost effective health care Addressing affordability of care Managing health care outcomes Tracking health care costs Using technology and data to help navigate the health care system Leveraging data with predictive analytics and social network analysis 6/21/2017

Data Lake Within the Data Ecosystem - Data Architecture Layers Raw Common Format Consuming and Sandbox Metadata Data Governance Hadoop Data Lake Landing Enterprise Application 6/21/2017

Requirements Gathering and Implementation Partnering with business data stewards and analytics and reporting resources guides delivery to focus on desired benefits and outcomes. Elicitation 1 Validation and Acceptance “The quality of the product, and the benefit it brings, will be remembered long after the project budget and deadlines have been forgotten about.” Adrian Reed  6/21/2017

Requirements - Elicitation Capture Capabilities and Needs Provided at a high-level from the business Tied to business goals and desired outcomes Drill Down to Detailed Requirements Business, Technical, and Non-Functional Data Quality and Certification Classify and Prioritize Breadth over depth to identify possibilities Manage scope, iterate, and evolve 6/21/2017

Requirements - Validation and Acceptance Traceability of Requirements To Design and Code To Testing Data Quality and Certification Audit and Balancing Framework Validate and Quality Certification Framework Acceptance and Success Criteria Defined by the Business Community Validated through User Acceptance Testing 6/21/2017

Communications Engage and Partner The Retrospective Collaboration with the business user community is needed, ranging from prioritizing the right thing to be working on to clarifying data quality, access, and usage. Engage and Partner 1 The Retrospective “When the trust account is high, communication is easy, instant, and effective.” Stephen R. Covey  6/21/2017

Communications – Engage and Partner With the Business Organization Working with Business Stewards and SMEs Leveraging Governance Teams and Committees Status Meetings and Escalations For status updates and transparency For decision making and directional guidance Fit Within the Data Ecosystem Alignment to data access and availability plans Alignment to data strategy and roadmaps 6/21/2017

Communications – The Retrospective Retrospectives conducted at selected intervals are used to: Recognize successes Reflect Obtain feedback Actionable improvements are identified for adoption going forward. Areas of Focus Worked Well? Not So Well? Improvements Update Best Practices Update Approach Data lake reflection 6/21/2017

Proof of Concept and Pilot Proving It Out Embedding Prove It and Show Me techniques within the development lifecycle provides more flexibility and ensures early business feedback. Proof of Concept and Pilot 1 User Acceptance Test “There’s two possible outcomes: if the result confirms the hypothesis, then you've made a discovery. If the result is contrary to the hypothesis, then you've made a discovery.” Enrico Fermi  6/21/2017

Proving It Out – Proof of Concept and Pilot Unknowns OR Well Defined Scope? POC for discovery and emerging toolset Pilot for fixed scope and anticipated delivery Impact Design OR Build? POC findings provide inputs to design Pilot acts as Phase 1 of a framework build out Findings OR Patterns to Reference? POC findings answer How To questions Pilot provides reference patterns and templates 6/21/2017

Proving It Out – User Acceptance Test Embed into Development Framework Works well for modular design Occurs after several sprints are completed Multiple Iterations Increasing levels of detail per iteration To provide data to users early and often Ideal Fit for Agile and Iterative Methods Aligns well with more frequent test cycles Provides feedback to development 6/21/2017

Controlled Self-Service Business Intelligence Controlled means providing reliable, consistent, and secure data for analytics and reporting, with enough data preparation to ensure trust. Prepare and Enable 1 Control and Lend Order “While the self-service aspect of modern BI platforms offers more freedom and power to business users, it doesn’t remove the responsibility of organizations to manage and maintain these analytics systems over time.” Brent Dykes  6/21/2017

Controlled Self-Service BI – Prepare and Enable Data Preparation Cleanse, integrate, and organize Profile, aggregate, and summarize Tools and Their Support Multiple tools (including familiar ones) Tool Coach and Helper as an assistant Metadata Inventory Catalog of data lake contents Metadata Repository for dictionary and lineage 6/21/2017

Controlled Self-Service BI – Control and Lend Order Data Standardization Business rules for data consistency Defined, vetted, and pre-built components Data Quality Management Quality rules to define measurement Rules execution and publishing of results Data Governance Partnership Business resources for rule definition Technology resources for configuration Data Lineage for data moving through zones 6/21/2017

Summary Leverage requirements validation techniques to align to business goals Frequent status and check in communications show transparency and establish trust Use Prove It and Show Me approaches to deliver more flexibility and earlier feedback Controlled self service depends on business collaboration and data management to be successful Align to Goals Establish Trust Early Feedback Business Partners 6/21/2017

Conclusion Applying data management disciplines and best practices to the data lake build out increases and enhances its effectiveness as an integral component of the data ecosystem. Collaborating with business data resources is essential to a successful implementation journey. 6/21/2017

Susan Swanson Susan_Swanson@bcbsil.com 6/21/2017