McKesson Corporation · NYSE:MCK
McKesson — Conference Presentation (May 2014)
32 slides from McKesson's conference presentation, May 2014
Slide 1: BAD Data – Who Suffers?
BAD Data – Who Suffers? End the pain Krista J. Casey, MBA Business Advisor McKesson Connected Care and Analytics
Slide 2: When bad data occurs, who pays?
When bad data occurs, who pays? • I.T. department usually not the first to hear about clinical initiatives/quality efforts • Analytics needs to be focused on both clinical and business initiatives – Business pays the price of bad data; emphasis should be on getting analytics as close to the business
Slide 3: Effective use of analytics quote
Effective use of analytics is ―not something you can buy from a vendor; it's an organizational and cultural value that has to grow and mature.‖ - James E. Gaston Sr. Director for Clinical and Business Intelligence at HIMSS Analytics 3 McKesson Corporation Confidential and Proprietary
Slide 4: How do we proceed?
How do we proceed? Start with master data management strategy, 1 built on an enterprise data warehousing plan. 2 During the shift to enterprise analytics Governance moves out of I.T.; becomes an enterprise-level function
Slide 5: What is Enterprise analytics?
What is ―Enterprise‖ analytics? • The core of ―enterprise‖ analytics is to create a technological and management infrastructure to get an enterprise-wide, accurate understanding of performance and develop a source of truth. • Enterprise analytics is a matter of survival.
Slide 6: Enterprise Analytics
Enterprise Analytics • Most providers/networks are still doing retrospective analysis • Can’t move into a predictive model for simulations and forecasting – Core of risk analysis – Enables organizations to understand knowledge management life cycles and how analytics is a corporate asset
Slide 7: Analytics and Information Governance
Analytics and Information Governance What Is It? Cultural behavior of responsible information management Formal framework to establish accountability/stewardship of an organization’s information assets – Like ―plant, property and equipment‖ data/information/analytics is a business asset. Provi
Slide 8: Information governance definition quote
“Information governance is the specification of decision rights and an accountability framework to encourage desirable behavior in the valuation, creation, storage, use, archival and deletion of information. It includes the processes, roles, standards and metrics that ensure the effective and effici
Slide 9: Analytics/Information Governance: Why Is It Necessary?
Analytics/Information Governance Why Is It Necessary? • Continue to foster and promote a supportive organizational culture • Create and govern documentation and standard data definitions allowing for consistent reporting • Establish and maintain report request process • Assign accountability for ste
Slide 10: Analytics/Information Governance: Why NOW?
Analytics/Information Governance Why NOW? Governance drives the roadmap for Analytics • Healthcare environment has shifted: – Regulatory data collection and submission – Need to manage the data, analytics – Accountability for data is CRITICAL • Analytics now requires: – A multi-disciplinary approach
Slide 11: Analytics/Information Governance: Who Owns It?
Analytics/Information Governance Who Owns It? • Analytics/information technology is an organizational resource, not a departmental resource • Executive Sponsor(s) and governing committees help communicate, maintain message throughout organization • Governance provides multi- disciplinary ownership o
Slide 12: Elements of Analytics/Information Governance
Elements of Analytics/Information Governance • Define standards for data sources, data elements • Establish ownership of data quality management function • Streamline/automate data collection and reporting efforts • Optimize use of existing information technology for ROI • Build out analytics/inform
Slide 13: Governance quotes from Gartner and HBR
“No company will fail due to a lack of information governance, but much of what the business wants to achieve won't work as well without it.” Data Source: ―Building a Business Case for Data Governance.‖ White Paper. 2012. New York, NY. Gartner, Inc. “…Companies that compete on analytics devote extra
Slide 14: What has been YOUR experience?
What has been YOUR experience? What have been the barriers? 14
Slide 15: Common Barriers
Analytics/Information Governance Common Barriers • Lack of executive-level sponsorship, involvement • Organizational politics • Lack of adequate organizational structure to support analytics – Inadequate staffing – Ill-defined reporting relationships – Lack of access to information • Multiple silo-e
Slide 16: High Performing Organization Characteristics
High Performing Organization Characteristics • Organizational strategies, goals are specific, well documented • Actionable metrics established for each major area – Volume/market penetration – Finance – Process quality – Patient care outcomes • Target values identified for each metric • Use analytic
Slide 17: Leadership alignment is critical
High Performing Organizations have organizational structures in place Leadership Leadership Executive Sponsorship alignment is critical Structures and Staffing Effective Performance Management
Slide 18: Analytics Culture
Analytics Culture* Data — Organization considers data to be a strategic resource; it is actively managed and the quality of data is maintained through an active process. Data are integrated across the enterprise. Enterprise — Analytics is function which is owned by the enterprise rather than being o
Slide 19: Key Analytics Cultural Questions
Key Analytics Cultural Questions 1 Who/What is the executive sponsor for analytics? – Advocates for analytics, assures adequate resources, models analytic decision-making across the enterprise Who/What makes strategic decisions regarding analytics e.g., what are key targets, priorities for data deve
Slide 20: Key Analytics Cultural Questions
Key Analytics Cultural Questions 4 Are metrics clearly defined and understood? – Standard data sources and definitions defined and utilized by the organization – Eliminate redundant or conflicting measures 5 How are analysts organized; to whom/what do they report? – Are they silo-ed within different
Slide 21: Governance Structure
Governance Structure Establish an oversight model that best fits your organization
Slide 22: General Recommendations
General Recommendations • One size does not fit all • Model should be appropriate for the organization • Leverage existing strengths; address significant gaps • Governance is ongoing and an iterative process • Evaluate structure periodically – Adapt to meet evolving needs of the organization – Revie
Slide 23: Governance Model
Governance Model Strategic Oversight Executive Executive Committee Sponsor(s) Strategic Oversight Performance Analytics Operational Management Oversight Tactical Oversight Committee Enterprise Departmental Staff Performance Tactical Execution Analytics Director Application Financial Clinical User Su
Slide 24: Strategic Oversight
Strategic Oversight Executive Committee involvement • Establish & communicate strategy • Sponsorship/Chair tactical oversight • Analytics is one piece of this Committee’s focus • Enable, Empower & Energize analytics discipline 24
Slide 25: Governance Model
Governance Model Tactical Oversight Executive Executive Committee Sponsor(s) Strategic Oversight Performance Analytics Operational Management Oversight Tactical Oversight Committee Enterprise Departmental Staff Performance Tactical Execution Analytics Director Application Financial Clinical User Sup
Slide 26: Tactical Oversight Scope
Tactical Oversight Scope • Subcommittee of existing Executive Steering Committee • Sponsor/Stakeholder-driven – Appropriate representation from across the organization • Multi-disciplinary: Comprised of departments that use and/or generate data – Finance – Business Operations (Managed Care, Govt. Re
Slide 27: Tactical Oversight Activities
Tactical Oversight Activities • Realize the organization’s strategic vision for analytics • Drive accountability toward performance targets • Prioritize analytics initiatives • Standardize data sources and data definitions • Establish accountability for data quality management • Resolve organization
Slide 28: Governance Model
Governance Model Tactical Execution Executive Executive Committee Sponsor(s) Strategic Oversight Performance Analytics Operational Management Oversight Tactical Oversight Committee Enterprise Departmental Staff Performance Tactical Execution Analytics Director Application Financial Clinical User Sup
Slide 29: Tactical Execution
Tactical Execution • Core project team comprised of departmental staff whose primary focus is to carry out daily analytics activities including: – Content design and development – Support for operationalizing data flows – Data validation – User support – Application maintenance • Ideally the core te
Slide 30: Assess Your Current Structure
Assess Your Current Structure • What governing structures, committees are already developed, active? – What is their objective or focus? – What are their roles and responsibilities? • How does the information you manage support your strategic goals? • How are your analysts aligned for the success of
Slide 31: Next Steps
Next Steps • Determine a governance model which fits your organization • Establish membership for each level/committee • Develop roles and responsibilities for each level/committee • Plan for and convene initial meeting of each level/committee • Engage Enterprise in progression of analytics • Initia
Slide 32: Questions and Feedback?
Questions and Feedback? Krista Casey, MBA, Business Advisor Krista.Casey@McKesson.com Interested in Test Driving the McKesson Analytics Explorer™ tool? Visit www.mckesson.com/explorertestdrive.com to learn more today!