Improving data governance; how can health informatics practitioners help gain stakeholder support?

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1 Improving data governance; how can health informatics practitioners help gain stakeholder support? Sarah Humphreys HISA Data Governance Conference March 2012

2 How to gain stakeholder support Private sector case study Framework Steps to build a business case

3 Common Data Governance issues: Case study 1: Private Sector example Background: Case study based on a global financial services organisation that has a presence in over 170 countries as well as Australia In Australia the company is predominantly involved in general and life insurance business Problem: The business relies heavily on information with a high dependency on accurate and complete data Inconsistent information reported against key performance indicators Inconsistent external regulatory and internal reporting Increasing data management costs and resourcing Increasing manual effort for data collection and manipulation in order to support decision making Information not trusted for decision making

4 Data Governance Business Case Framework BUSINESS CASE 1. Identify improvement objectives 2. Scope3 key areas: Stakeholder Analysis Current Landsca pe Identify pain points Root Cause Analysis Assess Maturity Organisatio n Policies & Procedures Architectur e & Standards 3. Develop a roadmap 4. Identify resourcing needed 5. Quantify Costs and Benefits Engage stakeholders

5 Power Business Case 1. Stakeholder Analysis High power, low interest High power, high interest Stakeholder Analysis Focus quadrant Target quadrant High interest, low power Low power, low interest Influencer quadrant Interest

6 Business Case 2. Current Landscape Current Landsca pe Current Landsca pe Evaluate current Information Management landscape: people processes, systems information Case Study Information: Required for regulatory reporting & performance management People: High resourcing levels involved in data collection and reporting activity. Little resourcing available for value-add insights / decisions, focus on consolidating numbers manually Processes: Reporting is manual and dependent on individual s knowledge and expertise to compile information. Monthly regulatory reporting cycles are very resource intensive Systems: Reporting and Business intelligence systems used haphazardly and inconsistently to meet information needs

7 Business Case 3 & 4. Pain points & root cause analysis Identify pain points Conduct root cause analysis Case Study Pain points Inconsistent data definitions for key performance indicators Inconsistent external and internal reporting from multiple data sources Inadequate data protection and security of customer data Increasing, unsustainable manual effort Root cause analysis Standards inadequate Standards and processes inadequate policies and procedures not well developed processes and systems not sustainable

8 Business Case 5. Maturity Assessment Ungoverned Awareness of problems but little action taken Little to no knowledge of Data Quality (DQ) Management Ad-hoc React to issues with limited standards or consistency Limited awareness of importance of DQ Limited business sponsorship / involvement Reactive Essential elements of data governance implemented (governance people, process & technology enablers) Initiatives in place to remediate DQ issues Proactive 3 Consistent governance embedded in the organisation Coordinated management of DQ issues Benefits monitored and realised Mature Governance 4 Business information can be leveraged as a strategic asset Organisation can make decisions quickly based on accurate information Leading Operational Case Study Governance

9 Business Case BUSINESS CASE 1. Identify objectives 2. Scope 3 key area Organisatio n Policies & Procedures Architectur e & Standards 3. Identify resourcing needed 4. Develop a roadmap 5. Quantify Costs and Benefits Objective: 1. Improve KPI reporting accuracy and timeliness 2. Reduce risks associated with manually intensive processes Case Study

10 Business Case Scope three key DG areas & resourcing Organisation Executive Sponsor Data Governance Council Data Stewardship working group Policies and Procedures Scope the work required to: 1.Define and agree polices and procedures 2.Define and agree a monitoring and reporting framework 3.Identify data issues that need to be addressed as a priority Standards & Architecture Scope the work required to: 1.Implement & maintain standards for high priority information areas 2.Standardise information architecture Data stewards / Data custodians Case Study ½ day per month for custodians. 2 hour meeting per month for council members 6 months 1 x FTE 6 months 1 x FTE

11 Data Governance Roadmap Establish Data Governance Council Benefits: Data governance council and data stewardship committees embedded as BAU activities with monthly meetings occurring Establish Policies and Procedures Benefits: Data security and protection policy agreed and implementation commenced Case Study Establish Architecture and Standards Benefits: Data definitions for key measures agreed and implemented. This has led to consistent reporting and understanding of key performance indicators

12 Quantify costs vs benefits Costs: Data governance committee & steward resourcing commitments Full time resourcing required to implement data governance Benefits: Tangible and intangible Reduce the pain points identified in the analysis phase Increased maturity equals increased decision making capabilities Reduce costs associated with processes identified during analysis Ultimately increased ability to improve healthcare & services

13 Key considerations Engage with stakeholders prior to and during the business case development Focus on stakeholders with low interest / high power rating Work with them to identify problem information management areas relevant to their role that data governance can assist Data governance is not a one off exercise business case covers the kick off only Ongoing journey to establish and maintain mature data governance there will likely be future business cases required

14 NSW Health Patient Activity: Information Management NSW ahead of it s time decentralised Data Warehouse infrastructure implemented 10 + years ago To be replaced with a centralised Enterprise Data Warehouse Project is currently focussed on building technical infrastructure Data governance in early stages of implementation Data governance organisation being setup in line with framework Standards being established

15 Thankyou Any questions?

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