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Angela Copeland



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    RF01 - Foundational Methodologies (ID 7)

    • Event: e-Health 2019 Virtual Meeting
    • Type: Rapid Fire Session
    • Track:
    • Presentations: 2
    • Coordinates: 5/27/2019, 10:30 AM - 11:30 AM, Area 5
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      RF01.02 - CCO’s Journey to Enterprise Metadata Management (ID 462)

      Angela Copeland, Data & Analytics Governance, Cancer Care Ontario; Toronto/CA

      • Abstract

      Purpose/Objectives:
      To enable Cancer Care Ontario’s (CCO) mandate supporting Ontario government on cancer and kidney care systems, and key health priorities such as wait times on health services, CCO collaborates with healthcare partners to improve the performance of our health systems by driving quality, accountability, innovation, and value. Metadata documentations were inconsistent, incomplete, and scattered across muiltiple artifacts, thus has been identified as a foundational pillar of enterprise data governance practice to deliver CCO’s mandate using data and analytics capabilities. Implementing Enterprise Metadata Management (EMM) ensures data users can access consistent supplemental documentation across clinical domains, understand the underlying meaning of the data they use or may want to use, leading to better trusted information for decision making.


      Methodology/Approach:
      EMM approach has been developed by defining and implementing a policy, guideline, procedural manuals, processes, and identification of roles and responsibilities (R&Rs) that center around a work cycle. To enable EMM practice adoption, CCO has introduced technology to support the implementation and maintenance activities to keep metadata current, complete, and correct at all times. Guideline establishes enterprise R&Rs, standardized repeatable and scalable process steps, and standardize templates to gather metadata, resulting in robust and maintainable EMM repository. Procedural manuals are established to ensure data asset specifics are captured. Customized sessions are conducted to ensure data stewards and users understand benefits of this work and how it can positively impact their work. Data Stewards receives close mentorship to ensure metadata content gathered reflect EMM standards. Technical teams standardizes the approach to link between technical and business metadata across data assets, a single scalable data model is used to capture business metadata across all data assets, and the technology supports organization wide access to the metadata. metadata work cycle.png


      Finding/Results:
      EMM implementation improves CCO data users’ understanding of the underlying meaning of CCO data, reducing unnecessary time to determine information accuracy, enable identification and resolution of conflicting information, thus increasing users’ trust and confident use of the data. People, process, and technology are all required to ensure the EMM practice is sustainable. Having commitment from all levels of organization (from executive team to junior analysts) to undertake this initiative is critical. By ensuring stakeholders are included in the process development and refinement, they feel they are part of this journey. The technologies meet current needs while also scalable to meet future requirements. This initiative may change how users interact with information to understand data; ongoing change management support is recommended to assist users through this transformation.


      Conclusion/Implications/Recommendations:
      Enterprise metadata management is required for organizations looking to realize value by leveraging its data and analytics assets


      140 Character Summary:
      CCO’s Journey to Enterprise Metadata Management is essential to transforming CCO to an insight driven organization.

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      RF01.04 - Data & Analytics Governance at CCO – Enabling Actionable Insights (ID 475)

      Angela Copeland, Data & Analytics Governance, Cancer Care Ontario; Toronto/CA

      • Abstract

      Purpose/Objectives:
      Cancer Care Ontario (CCO) is respected for its analysis and reporting of data to inform decision-making in the cancer and renal systems, as well as Ontario’s Wait Time Strategy. Over the years, the number of data assets collected and stored by CCO have increased substantially, and analytics is used across the organization. What didn’t occur was the establishment of standardized practices to support data & analytics usage. Each lines of business developed their own data management processes and analytics practices to meet their requirements. In 2016, CCO recognized an enterprise approach to data management, analysis and reporting of data was required to ensure on-going sustainability, continued growth and innovation within the data & analytics space.


      Methodology/Approach:
      Data & Analytics (D&A) Governance is a multidisciplinary approach that applies business management principals to the life-cycle of our data & analytic information assets. To support this philosophy, CCO embarked on a journey to create an Enterprise D&A Governance department with the key responsibility to set authorities, accountabilities and controls to formalize and consistently guide the management of enterprise data & analytics assets. A D&A governance framework has been developed that includes policies supported by guideline and procedural manual. An overarching D&A management guideline is drafted for key governance domains: Architecture, Data Quality, Metadata, Master Data, Concept/Methodology management, Security & Privacy, Lifecycle Management that describes principles and practices to be consistently performed on CCO’s data and analytics assets. The guideline describes the processes and stewardship model to effectively collect, process, provision, evaluate and archive CCO’s data assets and to effectively manage the information and analytics assets produced by internal analytics teams. Procedural manuals are data asset specific controls that provide step by step instructions to assist staff in implementing the various policies, standards and guidelines. A D&A governance structure is in place to approve guidelines, policies and standards; review consistently for changes in enterprise D&A artifacts; bring forward enterprise-wide data & analytics issues and act as a champion to formalize the practice within their business areas. The advisory forum ensures D&A priorities align with CCO’s strategy and are accountable to management committee. The community of practice (tactical groups) contribute to the development and maintenance of guidelines and help identify continuous improvement opportunities but are not part of the formal governance structure.


      Finding/Results:
      CCO’s data & analytics governance provides a coordinated approach to manage data and analytics assets in the most efficient way. Standard data governance and business processes reduce duplicate data management efforts and improve data understanding among analytics teams. Defining consistent analytics methodologies will standardize analytics concepts used across the organization which will lead to consistent analytical reporting at enterprise level. Integrated Data & Information stewardship model helped establish clear and consistent enterprise accountabilities and practice expectations for data management and analytics teams across CCO.


      Conclusion/Implications/Recommendations:
      Robust Data & Analytics governance will ensure CCO has reliable, high quality and trustworthy data available that will enable business users generate actionable insights.


      140 Character Summary:
      Provides an overview of CCO’s Data & Analytics Governance implemented enterprise-wide to manage, control and have oversight over CCO’s data and analytics assets