April 17, Ronald Layne Manager, Data Quality and Data Governance

Transcription

1 Ensuring the highest quality data is delivered throughout the university providing valuable information serving individual and organizational need April 17, 2015 Ronald Layne Manager, Data Quality and Data Governance

2 Agenda Defining Data Governance Why is Data Governance Important? A quick review of metadata Data Governance at GW Measuring Data Governance Sustaining Data Governance Page 2

3 Page 3 What makes up Data Governance?

4 Data Governance Defined Data Governance is the Execu=on and Enforcement of Authority Over the Management of Data and Data- Related Resources - - Robert Seiner Data Governance is the formula=on of policy to op=mize, secure and leverage informa=on as an enterprise asset by aligning the objec=ves of mul=ple func=ons - - Sunil Soares Enterprise Data Governance is the set of processes by which structured and unstructured data assets are formally managed and protected by people and technology to guarantee commonly understood trusted and secure data throughout the enterprise. - - Mike Ferguson Data Governance is the prac=ce of making strategic and effec=ve decisions regarding an organiza=on s informa=on assets. - - Anne Marie Smith Data Governance is the exercise of decision making and authority for data- related malers. It s a system of decision rights and accountabili=es for informa=on- relate processes, executed according to agreed- upon models which describe who can take what ac=ons with what informa=on, and when, under what circumstances, using what methods. - - Gwen Thomas Making Data Transparent and Trusted. - - My defini=on Page 4

5 Page 5 Why do we need Data Governance?

6 Why is Data Governance Important? The university s ins=tu=onal data is a valuable asset and must be maintained and protected as such. It is vital to have accurate and trusted data in order to make sound decisions at all levels of the ins=tu=on. Data Governance helps to provide transparency into data and results in confidence among faculty, staff, and management of the university to trust and rely on the data for informa=on and decision support.

7 Benefits of Data Governance Data lineage and auditability Improved data transparency and quality Consistent definitions Appropriate use of information Easier sharing of information Accountability for information use

8 A quick review of metadata The definition of Metadata is dependent on who you talk to. A technical Metadata user might refer to entities, attributes, source lineages, and mappings A business Metadata user might refer more to business terms, standard calculations, metrics, or policy standards. Information that describes various facets of an information asset to improve its usability throughout its life cycles.

9 Data Governance at GW Data governance at GW focuses on improving data quality, protecting access to data, establishing business definitions, maintaining metadata, documenting data policies and setting the foundation for analytics and reporting. Roles Responsibili=es Partners HR Job Descrip=ons Execu=ve Support Engaged Metadata Repository BI Integra=on Process Workflow Report Catalog Data Quality Master Data Integra=on Policy The What Process The How Page 9

10 People Operating Model Execu=ve Data Trustees Endorses the program and approves policy related decisions. Strategic Tac=cal Opera=onal Data Governance CommiBee Provides oversight and decision making authority over data related issues. Data Stewards Proposes data policies and standards to the Data Governance CommiLee. Data Custodians execu=on and maintenance according to the data policies and standards. Page 10

11 Data Governance Roles No one person, department, division, school or group "owns" data, even though specific units bear some responsibility for certain data. Several roles and responsibilities govern the management of, access to and accountability for institutional data. People Data Steward Responsible Data Trustee Accountable Data Custodian Supportive Subject Matter Expert Consulted Data Users Informed A Data Steward is a person that defines, produces or uses data as part of their job and has a defined level of responsibility for assuring quality in the definition, production or usage of that data. Data Stewards responsibilities include: Developing and maintaining data classification policies. Developing, implementing, and managing data access policies. Ensuring that data quality and data definition standards are developed and implemented. Resolving stewardship issues and data definitions of data elements that cross multiple functional units. Data Trustees are defined as institutional officers, (i.e. Vice Presidents, Vice Provosts, Deans, Chancellors, etc.) who have authority over policies and procedures regarding business definitions of data, and the access and usage of that data, within their delegations of authority. Each Data Trustee appoints Data Stewards for their specific Subject Area Domains. Data Custodians are system administrators responsible for the operation and management of systems and servers which collect, manage, and provide access to institutional data. Data Custodian responsibilities include: Maintaining physical and system security and safeguards appropriate to the classification level of the data in their custody. Maintaining Disaster Recovery plans and facilities appropriate to business needs and adequate to maintain or restart operations in the event systems or facilities are impaired, inaccessible, or destroyed. Managing Data User access as prescribed and authorized by appropriate Data Stewards. Following data handling and protection policies and procedures established by appropriate Data Stewards. A subject-matter expert (SME) are those individuals that support and consult the business and the technical professionals with their knowledge of business operations and the data that is necessary to operate and perform analysis. These people can be Business Analysts, Reporting Analysts, Data Architects, Data Modelers, and Project Management. Data users are university units or individual university community members who have been granted access to institutional data in order to perform assigned duties or in fulfillment of assigned roles or functions within the university; this access is granted solely for the conduct of university business Page 11

12 Everyone has a seat at the table People The Data Governance CommiBee meets once a month to review data quality issues, discuss proposed business terms, review policies and discuss other ins=tu=onal data related topics. This commilee is comprised of func=onal data stewards from across all func=ons and departments of the university. Academics Advancement Finance Research Human Resources Services Page 12 Page 12

13 People Data Governance Office The DGO facilitates and supports data governance and data stewardship activities, including: Keeping track of data stakeholders and stewards Providing liaisons to other disciplines and programs, such as data quality, compliance, privacy, security, architecture and IT governance Collecting and aligning policies, standards and guidelines from these stakeholder groups Facilitating and coordinating data analysis and issue analysis Facilitating and coordinating meetings of data stewards Collecting metrics and success measures and reporting on them to data stakeholders Articulating the value of data governance and stewardship activities Providing centralized communications for governance-led and data-related matters Maintaining governance records Administering metadata repository (Data Governance Center) We herd Cats Page 13

14 People Policies and Rules Data Policies provide a broad framework regarding how decisions should be made regarding data. Business Rules Translate policies into verifiable business rules or guidelines in order to implement and enact policies into the daily operations. Data Quality Rules ensure that data quality rules are aligned to the business rules, their scope, priority, and exception scenario s are correctly documented. Page 14

15 Process - Data Sharing Agreements People Data Sharing Agreements formalize the intended use of data. Data Sharing agreements govern the content, access method, security, and frequency for data maintained by the Granting Organization provided to the Requesting Organization. Data Sharing Agreements define: The Who - Intended audience who will see the data The What - The specific data being shared (Data Fields, Tables / Views) The When Frequency the data needs to be refreshed from the source The Where The granting system The Why The business justification on why the data is needed. The How - The mechanism for sharing the data (DB Links, Replication, Web Services, etc.) Data Stewards are responsible to ensure their data is used properly. A DSA is created when an application (or a repository) requires the use of a Data Steward s data. Page 15

16 People Technology Technology is a key enabler to maturing a data governance program. The following are areas where technology plays a part in advancing Data Governance: q Business Glossary q Metadata Management q Data Profiling q Data Quality Management q Reference Data Management q Policy Management q Data Modeling q Data Integration q Analytics & Reporting q Business Process Management Page 16

17 People Data Governance Center The Data Governance Center is the single source of truth of all our data governance and stewardship activities. It is used to manage all business definitions and KPIs, support our data stewards in their day to day activity, provide traceability between business and technical assets, policies and rules. It is a vital step toward achieving our vision of commonly understood consistent, trusted and high-quality data throughout the institution. Page 17

18 Measuring Data Governance If it s not measured, it doesn t get done. You cannot manage what you do not measure. Business Value Metrics Measures the business value that Data Governance adds. Typically have a cost associated with them. Reduced time spent cleansing and fixing data Reduced time arguing about what data means or how it is calculated? Improved early warning of data quality issues through profiling Operational Metrics A compilation of metrics that indicate the effectiveness of Data Governance. Involvement (Attendance, Usage, Ownership, Participation) Measures of results achieved (Terms added, issues resolved, policies approved) Data Quality (Accuracy, Completeness, Consistency, Uniqueness) Maturity (Assessment of how various facets of Data Governance have evolved) Page 18

19 Sustaining Data Governance Data Governance is not a one and done Data is constantly changing Data needs to be monitored Business rules need to be maintained Terms need to be updated and added Data Models need to be built and updated Policies and procedures need to be created and modified It is important to have ü Ongoing communication ü Ongoing training ü Periodic reviews and audits ü Discipline Page 19

20 Page 20 Questions?

21 Thank You Contact Info: Ron Layne GW Data Governance Web Site hlp://it.gwu.edu/data- governance