UCI Data Management and Governance Project

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To realize and be consistent with the UCI IT Principle - "Data are critical institutional assets" ( http://www.oit.uci.edu/consolidation/committee/uci-it-principles.php ),  a road map for campus data management and governance are needed. While non existent today, an incremental improvement can be achieved by leveraging the IT consolidation to look at data more comprehensively than previously possible.   Data Warehousing and Decision Support will become most effective once data management improvement is implemented and in reality will be difficult to achieve without it.  Moreover,  the replacement of core administrative and research systems such as the legacy financial ledger and purchasing systems with Kuali FS, Kuali Coeus Research Administration, the UCPath replacement of PPS, and the planned new Student Information System offer a unique and timely opportunity for starting a much needed campus data management program.  

Why is a data management program needed? Why an Enterprise Data Warehousing and Decision Support Initiative?

1. Business units across UCI are primarily concerned with entering and tracking data to meet their specific needs.  Consequently, enterprise data is frequently held in disparate applications across multiple departments within the university.  Often, data is copied from location to location due to the need to integrate this data to be more meaningful.  For example, student, financial, and employee data are downloaded and used in many shadow systems across UCI for local business needs.  An undesired outcome is a buildup of redundant, inconsistent, and often contradictory data, housed in isolated departmental applications from one end of the organization to another.

2. The confusion caused by this disjointed network of applications leads to poor customer service, redundant effort, occassionally inaccurate reporting, and ultimately, a higher cost of doing business.

3. To address the spread of data – and eliminate silos of university information – some universities have implemented enterprisewide data governance and data management programs, which attempt to codify and enforce best practices for data management across the organization.

4. Data management would help consolidate and integrate data from multiple sources, including student data, teaching/course load, grants, facilities/space, financials, purchasing,  and payroll in support of campus wide decision making and related information needs such as reporting, analysis, data drill-down, data visualization, and planning.    
5. Data management would provide information that is well-organized, easy to obtain, secure, accurate, timely, consistent, integrated, and appropriately detailed so that people throughout the campus -- staff, faculty, researchers, and executive-level administrators -- will be better able to assess their needs, set priorities, understand the impact of change, and fulfill their program responsibilities more efficiently.  
6. Data governance would provide a source of data for the campus that represents an agreed upon "truth", using a pre-agreed upon common interpretation of the data and setting minimum data quality standards; thereby reducing misinterpretation or misunderstanding of data and potential errors.
7. A data management program would allow the university to utilize the existing people, business policies and technology to achieve more effective data quality policies across multiple departments.  It would reduce the redundant project and programming effort taking place across the UCI campus today to identify, catalog, organize, scrub/clean, and document data that is of common interest;  leveraging FTE resources in a more effective manner and achieving synergies between units that are otherwise fragmented across UCI but working towards similar goals.

In order to start this process, the following road map or steps are suggested: 

1. Obtain buy-in from CIO and a few key campus stake holders, including the Office of Planning and Budget, and get at least 1 dedicated FTE resource.
2. Identify and inventory/catalog core campus systems that produce data of common interest.  Document respective data owning department(s), individuals, and data stewards.  Examples:  Student Enrollment Data - Registrar/ Elizabeth Bennett;  Student Admissions - Deborah Decker;  Employee Payroll Data - Accounting and Fiscal Services/Brenda Mathias...
3. Create a process that supports orderly release and tracking of restricted data that is subject to security regulation (HIPAA, SB1386, etc).
4. Research and adopt a data governance methodology.  Data Flux and DAMA offer some useful methodologies to consider.
5. Organize a data quality or data governance group and appropriate campus leadership structure.
6. Adopt a standard format to collect information necessary to catalog data and present data governance decisions to leadership.
7. Conduct an assessment of the data collection and management practices and data quality of systems that produce core common campus data.
8. Enter common data attribute names, descriptions, and business rules into a campus data dictionary.  Document data aliases.   Use Gartner's definition of Metadata from August, 2010
9. Document a data flow diagram per subject area, documenting where the data is transmitted to and how often.
10. Assess what it means to implement a "zero data defect" policy for this data set.   Some data sets may need to be officially certified for accuracy.
11. Implement a data change management strategy and policy, per data governance methodology selected.
12. Create a submission and queuing mechanisms that allows submission of decision support reports or data extracts to help campus staff in their daily business.  This process must be transparent and prioritization must be handled in a way that allows people to depend on timely and accurate responses to data questions.

The data governance maturity requirements targetting the UCI campus, based on the DataFlux White Paper on the Data Governance Maturity Model are separated into three areas, each with the goals: 

People

1. Data governance has executive-level sponsorship with direct CIO and Executive Leadership support.  Executive-level decision-makers view data as a strategic asset.  Management understands and appreciates the role of data governance – and commits personnel and resources.
2. Business users take an active role in collecting and delivering quality data.
3. Data stewards emerge as the primary implementers of data management strategy and work directly with cross-functional teams to enact data quality standards
4. A data quality or data governance group works directly with data stewards, application developers and database administrators
5. Organization has “zero defect” policies for data collection and management

Policies

1. New initiatives are only approved after careful consideration of how the initiatives will impact the existing data infrastructure
2. Automated policies are in place to ensure that data remains consistent, accurate and reliable throughout the enterprise
3. A service oriented architecture (SOA) encapsulates business rules for data quality and identity management
4. Real-time activities and preventive data quality rules and processes are implemented
5. Data governance processes are built into the Systems Development or System Acquisition Life Cycle
6. Goals shift from problem correction to prevention

Technology

1. Data quality and data integration tools are standardized across the organization
2. All aspects of the organization use standard business rules created and maintained by designated data stewards for core data sets.
3. Data is continuously inspected – and any deviations from standards are resolved immediately.  Ongoing data monitoring helps the campus maintain data integrity
4. Data models capture the business meaning and technical details of common data elements
5. A data stewardship group maintains common data definitions and business rules
6. Service-oriented architecture becomes the enterprise standard
7. More real-time processing is available and data quality functionality is shared and reviewed across different operational solutions

Risk and Reward

1. Risk: Low. Master data tightly controlled across the enterprise, allowing the organization to maintain high-quality information about employees, student, research, facilities, financials, and assets.
2. Rewards: High. Campus data practices can lead to a better understanding about an organization’s current business landscape – allowing management to have full confidence in all data-based decisions

A diagram to trigger thinking about data management is available at Campus Data Management