SOPHI A System for Observation of Populous and Heterogeneous Information Data Governance Plan. Version 1.0

SOPHI A System for Observation of Populous and Heterogeneous Information Data Governance Plan Version 1.0

DSBA-SOPHI Data Governance Plan 2

Table of Contents Executive Summary 1 1. Understanding Data Governance 2 1.1 What Data Governance Is 2 1.1.1 What Data Governance Isn t 2 1.2 Why Data Governance Is Needed 2 1.2.1 Management as a program, not as a project 3 1.2.2 Measurable goals 3 1.2.3 Planning 5 1.2.4 Areas of Responsibility 5 1.2.5 Expertise 7 1.2.6 Observatory Management Tools 7 1.2.6.1 DSBA Website Portal 7 1.2.7 Acceptance of Change 7 1.3 Implementation of Data Governance 8 1.3.1 Functional and Organizational Infrastructure 8 1.3.2 Technical Infrastructure 8 1.3.3 Policies and Procedures 8 1.3.3.1 Data Governance Policy 9 1.3.3.2 Data Governance Procedure 9 1.3.4 The DSBA Data Governance and Metadata Manager 9 1.3.5 The Metadata Observatory 10 1.3.6 The Data Quality Aspect of Data Governance 10 1.3.6.1 The Data Quality Issue Log 10 2. The Management of Data Governance 12 2.1 Overview 12 2.1.1 Stakeholder Involvement 12 2.1.2 Executive Council 13 2.1.3 Strategic Data Governance Steering Committee 13 2.1.4 Operations Group 13 2.1.5 Project Operations and Oversight Groups 14 2.1.6 Specific Roles Data Stewards 14 2.1.6.1 Data Definition Stewards 14 2.1.6.2 Data Production Stewards 14 2.1.6.3 Data Usage Stewards 15 DSBA-SOPHI Data Governance Plan 3

3. The Process of Data Governance 16 3.1 High Level Plan and Milestones 17 3.2 On-Going Data Governance 17 DSBA-SOPHI Data Governance Plan 4

Document History Version 1 Initial Review by stakeholders 10/10/14 DSBA-SOPHI Data Governance Plan 5

DSBA-SOPHI Data Governance Plan 6

Executive Summary Data management is the exercise of guidance over the management of data assets and the systems making these assets available. Data governance refers to the overall management of the availability, usability, integrity, and security of data as employed within an enterprise. A sound data governance program includes a dedicated decision making council, a defined set of best practices and procedures, and a plan to execute and review these procedures. In practical terms, this means putting personnel, policies, procedures, and organizational structures in place to ensure that data is accurate, consistent, secure, and available in order to accomplish the mission of the Data Science and Business Analytics (DSBA) program. Effective data governance makes the DSBA program as well as associated faculty and industry partners more efficient by saving money, optimizing resource usage, allowing re-use and novel correlation of diverse data, and supporting advanced data analytics. However, data governance requires more than collaboration amongst a few IT staff members supporting individual project siloes and associated Investigators. It requires participation and commitment of IT and project management, as well as senior-level executive sponsorship and active cooperation between educational and research communities of interest. Through assigned responsibilities and agreed upon rules of engagement, a formalized yet dynamic data governance program enables the DSBA to effectively and confidently manage data assets and technologies. With regard to the DSBA SOPHI (System for Observation of Populous and Heterogeneous Information) data governance is planned, managed, and implemented through a three-tiered structure: The Executive Council provides strategic direction, ensuring that data governance efforts address all relevant and mission-critical needs of the DSBA and the University of North Carolina at Charlotte. The Strategic Data Governance Steering Committee (Strategic Committee) defines plans and policies to implement guidance from the Executive Council. The Strategic Committee prioritizes data governance efforts and communicates with stakeholders, users, and other communities of interest. The Operations Group implements plans and policies of the strategic committee and analyzes and resolves any technical or tactical problems that arise. This committee identifies data stewards and data compliance needs/personnel to oversee data lifecycle requirements. Clear and ongoing communication is paramount for the success of the data governance program. To realize success with this data governance effort, management bodies and implementation teams must inform stakeholders what actions are underway and why, must inform all relevant communities of interest about how data governance will benefit them, and must listen to stakeholders and communities of interest to incorporate their ideas and feedback into the SOPHI data governance model. Input and feedback make governance efforts more effective in achieving mission-critical goals and is vital for successful data governance. DSBA-SOPHI Data Governance Plan 7

1. Understanding Data Governance 1.1 What Data Governance Is Data governance is a component of data management that can be defined in several ways. The CIO Magazine says "Management is the decisions you make, governance is the structure for making them." One source 2 defines it as "Data governance refers to the organizational bodies, rules, decision rights, and accountabilities of people and information systems as they perform [data] information-related processes." Depending on their specific needs, different organizations will choose different management structures to implement data governance. It is less important to follow a particular organization chart than it is to ensure that data governance management makes data: Reliable Consistent Complete Easily available to those with a legitimate need for it Unavailable to those without a legitimate need or authorization for it These goals should guide the DSBA in planning and managing its data governance program. 1.1.1 What Data Governance Isn t Understanding what data governance is not can help focus on what it is. In particular, data governance is not: Change management Data cleansing or extract, transform and load data (ETL) Data warehousing Database design Data governance applies to each of these disciplines but is not included in any of them. 1.2 Why Data Governance Is Needed Historically, data has been collected and managed primarily at the level of individual departments and individual research groups for their own needs. Each group has developed procedures, data formats, and terminology that fit its unique situation and preferences. In addition, each group has often procured storage hardware and computing infrastructure to fit its unique situation and preferences. Today, however, the need to strategically allocate budgets and the increasing importance of data science and advanced analytics to the mission of UNC-Charlotte require programs and initiatives such as the College of Computing and Informatics and the DSBA to report on their activities at the enterprise level. This means that we must: Migrate data and processes from legacy systems Integrate and synchronize data from independent research/departmental silos DSBA-SOPHI Data Governance Plan 8

Reconcile inconsistent technology and data support practices through standardize lifecycle management Transparently communicate our processes, procedures and capabilities. Data governance makes it possible to fulfill these requirements. As a component of data management, data governance provides and enforces enterprise-wide data standards, common vocabulary, common audit and utilization reporting, and the development and use of standardized procedure. The benefits of data governance include enterprise standardization for data and systems, the ability to make use of merged data for additional knowledge discovery, and increased leverage when dealing with external data suppliers. Formalized data governance will enable CCI and the DSBA to more efficiently integrate, synchronize and consolidate the data and technology resources of independent research and educational groups, strategically focus technology expenditures, exchange data services with other groups and collaborative partners in a controlled format, and manage technology change throughout data and project lifecycles. 1.2.1 Management as a program, not as a project The management of data across an enterprise relies on commonly agreed-upon data definitions and practices. Data governance defines processes and procedures for reaching this goal. The DSBA will manage data governance as a program rather than as a series of disconnected, one-off projects. Program management is a best practice for data governance. Program management differs fundamentally from project management. Project management focuses on the achievement of immediate tasks with specifically allocated resources and time. Program management, on the other hand, manages multiple related tasks, each of which makes its own contribution to overall strategic goals. Program management allows the data governance team to use work from earlier projects in later projects, avoid duplicated effort, and ensure that all the program s projects work smoothly together in support of desired strategic goals. 1.2.2 Measurable goals Measurable goals are essential to monitor the effectiveness of the data governance program, just as they are essential in other areas of management. Some authorities see measurable goals as part of the definition of data governance. For example, Jeanne Ross and Peter Weil of the Massachusetts Institute of Technology (MIT) say that governance should ensure that decisions match company-wide objectives by establishing mechanisms for linking objectives to measurable goals. 3 Setting measurable goals is not enough however: the DSBA must choose the right goals to measure. Anything an organization measures will tend to improve sometimes, at the expense of other things that the organization does not measure. For example, if a DSBA-SOPHI Data Governance Plan 9

manufacturing plant measures how many parts workers produce per minute but pays no attention to defects or worker attrition, it will get an increase in all three factors one of them desirable and two undesirable. Defining and using measurable goals requires applying the more general discipline of business performance management (BPM) to data governance. The following figure shows an outline of the process. Figure: Defining data governance goals and measuring their achievement. Two key steps can help identify the right goals to measure: 4 1. Identify and define value metrics linked to the goals of data governance, such as increased data reliability/consistency, increased accessibility and/or access control, improved audit/compliance reporting. 2. Identify and define additional analysis metrics linked to the processes of data governance and to possible negative side effects of monitoring the value metrics. For instance, how many projects utilize service encapsulation or the advanced analytics capabilities the DSBA supports. The operations group assisted by faculty project directors and other stakeholders and in collaboration with University Research Computing, the CCI Technology Solutions Office, UNC-Charlotte Information Technology Services, and other pertinent IT partners will develop scorecards and assessment tools to monitor goal performance. Balanced scorecards, in particular, are useful to monitor the achievement of non-metric goals. 5 DSBA-SOPHI Data Governance Plan 10

1.2.3 Planning The DSBA will perform data governance planning at three levels, with two additional levels providing input and support: o o The Executive Council sets the overall mission and strategic goals of data governance. It also obtains needed funding and resources. The Strategic Data Governance Steering Committee (Strategic Committee) develops the high-level task plan to achieve the strategic goals mandated by the Executive Council. The Operations Group develops short-term goals and tasks to implement the high-level plan mandated by the Strategic Committee. To do so, it includes data stewards and subject matter experts as members. Project managers and other stakeholders in the DSBA and its affiliates provide ideas and feedback to the formal management organization for data governance. Information Technology professionals provide direction, suggestion, and feedback to the strategic purchase, placement and management of technology resources. Section 2 of this document ( The Management of Data Governance ) provides more detail about these management groups. 1.2.4 Areas of Responsibility The DSBA staff has functions dedicated to Data Governance, as well as the management bodies described above. In accordance with the governance of the University of North Carolina at Charlotte, contractual obligations and licensures naming UNCC are entered into at the sole discretion of the Chancellor, the Office of Technology Transfer, and/or General Council in the Office of Legal Affairs. The table below summarizes other areas of data governance responsibility tasked to the DSBA staff. Sub-function Define Data Governance Process Description Design and implement a governance framework for managing a consistent observatory of data elements. The governance framework should: Define a governance process hierarchy with participation from faculty, IT operations and administration Establish a set of procedures (checklists and flowcharts) used to define, review and approve DSBA-SOPHI Data Governance Plan 11

Implement Data Governance Process Develop and Implement Enterprise Metadata Architecture Create and Maintain Master Data Management Standards requests for DSBA resources Designate data stewardship and compliance responsibilities amongst project, DSBA and IT personnel Define the roles and responsibilities of data stewards and compliance managers Coordinate project oversight committees (operations groups) Identify and coordinate data stewards and compliance managers Follow procedures to define, review and approve requests for DSBA resources Enable the creation, storage, manipulation, control, integration, distribution, use and change management of project data / metadata. Enterprise Metadata Architecture consists of: Create and maintain a metadata strategy Define and execute change management procedures for DSBA data / metadata repositories Serve as the liaison among project affiliates to: Identify authoritative sources of shared data Define consensus for the logical data structures of shared data elements Capture, as part of the enterprise metadata, the rules that govern creation and update of shared data and process entities Table 1: Data governance responsibilities of the DSBA The DSBA team will support affiliated projects and participate in the Operations Group and Strategic Committee to provide program continuity. The data stewards and compliance managers will implement their directives in the data domains for which they are responsible. DSBA-SOPHI Data Governance Plan 12

1.2.5 Expertise At the top level, members of the Executive Council will provide global understanding of the needs and issues faced by the DSBA. Members of the Strategic Committee will incorporate this global understanding into a lower-level strategic plan for data governance in specific operational areas. The DSBA Data Scientist will provide expertise to identify general issues of data governance that the effort needs to address. For instance data standardization, compliance concerns, and data disclosure obligations to name a few. This individual will also help to identify the metadata that needs to be collected for data governance. Affiliated faculty and professionals who are experts in specific data domains participate as members of project operation and oversight groups. These individuals know their data domains and the analytic processes that consume data in their domain. As such, they will identify how prospective changes in the management of their data will affect project continuity and success. They will also assess and help to improve data and service quality in the area, and will present recommendations for identified data quality and quality of service issues. 1.2.6 Observatory Management Tools The DSBA will conduct research and tool evaluation to acquire, develop and maintain a tool suite supporting the overall data and resource management effort. With respect to data governance, the tool suite will enable management of data quality, profiling and inventory control as well as enable comprehensive usage audit and reporting. 1.2.6.1 DSBA Website Portal The DSBA team will develop and maintain a portion of the http://dsba.uncc.edu website to communicate data governance related matters. The site will provide guidance, best practices, policies and procedures related to data management for the communities of interest. 1.2.7 Acceptance of Change As with all new standards, administration expects that research and educational efforts will support and comply with the SOPHI data governance program. Data governance is a service to the University and DSBA affiliates that will deliver higher data quality, as well as consistent data and computing resource use across the organization and in collaboration with business partners. It will enable the DSBA program to have positive, impactful consequences for research and education. That said, change - even beneficial change - is always uncomfortable. We all know how to do things the way we ve been doing them: for a long time, these old ways have DSBA-SOPHI Data Governance Plan 13

seemed to be good enough. However, with ever-limited resources expected, changes are required to sustain our growth and to ensure our excellence in research and education. A well-planned, stepwise approach to change will help. Most important is the willingness of those involved to listen to each other and work collaboratively to achieve the best result for everyone. 1.3 Implementation of Data Governance Data governance is a vital keystone in the process of building enterprise-wide data management. As such, it s one of the essential foundation pillars of the DSBA SOPHI. The DSBA team will work with affiliated faculty and industry professionals to introduce the SOPHI model of data governance to the University. The team will work closely with stakeholders, whose feedback and comments (both positive and negative) will help to improve policies and procedures to better serve the needs of the DSBA initiative. Data governance implementation includes various tasks, such as Master Data Management (MDM), Enterprise Data Management, Data Stewardship and Data Compliance management. MDM supports the integration of Data Governance and Data Quality Control. Data Compliance management supports the integration of Regulatory Compliance, License Obligation and Disclosure Assurance. Data governance management bodies will share responsibility for such tasks with the DSBA team and affiliated project staff. 1.3.1 Functional and Organizational Infrastructure The creation of functional and organizational infrastructure has been tasked to the DSBA staff through mandate from the University Chancellor and formation of the DSBA initiative. DSBA is empowered to execute data management tasks and recommend resource and support functions that may be required. Data governance management will be structured as described in Section 2.1 of this document. 1.3.2 Technical Infrastructure DSBA will support UNC-Charlotte as it successfully and effectively deploys new technology and architectural principles such as Service-Oriented Architecture (SOA), Data as a Service (DaaS), Analytics as a Service (AaaS), Visual Analytic Services (VAS) and Enterprise Information Integration (EII). All of these principles depend on high quality and consistent use of information and resources across the organization. 1.3.3 Policies and Procedures As data governance encompasses the people, processes and procedures to create a consistent, enterprise view of data resources in order to increase consistency and confidence in decision-making, decrease the risk of regulatory infractions and improve DSBA-SOPHI Data Governance Plan 14

data security, the data governance policy serves as the backbone of the data governance program. It supports all requisite actions and ensures that the governing of data is not spurious and is not optional. The Executive Council will communicate and approve the data governance policy. 1.3.3.1 Data Governance Policy Currently, the DSBA team and the Strategic Committee are defining an enterprise data governance plan based on industry best practices. The policies are: Participation in the SOPHI data governance program: Affiliated project directors and staff will participate in the SOPHI data governance program and will represent relevant Data Science and Business Capability Areas (BCAs) in the decision making process. Data stewardship and compliance responsibilities: Affiliated project directors and staff will designate data stewards and compliance managers from their areas of interest. The data stewards and compliance managers will have day-to-day responsibility for coordinating the data governance activities for which they are responsible. 1.3.3.2 Data Governance Procedure Data governance procedures are developed by the DSBA team and the Strategic Committee and approved by the Executive Council. These procedures are communicated in the following documents: 1. Data Standards and Standardization Policies and Procedures 2. Data Structures and Storage Policies and Procedures 3. Resource Allocation and Service Request Policies and Procedures 1.3.4 The DSBA Data Governance and Metadata Manager The DSBA Metadata Manager manages a repository of information connecting each data steward, compliance manager and project director with the data for which he/she is responsible. Conversely, this repository will connect each data domain / BCA with the data steward(s) who oversee it. This kind of information is called metadata because it is data about data. This information repository enables administrators, data steward coordinators, and stakeholders to identify and communicate quickly with data stewards. In addition, data domain scientists, data analytics scientists, and industry professionals can use the repository to identify potential collaborations. DSBA-SOPHI Data Governance Plan 15

Until which time the scope of DSBA activity requires the delegation of data governance and metadata management tasks the responsibility for such will be tasked to the DSBA Lead Data Scientist. 1.3.5 The Metadata Observatory As its name implies, the enterprise-wide Metadata Observatory (MO) contains information about the data assets maintained in the DSBA-SOPHI observatory and itself adheres to DSBA-SOPHI models as a reflective project. It contains such information as: Community (Executive Council, Strategic Committee, Operations Group, Project Operations and Oversight Groups, Industry Partners, Data Providers, ) agreedup index information Current-state information about data formats used by various systems or projects, as well as the terminology used by individual groups to describe the data. Target-state information about desired common data formats, data definitions, methods for reconciling incompatible data sources, data lifecycle milestones 1.3.6 The Data Quality Aspect of Data Governance Identifying and acquiring a data quality tool that can automatically check data sets to ensure that they meet the data quality standards dictated by the data governance policy and project operations and oversight group will be of great advantage to the DSBA- SOPHI and to UNC-Charlotte. Such tools will support the DSBA team in its efforts to ensure the availability, usability, integrity and security of the data assets maintained in the DSBA-SOPHI observatory through application of a well-defined set of procedures and a means to efficiently execute those procedures. This Data Assurance tool will capture quality metrics in a consistent electronic format allowing further analysis and sharing of quality assessment with project staff and data stewards for review and/or corrective action. The Data Assurance tool can be an indispensable check on the validity, accuracy and compatibility of data throughout the data asset lifecycle. It can catch errors that, further down the line, may have negative impact on University research credibility and which may be cost-prohibitive to correct. 1.3.6.1 The Data Quality Issue Log A Data Quality Issue Log enable data governance stakeholders and affiliated users to record problems and/or other issues that they find with the data. Data stewards and data steward coordinators should review the log on a regular basis and should record the actions they take to resolve problems and issues they find in the log. They should also note whether problems or issues are local, affecting an individual DSBA-SOPHI Data Governance Plan 16

project, or strategic, affecting the entire (or a large portion of) DSBA-SOPHI governance effort. Data stewards also use the Data Quality Issue Log for recording information about data quality problems, solutions, and results. The DSBA team monitors the Issues Log and addresses the issues as appropriate. DSBA-SOPHI Data Governance Plan 17

2. Management of Data Governance 2.1 Overview Within the DSBA data governance will be conducted with a three-tiered management structure, with two additional levels providing input and support: o o At the top level, the Executive Council sets the overall mission and goals of the DSBA and DSBA-SOPHI data governance. It also obtains needed funding and resources. At the second level, the Strategic Data Governance Steering Committee (Strategic Committee) recommends strategic goals and develops the high-level task plan to achieve the strategic goals mandated by the Executive Council. At the third level, the Operations Group develops short-term goals and tasks to implement the high-level plan mandated by the Strategic Committee. To do so, it includes data stewards and subject matter experts as members. Project managers and other stakeholders in the DSBA and its affiliates provide ideas and feedback to the formal management organization for data governance. Information Technology professionals provide direction, suggestion, and feedback to the strategic purchase, placement and management of technology resources. 2.1.1 Stakeholder Involvement A common business adage states marketing is everyone s business. Likewise, security agencies think, security is everyone s business while medical professionals think, health is everyone s business. Though all of these viewpoints are over-simplifications, they do contain a grain of truth. For any set of goals, some individuals or groups must bear primary responsibility for planning and achieving them: Tasks that are everyone s responsibility can easily end up being no one s responsibility. Nevertheless, in data governance as in marketing, security and health the job cannot be done successfully by a sole entity. To maximize the success of the DSBA program, management of the program should include representatives from administration, IT, and affiliated project stakeholders as well as seeking input from industry partners and service users. At every level, the data governance team must seek the advice and the involvement of data stakeholders. This improves data governance because many new ideas come from those outside the formal management structure. This also encourages cooperation with the inevitable changes that data governance will require, whether they are changes in work processes or they are as simple as adapting to standard terminology and reporting. DSBA-SOPHI Data Governance Plan 18

2.1.2 Executive Council The Executive Council includes the executive director for the DSBA, the DSBA Industry/University liaison and the deans of DSBA affiliated colleges and/or their designees. The council sets the overall mission and strategic goals of the DSBA program as well as securing the funding, resources and cooperation needed to sustain the DSBA effort. Key to the Executive Council is its ability to make decisions on an enterprise perspective that is, on what is best for the University as a whole instead of merely desirable for individual projects/faculty. In addition, the Council will be available to resolve strategic problems as they arise. If other levels of data governance management are unable to resolve such problems, each lower level will escalate the problem to the next level up, ultimately reaching the Council. The Executive Director of the DSBA will chair the Executive Council. 2.1.3 Strategic Data Governance Steering Committee The Strategic Data Governance Steering Committee (Strategic Committee) includes representatives from DSBA affiliated projects, University Administration and Services (OTT, Compliance, General Council), DSBA enterprise architects (data governance and metadata manager / data scientist), and the DSBA program manager. The Strategic Committee develops a task sequencing-plan for the operational working group and will be available to resolve problems escalated from lower levels of data governance management. It reports to the Executive Council, which serves in turn as a decision maker for escalated issues. The DSBA senior program manager will chair the Strategic Committee. 2.1.4 Operations Group The Operations Group includes members of the DSBA staff (Industry/University liaison, Program Manager, Data Scientist, Research Professor), subject matter experts most literate on data and database systems as well as analytics systems in their domain, IT representatives (OTS, ITS, URC), and technical end users. The Operations Group provides tactical-level implementation of the policies and decisions from higher-level data governance management. It will also receive assignments and their priorities from the Strategic Committee. Members of the Operations Group will outline the necessary components of the data governance program to meet the strategy as outlined by the Strategic Committee and endorsed by the Executive Council. For instance, the operations group may be tasked with conducting impact analysis to determine resource usage and/or how changes in specific data or computing assets will affect affiliated projects. DSBA-SOPHI Data Governance Plan 19

If an affiliated project identifies data problems, the operations group will invite a representative of the project either to (a) discuss the problem and possible solutions, or (b) help research and propose a solution to be conducted by the Group. The Group will then develop a recommendation and present it to the Strategic Committee for approval. If members of the Operations Group cannot reach agreement on an issue, they will escalate multiple solutions to the Strategic Committee for decision. The DSBA lead data scientist will chair the Operations Group. 2.1.5 Project Operations and Oversight Groups Individual project operations and oversight groups will be comprised of affiliated project directors (or designees), DSBA staff, and domain data stewards. The primary tasking of the project operations and oversight group is to evaluate and recommend DSBA resource allocation request for their community of interest to the Strategic Committee. This group will also act as a point of contact for their community of interest to voice comment or concern with regard to DSBA operations. The chair for project operations and oversight groups will be elected by each group independently. 2.1.6 Specific Roles Data Stewards Data Stewards will have several crucial responsibilities, including defining, approval and maintenance of data lifecycle and governance procedures and advising affiliated project directors and staff on their implementation. The main objective of data stewardship is to assist in managing the DSBA data assets to improve their reusability, accessibility and quality. Stewards will also have a hand in high-level information requirements and data structure definition. They will help to develop and monitor control policies for data. They also serve as overall coordinators for enterprise data delivery efforts. Data Stewards will work with project directors and staff to continually improve data flow. Below is a brief description of the types of responsibilities tasked to data stewards who participate with the DSBA program 2.1.6.1 Data Definition Stewards The task of managing data definition falls to a data definition steward. This responsibility includes identifying the specific data formats and structures that are utilized within their community of interest as well as identifying and recording appropriate metadata. Data definition stewards are the most appropriate point of contact for identifying opportunities to share (or re-use) data and to insure data quality standards. 2.1.6.2 Data Production Stewards The data production steward is primarily a technical role responsible for ingesting and modifying primary data stores in the observatory system. This responsibility includes DSBA-SOPHI Data Governance Plan 20

auditing and reporting on the usage of storage resources, validating data that enters and exits the system and communicating concerns, issues, and problems to the appropriate operations group(s). 2.1.6.3 Data Usage Stewards Data users potentially include constituencies outside of the university who may or may not follow the guidance of the SOPHI Data Governance policy. Data Usage Stewards are tasked with acting as liaison to these users as well as those affiliates within the university to insure appropriate data usage and to advise on concerns of compliance and data security. The usage steward is responsible for helping interested and appropriate parties to access and use data for its intended purpose as well as to access available metadata about how data was defined, produced, and stored in the observatory. DSBA-SOPHI Data Governance Plan 21

3. The Process of Data Governance The process of data governance is determined by its goals. Its strategic goals are to standardize, harmonize, and integrate data across the breadth of domains with which the DSBA interacts. To achieve these strategic goals, the SOHPI data governance model: Adopts formal policies and procedures to insure data consistency, data standardization, data reuse, and data exchange. Creates a formal decision-making structure to approve policy and resource allocation. Provides a central mechanism for communicating data-related initiatives. Provides a central point of contact acting as liaison between technical and business/research groups, both internal and external. To execute these principles, the Data Science and Business Analytics program has established a data governance management and reporting structure consisting of an Executive Council, a Strategic Steering Committee, an Operations Group and individual project oversight groups. All of these management bodies will work with users and affiliated units of the university to design and implement data governance policies and procedures that serve both the general needs of the DSBA and affiliated colleges and the specific needs of individual departments, projects and communities of interest. As an example, the figure below displays the interaction of the management bodies involved in a standards approval process. Actors Activities Executive Council Approve Standard Strategic Committee Determine Task Priority Evaluate Standard Reject Standard Select Process Operations Group Create Standard Revise Standard Determine Deliverable Project Oversight DSBA-SOPHI Data Governance Plan 22

Suggest Task/Need Vote to send to Strategic Committee 3.1 High Level Plan and Milestones The high-level data governance plan for the DSBA-SOPHI and related milestones cover the implementation of three key areas. These areas are Master Data (Resource/Asset) Management, Enterprise Metadata Management and Data Stewardship. The implementation of Master Data Management focuses on the creation and maintenance of standards and the development of formalized procedure for asset management in collaboration with affiliated project constituents. Documentation of core asset management procedure and data standards is targeted for completion by the end of November 2014. The implementation of Data Stewardship formalizes the accountability for managing the diverse data assets of the DSBA-SOPHI. Each Project Operations and Oversight Group (POOG) will identify two Data Stewards tasked with the responsibility to manage and make decisions regarding the data within their POOG domain. Formal Data Stewardship responsibilities are targeted to begin in January 2015. The DSBA plans to initiate foundational capabilities for the SOPHI project in mid December 2014 and be fully operational by May 2015. As a foundational capability of the SOPHI project, we plan to execute the SOPHI data governance model in the first week of December 2014. As a first step, the Operations Group was formed in June 2014 and conducted an organizational meeting on June 26, 2014. Shortly thereafter the Strategic Committee was formed and conducted an informal discovery meeting regarding DSBA-SOPHI Data Governance on July 25, 2014. 3.2 On-Going Data Governance The data governance management bodies as described in section 2, The Management of Data Governance, will meet as needed to accomplish the goals and expectations of the DSBA. The Operations Group is scheduled to follow an agile development plan with bidaily scrum meetings and three-week interval sprint demonstration meetings. These meetings began June 26, 2014. Meetings with the Data Governance Steering Committee and Executive Council will be conducted as necessary. The DSBA team will provide a monthly status report to the Executive Council regarding the efforts underway. All other documentation and data quality logs will be maintained through the DSBA.uncc.edu website. DSBA-SOPHI Data Governance Plan 23