1. How to transform the landscape of analytics with data governance
Richard R. Burnette III, PhD
Associate Provost for Metrics, Analytics and Strategic Planning, and Institutional Data Administrator
Florida State University
Braden J. Hosch, PhD
Associate Vice President for Institutional Research, Planning & Effectiveness
Stony Brook University
RICK Introduce session and hand to Braden
Rick Burnette AVP and my colleague Braden Hosch AVP at Stony Brook. Both of us have been deeply engaged in empowering our campuses for data-informed decision making.
Our session is a primer. We could do a weekend long symposium on the topic so we are focusing today on the Definitions, rationale, value propositions, dimensions and scope, structures, roles and responsibilities, and finally implementation and assessment of data governance systems.
We have a lot to cover and will be jumping in and out throughout the conversation and we plan to leave time for your questions. – Braden
© Braden J. Hosch & Richard R. Burnette III 2019
Drs. Hosch and Burnette share equal co‐ownership of the copyright and related intellectual ownership of any of the content and materials presented, utilized, and distributed in this session. The parties expressly state that they do not intend to create a partnership whereby any co‐owner shall be liable for the act of any other co‐owner.
Suggested citation: Burnette, R. & Hosch, B. (2019, November 10). Primer on How to Transform the Landscape of Analytics with Data Governance. Session presented at the Association of Public and Land-Grant Universities.

2. Data governance is a strategic priority
Proliferation of data and applications
Privacy expectations/ regulations
Cloud data mobility
Fair and ethical use
Braden

3. The 5-second elevator definition
a set of guidelines for how people behave and make decisions about data
Data governance is …
BRADEN
What’s particularly important about this definition is that it is simple and it’s easy to remember.
It also provides opportunity for the immediate follow-up that data governance is about people more than data. Specifically, it is about how people act or do things. Successful data governance requires action.

4. What is Data Governance?
John Ladley – Data governance is the organization and implementation of policies, procedures, structure, roles, and responsibilities which outline and enforce rules of engagement, decision rights, and accountabilities for the effective management of information assets.
Implement
Policies
Procedures
Structures
Roles
Responsibilities
Identify
Rules
Rights
Accountabilities
Expectations
Opportunities
RICK
John Ladley – Data governance is the organization and implementation of policies, procedures, structure, roles, and responsibilities, which outline and enforce rules of engagement, decision rights, and accountabilities for the effective management of information assets.
On click – Data Governance structures implement:
Policies
Procedures
Structures
Roles (for people to enforce governance)
And responsibilities
They then identify
Rules
Rights
Accountabilities
And I would like to add
Expectations and
Opportunities

5. Important characteristics of DG definitions
Data governance IS
Data Governance IS NOT
More about people and behavior than data
A system that requires and promotes shared agreement
Formal (i.e. written down)
Adds value by supporting institutional mission/goals
IT’s responsibility
Solved by technology
Equally applied across all data assets
BRADEN
If you have developing data governance on your campus, is it centered around IT or business units? Why is this and how might institutional history or agendas of various officers shape how data governance works on the campus?
Is there an expectation on the campus that purchasing a piece of software will solve the data governance problem, if the problem is even recognized?
Is there an expectation that all data will be subject to a single governance system? What types of data does the institution have to manage (and how do you even know what you have)?

6. Complementary Elements of Data Governance
Strategic Empowerment
Rules & Constraints
Risk v Reward
RICK
This view of data governance creates a synergistic complement where the identification of rules and constraints actually elevate the strategic power of data.
Governance creates reliability/consistency that can be used to empower the institution and reduce risk by allowing for data-informed decision making while reducing the real financial and reputational risk of data breaches, misinformation, data hoarding, and inconsistency in sourcing.

7. Why Do We Need Data Governance?
Maximize Data Investments
Gain Deeper Insights
Promote Efficiency
Ensure Trust
Reduce Risk
Improve the Experience
RICK
Higher Education is a competitive market. There are questions on the value of higher education and we are all trying to prove our worth, improve our experiences while creating an environment where our students, faculty and staff are empowered to be creative, innovative and successful. This all requires a mature and governed data environment.
Data Governance is necessary to accomplish the shared goals of the university. Goals like Student Success, Metric Attainment, and Institutional Advancement and empowerment
However, that empowerment through data requires trust and involves risk.
DG is needed to be able to accomplish and sustain evidence-based decision making.

8. Principles of Data Governance
Consistency of data in its sourcing and in its vocabulary, definitions, and taxonomies
Quality which is proactively assessed and standards applied
Responsibility and accountability defined across the data lifecycle and recorded in the information asset register
Business alignment which ensures that data is regarded and treated as a key business asset
Secure access to relevant users, kept secure through access control
Insight
BRADEN
This set of principles is grounded in the literature, and derives from organizational models employing a chief data officer (CDO – not to be confused on college campuses with the Chief Diversity Officer). These are shown in the order in which they were published – in some ways the business alignment is most important, followed by providing trusted insight. Others are also significant but derivative from these purposes, which DG is intended to enable.
Source: Carruthers, C. & Jackson, P. (2018). The chief data officer’s playbook, London: Facet, p. 145

9. What are the Data Dimensions
Use
Access
Management
Integrity
Privacy
Security
Visible Elements
RICK
I like to think of this image as Maslow meets Data Governance. The foundational elements at the bottom allow for a better experience at the top. In Maslow’s terms, someone is not going to be self-actualized if their data aren’t secure, private, clean, and well managed.
On click: This representation could have also been an iceberg as the user should only see the top levels, but the bulk of the governance is below the surface.

10. What Data are we Governing?
ERP Data and Operational Data
Administrative
SIS, HR, Financials, CRM, Departmental, Organizational Performance Data
Instructor and Student Performance
Teaching & Learning
LMS, Lecture Capture, Clickers, Attendance, Engagement, Grades, Progression, and Course and Faculty Evaluations
Data on Researchers, Research & Grants
Research
Publication and Citation Histories, Proposals Submitted, Research Content & Results, Grant Dollars, PI and Co-PI data, Graduate and Undergraduate Research
Both Internal and External Data
Other Types
Meta-data, Unstructured Data, Geo-location, Event Attendance, Organization Involvement, Social Media, Sentiment Analyses, Survey Data, Business Transactions, Vendor Data
RICK
This slide addresses some of the data inputs we cover in data governance. These are some representative examples. There are many others.
In the past, most data was in the transactional systems that constitute our ERP solutions, but the amount of data we are collecting and the amount of data we can access are both growing.
We have to be able to manage both structured and unstructured data? We think of structured data as the data that are already in our databases that are well-defined. These databases have schemas so we can determine what each data point is to some extent – unless a field has been modified. Unstructured data is the data we can pull or acquire from social media, geo-location devices like Wi-Fi, and the semi-structured meta data about how long a student was logged into the LMS or the latency before response to an .

11. Key features of data governance systems
Documents
Charter / framework
Principles & values
Purpose & scope
Roles & responsibilities
Written & published policies
Data dictionaries
Communication strategies
Groups
Senior leadership [buy-in]
Policy council
Data steward council(s)
Information security council/program
Positions/office to support DG
Individual roles
Data stewards
Data custodians/ caretakers
Data users
BRADEN
These key features appear in many data governance systems. Not all systems will have all features but mature systems will typically include or address all of these in some ways. The written and published aspects of policies, procedures, dictionaries, etc. are of particular initial importance. If they are not published (preferably on a web page), they are secret procedures and will not be widely adopted.

12. Common Elements of the Structure
Committees
Executive/Steering – Senior officials focused on holistic goals of institution
Data Strategy – Occasionally separate from above focused on better data use
Operational Governance – Responsible for executing policies and procedures
Data Standards – Maintains data elements and monitors quality and delivery
Roles
Chief Data Officer (CDO) – Often overseas the execution of institutional goals
Data Trustees – Subject matter owners responsible for data integrity and clarity
Data Stewards – Responsible for assigning access and assuring standards met
Data Custodians – Usually IT maintaining security, backups, recovery, availability
Data Consumers – Functional staff who interact with data and report on data
RICK
Now that we have a sense of the purpose we need to look at some of the committees and roles we will need to create or codify.
There are various workable structures and the best thing to do is to consult the references below to see which will work for you. The primary committees may include an Executive Steering Committee, a Data Strategy Committee, the primary operational governance committee and possibly a group that focuses on the more nitty gritty aspects of data elements that I call the Data Standards Committee. Of course these committees may have different names and there are other committee names that aren’t listed.
There are many roles that I will identify below, but here are some of the key ones: Chief Data Officer, Data Trustees, Data Stewards, Data Custodians, and Data Consumers

13. Structure – Generic Example
Executive Steering Committee
Authorized to change the organization
Drives cultural change
Supports the program enterprise-wide
Provides funding for the Data Governance Program
Data Governance Board
Made up of high-ranking representatives of data- owning business functions who can make decisions about data for the company
Assign members of the Data Stewardship Council
Approve decisions of the Data Stewardship Council
Approve data-related policies
Business Data Stewards
Experts on use of their data domain data
Able to reach out to SMEs to gather information and make decisions
Typically someone who others come to as the most knowledgeable about the meaning of the data (and how it is calculated)
Makes recommendations on data decisions and write data-related procedures
BRADEN
This graphic from David Plotkin becomes important in later slides. Spend some time discussing levels and roles. Theoretically, an executive group authorized data governance, and mid- to senior group sets policy and procedure, and mid-level administrators serve as stewards to do the hard work of building dictionaries, resolving issues, and addressing problems.
The box on the left of the pyramid – the Data Governance Office – is important. Many participants will ignore it or report they have no resources for it. Discuss why it is important to have a coordinator of DG functions. This office can reasonably fit under IR, IT, or somewhere else.
Plotkin (2014). Data stewardship: An actionable guide to effective data management

14. EXECUTIVE STEERING COMMITTEE INFORMATION GOVERNANCE COUNCIL
Report
COMPLIANCE
Privacy
Audit
Security
Monitor
EXECUTIVE STEERING COMMITTEE
Leadership
Vision
Funding
Oversight
Chief Data Officer
Human Resources
Fair & Ethical Use
INFORMATION GOVERNANCE COUNCIL
Procedures
Arbitration
Policies
Permissions
Strategy
Evaluation
Oversight
Data Sciences
Financials
Functional Governance Groups
Student Information
Data Strategy
RICK
Data Governance Hamburger
Operates under an umbrella of compliance
Executive Steering Committee
Information Governance Council – Data Governance Committee – Primary procedural and policy organization
Affiliated Functional Groups – Groups by data domains and others that are cross-cutting groups dealing with strategic issues like data strategy, data science and Fair and Ethical use of data
Data Custodians provide the foundation by managing the IT and data infrastructure
Alumni & Foundation
Business Intelligence
Access
Monitoring
Architecture
Movement
Quality
DATA CUSTODIANS
Security

15. Information Governance Council Purpose
Create a data governance imperative
Promote a data-engaged campus
Create and update policies
Create access control mechanisms
Manage conflict resolution
Promote shared data management
Authorize data movement and storage
Reinforce reporting controls
Define and arbitrate fair and ethical use
Evaluate and assess effectiveness
RICK
Read the slide
You will see that we have discussed many of these things in our discussion of the dimensions of data.
What is important here is that the purpose isn’t limited to control. There has to be an institutional expectation/imperative for data governance that leads to a data-engaged campus. In addition to the common dimensions, the data governance process should provide a mechanism for managing and resolving conflicts and more recently, to arbitrate challenges to fair and ethical data use.
Furthermore, this group should be evaluation and assessing the effectiveness of the information and data governance structures.

16. Data Steward Responsibilities
Oversee management of selected data assets
Participate in data governance and carry out decisions
Assist in creation and maintenance of data dictionaries, metadata
Document rules, standards, procedures, and changes
Ensure data quality and manage specific issues
Communicate appropriate use and changes
Manage access and security
BRADEN
Specific responsibilities of data stewards are listed here. It is useful for institutions to articulate these responsibilities for each data steward. Mature organizations that take data governance seriously may insert such responsibilities into position descriptions or employee performance programs so as to formalize the employee’s responsibility to carry out these functions as part of their normal job.
Reference

17. Functional Data Stewardship Council/Committees
Coordinate data stewards in related area
Set / review definitions, data quality rules, creation/usage rules, metadata
Consider and approve changes to code sets
Enforce data dictionary standards in area
Review data quality in functional area; identify practices promoting data quality
Respond to inquiries about process, content, limitations and uses of data, especially in cross-functional settings
Elevate issues that require resolution
Communicate proceedings, including notice of changes and decisions
BRADEN
Data stewards will meet regularly in a formal group or groups to establish shared procedures, definitions, etc., since often data issues require cross-functional input. Institutions ay have one or more functional data governance committees to carry these functions out. Often these groups are domain specific, depending on how the institution has organized its data. Ex.:
Student/academics committee
Finance committee
HR committee
Source: Stony Brook Data Governance Framework

18. Data users Report data quality issues to appropriate data steward
Expectations should be set for data users. Example formal responsibilities (Stony Brook)
Recognize that institutional data are potentially complex. Make efforts to understand the source, meaning and proper use of the data
Include information about the data source and criteria to guard against misinterpretations of data.
Respect the privacy of individuals whose records they may access.
Ensure that passwords or other security mechanisms are used for sensitive data
Report data quality issues to appropriate data steward
BRADEN
Stony Brook added a set of responsibilities for data users to its data governance system to acknowledge that each member of the community is a data citizen who also play a part in data governance
Make an effort to understand reports and the complexities of definitions and structures
Document sources and provide interpretation
Protect privacy and guard against disclosure
Secure sensitive data
If you see something, say something

19. Keys to Implementation
Create value statement for DG
Prepare a roadmap
Design the program
Identify rough costs and staffing
Identify structure and roles on governance committees
Plan meetings and document via shared media
RICK
Policy Statement (from UNC Chapel Hill)
Institutional Data is a strategic asset of the University. As such, it is important that it be managed according to sound data governance procedures.
This policy serves three primary purposes:
1. It assigns stewardship responsibilities for University administrative data
2. It establishes standards for the custodianship of such data
3. It empowers the Institutional Data Governance Committee to establish standards and/or procedures for storage, retrieval, destruction, backup, and access as needed to ensure proper management and protection of Institutional Data.
The Vice Chancellor for Information Technology and Chief Information Officer has authority and responsibility for preserving the security and integrity of Institutional Data. Proper stewardship and custodianship of University Institutional Data will facilitate access to data that supports the work of those with official educational or administrative responsibilities within the institution and will inform users of data of their responsibilities.
Institutional Data is available to individuals carrying out their University work responsibilities on an as needed basis. An individual interested in utilizing Institutional Data for any other purpose must request the Institutional Data through a public records request. The Public Records Officer and the Office of University Counsel have responsibility for interpreting the laws governing data access and related issues. Note: Nothing in this policy precludes or addresses the release of Institutional Data to external organizations, governmental agencies, or authorized individuals as required by legislation, regulation, or other legal vehicle.
Preparing a roadmap. Once you have a value statement and executive support, you can start mapping out the timeline.
You need to understand what you want to accomplish, the costs and staffing associated with positions like chief data officers or chief information security officer, or for compliance software. Chances are that you have many of the components and roles.
Draft the roles and structure you would expect in your committees including alternative representations.
The working group will need an overall lead and likely several sub-leads (for each dimension team). Identify them and set expectations for documentation and meetings.

20. Additional Keys to Implementation
Identify early wins
Prepare policy documents
Create mechanisms for documenting compliance
Adopt Maturity Model
Brand process with visible brand
Prepare training protocols
Measure and report on activities
RICK
Identify early wins – use cases of high need and of reasonable scope
Prepare policy documents (see references below)
Create mechanisms for documenting compliance
Adopt a Maturity Model – this allows an outside entity to evaluate the effectiveness of your program on each dimension
Brand process with visible brand
Prepare training protocols
Measure and report on activities

21. Technology applications for data governance
Technology can support data governance
Data dictionary management
Data quality analysis
Master data management
Issue and process management
Technology will not
Build organizational structures, responsibilities, accountabilities
Mend dysfunctional organizations
Implement organizational or cultural change
BRADEN

22. Example Data Governance Maturity Model
Level 1
Level 2
Level 3
Level 4
Level 5
Informal
Developing
Adopted and Implemented
Managed and Repeatable
Integrated and Optimized
Organizational Structures
Attention to Data Governance is informal and incomplete. There is no formal governance process.
Data Governance Program is forming with a framework for purpose, principles, structures and roles.
Data Governance structures, roles and processes are implemented and fully operational.
Data Governance structures, roles and processes are managed and empowered to resolve data issues.
Data Governance Program functions with proven effectiveness.
Culture
Limited awareness about the value of dependable data.
General awareness of the data issues and needs for business decisions.
There is active participation and acceptance of the principles, structures and roles required to implement a formal Data Governance Program.
Data is viewed as a critical, shared asset. There is widespread support, participation and endorsement of the Data Governance Program.
Data governance structures and participants are integral to the organization and critical across all functions.
Data Quality
Limited awareness that data quality problems affect decision-making. Data clean-up is ad hoc.
General awareness of data quality importance. Data quality procedures are being developed.
Data issues are captured proactively through standard data validation methods. Data assets are identified and valuated.
Expectations for data quality are actively monitored and remediation is automated.
Data quality efforts are regular, coordinated and audited. Data are validated prior to entry into the source system wherever possible.
Communication
Information regarding data is limited through informal documentation or verbal means.
Written policies, procedures, data standards and data dictionaries may exist but communication and knowledge of it is limited.
Data standards and policies are communicated through written policies, procedures and data dictionaries.
Data standards and policies are completely documented, widely communicated and enforced.
All employees are trained and knowledgeable about data policies and standards and where to find this information.
Roles & Responsibilities
Roles and responsibilities for data management are informal and loosely defined.
Roles and responsibilities for data management are forming. Focus is on areas where data issues are apparent.
Roles and responsibilities are well-defined and a chain of command exists for questions regarding data and processes.
Expectations of data ownership and valuation of data are clearly defined.
Roles, responsibilities for data governance are well established and the lines of accountability are clearly understood.
BRADEN
Have participants complete Activity 4 for their own institution. This should take five minutes. When engaging in this exercise back on campus, multiple stakeholders should be involved. Participants have a version of this maturity model in their exercise packet – the slide itself may be too small to read.

23. Person Roles CDO – Chief Data Officer
CISO – Chief Information Security Officer
Chief Privacy Officer
Chief Compliance Officer
Institutional Data Administrator
Data Stewards
Data Custodians
Data Manager
RICK
Describe the various person roles. These are the descriptions they are likely to see, but the definitions tend to change slightly in different models.
CDO – Chief Data Officer
CISO – Chief Information Security Officer
Chief Privacy Officer
Chief Compliance Officer
Institutional Data Administrator
Data Stewards
Data Custodians
Data Manager
Data Owner

24. Key Policies Strategic Vision/Policy for Data Use Information Privacy
Data Access and Use
Data Management (includes 3rd Party)
Cybersecurity
and Media Use
Survey Administration
Data & Device Security
Fair and Ethical Use
RICK
This is a listing of the breadth of some of the policies you may have to address. Some are global and some are unique for students or faculty.
Many institutions use compliance software to associate policies with the various dimensions of the data governance landscape. These software product can be used to track compliance at the institutional or unit level. You can set the time frame and apply the associated compliance pieces with groups or projects.

25. Takeaways Data governance is more about people than data
All higher ed change management principals apply
Process and written documents are essential
Leadership support
Broad-based consultation, including faculty
Opportunity for consultation
Representation
Software can help, but it won’t fix broken processes or organizations
Starting data governance is hard work; sustaining it is harder
BRADEN
Wrap up – begin conclusion with points above. If there is just one take away participants remember is that data governance is more about people than about data.

26. Questions? Rick Burnette Braden Hosch
Associate Provost for Metrics, Analytics and Strategic Planning, and Institutional Data Administrator
Florida State University
Braden Hosch
Associate Vice President for Institutional Research, Planning & Effectiveness
Stony Brook University
© Braden J. Hosch & Richard R. Burnette III 2019
Drs. Hosch and Burnette share equal co‐ownership of the copyright and related intellectual ownership of any of the content and materials presented, utilized, and distributed in this session. The parties expressly state that they do not intend to create a partnership whereby any co‐owner shall be liable for the act of any other co‐owner.
Suggested citation: Burnette, R. & Hosch, B. (2019, November 10). Primer on How to Transform the Landscape of Analytics with Data Governance. Session presented at the Association of Public and Land-Grant Universities.