The Federation of American Scientists Brings MetroLab Network Into Organization

June 2, 2025 – The Federation of American Scientists (FAS) is excited to announce it has acquired MetroLab Network (MLN), bringing together two teams with a shared commitment to harnessing science, technology and innovation to drive impact in new ways in communities across the country.

“MetroLab launched ten years ago with a vision to bring technical expertise and breakthrough innovations from the lab into the civic and community space where their impact can be transformational,” FAS CEO Daniel Correa said. “Today, we are teaming up to realize a shared vision, matching the deep science and tech expertise at FAS with Metrolab’s leadership, expertise, and expansive network across civic and university actors. I can’t wait to work hand in hand with Kate and her team to build the next chapter together.” 

This move will provide FAS with reach across all levels of government, connecting people, ideas, and initiatives from local communities to federal policies and endeavors. Today’s announcement is the culmination of several months of collaboration and planning, and will bring scale and reach to the work both organizations have been building over the last several years.

“MetroLab has focused on community partnerships and catalytic innovation for a decade. This new chapter will enable that work to grow and scale,” MetroLab’s Kate Garman Burns, who will join FAS as a Director, said. “We work with universities and local governments to bring innovative policy solutions to communities – and that work complements what FAS has been doing at the federal level. I could not be more excited to see what we can do together.”

As part of a new policy team at FAS, MetroLab will bring several existing programs to FAS that will continue and evolve within FAS, including its Civic Innovation Challenge, Mobility Center of Excellence, Local Government R&D Agenda, and Data Governance Policy Guidance. MLN also brings along its impressive network of community partners, including 82 local governments and over 120 universities. In addition, MLN board member Tom Schenk is joining FAS’s board of directors – he was officially elected on May 13.

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MetroLab Network (MLN) drives positive impact in communities by empowering collaboration between two critical public institutions: local government and universities. We believe science and research can live and flourish in cities and their city halls – bringing innovation and solutions to the front door of communities. We believe transformative partnerships between these institutions are a necessary evolution to the innovation and scientific movement of tomorrow.  The challenges cities and counties face are complex, and to create transformative change we bring together an ecosystem of  research and doers.

The Federation of American Scientists (FAS) works to advance progress on a broad suite of contemporary issues where science, technology, and innovation policy can deliver transformative impact, and seeks to ensure that scientific and technical expertise have a seat at the policymaking table. Established in 1945 by scientists in response to the atomic bomb, FAS continues to bring scientific rigor and analysis to address national challenges. More information about FAS work at fas.org.

Definitions and Data Classifications

This is a section of the Model Data Governance Policy & Practice Guide for Cities and Counties. Learn more about the report and find the other sections here.

Section Notes

Purposes. Agreed upon definitions are key to any legal or policy regime. Definitions allow practitioners to classify technologies and standardize operations. A core set of definitions reflecting municipal uses of Data will be vital to standardizing practices across departments and jurisdictions. This Section seeks to establish definitions and Data classifications to standardize language and approaches to interdepartmental, inter-jurisdictional, and other external data sharing.  

Prominent Challenges Addressed. The initial working group that led to the MetroLab Data Governance Task Force identified several scenarios, challenges, and considerations regarding  “definitions” and “data classifications,” including:

Definitions

For purposes of this Policy, the following terms shall have the following respective meanings: 

Applicable Third Party: An individual or organization, other than a Jurisdiction employee, engaged by contract or otherwise working with or for the Jurisdiction in any one or more aspects of Data Handling. 

Chief Data Officer: A Jurisdiction employee designated by the Controlling Authority to perform the  functions of a “Chief Data Officer” set forth in Section 5.

Community Advisory Board (sometimes herein referred to as the “CAB”): The group established  and maintained to provide well-informed, timely, and independent advice to the Jurisdiction on  significant Data Handling matters in accordance with Section 5 of this Policy. 

Community End User Testing Group (sometimes herein referred to as the “CEUTG”): The group  responsible for providing feedback regarding the use and accessibility of the Data resources,  websites, applications, and other citizen interfaces, through an Open Data Program or otherwise,  as described in Section 5.1

Controlling Authority: The individual(s), body, or other entity with the legal authority to make a decision on behalf of the Jurisdiction with regard to adopting a policy, designating an individual, body or other entity to serve a function, or other significant matter described in this Guide. 2

Convener: The person or institution designated to lead the administration of the Community  Advisory Board as provided in Section 5. 

Data: A subset of information, whether quantitative or qualitative, that is regularly used by, maintained by, created by or on behalf of, and possessed, owned, or licensed by the Jurisdiction in non-narrative, alphanumeric,  or geospatial formats. Data are an asset independent of the systems or formats in which they reside.3

Data Governance: The policies, practices, and mechanisms adopted by a Jurisdiction to manage its Data Handling.

Data Governance Oversight Committee: The committee established and maintained as such in  accordance with Section 5.  

Data Governance Principles: The principles set forth in Section 2 of this Guide and such other principles regarding governance of Data Handling that the Jurisdiction adopts.

Data Governance System: The processes and procedures set forth as such in Section 5. 

Data Handling: The collection, creation, storage, use, transfer, dissemination, and disposal of Data, and  use of Data Platforms, and related security, risk mitigation, and breach damage containment  measures. 

Data Intermediary: An individual or organization, other than an employee or unit of the Jurisdiction, that assists the Jurisdiction in collecting, storing, disseminating, communicating, analyzing, or disposing of Data sought for use or sharing by the Jurisdiction.4

Data Security Policy: The “Data Security Policy” described in  Section 3.B.2.

Dataset: A collection of Data organized or formatted in a specific or prescribed way. Typically, a  Dataset consists of one or more tables and is stored in a database or spreadsheet. Files of the  following types are not Datasets: text documents, emails, messages, videos, recordings, image  files such as designs, diagrams, drawings, photographs, and scans, and hard-copy records. 

Data Platform: The methods, machinery, software, and related tools and systems utilized by the  City or Applicable Third Parties to collect, store, use, or make public any Dataset, including,  without limitation, those utilized in any Open Data Program. 

De-Identify: To remove all Personally Identifiable Information from Data.5

Encrypted: Any Data format with content designed to be protected and accessible only by private  parties specifically intended as an audience. 

Machine-Readable: Any Data format in which a computer can read and process information.  

Open Data: Data made open and freely available to all online in a Machine-Readable, open  format that can be easily retrieved, downloaded, and reused utilizing readily available and free Web search applications and software.6

Open Data Program: A City program dedicated to making specific Datasets available as Open  Data to the public, including, without limitation, programs that engage civic technologists, the research community, and other partners to make use of such Datasets in support of the  program’s goals.7

Open Data Programs Manager – The Jurisdiction employee designated by the Controlling Authority to  manage the City’s Open Data Programs and to perform the functions pertaining thereto described in Section 5.

Payment Card Industry (PCI) Data Security Standard: Standards adopted by the Payment Card Industry Security Standards Council to protect payment information for safe financial transactions.8

Personally Identifiable Information (“PII”):  information that identifies, relates to, describes, is reasonably capable of being associated with, or could reasonably be linked, directly or indirectly, with a particular individual (sometimes shortened to “personal information”). Examples include but are not limited to:

Note: Cybersecurity insurance or other policies may have different definitions of PII that could impact policies and processes. 

Principal Data Handling Administrator: The Chief Data Officer or other individual designated by the Controlling Authority to  be primarily responsible for oversight of adherence to this Policy. 

Privacy Laws: All laws containing provisions for the protection of a person’s privacy by regulation  of the collection, storage, use, and/or release of any PII of such person. 

Public Disclosure Law(s): All open meetings, open records, public records, freedom of information, or  similar laws pertaining to disclosure, notice, or other transparency requirements to which any  Data Handling activities of the Jurisdiction are subject.  

Re-Identify: To convert anonymized or De-Identified Data into PII. 

Sensitive Data: Information that the Jurisdiction determines should be safeguarded and protected  against unwarranted disclosure for legal or ethical reasons, for reasons pertaining to personal  privacy, or for proprietary considerations, and includes, without limitation, PII. 9

Unit Data Steward: The Jurisdiction employee designated by the Chief Data Officer as the person in a  Jurisdiction agency or department responsible for performing the functions of a “Unit Data Steward”  described in Section 5. 

Data Classifications Recommendations

Note: The following Data classifications recommendations in this subsection assume that the Jurisdiction’s Data Handling  experience is fairly mature.  An alternative set of recommendations for Jurisdictions with less mature Data Handling experience is presented at Alternative Data Classifications for Less Mature Data Handling Systems. 

If Data is not already classified by a third party, cities and counties should establish Data classifications by level of sensitivity. Sensitivity levels inform data collection, retention, storage, dissemination, and disposal. Classifying data protects privacy, limits data misuse, maximizes data usage, and facilitates sharing of open data sets.  The following suggested classifications could be established by rule or practice and incorporated into training, security measures, and data-related decision-making.  Data classifications should be reviewed regularly and updated as necessary. These classifications will also inform the parameters of a local government’s Data Security Policy.  

Level 0—Open  

Any Dataset regularly published in Machine-readable format by Jurisdiction or its Units on the Jurisdiction’s website, or otherwise treated as Open Data is considered “Level 0—Open” unless the Jurisdiction or a Unit makes a proactive determination to raise the classification. 

Level 1—Public, Not Proactively Released 

Data available for public access or release, not subject to any restrictions under any Public Disclosure Law or Privacy Law. 

Level 2—For Internal Government Use 

A Dataset that the Jurisdiction determines is subject to one or more Public Disclosure Law exemptions, but is not highly sensitive, and may be distributed within the Jurisdiction government without restriction by law, regulation, or contract. Data that is normal operating information but is not proactively released to the public. Viewing and use is intended for employees; it could be made available Jurisdiction-wide or to specific employees in a department, division, or business unit. Certain data may be made available to external parties upon their request. 

Level 3—Sensitive  

Data intended for release on a need-to-know basis. Data regulated by privacy laws or regulations or restricted by a regulatory agency or contract, grant, or other agreement terms and conditions. 

Level 4—Protected 

Data that triggers a requirement for notification to affected parties or public authorities in case of a security breach. 

Level 5—Restricted 

This data poses direct threats to human life or catastrophic loss of major assets and critical infrastructure (e.g., triggering lengthy periods of outages to critical processes or services for residents). Before classifying data as Level 5 Restricted, you should speak with leadership in your Unit and the Jurisdiction’s Chief Data Officer.

A Data classification flowchart and examples of each category of Data follows below:10

Data Classification Examples
Data classificationExamples
Level 0 Open• Open Data
• Public websites
• Press releases
• Job announcements
• Public reports
• Bid/contract/RFP announcements
Level 1 Public, Not Proactively Released• Certain financial data and reports
• Health or building inspection information
• Notices about future construction projects
• Organizational charts
• Internal memos
Level 2 Internal City Government Use• Employee phone directory
• Draft reports, memos, and meeting minutes
• Internal project documents
• Intranet
• Fuel consumption/fleet management data
• Learning management data
• Some financial data
• Some audio and video recordings
• License plate numbers
Level 3 Sensitive• Personnel records (including employee name + employee number, performance appraisals)
• Personally identifiable information (PII) not triggering statutory notification requirements
• Certain public safety/criminal record data
• Sensitive Security Information (SSI)
• Physical security access logs
• Investigative data (e.g., related to citations, legal proceedings)
• Trade secrets/proprietary/commercially sensitive data
• Internal risk management and mitigation data
• Central property management information
• Browser history
• Privileged communications
• Biometric information
Level 4 Protected• Social security number
• Driver’s license number
• State ID number
• Payment Card Industry (PCI) data and other customer financial information
• Protected health information (PHI)
•Password and PIN numbers
•Student records (FERPA)
• Federal tax information
• Some criminal justice information
Level 5 Restricted• Certain network/infrastructure information
• Certain water infrastructure
• Some emergency response information
• Some data obtained from federal government

Privacy and Other Data Governance Principles

This is a section of the Model Data Governance Policy & Practice Guide for Cities and Counties. Learn more about the report and find the other sections here.

Section Notes

Purposes. This Section offers recommendations on privacy protection principles as well as other Data Governance core principles and discusses the role of resolutions in establishing such principles.

Prominent Challenges Addressed. The initial working group that led to the MetroLab Data Governance Task Force identified several categories of challenges and considerations relating to privacy protection and other principles for city or county Data Governance, including: 

Privacy Principles and Resolutions

“Rules are not necessarily sacred, principles are.”

– Franklin D. Roosevelt

Privacy is an essential component of Data Governance. It is the right that determines the protection of an individual’s information.1 Depending on the level of data governance maturity and resources, there are three approaches to building in privacy as a key Data Governance pillar:

  1.   Establishing privacy principles by way of resolution.
  2. Conducting privacy impact assessments.
  3. Establishing privacy policies.

Privacy Principles

While cities, counties and states use many rules and regulations, a common first step is to establish privacy principles, often by way of resolution passed by the Jurisdiction’s governing body. Beginning in 2015, cities started publishing privacy principles to help establish trust with the community and express a commitment to using data for good and seeking to avoid unintended consequences. Or, as Columbus, Ohio says it, “the Data Privacy Plan starts with a statement of principles that illustrates Smart Columbus’ commitment to the ethical use of data. 

For a comparative look at (simplified/edited) city privacy principles, please see the below table:

Portland, OR (available here)Kansas City, MO (available here)Columbus, OH (available here)
Transparency: managing and collecting information in a described way clearly, accurately, and shared in an accessible way.Kansas City values privacy and considers risks to the well-being of the public before collecting, using, or disclosing personal information.Smart Columbus is as open to the public as it can about how it collects personal data.
Data will be secured and protected throughout its lifecycle.The City will only collect information that is needed to deliver city services, and the data will be kept only as long as legally required or valid for a business purpose.Smart Columbus will notify individuals when it collects their information.
Prioritization of the needs of marginalized communities regarding data and information management.When appropriate, the City will disclose how personal data will be used and give the option to choose how it is used whenever possible.Smart Columbus will use an individual’s information only for the purposes stated in the notice, and to which the individual consented.
Fair stewardship of data and information with non-discriminatory protections and understanding impacts of unintended consequences.The City will restrict improper access to data, securing cyber systems and storage resources.Smart Columbus projects will collect only the minimum amount of personal information that they need to accomplish their purpose.
Third parties working with city data must not expose confidential or private information.Business partners and contracted vendors who collect or receive personal data must agree with city privacy requirements.Smart Columbus will apply robust information security controls that take into account the sensitivity of project data and the risk of individuals that it poses if released.
The City will create procedures for reviewing, sharing, assessing, and evaluating automated decision system tools around equity, fairness, transparency and accountability.Residents should have an effective and responsive mechanism for exercising privacy complaints. The City will receive, investigate, and respond to individuals’ complaints.Smart Columbus will ensure that the data it releases on the Smart Columbus Data Portal does not contain information about identifiable individuals.
All data must bring value to the City, the City will collect only the minimum amount of personal information to fulfill a well-defined purpose.Smart Columbus will institute the processes necessary to ensure that it follows and meets each above principle.

For examples from other Jurisdictions, see the Privacy Principles section of the Resources Library. 

While the language varies, there are consistent themes across city and county privacy principles. These themes include:

Key takeaway: Privacy principles are a way to establish a commitment to privacy values that will provide guidance and parameters as the Jurisdiction moves forward in developing its privacy practices. Use these central themes as a guiding list to consider and employ best practices for community engagement described in Section 5.

Privacy Impact Assessment

Another way to protect resident privacy is to build a privacy review into IT processes by conducting  privacy impact assessments. The following language comes from the City of Seattle, WA:3

“A Privacy Impact Assessment (“PIA”) is a method for collecting and documenting detailed information collected in order to conduct an in-depth privacy review of a program or project. It asks questions about the collection, use, sharing, security and access controls for data that is gathered using a technology or program. It also requests information about policies, training and documentation that govern use of the technology. The PIA responses are used to determine privacy risks associated with a project and mitigations that may reduce some or all of those risks. In the interests of transparency about data collection and management, the City of Seattle has committed to publishing all PIAs on an outward facing website for public access.” 

The centralized IT department asks questions of the department seeking to procure the specific technology. Questions addressed in these assessments include some of the following:

Regularly asking departments to understand these detailed aspects of technology and data use allows for a thorough analysis of privacy risks. 

For other source of guidance on Privacy Impact Assessments see the Privacy Impact Assessments section of the Resources Library.

Privacy Protection Resolutions

In addition to privacy principles, Jurisdictions can consider resolutions that support privacy-forward processes. This includes: 

In addition to these processes, consider when additional notice should be given when Data is shared internally to a Jurisdiction.  For example, if a parks department gives Data to a policy department, a new data use or shift in resident expectation (that wasn’t shared at the onset of data collection) has potentially occurred. Intentionality should be at the core of Data collection. Jurisdictions often fail to address the “why” of Data collection. Data minimization is key to protecting residents’ privacy rights; accordingly, being intentional about why and how resident Data is collected is of paramount importance. Municipalities should standardize Data collection requirements and justification for the same. In considering the  recommendations offered in this Section 2 and in Section 3, one should be mindful that there are three conditions that justify data collection: 

  1. Mandated by law. 
  2. Requirement imposed by an external funder of a program. 
  3. Required to ensure optimal allocation of resources.

In addition to considering a resolution, staff can develop internal core principles for a Jurisdiction’s   leadership and staff to exercise appropriate care and diligence.   Some suggested internal principles: 

Other Data Governance Principles and Resolutions

“The basis of our governments being the opinion of the people, the very first object should be to keep that right; and were it left to me to decide whether we should have a government without newspapers, or newspapers without a government, I should not hesitate a moment to prefer the latter.  But I should mean that every man should receive those papers and be capable of reading them.”

– Thomas Jefferson (Letter to Edward Carrington, January 1787)

As discussed in the Preamble to this Guide, in addition to protection of Data privacy, the Task Force embraces the use of Data by cities and counties to address complex challenges and improve government services. Moreover, as addressed in Section 5 of this Guide, local government can and should engage the communities they serve in well-informed ways to collectively leverage properly available data for public good. Accordingly, in addition to the Privacy Principles and Resolutions discussed in Subsection B. above, it is recommended that Jurisdictions consider adopting by resolution principles along the following lines: 

The Task Force has identified various resources supporting the value of a Jurisdiction adopting those two principles and containing descriptions of practices to implement them that users of this Guide are encouraged to consider—see the Open Data Policies section of the Resources Library. 

In particular, we wish to highlight Washington DC’s data policy reflecting the intentional effort of using Data as a tool to improve services. 

From “Purpose” in D.C. Data Policy:9

“3. The greatest value from the District’s investment in data can only be realized when enterprise datasets are freely shared among District agencies, with federal and regional governments, and with the public to the fullest extent consistent with safety, privacy, and security. ‘Shared’ means that enterprise datasets shall be:

  1. Open by default, meaning their existence will be publicly acknowledged, and further, if enterprise datasets are not shared, an explanation for restricting access will be publicly provided;
  2. Published online and made available to all at no cost;
  3. Discoverable and accessible;
  4. Documented;
  5. As complete as can be shared;
  6. Timely;
  7. Unencumbered by license restrictions; and
  8. Available in common, non-proprietary, machine-readable formats that promote analysis and reuse.

By so sharing, the District can:

  1. Improve the quality and lower the cost of government operations;
  2. Make government more open, transparent, and accountable;
  3. Enhance collaboration between public bodies, with partner organizations, and with the public; and
  4. Further economic development, social services, public safety, and education by making data available to work with and study.”

Data Integrity and Data Protection/Cybersecurity

This is a section of the Model Data Governance Policy & Practice Guide for Cities and Counties. Learn more about the report and find the other sections here.

Section Notes

Purposes. Doing public good with Data requires that the Data is of sufficient quality/integrity, is properly accessible, and is stored safely. Recent cybersecurity incidents faced by city and county  governments very clearly highlight the importance of strong information security and privacy preserving practices when governments collect sensitive personal information.  This Section 3 offers recommendations regarding data quality/integrity and data protection and security standards and practices, as well as measures to lessen risks of damage from data breaches or cybersecurity attacks, in relation to intra- and inter-departmental data processing activities. Note: various levels of designated Data classifications require different and unique process considerations. Please refer to Section 1 for recommendations regarding Data classifications.

Prominent Challenges Addressed. The initial working group that led to the MetroLab Data Governance Task Force identified several scenarios, challenges, and considerations in connection with Data integrity, Data Protection, and cybersecurity, including:

Quality and Security Measures, Compliance, and Audit Mechanisms

Note: cybersecurity is a complex endeavor with several processes to consider. This section is a high-level overview, with auditing being a particularly key tool to ensure cybersecurity measures are in place.  

Data Quality and Integrity

“Data Quality” is critical to avoid garbage in garbage out. Once data is acquired, every data pipeline should go through a Data quality check. A Data quality check includes assessment of Data accuracy, validity, timeliness, and completeness. Jurisdictions  can set up review processes and steps to assess Data quality. Baseline Data assessment should include the following:

As capacity allows, Jurisdictions can embed the baseline quality checks and other auxiliary tests in the acquisition process itself. Datasets that fail baseline assessment should trigger a warning to the Data owner and initiate a review and correction process.1

“Data Integrity” goes beyond Data Quality which is primarily limited to checking for errors or anomalies in the dataset. Ensuring Data Integrity requires ensuring (to the best of your ability) that the Data is internally consistent and as free of bias as possible.2

Data Security Policy

The Jurisdiction should adopt a formal, written “Data Security Policy” for establishing and communicating  Data security requirements across all Jurisdictions departments and agencies. The Data Security Policy  should: 

Data Handling Systems

All Data or data systems (hardware or software)used by the city or county, its representatives, and Applicable Third  Parties, or interconnected to the Jurisdiction’s network(henceforth referred to as a “Data Handling System”) shall provide mechanisms for compliance with the Jurisdiction’s  Data Security Policy.  Such mechanisms should include, without limitation, the following: 

a. All Data Handling Systems shall be subject to a security assessment and  tested for vulnerability to unauthorized access or use prior to deployment. These scans should be done on regular schedule as determined by the Chief Data Officer. If a  Data Handling System employs any means of credit card transactions or  interfaces with third party systems that employ such transactions, such Data Handling System shall comply with the provisions of industry standards such as the Criminal Justice Information Standard (CJIS) or the Payment Card  Industry (PCI) Data Security Standard.

b. The city/county shall take additional precautions with respect to all Internet-accessible  Data Handling Systems to safeguard against unauthorized information  access or manipulation by outside actors. The Chief Technology Officer (or other appropriate leader of security and/or Information Technology on a city-wide basis) shall from time  to time promulgate a series of tests using up-to-date federal standards for  information assurance to ascertain the security of all Data  Handling Systems against: 

Compliance

A compliance approach is necessary by supporting a structured team or implementing a standard process. Working with IT department teams to ensure that those requirements are implemented, and documentation is maintained is critical. This is a significant amount of work. If capacity is restricted, consider whether this is internal or external compliance (i.e., holding vendors accountable to requirements and audit checks).  

The Jurisdiction’s Data Security Policy and Data Handling Systems shall comply with all applicable laws,  regulations and the Jurisdictions policies and practices. The city/county shall comply fully with applicable Public Disclosure Laws.5 Legal notices and copyrights shall be included for disclosure purposes. 

Security Audits

The city/county should conduct a periodic “Security Audit” at such intervals as are determined by the Controlling Authority and under the supervision of a recognized independent audit authority approved by the Controlling Authority.  The primary functions of the Security Audit are to evaluate all  Data Handling Systems  and other mechanisms in place to ensure compliance with the Data Security Policy, protect  information assets, and properly dispense information to authorized parties. Security Audits shall  include evaluation of each pertinent system’s internal design. Such evaluation must include, but  is not limited to, efficiency and security protocols, development processes, and governance or  oversight. Installing controls is necessary but not sufficient to provide adequate security. Security  Audits must include a report on the implementation of this Policy. The auditor must consider  whether the controls are installed as intended, if they are effective if any breach in security has  occurred and, if so, what actions can be taken to prevent future breaches. These inquiries must  be answered by independent and unbiased observers employed by the auditor performing the  task of information systems auditing. The following principles and actions should be among those  included in each Security Audit: 

Web Presence Audits

The extension of the Jurisdiction’s presence beyond its internally controlled Data Handling Systems, network, and management domain (e.g., the adoption of social media by the enterprise along with the proliferation of cloud-based tools such as social media management systems) requires the city/county  to incorporate Web Presence Audits into the Security Audit. The purposes of such Web Presence Audits are to ensure that the Jurisdiction and Applicable Third Parties are taking the necessary steps to: 

Network and Communications Systems Audits6

The city/county should audit its network, including all interfaces and interconnections with third party  networks and infrastructure, and its communications systems, whether controlled internally or  purchased as a service, for compliance with the Jurisdiction’s Data Security Policy. The “Network and  Communications Systems Audit” should ensure that the Jurisdiction’s network and communication systems: 

For sample approaches to Data Security Policies, Data Handling Systems, cybersecurity, and  related policies and practice tools, see the resources linked in the Data Management and Cybersecurity sections of the Resources Library.

Special Provisions for Open Data Programs

With respect to all of its Open Data Programs, it is recommended that the Jurisdiction:7

  1. Make Data it collects discoverable and accessible to the public only through Data platforms that adhere to its adopted Data Governance principles and comply with its policies on Data quality and Data Integrity and its Data Security Policy.
  2. Assess the Datasets to publish as Open Data, in accordance with standards and procedures established from time-to-time by a Data Governance Oversight Committee of the type described in Section 5 of this Guide), to identify risks of harm to personal privacy or personal safety and take steps to mitigate such risks. 
  3. Document the process for reviewing new Open Data requests, including who approves or denies the request and the rationale for the decision, and make the request, decision, and rationale available to the public.  
  4. Perform an annual risk assessment of the Open Data Program and the content available to the public pursuant thereto and present such report to the Data Governance Oversight Committee for its review, comments, and recommendations as to efficacy and risk mitigation strategies.
  5. Provide a public process to allow individuals to review and contest Data that concerns their own individual personal information, whether or not such information is PII.
  6. Provide to the Data Governance Oversight Committee an annual “Open Data Program Plan” and annually report on the assessment of progress towards achievement of the goals described in the Open Data Program Plan for the previous year.
  7. Include in its Open Data portal and any similar Jurisdiction-maintained mechanism for publishing Open Data appropriate Limitation of Liability Provisions.8
  8. To the extent prudent the Jurisdiction should:
    • Publish high quality, public Data with documentation online.
    • Ensure publishable Data is in the public domain and can be easily retrieved.
    • Minimize limitations on disclosure of public information while safeguarding Sensitive Data.
    • Encourage innovative uses of publishable data by agencies, the public, and other partners.

The “Operationalizing How”: Oversight, Organization, Inter-Departmental Process, and Community Engagement

This is a section of the Model Data Governance Policy & Practice Guide for Cities and Counties. Learn more about the report and find the other sections here.

Section Notes

Purposes. Data is an asset only if it is responsibly used to enhance the efficiency of cities and counties and improve residents’ quality of life. While protecting data from outside threats is a major concern in a Jurisdiction’s Data Governance, just as important is standardizing internal departmental procedures to safeguard data throughout its lifecycle. Such procedures should ensure data integrity, interoperability, accessibility, and security from the prying eyes of unauthorized individuals–even unauthorized individuals who work for a Jurisdiction department or agency.  

Prominent Challenges Addressed. The initial working group that led to the MetroLab Data Governance Task Force identified several categories of challenges and considerations in “operationalizing” city or county Data Governance, including: 

Some Threshold Considerations on Operationalizing Data Governance:

Roles and Responsibilities of Jurisdiction’s Primary Data Governance Personnel 

It is recommended that a city or county have a “Data Governance System” to provide consistently applied processes, with checks and balances, for managing all aspects of Data Handling by the Jurisdiction and Applicable Third Parties. The Jurisdiction should adopt, implement, and maintain mechanisms for oversight of its Data Handling System to ensure compliance with its Data Governance Principles and Data Security Policy, and consider including in its Data Governance System, in addition to any other components it deems appropriate, the interdependent roles, responsibilities and processes set forth in the following provisions of this Section 5. 

Chief Data Officer. The Controlling Authority should designate a Chief Data Officer to oversee all significant aspects of Data Handling and compliance with this Policy on a day-to-day basis,  and also  appoint an Open Data Programs Manager to oversee the implementation and management  of the Jurisdiction’s Open Data Programs and related policies and infrastructure.  In some municipalities, the duties and functions of the Chief Data Officer may be shared with a “Chief Information Officer” or other Jurisdiction employee responsible for overseeing data security and data integrity measures, or the same person may hold both positions. The roles and responsibilities of the Chief Data Officer should include, in addition to such other matters as the Controlling Authority may designate:2

  1. Managing the safeguarding of the Jurisdiction’s Sensitive Data. 
  2. Ensuring that the data and network security provisions described in Section 3, including, without limitation, the tests and audits described therein, are implemented. 
  3. Help Jurisdiction departments/agencies make better use of available Data. 
  4. Connect citizens with Jurisdiction Data to promote public benefits. 
  5. Maintaining and keeping up-to-date systems designed to ensure compliance with Privacy Laws, Public Disclosure Laws, and other applicable laws and regularly engaging with Jurisdiction Legal Counsel and Information Technology (IT) Staff in those efforts—including on matters of encryption, cybersecurity, and evolving best practices in view of the evolution of pertinent technologies. 
  6. Designating and training a Unit Data Steward for each Jurisdiction department and agency, with input from each such unit on such designation and training. 
  7. Coordinating with, as applicable, the “Chief Innovation Officer,” “Chief Information Officer”, and/or “Chief Technology Officer (as applicable with respect to matters relating to data security), the Open Data Programs Manager, and all Unit Data Stewards and creating systems and structures that promote teamwork and feedback loops to help reap the benefits of Data gathering and analytics in a manner consistent with the Jurisdiction’s Data Governance Principles,  and in accordance with consistently applied quality assurance, accountability, and ethical standards.3 Among other things, this coordination function should include attention to intra and inter-departmental data sharing (“Internal Data Sharing”). Data sharing with external parties such as vendors and educational institutions is addressed in Section 4 above. However, as part of data governance,  Internal Data Sharing can be equally challenging. External data sharing standards should be applicable to internal data sharing as well especially if the department is independent or quasi-independent such as the police department. Diligence regarding Internal Data Sharing before it occurs should include:
    • Reaching agreement on purposes for which the requested data can be used and making sure they are consistent with the Jurisdiction’s Data Governance Principles.
    • Ensuring that the requested Data use clearly demonstrates the benefits and value of data sharing. 
    • Committing to a retention duration and ensuring that Data is deleted post expiration of the retention period.
    • Agreeing on the degree of access the department personnel will have to the data. 
    • Determining whether there are special considerations to address when the Internal Data Sharing may involve particular types of governmental operations—such as city or county law enforcement, relationships with state or federal law enforcement (e.g., taxing or immigration law authorities), or public schools.    

Examples of approaches to Internal Data Sharing appear in several resources cited in the Resources Library. 4

Open Data Programs Manager. The Open Data Programs Manager – who in some Jurisdictions might be the same individual as the Chief Data Officer – would manage the Jurisdiction’s Open Data Program, and in performing that function:  

Unit Data Stewards. The Chief Data Officer will designate a Unit Data Steward for each  department and agency (each a “Unit”), in each case in consultation with  the Unit. A Unit Data Steward must be a Jurisdiction employee with other significant duties  within the applicable Unit or significant prior experience with the particular functions  and practices of that Unit. The responsibilities of a Unit Data Steward include: 

  1. Developing and maintaining a concrete understanding of:
    • the inner workings and outer relationships of the Unit regarding Data Handling;
    • the ability to recognize and classify Sensitive Data collected or generated by the Unit; and 
    • familiarity with the requirements of Privacy Laws and Public Disclosure Laws that may apply to the Unit’s Data Handling.  
  1. Updating the Chief Data Officer on new data availability, data issues, and system changes.
  2. Making recommendations to the Open Data Programs Manager as to what  Data collected or generated by the Unit the City should make available to  the public as Open Data.  
  3. Offering suggestions to other personnel in the Unit as to ways responsible and unbiased analysis of Data available to the Unit can improve the efficiency, quality, and positive impact of the work of the Unit. 

A general overview of the foregoing organizational structure follows (Source: Authors):

For other examples of Data Governance organizational structures adopted by Jurisdictions, see resources listed in the Data Management, Data Governance Policy, and Operationalization sections of the Resources Library.6

Comprehensive Oversight Model. 

This subsection provides an example of a comprehensive model for Data Governance oversight. It includes multiple layers and groups of staff and external stakeholders, including community members. Elements of these groups could be implemented in different ways depending on the Jurisdiction’s resources and organizational capacity. Subsection D. below provides additional recommendation on how to involve the community in the design, maintenance, accountability, and oversight of the Jurisdiction’s Data Governance System.  

Data Governance Oversight Committee

The Jurisdiction should create a “Data Governance Oversight Committee” comprised of the Chief Data Officer, the Chief Information Officer, and the Open  Data Programs Manager, Legal Counsel (e.g., a designated City Attorney), and the Community Advisory Body (CAB) “Convener” described in D.  below, and have the following authority, responsibilities, and general operating rules: 

  1. Act as an “executive committee,” chaired by the Chief Data Officer, in overseeing adherence to all significant elements of the Jurisdiction’s Data Governance mechanisms, including, without limitation, making  recommendations on matters that allow for optional means of compliance or expressly contemplate  discretionary actions. 
  2. Recommend (i) modifications to the Jurisdiction’s Data Governance System when the  Committee deems such modifications necessary to better adhere to the Jurisdiction’s Data Governance Principles or to respond to developments in technology or  other circumstances that necessitate such modifications to facilitate such adherence, and (ii) steps to clearly communicate such modifications to Jurisdiction personnel (including unit-level Data Stewards), to other participants in the Data Governance System, and to the public.  
  3. Review all Jurisdiction audit reports relating to Data Handling and make any recommendations to the Chief Data Officer deemed  appropriate based on such reports. 
  4. Periodically review the Jurisdiction’s training programs relating to Data Handling and      provide recommendations to the Chief Data Officer regarding such programs.
  5. Provide advisory input to the Chief Data Officer on other matters or decisions regarding Data Handling on which it is asked to provide such input by the Chief Data Officer or by the Community Advisory Board (CAB).  
  6.  In its work on significant Data Handling matters actively engage the CAB to  gather               informed and timely community input and channel it to the Committee, and then deliver such community input, together with any observations or  recommendations it makes based thereon to the Chief Data Officer. 
  7. Hold regular periodic meetings to facilitate performance of its functions and hold special meetings, whenever the Chief Data Officer or a majority of the Committee deems necessary or appropriate and develop other operational rules the  Committee deems appropriate to perform its functions in a manner consistent with  the Jurisdiction’s Data Governance Principles.

Community Advisory Board (CAB)7

The Jurisdiction should create a “Community Advisor Board (CAB)” consisting of a “Convener,” who shall be a non-voting ex officio member of such Board, and a reasonable number of regular Board members. The regular Board members should be or represent diverse community stakeholders. Accordingly, efforts should be made to  include as regular Board members representatives of: neighborhood  associations, educators from varied disciplines (including, among others,  human sciences such as ethics, philosophy, psychology, and sociology), the  business community, the technology community, and nonprofit  organizations that promote public health and safety, workforce  development, and equitable opportunities for well-being for vulnerable  populations such as disabled, aging, and low-income residents.  

Functions of the CAB

  1. The CAB’s primary function is to provide the Data Governance Oversight Committee with informed, timely, and diverse community input and recommendations on Jurisdiction Data Handling matters and decisions (i) on  which the Data Governance Oversight Committee requests such advisory  input and (ii) that the CAB determines should be brought to the attention of the Data Governance Oversight Committee.8 In performing its primary function, the CAB shall seek to
    • (i) advance adherence to the Jurisdiction’s Data Governance Principles, and
    • (ii) develop systems and methods for gathering,  memorializing, and reporting to the Data Governance Oversight Committee informed, timely and diverse community input and recommendations that are well designed and tailored for particular Data Handling matters and decisions it is addressing (i.e., not “one-size-fits-all”).
  2. The CAB should all also collaborate with the Community End User Testing Group described in subsection 5.D to facilitate diversity and timeliness in participation by  community stakeholders in that Group’s work.         
  3. The CAB should have regular meetings, at appropriate intervals determined by the Jurisdiction, as well as special meetings when called by the Convener (with  notice reasonable in the circumstances presented). The CAB shall fix its own  operating rules and procedures in a manner appropriate for its above-described functions.  

Designation and Functions of the Convener:

  1. Subject to 3. below, the Convener should be an individual designated by the Controlling Authority under such process and for such term of service as the Controlling Authority determines.9
  2. Subject to 3. below, the regular Board members should be individuals designated jointly by the Chief Data Officer and the Convener to serve for such term of service as is determined by the Controlling Authority.10
  3. In no event should any person be appointed as Convener or a regular Board member if such individual is (i) an employee of the City; (ii) a contractor  with the City; (iii) an owner, officer, employee, agent, or representative of  a for-profit business engaging or seeking to engage in a contract or other  commercial relationship with the City; or (iv) a spouse, parent, child, sibling  (including those related by marriage) or significant other of, or any person  who resides with, a person described in (i), (ii), or (iii). 
  4. The Convener should:  
    • Present an annual budget for the CAB to the Controlling Authority to secure resources needed for the CAB to operate.
    • Set the agenda for each CAB meeting, with input from the regular Board members.
    • Call special meetings of the CAB as and when needed.
    • Administer the conduct of all CAB meetings.
    • Manage the process of having the Board prepare and deliver reports its  input and recommendations to the Data Governance Oversight Committee.  
    • Serve on the Data Governance Oversight Committee and, in that  connection, monitor the extent to which the CAB’s input to that  Committee is taken into account in its work, and report to the regular CAB  members on the disposition of its input and recommendations. 
    • Prepare and deliver to the CAB and the Data Governance Oversight Committee an annual report summarizing the activities and impact of the  CAB for the reporting year. 

Civic End User Testing Group (CEUTG)11

The practice of having a “Civic End User Testing Group” can serve important purposes that relate to Data Governance but also advance a Jurisdiction’s public service objectives in the context of testing operations where “data” is not the primary focus.  In essence, such a group can bring a diversity of community perspectives to bear in the co-design of improvements to Jurisdiction systems with which community members interact. 

Under the direction of the Data Governance Oversight Committee, the Jurisdiction should create a Civic End User Testing Group (“CEUTG”). The CEUTG would provide feedback regarding the use and accessibility  of the Jurisdiction’s Open Data resources, websites, applications, and other citizen interfaces. 

  1. The CEUTG should be composed of community users possessing a variety of    technological  skill levels. The CEUTG will seek input from the Community Advisory Board (CAB) on  inclusiveness and diversity of community users. 
  2. The Jurisdiction would solicit participation in user testing through its existing websites and  applications or other means, with advisory input from the CAB, and in doing so may pose  eligibility questions to ensure participants represent a variety of skill levels. 
  3. The Jurisdiction might incentivize participation in the CEUTG testing by providing testers with  small monetary awards for completing applications and testing.12
  4. The CEUTG would report feedback from its user testing activities directly and  simultaneously to the Data Governance Oversight Committee and the CAB.

For other examples and practice tools regarding end-user testing as part of city or county Data Governance Systems, see resources listed in the Community Engagement and Resident Feedback section of the Resources Library. 

Community Engagement Needs and Methods.

Community participation in a city’s, or county’s Data Governance is essential for Data to become a community asset. Oftentimes, Data-related policymaking can be opaque or unaccountable to those experiencing the greatest risks of Data harms. There are important opportunities for communities to contribute to the design of Data Governance policies and practices, help hold organizations accountable, and improve communications. Creating spaces for communities to meaningfully contribute requires resources, time, and relationship building. These investments will improve Data Governance impacts and outcomes. A Community Advisory Board (CAB) as described in Subsection 5.C above would obviously be one key element of community engagement—one that we recommend can and should play role in supporting it—but the need for community engagement extends well beyond the oversight function of that body.

Engagement Planning

It is recommended that a Jurisdiction begin with proactive planning for any interaction or request of a community member’s time. Community members (and staff) have limited time and overlapping urgent priorities. Before designing an engagement, identify the goal and what can be provided to the participating community members in terms of how their input will be used and how the Jurisdiction will report back on the final impact of their participation. Depending on the identified goals of the community engagement, different forms of public participation may be necessary or useful:

The Jurisdiction might in this connection review Facilitating Power’s Spectrum of Community Engagement to Ownership or the International Association for Public Participation’s Spectrum of Public Participation for more details on different types of participation. Being explicit about the Jurisdiction’s Data Governance goals and how they relate to the ways community members interact with the Jurisdiction allows communities to know what to expect and how they can participate. 

Data Governance has many components. Information sharing events and materials may help prepare a variety of audiences to be able to sit at the same table for more collaborative engagements and involvement in the Jurisdiction’s Data Governance practices. Design presentations or materials with accessible language and examples that connect to community member’s daily lives or common interactions they have with the city or county. For example, community members are often required to share personally identifiable information (PII) when paying a bill or perhaps share anonymous demographic information about themselves when accessing a new service. Understanding how that Data is managed, who has access to it, and a clear reason for how and why you will use that Data are outcomes of Data Governance that will benefit community members. 

Data that the city or county is collecting or managing through its Data Governance System is often about and from communities. Community members are experts on their lived experiences. Knowledge and expertise of community members most susceptible to harm from Data are also required to disrupt existing harmful Data collection and analysis practices. By partnering with communities and leading with community driven needs, challenges, and strengths, the Jurisdiction may be able to prioritize where to focus Data Governance efforts if resources are limited. Here are several examples of Data Governance practices that would be served well by collaborative, co-design, or defer to styles of community engagement:

If throughout the development and implementation of Data Governance community engagement, the types of approaches utilized all fall on the informing or consulting end of the engagement spectrum, trust and partnership with communities may not be improved. It may require more staff time and resources to design interactions on the involve, collaborate, and defer to end of the spectrum, but the potential to thereby increase trust is also much greater. Note that there may be other aspects of existing Data Governance structures and/or leadership preventing meaningful contribution from communities. Identifying such impediments is necessary to find solutions or to communicate these limitations directly with community members.

Accountability

As Data Governance policies and practices are adopted, communities can play several roles for accountability. 

Section 5.C of this Guide details a comprehensive oversight model with a formalized Community Advisory Board and Civic End User Testing Group. This model requires that budgets available for the groups, stipends, and staff resources to adequately support the groups. Committee members in voluntary oversight or advisory bodies may quit if commitments are burdensome. To be effective, committee members need information and support. 

If a group is an advisory body, there needs to be clarity on who is ultimately the decision maker and how these decisions are made. This supports understanding of how information provided by committee members is or is not used. If a group is an oversight body, they need access to information about how implementation is going, where challenges are arising, and authority to make sure commitments are met. For example, a 2019 City of Portland Audit found that “if a government body commits to public oversight, it must work to ensure that participation is meaningful because ineffective participation can jeopardize public trust and waste resources and time.”

Accountability with communities may also be achieved through implementing other types of engagements. Accessible information sharing and meaningful education opportunities to create awareness about adopted Data Governance Principles and Data Governance practices are first steps. If these are implemented along with a clear contact at the organization, a community member or community-based organization can raise a flag if they see a practice being violated by staff. This places the burden on civic engagement and advocacy to flag but could be a minimum starting point. 

Community Involvement and Partnership

Community involvement and collaboration-style engagements can also be used for accountability touchpoints. This would require staff to prepare accessible report outs on implementation progress and what decision points communities can weigh in on to help assess the Jurisdiction’s Data Governance. Active involvement of communities in the design of Data Governance policies and practices allows communities to be able to identify if and how community needs they know were shared are being incorporated or not. 

One last model that could be incorporated into any of the above pieces is a community-government partnership model where community leaders are hired and paid as consultants. For example, the City of Portland Smart City PDX has implemented several iterations of this model documenting lessons learned from each year. Qualifications for community leaders can come from a range of experience including volunteering, organizing, or work. Below are examples of how Smart City PDX defines community leader excerpted from the 2021 Request for Qualifications: 

“As a Community Lead, you are eager to build inclusive technology and collaborative decision-making spaces through thoughtful partnerships between frontline communities and the Smart City PDX program. You are a connector – ready to think about how to link digital justice with the many priorities communities are already navigating. You are an organizer – ready to bring your community and their voices into the digital justice movement. Most of all, you are excited to work with a team of people who each have different skills, visions, and perspectives on what digital justice looks like.”

In this model, community leaders become a part of the Jurisdiction’s team to help design and implement new practices of centering community. These leaders have existing relationships with impacted communities. These positions help expand the team. They could be used to support a successful advisory committee body. Community leads can help design and implement engagement events that may bring new participants, beyond those who would attend events designed and led by staff alone. This same model could be achieved by contracts with community-based organizations to help build new relationships, expand involvement, and ultimately achieve data governance that serves your communities. 

For other examples of collaboration-style community involvement in the design of Data Governance policies and practices see resources listed in the Community Engagement and Resident Feedback section of the Resources Library.  

Liability Limitations, Governmental Immunity, and Cyber-Insurance

As indicated earlier in this Section 5, it is essential that a Jurisdiction’s Legal Counsel be regularly and closely involved in its Data Governance System and related oversight mechanisms. A city or county attorney’s roles should include, among other things, identifying potential risks of liability and recommending measures to help eliminate or mitigate the Jurisdiction’s exposure to liability associated with its Data Handling. 

Liability Limitation Measures

The Jurisdiction, with advice from its Legal Counsel, should in its Data Handling activities adopt and adhere to appropriate terms of use, disclaimers, exclusion of warranties, and other limitation of liability statements or provisions, monitor the effectiveness of such provisions, and seek to modify them when deemed necessary or appropriate based on experiences, technological developments, or other circumstances.  These types of provisions of course must take into account applicable laws and should seek to follow best practices.  Readers are encouraged to review relevant sections in resources listed in the Data Management, Open Data Policies, and Data Governance Policies sections of the Resources Library.  

Governmental Immunity and Cyber-Insurance.To the extent, if any, that the Jurisdiction’s Legal Counsel determines that “Governmental  Immunity” does or may not apply to any part(s) of the Jurisdiction’s Data Handling endeavors, or that it is otherwise desirable, the Jurisdiction might consider purchasing appropriate cyber-insurance for coverage related to loss or damage resulting from a Data hack/breach or spillage of Data.  Issues relating to whether or not a city or county has “Governmental Immunity” against liability for damages caused by Data hacks or breaches are quite complex, and can vary among Jurisdictions by reason of differences in state laws and other circumstances. A Jurisdiction should have its Legal Counsel explore them as well as the terms and implications of obtaining cyber-insurance.

For additional background in this connection, see relevant readings listed in the Additional Background Reading section of the Resources Library.  

Data Use Rights and Data Sharing Agreements

This is a section of the Model Data Governance Policy & Practice Guide for Cities and Counties. Learn more about the report and find the other sections here.

Section Notes

Purposes. Cities and counties regularly engage in “data sharing” in many ways, including through Open Data Programs that make selected types of Data publicly accessible; in agreements with vendors, research organizations, or other for-profit or non-profit organizations of various types; in arrangements with other cities or counties, or with state or federal law enforcement or other agencies; and “internally” where two or more city or county departments or agencies identify data sharing requirements, needs, or potential benefits.  

Prominent Challenges Addressed. The initial working group that led to the MetroLab Data Governance Task Force identified several scenarios, challenges and considerations regarding “Data Use Rights” and “Data Sharing Agreements,” including:

Special considerations regarding residents’ rights regarding their Data acquired by a Jurisdiction are addressed in the Data Governance Principles in Section 2 of this Guide, and Data sharing through Open Data Programs is addressed in Section 3.C.  Both a Jurisdiction’s “Internal Data Sharing” (e.g., among Jurisdiction departments) and Data sharing with other governments are addressed in Section 5.B.  This Section 4 focuses on Data sharing provisions included in documents for technology procurements from vendors (e.g., in RFPs) and negotiated Data sharing agreements with other non-governmental “external” parties, such as parties whose activities are subject to regulation by the Jurisdiction or organizations involved in research that might help inform Jurisdiction policies and practices or otherwise promote public good.  

For a list of several sources providing background, and sample policies and practice tools associated with the issues addressed in this Section 4, see the Data Sharing Agreements and Additional Background Readings sections of the Resources Library.   

Data Sharing Challenges/Common Considerations and Principles

Context and Threshold Guiding Principles

With the volume, velocity, and variety of data expanding exponentially, Jurisdictions are increasingly employing Data sharing to innovate, fill knowledge gaps, and facilitate other parties’ public good initiatives. For the purposes of this Guide, Data sharing, and acceptable use considerations are focused on Data the Jurisdiction (i) collects directly, (ii) receives through an agreement with a Data Intermediary or other Applicable Third Party engaged to collect the Data for the Jurisdiction, or (iii) has obtained from an Applicable Third Party and has permission to share.

The key to effective and appropriate Data sharing is for all impacted parties to have a common understanding of what Data will be shared, why Data sharing is warranted, the intended outcomes of the Data sharing, permitted and non-permitted uses of the Data,  the Data management approach to be employed, and the roles and responsibilities of each party. The parties involved, and associated guiding principles, include:

In line with Section 2, those parties and guiding principles should be considered and intentionally addressed when negotiating Data Sharing Agreements and Data Use Rights. Included below are a few additional comments related to principles and practices around ethical Data use and risk management. 

Ethical Data Use and Risk Management

Ethical Data use means using the Data to improve lives without introducing greater risk to those lives. While ethical Data use can be relatively straightforward for the Jurisdiction when collecting information in order to provide a service, the considerations are different when such administrative Data will be put to different uses through Data sharing. To determine if the Data sharing will result in continued ethical Data use, the following questions should be among those asked:

Risk appetite varies among Jurisdictions, with some being highly risk averse and others willing to accept some risk for potentially greater community rewards. Data sharing has a risk management component that needs to be aligned with the Jurisdiction’s tolerance for risk. One consistent output of all Data sharing should be public communications – what Data sharing is occurring, why, and to what benefit?  When managing risk, consider how the Data sharing message will be perceived by constituents and the Jurisdiction’s leadership.

Data Sharing Provisions in Procurements

This subsection addresses provisions pertaining to Data sharing in the context of a Jurisdiction’s procurements of technology from vendors of three types:

The Jurisdiction’s procurement processes for engagements that directly or indirectly involve any aspect of Data Handling (“Data Handling Procurements”) should follow the same principles, policies, and guidelines that apply to Data management within the organization.  Accordingly, Data Handling Procurements,  and related requests for proposals (“RFPs”), requests for information (“RFIs”), and requests for  quotes (“RFQs”) should reflect the following principles and practices: 

  1. The Jurisdiction shall recognize that the products and services it buys have inherent social,           human, health, environmental and economic impacts, and that the Jurisdiction should          accordingly make procurement decisions that embody, promote, and encourage a                 commitment to the community it serves. 
  2. Procurement officials and associated Jurisdiction employees shall apply due diligence in seeking to prevent or mitigate harm or inequity resulting from Data Handling. 
  3. To the extent deemed applicable by the Controlling Authority in the particular request regarding a Data Handling Procurement, require that respondents communicate how they will adhere to the Data Governance Principles adopted by the Jurisdiction and the additional guiding principles specific to Data use and Data sharing set forth in Subsection 3.B above. 

In addition, the following specific measures and questions drawn from three sources shared by Task Force members might be considered for inclusion in RFPs, RFIs, and RFQs, as applicable:

From City of Asheville, NC Technology Procurement Governance Checklist:

Asheville uses a checklist to evaluate whether a vendor’s product meets the City’s data, security, accessibility, and other standards and makes the checklist available to vendors. See the “Questions” and “Why is it Important?” explanations associated with each of the twenty-one following named “Items” addressed in Asheville’s checklist:1

From Platform Urbanism Data Sharing Policy Guidelines: 

While the context of Platform Urbanism Data Sharing Policy Guidelines2 is regulation of sharing economy platforms, the following seven named guidelines it sets out can also have relevance to a Jurisdiction’s procurements as well:

  1. Justify and focus data sharing requirements by defining government objectives and documenting use cases.
  2. Commit to minimizing platform data collection to the least invasive information needed to meet program objectives.
  3. Specify fields and frequencies to cater data granularity appropriately.
  4. Require machine-readable, open formats and standards, and consider appropriate data transfer approaches.
  5. Commit to program transparency, public oversight, and ongoing feedback.
  6. Establish organizational structure for [data sharing requirements] implementation, including roles, responsibilities, and enforcement mechanisms.
  7. Classify, protect, and permission Sensitive Data.

Sources underlying those seven guidelines and “Example Policy Language” are available in the PUDS Policy Guidelines site.3

Some Additional Recommendations for Data Sharing in the Procurements Context:

Also consider incorporating each of the following in planning and setting the proposed terms of a technology procurement that will involve Data Handling:4

Data Sharing Agreements with External Parties in Other Contexts

This subsection addresses Data Sharing Agreements which do not involve any payment by the Jurisdiction to an “external” party of the either of the following types:

Regardless of which of those types of parties are involved, the overarching considerations are common to both, and essentially mirror the considerations addressed in Subsection 4.C above regarding Data sharing provisions in technology procurement processes and documents—and implicate the same issues to address and due diligence and other recommendations made in that subsection.  

In general, the fact that context does not involve a financial payment by the Jurisdiction to the one or more other parties in the Data sharing arrangement does not render any of those recommendations irrelevant.  Indeed, some Task Force members expressed the opinion that special types of Data Sharing Agreements for arrangement with nonprofits or in other non-procurements settings are unnecessary. However, other Task Force members felt that there is value in having special forms/templates for non-procurement scenarios, as there can be some additional considerations to take into account in such situations. Such additional considerations might include, for example:

For references to some resources containing sample Memorandum of Understanding (MOU) approaches to multi-party data collaborations among government agencies and nonprofits on matters involving education, homelessness, housing, and other issues, see the Data Sharing Section of the Resources Library.

Resources Library for the Model Data Governance Policy & Practice Guide

This is a section of the Model Data Governance Policy & Practice Guide for Cities and Counties. Learn more about the report and find the other sections here.

This library of Data Governance resources has been compiled in conjunction with the Task Force initiative, and contains links to a wide range of policies, practice tools, and associated background readings. Both the Guide and the Resources Library are meant to be “living” instruments accessible on the MetroLab Network website that can be updated, expanded, and refined over time.

Privacy Principles

The City of Seattle Privacy Principles | Seattle, Washington – 02/23/2015

Six privacy principles adopted by the city of Seattle regarding data collection.

Privacy Principles – Resolution No. 88701 | Oakland, California – 03/03/2020

The city council of Oakland established a resolution NO. 88701 that states seven privacy principles to protect the privacy of all Oakland residents, visitors, and the public. 

Data Privacy and Information Protection Principles for the City of Portland | Portland, Oregon – 06/19/2019

Data Privacy and Information Protection Principles was adopted by the city of Portland in order to protect private and sensitive data managed by the city or those working on behalf of the city.

Data Privacy Principles | Kansas City, Missouri 

Kansas City created data privacy principles in order to be a more transparent and accountable government while protecting the privacy of their citizens.

Data Privacy Principles | ​​Minneapolis, Minnesota

The city council of Minneapolis adopted a data privacy principles resolution to incorporate shared values regarding data governance.

Data Privacy Principles | Hillsboro, Oregon 

The city of Hillsboro adopted five data privacy principles in order to improve their data governance.

Privacy Principles | ​​Mesa, Arizona

The city of Mesa adopted six privacy principles in order to protect the privacy of public information.

Public Data Principles | Boston, Massachusetts

The city of Boston adopted six public data principles to guide their data collection process and management infrastructure.

Link: https://www.boston.gov/departments/new-urban-mechanics/building-public-data-principles 

Privacy Principles | Arlington, Virginia 

The city of Arlington created privacy principles in order to improve and guide their data governance.

Privacy Principles | Syracuse, NY 

The city of Syracuse built a section of privacy principles as part of their data privacy policy guide. 

Privacy Principles | San José, California – 12/08/2020

The city of San José passed a digital privacy policy in which they included their privacy principles. 

Privacy Principles for Mobility Data 

These privacy principles are designed to guide the management of data in the mobility ecosystem. 

Data Privacy Plan | Columbus, Ohio – 02/08/2019 

The city of Columbus drafted a data privacy plan that provides an overarching framework for the ways in which Smart Columbus will protect the security of personal information that it collects and uses, and the privacy of the individuals to whom this information pertains. 

Privacy Impact Assessments 

Seattle Privacy Impact Assessment

This assessment conducts a review of how data is being collected and managed while determining different privacy risks. 

Privacy Impact Assessment Policies Help Cities Use and Share Data Responsibly with their Communities by Future of Privacy Forum 

This is a model dedicated for the government and communities working with personal data collected from smart city solutions. 

Data Management 

Data Management Strategy Overview | Dallas, Texas 

The city of Dallas created a Data management strategy in order to improve their data governance practices. 

Texas Data Management Framework Fast Start Learning Guide | State of Texas – Office of Chief Data Officer 

This guide provides a summary of the DAMA DMBOK with a remark on practical principles and application for government. 

San Francisco Data Management Policy, Section 1.0 – 01/17/19

San Francisco created a Data Management Policy. 

Fostering Civic Trust: A Policy Guide for Municipal Leaders by USIgnite 

This guide shows municipal leaders how to make informed decisions by explaining an ecosystem of civic trust: equity, data governance, privacy, cybersecurity, community engagement and equity. 

US Ignite’s Fostering Civic Trust Guide (pg. 21-26)

This guide, created by USIgnite, provided a policy guide for municipal leaders; however, section 1.3 highlights the key consideration for data sharing specifically. 

The Data Assembly – Responsible Data Re-Use Framework 

The GovLab and Henry Luce Foundation created this data assembly report in order to highlight the importance of re-use data especially after the covid-19 pandemic. 

Responsible Data Stewardship from Open Data Institute (OPI) – London 

This guide illustrates how to implement responsible data stewardship, and it highlights several principles to consider. 

Mobility Data State of Practice from Open Mobility Foundation  

This document compiles several data resources for cities to use as examples such as privacy principles, open data policies, data sharing, and so on. 

Beeck Center Data Labs Playbook by Georgetown University

This is a Data Labs Playbook that serves as a guide for public servants who are interested in launching a data-informed project in their state.  

Data Management by NYC – Guidelines for Internet of Things 

This is an example of how NYC manages, collects, and processes data. 

Defining a Data Intermediary from Civic Switchboard Guide

These are three strategies to identify a data intermediary. 

Dataset Inventory | San Francisco, California 

This is an example of a dataset inventory provided by the city of San Francisco. 

San José Digital Privacy and AI Manual – 05/16/2023

This manual aims to guide the city of San José on matters of digital privacy and artificial intelligence. 

TOOL: What Works Cities (WWC) 

WWC has a certification program that cities can take in order to become more data driven. 

TOOL: Resource Library from the city of San Francisco 

The city of San Francisco has been working closely with data; therefore, they created this resource library that includes their data resources such as data toolkits and open data metrics. 

Open Data Policies

Open Data Policy and Technical Standards Manual for the City and County of Honolulu | Honolulu, Hawaii – 06/2022

This is a manual regarding open data policy created by the city of Honolulu in order to improve their open data practices. 

Open Data Policy | Boulder, Colorado 

This open data policy was established to provide transparent and secure open data in Boulder county. 

Open and Protected Data Policy | Boston, Massachusetts

The city of Boston created an open and protected data policy in order to improve their data governance. 

Executive Order – 02-2013: Establishment of Open Data Policy and Portal for Public Information | South Bend, Indiana 

South Bend established an executive order NO. 2-2013 that will describe open data policy and portal for public information. 

Seattle Open Data Policy – 02/01/2016

This Open Data Policy defines the principles governing City of Seattle Open Data and describes the expectations for department participation and governance of the Seattle open data program. 

City of Charlotte Open Data Policy | Charlotte, North Carolina – 01/01/2015 

The city of Charlotte adopted an open data policy. 

Open Data Policy – Section 2-2130 | Kansas City, Missouri – 10/22/2015

The city council of Kansas City passed an ordinance NO. 150865 that states an open data policy in order to promote openness and transparency while protecting the privacy of their citizens. 

Open Data – Executive Order | Louisville, Kentucky – 10/15/2013

The city of Louisville passed an executive order that established their open data policy in order to improve their data governance. 

Seattle Open Data Program

Seattle has an open data program, in which they also include several open data resources. 

Open Data Standard Operating Procedure (SOP) Manual | City of Aurora, Illinois – Revised 12/02/2022

This is a manual created by the city of Aurora that focuses on open data in order to improve their data practices. 

TOOL: US City Open Data Census 

This site illustrates an overview of the degree of openness of specific key dataset in cities across the United States.

TOOL: Open Data from New York City 

New York City has a website that provides different resources to find or request open data. 

TOOL: Mapping Data Ecosystem from the Open Data Institute (OPI) – London 

This guide provides a tool for documenting and mapping the data ecosystem. 

TOOL: City of Aurora Open Data Portal

This is an open data portal that allows the public to access open data of the city of Aurora. 

Data Governance Policies

Protected Data Privacy Policy – Executive Order NO. 143 | City of Denver, Colorado – 02/16/2018 

The mayor, Michael B. Hancock, passed an executive order NO. 143 that established a protected data privacy policy for the city and county of Denver. 

Establish Privacy and Information Protection Principles for how the City collects, uses, manages, and disposes of data and information presentation –  Resolution NO. 37437 | Portland, Oregon – 06/19/2019 

The city of Portland passed a resolution NO. 37437, which established privacy and information protection principles that the city will follow when gathering data. 

District of Columbia Data Policy – Mayor’s Order 2017-115 | Washington DC – 04/27/2017 & Amended 06/18/2018

This is a mayor’s order that provides a comprehensive data policy for the District of Columbia government. 

Personal Data Collection and Protection Ordinance | City of Chicago, Illinois 

The city of Chicago passed an ordinance that regulates how operators collect sensitive customer personal information through the Internet about individual consumers in the city of Chicago. 

Data Collection Policy | South Bend, Indiana

The city of South Bend has incorporated a data collection policy that states how they collect, manage, and publish data. 

Resolution – 20111208074 | Austin, Texas – 12/08/2011

The city of Austin passed a resolution NO. 20111208074. 

Data Privacy Guidelines – 01/2021 | Long Beach, California

Long Beach created a data privacy guideline that incorporates data privacy principles and guidelines when working with data. 

Seattle Privacy Program 

Seattle has implemented a privacy program.

Using MDS under GDPR 

This guide is specifically for European nations since it is related to the General Data Protection Regulation, which is European Union regulation; however, it can be an interesting reading for cities.

Data Quality Standards and Review Process | New York City, New York 

NYC Open Data Team and Office of Technology and Innovation created a guide with data quality standards for Open Data Coordinators and Data owners to follow. 

Privacy Policy for Smart.Columbus.gov | Columbus, Ohio 

The city of Columbus created this privacy policy that will be applied when the city or its partners are collecting personal information. 

California Consumer Privacy Act (CCPA) – 2018 

This act allows consumers to have more control over their personal information when businesses are collecting that information. 

NYC Guidelines for the Internet of Things (IoT) – Privacy + Transparency 

This website illustrates how the city of IoT provides privacy and transparency to their residents. 

Data Sharing Agreements

Data Sharing MOU (Memorandum of Understanding) template

This template, made by Arlington County, illustrates a MOU between university partners, and data classification guidelines as well. 

Data Labs: Roadmap to Recovery Example Data Sharing Agreements (or MOUs/MOAs)

This is a collection of examples and articles about Data Sharing MOUs from Beeck Center (at Georgetown University).

Enterprise Memorandum of Understanding by the state of Connecticut  

This is an example E-MOU presented between several parties such as the State Board of Education, Office of Early Childhood, and so on, and Connecticut Office of Policy and Management, and Connecticut Department of Labor. 

Memorandum of Understanding by the state of Kentucky

This is an example between different state agencies to audit/evaluate education programs and to authorize the release and use of confidential data. 

SBA Disaster Data Sharing MOU and Instructions 

This document, provided by the U.S. Small Business Administration, drafts an example for local and state governments who are entering into a MOU with the Office of Capital Accessd in order to prevent duplication of benefits. 

 NCDHSS Data Sharing Guidebook

This guide, created by the North Carolina Department of Health and Human Services, provides clear processes for data sharing and integration.

State of Connecticut Data Sharing Playbook 

This is an example of an operationalization focused playbook that includes data sharing that includes templates and walkthroughs. 

Assessing risk when sharing data: a guide from the Open Data Institute (OPI) | London 

This guide provides the early steps organizations need to take when sharing data in order to reduce and manage the risks. 

Platform Urbanism Data Sharing (PUDS) Policy Hub and its Best Practices Recommendations 

This is a two-part resource providing relevant recommendations and supporting materials regarding Data Sharing linked to policies from several jurisdictions. 

Data Labs 

Data Labs presented a series of case studies for data owners, especially those working with PII (Personal Identifiable Information) on how to improve their data practices. 

Beatriz Botero Arcila, The Case for Local Data Sharing Ordinances, 30 Wm. & Mary Bill Rts. 1015 (2022) 

This is an academic article that describes the current relationship between the Fourth Amendment and privacy laws when sharing data. 

Megan Marini, Troy Simpson, and Priyanka Jain, A Rhode Trip: Lessons for the Future of Mobility From the Little Roady Autonomous Microstransit Pilot (2022)

This article talks about the Little Roady pilot project and the challenges of data sharing limitations. 

TOOL: What Works Cities (WWC)  

WWC has a certification program that cities can take in order to become more data driven. 

TOOL: Data Collaboratives Explorer from GOVLAB

GOVLAB provides data collaboration between public and private partnerships across the globe, so they can create public value. 

TOOL: Contractors for Data Collaboration (C4DC)

C4DC aims to improve understanding of the legal conditions that can enable effective data collaboration.

Cybersecurity

Integrating Cybersecurity and Enterprise Risk Management (ERM) – NISTIR 8266 (National Institute of Standards and Technology – US Department of Commerce) 

This is a publication by NISTIR that highlights the importance of cybersecurity risk in all enterprises because of the frequency and increase of cyberattacks. 

The NIST Privacy Framework: A Tool for Improving Privacy through Enterprise Risk Management

This tool provides better privacy practices that will support organizations to protect individuals’ privacy.

Framework for Improving Critical Infrastructure Cybersecurity by NIST 

This website provides several standards, and a cybersecurity framework. 

ISO/IEC 27001 – Information security management systems 

These are standards for information security management systems (ISMS) for companies of all sizes. 

ISO/IEC 27002 – Information Security, cybersecurity, and privacy protection – Information security controls

This is a document that provides organizations with generic information on security controls. 

Standards for Security Categorization of Federal Information and Information System – FIPS 199

This document is provided by NITS, and it states a standard for categorizing federal information and information systems. 

Citywide Cybersecurity Policy | San Francisco, California 

San Francisco created a citywide cybersecurity policy in order to support, maintain, and secure data systems.

Information Security Policy | Chicago, Illinois 

This policy states the role for data security, requirements for protecting sensitive data, and mission critical systems, and protects the city’s systems and data

Cybersecurity Best Practices for Smart Cities 

This guidance was created by different countries’ cybersecurity agencies in order to integrate information and communication technologies, community wide data, and operational technology. 

PCI (Payment Card Industry) – Privacy Security Council 

This is a global forum dedicated to protect account data security by developing standards and resources in the payment card industry. 

Security from NYC – Internet of Things 

This is an example of how the aspect of security regarding data is handled in NYC. 

San Francisco City-wide IT focused – Disaster Preparedness, Response, Recovery, and Resilience Policy 

San Francisco developed a DPR3 that will ensure the delivery of public services during, and after a disaster.

Data Classifications

Data Classification Standard | San Francisco, California

The city of San Francisco created a data classification standard that requires departments inside the city government to categorize and label data per classification levels. 

Data Policy – Dataset Classification | Washington DC, District of Columbia

Washington, DC created a data policy that also provides dataset classification that other cities can utilize. 

Data Security: Policies and Regulations Impacting Research Data from University of North Carolina 

This provides a definition for sensitive data, and level of sensitive information followed by University of North Carolina. 

Operationalization

Appointment of Chief Data Officer – Resolution NO. 2019-30869 | Miami, Florida – 06/26/2019 

This is an example of a resolution issued by the mayor and city of Miami to appoint a data chief officer and its responsibilities. 

Eight Strategies for Chief Data Officers to create and demonstrate value by Harvard Business Review 

This article illustrates different strategies that Chief Data Officers can implement to strengthen their value. 

DatSF Guidebook: Data Coordinators Edition

This guide is directly to Data Coordinators in the city and county of San Francisco to provide guidance in their new role. 

The Chief Data Officer in Government 

This is a playbook created by Deloitte Center for Government Insights that provides roles and responsibilities for a government Chief Data Officer (p. 3-4). 

Checklist: The city of Asheville, NC Technology Procurement Checklist 

This is an example of questions that the city of Asheville asked when purchasing technology. 

TOOL: Data Ethics Maturity Model: Benchmarking your approach to data ethics from the Open Data Institute (OPI) |  London 

This tool allows organizations that work with data to benchmark their maturity in relation with data ethics. 

Community Engagement and Resident Feedback

CUTGroup Chicago – Engaged their community (pg. 1)

This is the completed guide that explains the CUTGroup in detail. 

Chicago Tech Collaborative CUTGroup

It shows the latest projects CUTGroup Chicago has been working on. 

CUTGruops Detroit

It is a tester pool that brings Detroit residents to evaluate civic websites and apps, and it also provides several data literacy training sessions.

CUTGroups Seattle 

This article describes the creation of Civic User Testing Group in the city of Seattle.

The Spectrum of Community Engagement Ownership 

This guide was provided by the Facilitating Power and it draws a pathway for local democracies to strengthen and transform.

An opportunity for community leaders 

This article presented by SmartCity PDX, in which states two innovative ways for engaging community leaders. 

Community Leads Request for Smart City PDX Program 

This is an example provided by the city of Portland regarding a position as community leads in order to close the gap between technology and marginalized communities. 

Link: 

https://static1.squarespace.com/static/5967c18bff7c50a0244ff42c/t/611adc797143ff4af062e56e/1629150329289/Community+Leads+Request+for+Qualifications+Due+Sept+7+2021+at+5pm.pdf 

Participatory Data Stewardship 

This is a report provided by Ada Lovelace Institute that provides a framework for involving people in use of data. 

City Leader Guide on Civic Engagement from Bloomerang Harvard 

This guide provides analytic tools for city leaders to engage residents in public problem-solving, and facilitating innovative practices. 

OECD Innovative Citizen Participation and New Democratic Institutions: Catching the Deliberative Wave

This is an informative resource that provides several models of deliberative processes. 

IAP2 Spectrum of Public Participation

This is an example of how to collect public participation.

Seattle Community Technology Board 

The city of Seattle created a community technology board, which provides recommendations to the Mayor and City Council regarding information and communications technology as well as effective electronic civic engagement.

TOOL: KC Digital Drive, Code for KC and Missouri Western University launch Kansas City’s First Civic UX testing group 

Civic UX Testing group is a tool created by KC Digital drive with the goal to build technology applications to help resolve social and civic issues.

Chicago Tech Collaborative’s Civic Design & User Testing initiative (“CUTGroup”)

CUTGroup is a civic engagement program that provides a space to Chicago residents to contribute to emerging technology while providing feedback to public, privacy and social sector partners. 

Additional Background Readings

Legal Technology Laboratory 

The Legal Technology Laboratory is a community of practice platform designed to promote collaborations by innovators at intersections of law, technology, education, and entrepreneurship and empower multidisciplinary teams to address social, civic, and economic development challenges with technology-assisted solutions and data-driven policy development.

General Services Administration, Rules and Policies – Protecting PII – Privacy Act (11/15/2018) 

The U.S. General Services and Administration drafted a set of rules and behaviors to follow when handling personally identifiable information. 

NYC Internet of Things 

NYC Internet of Things along with other 35 cities help government and their partners to responsibly deploy connected devices and IoT (Internet of Things) technologies

​​Cf. REGULATION (EU) 2016/679 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL 

On 27 April 2016. On the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation-“GDPR”) – Paragraph 32. This is a regulation established by the European Parliament that states the importance of protecting one’s personal data.

Algorithmic bias detention and mitigation: Best Practices and policies to reduce consumer harms

This article was published by the Brookings in 2019, it describes the current mass-scale digitization of data and the consequences of it in different sectors such as in economics, transportations, retail, and other areas. 

Bias Mitigation in Data Sets 

This article describes the different ways bias can happen in datasets, and it was published by SOCARXIV, an open archive of social science.

Michael Juerens, Social Media Risks Create an Expanded Role for Internal Audit from The Wall Street Journal (08/06/2013)

The Wall Street Journal published an article describing the different threats that social media can create; therefore, it is crucial to create internal audits. 

Beatriz Botero Arcila, Sharing Data in the Sharing Economy: Policy Recommendations for Local Governments. 9 Indiana J. Law and Society Equity 1 (2021)

Indiana Journal of Law and Social Equality published this academic article that provides policy recommendations when daring data in the sharing economy.

The City needs to make realistic commitments to voters and ensure they are delivered

This is an example of an audit made to the city of Portland regarding accountability for voter-approved taxes. 

Data Labs 

Data Labs presented a series of case studies for data owners, especially those working with PII (Personal Identifiable Information) on how to improve their data practices. 

Privacy Guide For Cities & Public Agencies from Open Mobility Foundation – 03/17/2023

This guide was developed by the Open Mobility Foundation and the Privacy, Security, and Transparency Committee to orient cities as technologies transform cities’ transportation networks. 

Mozilla’s framework notes for data commons governance 

This article provides a framework when applying Ostrom’s principles to data commons governance. 

The softer side for data governance: a playbook for non-technical users from the Open Data Institute (OPI) | London 

This playbook is directly to non-technical users leaders in the healthcare field who work with data on their daily basis.  

Sovereign immunity in the age of continuous cyber warfare – 07/15/2015 

This article describes the importance of sovereign immunity, and its implication against cyber attacks. 

Distinguishing Between Governmental and Property Functions (Chapter 2 of Local Government Immunity to Lawsuits in North Carolina (2018) 

This chapter talks about the origin of the distinction in immunity cases and its relationship with the judiciary’s role. 

Sean Andrés Rapela, The Ugly Truth Cyber Security Insurance & Governmental Data Breaches, 21 J. High Tech. L. 242 (2021) 

This academic article states the increased number of cyberattacks while also proposing a cyber relief program. 

Rebekah Luna, Stranger Danger!: How Hackers Break Into School Databases to Steal Student Data, and What Legislatures Should Do About It, 54 Tex. Tech L. Re. 381 (2022) 

This academic article refers to schools as easy targets for cyber attacks due to the lack of legislation in this matter. 

How to know you are a “data intermediary” under the Data Governance Act from the International Association of Privacy Professionals (IAPP) 

This is an article that describes the roles of data intermediary while following the Data Governance Act

FOIA (Freedom of Information Act) 

This is the central website for FOIA that cities can check for any new regulations. 

European Union’s General Data Protection Regulation (GDPR)

This is a resource that will help organizations and individuals when working with the General Data Protection of the European Union. 

Privacy Definition & Legal Meaning in the online version of Black’s Law Dictionary (2ND Ed.)

This website provides a legal definition of privacy, and divides it into four categories. 

Cities Partner on Model Policy for Handling Municipal Data

This article was published by Government Technology in 2020, and it provides some insight regarding the early stages of the Data Governance Policy. 

Model Data Governance Policy & Practice Guide for Cities and Counties

Preamble: The Why, What, And How Of This Guide

“Data is like garbage. You better know what you are going to do with it before you collect it.”

-Mark Twain

Purposes, Intended Uses, and Scope

Purposes. Data Governance is an important, complicated consideration in the modern era of local governments. As cities and counties (each sometimes hereinafter referred to as a “Jurisdiction”) discover ways to use, retain, and organize data, it is imperative to learn from one another as we consider the complex nature of governance.  Sharing effective approaches to policy development and implementation tools in a “community of practice” manner, as envisioned in the formation of the MetroLab Network Data Governance Task Force that produced this document, enhances the ability of local governments to both use data to provide increasingly efficient and beneficial public services, and to protect the public and mitigate risks of misuses of data.

Intended Uses. This Policy and Practice Guide (the “Guide”) is intended  to be several things:

  1. A useful tool for practitioners, co-developed by practitioners (with the guidance and input of expertise from individuals from academia and other organizations with relevant expertise). 
  2. A living guide housed on the MetroLab Network website, and curated, updated, and refined there through a multi-functional online platform to help local governments as they keep pace with rapidly evolving Data collection and dissemination technologies and other changes in circumstance.
  3. A reminder of several legal considerations that permeate Data Governance—while a few lawyers were involved in this project, it is incumbent upon us to tell you that this is not legal advice, and the local governments should of course obtain legal advice from their attorneys on legal issues affecting Data governance policies and practices. 

Scope. The suggested governance approaches in this Guide are for Data that is owned or in possession of a municipality—this includes Data that the Jurisdiction directly collects, or Data received by a local government intentionally (i.e., the local government has contracted with a third party or is working with a third party on a project/pilot, such as a grant). While this Guide addresses a wide range of local government interactions with such Data internally and externally, it does not include governance approaches or specific policy considerations or recommendations on issues of surveillance or uses of artificial intelligence.

A Note on Maturity Levels: We recognize that cities and counties are at different levels of established processes with respect to Data Governance. We have included the full gamut of recommended policies. This Guide includes resources and recommendations for varied maturity levels and the website search tools will be maintained in a way designed to help users at varying stages in their Data Governance journeys navigate to the resources most pertinent to their needs and circumstances. 

Simply put: got data? Use whatever portions of this Guide fit well with your needs and circumstances!

Data Ethics and Data Empowerment

We would like to highlight the ethos of this collaborative group of practitioners and Data Governance subject matter experts. First, what drives much of our passion and curiosity to ensure a proper governance structure is our sense of obligation to protect and provide for residents. Data is a powerful tool, a tool that is required to provide services for everyday necessities like water, electricity, and food stamps. Therefore, it is incumbent upon local governments (and other levels of government alike) to protect and take care of people’s information. And while Data is something to protect, it is also something that can unlock answers to complicated challenges and improve local government services. Thus, we also wish to advocate for its (proper) use. Data is also a powerful tool for good. The combined importance of those two themes is the “why” behind the publication of this Guide. Local governments have a moral obligation to protect individual Data, and an obligation to use it to hold it accountable as a service provider. We know both can and should coexist.

Project History and Methodologies

Project History

Origin. Students and faculty in an interdisciplinary and multi-institutional projects-based, graduate-level civic and social entrepreneurship course at the University of Missouri-Kansas City (UMKC) developed a Draft Model Data Handling Policy (“Draft Data Handling Policy”) in collaboration with personnel in Kansas City, MO city government and in Kansas City, KS/Unified Government of Wyandotte County, and other individuals.1  Many elements of that document reflected: (i) studies of data-related and “Internet of Things” (IOT) policies or guidelines in various cities in the U.S. and some in other countries, many of which were “Open Data Portal” policies; (ii) research on several issues presented by municipal data initiatives; and (iii) review of a sampling of data sharing agreements that some cities had entered into with for-profit companies and other organizations in varying contexts. 

MetroLab Network Vetting of Draft Data Handling Policy.  The Draft Data Handling Policy was vetted at a Roundtable Session at the September 2019 MetroLab Network Summit held in Boulder, CO. That session validated the proposition that many local governments were in the process of developing or were interested in developing relatively comprehensive Data Governance policies with wider scope than seen in Open Data Portal policies. It also provided great feedback and suggestions for a next iteration of that draft document that would, among other things, have more practice tools, be less “prescriptive,” and offer options for local governments at varying levels of maturity in their data collection, data security and data sharing activities and processes. Following up on the Boulder session, a call for collaborators from across the United States to participate, with MetroLab network assistance, in the co-development of the next iteration of the Draft Data Handing Policy, as described in an April 2020 article in the online GovTech publication.2

2022 Formation of the MetroLab Network Data Governance Task Force.  After a hiatus occasioned by the COVID-19 pandemic, in the winter and spring of 2022 UMKC Professor Tony Luppino renewed the call for collaborators to build on an April 2020 version of the Draft Data Handling Policy and organized an initial co-working group for its next iteration. At a Roundtable Session at the MetroLab Networks Summit in Chicago, IL in June of 2022 several members of that group presented reasons to take a “community of practice” approach to that endeavor. That session led to a collaboration, among Prof. Luppino, MetroLab Network leadership, and Miles Light of the Future of Privacy Forum to expand and formalize an initial project co-working group, which in turn resulted in the MetroLab Network formally launching a national task force to bring practitioners and subject matter experts together. That MetroLab Data Governance Task Force (sometimes referred to in this Guide as the “Task Force”) is comprised of  city and county staff members, metropolitan planning organizations staff members, educators, and other researchers from diverse disciplines and jurisdictions across the United States—a group of approximately 50 individuals from some 20 cities and counties that made this Guide possible.3 

Methodologies

The Task Force utilized the following principal steps/methodologies in producing this June 2023 Guide: 

  1. Online meetings of “Sub-Groups” of volunteers to explore specific topics and “standard headaches” (i.e., “Challenges”) identified in May 2022 by the initial co-working group.
  2. Asynchronous postings in an online platform of comments on the April 2020 Draft Data Handling Policy, resources to consider in addressing the Challenges presented, and use cases to help guide thinking on the development of potential policies and practice tools.
  3. Initial drafting of Guide text, working off of the April 2020 document as a starting place, by a group of volunteer drafters and editors from within the Task Force.
  4. A series of live online co-working sessions open to all Task Force members to explore key discussion questions relating to text sections of this Guide, with results then taken into account by the editors in producing a refined draft.
  5. An “all hands” online meeting, and an additional call for postings in the Task Force’s online platform to gather feedback on that refined draft and gather “practice tools” suggestions for inclusion in the Guide’s appendices.
  6. Presentation of the Guide in an online platform on the MetroLab website in June 2023 as both a downloadable document and electronic version that users can navigate on the site.

Format of this Guide 

This Guide has five Sections essentially corresponding to key topic areas identified by the 2022 Initial Co-working Group and subsequently refined by the Task Force.  Each Section begins with a brief “Section Note” summarizing its purpose/subject matter and noting the most prominent Challenges identified by the initial co-working group addressed in the Section, and then proceeds with recommendations of principles, policies, and/or practices a city or county might consider adopting in addressing such Challenges.  It also contains several footnotes citing or linking to sources or providing other information for readers. References in the Guide to materials posted on websites mean such  materials as they existed on those websites on June 20, 2023. In addition, a library (Resources Library) of Data Governance resources has been compiled in conjunction with the Task Force initiative, and contains links to a wide range of policies, practice tools, and associated background readings. Both the Guide and the Resources Library are meant to be “living” instruments accessible on the MetroLab Network website that can be updated, expanded, and refined over time.

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MetroLab
Definitions and Data Classifications

A core set of definitions reflecting municipal uses of Data will be vital to standardizing practices across departments and jurisdictions.

06.20.23 | 7 min read
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MetroLab
Privacy and Other Data Governance Principles

While cities, counties and states use many rules and regulations, a common first step is to establish privacy principles, often by way of resolution passed by the Jurisdiction’s governing body.

06.20.23 | 9 min read
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MetroLab
Data Integrity and Data Protection/Cybersecurity

Doing public good with Data requires that the Data is of sufficient quality/integrity, is properly accessible, and is stored safely.

06.20.23 | 9 min read
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MetroLab
The “Operationalizing How”: Oversight, Organization, Inter-Departmental Process, and Community Engagement

While protecting data from outside threats is a major concern in a Jurisdiction’s Data Governance, just as important is standardizing internal departmental procedures to safeguard data throughout its lifecycle.

06.20.23 | 20 min read
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MetroLab
Data Use Rights and Data Sharing Agreements

With the volume, velocity, and variety of data expanding exponentially, Jurisdictions are increasingly employing Data sharing to innovate, fill knowledge gaps, and facilitate other parties’ public good initiatives.

06.20.23 | 10 min read
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MetroLab
Resources Library for the Model Data Governance Policy & Practice Guide

This library of Data Governance resources has been compiled in conjunction with the Task Force initiative, and contains links to a wide range of policies, practice tools, and associated background readings.

06.20.23 | 17 min read
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