Research Request: Data Governance Projects

Wondering if there are any cities that have gone through the Data Governance process and really implemented a great project. Would these cities and/or the Alliance share with us their data governance policies, training materials, tools used and possibly a listing of lessons learned that would enable us get city staff to embrace this critical issue?

PROGRAMS AND PROJECTS | Nov 25, 2019

Request Prompt:

"Wondering if there are any cities that have gone through the Data Governance process and really implemented a great project. Would these cities and/or the Alliance share with us their data governance policies, training materials, tools used and possibly a listing of lessons learned that would enable us get city staff to embrace this critical issue?" 

Summary of Findings: 

There is a great deal of research and materials available from different organizations that are leading in data governance. Some important characterizations and consensus among the research and from cities’ own experiences is that many tend to use directives and resolutions from the top down to institute their data governance policies (Scottsdale; San Francisco; Louisville; Evanston; Philadelphia; New York City; Chicago). Often, data governance projects are in line with a city’s initiative or goals, i.e. transparency or effective budgeting purposes (Louisville; Mesa; Scottsdale). Additionally, cities tend to use resources and outside guidance to implement data governance, develop data governance policies, and processes. Particularly popular are those from Bloomberg’s What Works Cities and the Sunlight Foundation (Mesa; Scottsdale; Evanston; Louisville). Other resources include Socrata, GovEx, and Code for America (Mesa; San Francisco), which help cities publish their data (see Tools). Hillary Beata with the City of Evanston reported that What Works Cities’ roadmap to pursue open data initiatives is helpful in establishing a data governance framework as the certification requires a data governance policy, which is fundamental going forward for open data pursuits.

Another trend in data governance projects is that there tends to be a “champion” or someone willing to see the project through. Each of the interviewed cities, and cities that have a data governance project, each assembled a data governance team to help create and establish the policy for the city, and also used relied on their IT team to develop the platform simultaneously (Scottsdale, Mesa, Louisville, Evanston, San Francisco). In a survey of 65 cities, 75% of cities that had undertaken big data initiatives had success using a team approach or multidepartment governance structures for data initiatives (Ho & McCall, 2016; The Pew Charitable Trusts, 2018). Hillary Beata supports this in her suggestion that implementing the structure leads to cultural change (Evanston). Also, Ho & McCall found that many cities create and benefit from chief data officer positions, which is valuable to help bring in persons with the skillset necessary to make the project happen (Ho & McCall, 2016; Wiseman, 2017).

Challenges identified for implementing data governance include 1) Staffing and identifying employees with experience in both policy and data analytics; 2) Data accessibility, 3) Data quality, which can impair data analysis, and 4) Data sharing which has legal, ethical, and privacy considerations which prohibits some data from being shared (The Pew Charitable Trusts, 2018; Scottsdale)

Learning lessons identified include making sure that the implementation of data governance is accompanied by departmental engagement, particularly with directors but also with department staff, and that education and training is provided from the beginning and on an ongoing basis (Gardner, 2019; Scottsdale; Evanston). Staff involvement and communication is key to get buy-in for successful and timely implementation (Gardner, 2019; Adler, 2017 ). It was also important that the IT department had support from the top to help them execute tasks that require a great deal of coordination so that they can do the best job they can (Wiseman, 2017; Scottsdale). Additionally, when considering the type of data that the city will be collecting and reporting on, there is a need to get input from departments on their data needs, and to identify what they will be able to do with data that is being collected (Evanston;  Gardner, 2019; ). There is also a need to generate public input to understand what data they might be interested in or find valuable to businesses or groups, and in what form that data should be made available (Wiseman, 2017; Montgomery County, 2013) Some cities strive for open data and allow access to as many data sources as possible, but it may not be a goal for all cities, and if it is, it may not be possible to do this all at once. Data governance can be implemented all at once department wide, or slowly implemented one department at a time to take the time to understand where data governance will be able to help department processes and improvements (Gardner, 2019; Scottsdale). Additionally, due to privacy concerns, data should go through a legal team if a city means to open the data up to the public (Ho & McCall, 2016; Scottsdale; San Francisco; Mesa).

Data Governance Info Sheet

Table of Contents:

  1. Cities and Data Governance Resources
  2. City Interviews
    1. City of Evanston, IL
    2. City of Mesa, AZ
    3. City of Scottsdale, AZ
  3. Tools (i.e. Resources for trainings, data governance and open data policy development, etc.).
  4. Guiding Literature and Best Practices

Cities and Data Governance Resources

  1. City of Chicago, IL
    1. FOIA - The State of Illinois Freedom of Information Act (FOIA) guarantees access to records and documents maintained by government.
    2. Resources
      1. Data policy for Chicago
      2. Open Data Executive Order
      3. Data Sharing Policy
      4. A Comprehensive History of Open Data Chicago
      5. Data Dictionary
      6. Data Portal
        1. The City of Chicago's Data Portal is dedicated to promoting access to government data and encouraging the development of creative tools to engage and serve Chicago's diverse community. The site hosts over 600 datasets presented in easy-to-use formats about City departments, services, facilities and performance. The catalog presented below lists datasets alphabetically and links back to each set on the Portal.
      7. Metro Chicago Open Data Examples
  2. City of Evanston, IL
    1. Hillary Beata, Digital Services Specialist
    2. Data policies established by What Works Cities
      1. hbeata@cityofevanston.org
    3. See interview
      1. Open Data Guidelines
      2. Data Governance Policy City of Evanston.pdf 
      3. 71R17 Approving an Open Data Policy_Resolution_Evanston.pdf 
  3. City of Louisville, KY
    1. Michael Schnuerle, Chief Data Officer
      1. michael.schnuerle@louisvilleky.gov
    2. Resources
      1. Louisville Metro Open Data Policy.pdf
      2. https://opi.lsvll.io/innovation/data-governance/
      3. https://data.louisvilleky.gov/inventory
      4. Louisville Open-Data-Executive-Order-2013-signed.pdf 
  4. City of Mesa, AZ
    1. Data policies established by Data Office.
    2. Evan Allred, Data Governance Administrator
      1. evan.allred@mesaaz.gov
    3. See Interview
      1. Dataset Best Practices.pdf
      2. Mesa Data Governance Responsibilities.pdf
      3. Datasource to Published Dataset.pdf
      4. Dataset Best Practices.pdf
      5. Metadata Standards - Adopted.pdf
      6. Process to Request Interdepartmental Data.pdf
  5. New York City, NY
    1. Kelly Jin, Chief Analytics Officer with Mayor's Office of Data Analytics
    2. Resources
      1. Open Data Policy and Technical Standards Manual (2018)
      2. Data Portal
      3. New York City Open Data: A Brief History
      4. New York City Privacy Protocols and Why They Matter
      5. NYC Data Driven Governance
      6. Open Data Report New Sets NYC
      7. NYC Open Data Law
  6. City of Philadelphia, PN
    1. Daniel J. Paolini, Chief Information Officer
      1. Department of Behavioral Health and Intellectual disAbility Services
    2. Resources
      1. Open Data Executive Order
      2. Philadelphia Data-Governance-Framework-Strategic-Plan-v1.pdf 
      3. Philadelphia Data-Governance-Framework-Implementation-Plan.pdf 
      4. Metadata Catalog (in beta)
      5. Inventory Template
      6. Open Data Inventory
  7. City of San Francisco, CA
    1. Jason Lally, Data Services Manager
    2. Resources
      1. Data Policy_FINAL DRAFT_ San Francisco.pdf 
      2. 425-SanFranciscoOpenDataStrategicPlan2014.pdf 
      3. DataSF Guidebook: Data Coordinators Edition
      4. 5 Ways to Scale the Mountain of Data in Your Organization blog (discusses alternatives to a full scale inventory)
  8. City of Scottsdale, AZ
    1. Data policies in place as part of What Works Cities certification.
    2. Kari Johnson, Business Intelligence Manager
      1.  kajohnson@scottsdaleaz.gov
    3. See interview
      1. Scottsdale Open Data Program Administrative Regulation 297.pdf 
      2. Open Data Policy and Program Resolution_Scottsdale.pdf 
      3. Data Portal

 

Interviews:

Hillary Beata, Digital Services Specialist

City of Evanston, IL

Contact information: hbeata@cityofevanston.org

Resources:

Open Data Guidelines

Data Governance Policy City of Evanston.pdf 

71R17 Approving an Open Data Policy_Resolution_Evanston.pdf 

            The City of Evanston’s council also passed a resolution for What Works Cities. They are applying for a What Works Cities certification, although have yet to receive their certification. However, they found this certification a valuable roadmap for setting standards and policies to ensure the best practices for data governance. This certification requires the formation of a governance team, which they have grown to include a member of every department of the city.

            The key to getting staff on board is establishing a better understanding of how gathering data can be used by others, for individual benefit and external benefit. She stated that cities should understand needs and projects that could pilot ideas and assemble success stories and lessons learned from failures.

She also stated that structure leads to cultural change: If you are able to get a group together in the same room, and create a team of early adopters, data experts, and assigned staff, this leads to other departments not wanting to “miss out”.

Evanston said they had big wins by starting with police and fire departments: The police department was a 2-year project to automate police data with the goal of eliminating lag time and streamlining for easier updates. They were able to develop a model this way that led to processes, contexts, visualization, and ways to empower staff to show what they do. It allows you to create a new tool, show how much you do. Then, the fire chief came to the data team, with a need to integrate their data into one spot to make it more useful. They heavily used interns to take project goals into dashboards.

Within the IT department, they were familiar with the entire city, and capable of conducting one on one support and approaches to assist with the projects. They had IT department do some presentations to explain aspects of projects. IT departments says be a detective, and let people know that you are deeply interested in helping and have the tools to do so. They said they had to wait for accomplishments and confidence to come out of projects before other departments started getting on board.

Their internal data governance policy came from google search, what was the best fit for Evanston. They resolved to use Sunlight Foundation for their internal policy.

 

Kari Johnson, Business Intelligence Manager

City of Scottsdale, AZ

Contact Information: kajohnson@scottsdaleaz.gov

Resources:

Scottsdale Open Data Program Administrative Regulation 297.pdf 

Open Data Policy and Program Resolution_Scottsdale.pdf 

http://data.scottsdaleaz.gov/

            Scottsdale, Arizona’s data governance project began in 2016 with a resolution that was passed to promote open data (see attached). City of Scottsdale contracted with Bloomberg’s What Works Cities, Sunlight Foundation, and Behavioral Insights team. They had someone at the top to champion this project, which was fundamental to getting the project up and running. This in combination with the contract with Bloomberg allowed for the quick establishment of necessary structures and the allocation of resources to institute the new resolution from the top-down. Scottsdale had over a period of 6 months to get the project up and running. Using the What Works Cities and Sunlight Foundation allowed for a very structured project. They formed a small team of six to make up their Data Leadership Team, which included people who are on their Performance Measure team. This team consisted of their finance director, water director, planning director, the data manager, the public information officer, and assistant city manager. Getting these different people together, they were able to craft their policies and procedures with the guidance and consultation from What Works Cities and Sunlight Foundation. Simultaneously while the data governance policy and procedures were being assembled, they were trying to create the data coordinator piece, begin a data portal, and get a test site up and running. A great deal of the materials they put together came from the Sunlight Foundation and San Francisco; for example, the data coordinator handbook that they used came from San Francisco.

            They then used their IT team to create their administrative regulation and permissions, and an approval system flow to establish a process of submitting data inventory and getting it on the city website. They had everything go through their legal team before uploading the data. The data they were collecting on was at the citizen’s request for top ten as well as what the city found was being hit most on the open portal. They therefore incorporated all departments from these top 10 into the data governance project at the same time. They conducted trainings on how to put their data together and submit it. They collected 40 data sets and wrote all the ETL’s to update the data online.

            Lessons learned from the implemented process was that they had a great deal of support from the top to make sure that all departments were on track helping to compile their data; this was critical to stay on deadline, since you don’t want to have your data coordinator banging on doors and begging people for their data. She found it was important to the process not only to use top-down communication, but bottom to top. It was just as important to make sure departments understood why they were putting the needed data together, and to know that they are participating in a new city initiative, to get feedback from departments. This would ensure that departments understood why they would need to set aside time for the project. She stated that there is a need to let departments know that this is an ongoing task and said that good management, education, and communication was critical, particularly from the beginning, but also throughout the process. Doing so would give context to the work and promote a sense of inclusion. The tradeoff of having to get a project done so quickly with so many moving pieces meant that there was not as much time for communication with departments as there could have been. Using the What Works Cities was helpful for a city that was willing to have its departments prioritize this as an initiative to meet deadlines, and successfully implement a data governance project.

 

Evan Allred, Data Governance Administrator

City of Mesa, AZ

Contact information: evan.allred@mesaaz.gov

City of Mesa – Data and Performance Management

Resources:

Dataset Best Practices.pdf

Mesa Data Governance Responsibilities.pdf

Datasource to Published Dataset.pdf

Dataset Best Practices.pdf

Metadata Standards - Adopted.pdf

Process to Request Interdepartmental Data.pdf

 

            The City of Mesa began implementing their Data Governance project in 2018. The project came from the top down, which the Mayor and City Manager redefined city priorities, and identified the opportunity for departments to help contribute to city priorities and data driven decision making. They partnered with What Works Cities to work with their collaborative partners at the Sunlight Foundation, GovEx, and Results for America, which helped them to carry out their data governance projects. They created their own data governance policy, created an Open Data Leadership Board, and their Data Governance team in 2018. They adopted metadata and dataset standards and best practices, a process for publishing city data, and a process for sharing city data across departments (see attached). Now, the city is preparing to take the next step in improving their data governance practices. This past summer their data governance board partnered with GovEx and researched data governance plans and policies across North America. As a result of this summer’s work they are preparing to revamp their data governance approach. Evan has attached their data processes and internal policies used in Mesa for your viewing.

 

Tools:

Accela:

https://www.accela.com/civic-platform/

cloud based platform for government software solutions

https://www.accela.com/civic-platform/saas/

Software as a Service (SAAS) for small and medium governments and agencies

 

The Chief Data Officer in Government: A CDO Playbook

https://documents.deloitte.com/insights/CDOplaybook

How can government leaders improve service delivery and social impact through enhanced open data strategies?

 

Civic Analytics Network (a network of urban CDOs)

Civic Analytics Network, hosted by the Harvard Kennedy School and funded by the Laura and John Arnold Foundation, provides city CDOs with a forum to share ideas, chal­lenges, and solutions

 

CKAN https://ckan.org/

The world’s leading Open Source data portal platform

CKAN is a powerful data management system that makes data accessible – by providing tools to streamline publishing, sharing, finding and using data.

 

Data.gov | https://www.data.gov/

Data.gov provides data, tools and resources to conduct research, develop web and mobile applications, design data visualizations and more.

 

Data Literacy Project

https://thedataliteracyproject.org/learn

Get a data literacy certification and take e-courses to learn about data, analytics, and data driven decision making. Additionally, access resources on embracing a data challenge, developing a data literate workforce, and how to launch a data literacy initiative.

 

Data Management Association International | https://www.dama.org/

DAMA International is a professional organization providing research, education, publications, standards’ promotion and activities to enhance the practice of data management.

 

Georgia Technology Authority | http://gta.georgia.gov/enterprise-governance-and-planning-main-page

Georgia Technology Authority’s Enterprise Governance and Planning Division promotes an enterprise approach to technology by establishing statewide policies, standards and guidelines based on industry best practices and federal requirements.

 

Github | https://github.com/

The open source code-sharing website which allows users to upload, view, and edit each other’s files.

 

Government Technology | http://www.govtech.com/

Government Technology covers IT's role in state and local governments. Coverage includes IT case studies, emerging technologies and the implications of digital technology on the policies and management of public sector organizations.

 

Govex

https://labs.centerforgov.org/guides/

This includes guides and tips for everything from creating a data sharing agreement, topics for data governance boards to focus on, changing culture in government, policy touchpoints for open data programs.

http://courses.govex.academy/catalog

Self-guided courses on creating data governance board, data governance policy, foundations for data governance structures, data management, data inventory, data standards, etc.

https://govex.jhu.edu/

One source of information on local governments is Andrew Nicklin, Center for Government Excellence, Johns Hopkins University - anicklin@jhu.edu 

 

National Neighborhood Indicators Partnership (NNIP):

https://www.neighborhoodindicators.org/data-tech/course-catalog/data-101-data-visualization-data-literacy-and-storytelling

NNIP offers Data 101: Data Visualization, Data Literacy, and Storytelling consisting of materials for foundational data literacy, including exploration of basic concepts in data visualization, storytelling, and mapping—all using printed materials without computers.

 

Qlik https://www.qlik.com/us/company/academic-program

Consultation, training and support for big data analytics, developer platforms, and data integration.

 

Socrata

https://dev.socrata.com/publishers/

Helps cities publish their data

 

Sunlight Foundation

https://sunlightfoundation.com/opendataguidelines/

Helps cities establish data policy

 

Texas Health Human Services Commission | http://www.hhsc.state.tx.us/hhsc_projects/oehc/index.shtml

The Texas Health and Human Services Commission has an enterprise data program that provides an organization and policy framework to support data management standards across the state’s health and human services agencies to improve data sharing and data quality.

 

What Works Cities

https://whatworkscities.bloomberg.org/certification/

Data policy is required to move forward with certification

 

Guiding Literature and Best Practices:

Assessing Your City’s Data-Driven Approach

https://www.govtech.com/data/Assessing-Your-Citys-Data-Driven-Approach.html

This self-assessment tool can help city leaders determine the degree to which they are using data to drive better results for the public.

 

A 2018 study by the Pew Charitable Trusts, How States Use Data to Inform Decisions

https://www.pewtrusts.org/-/media/assets/2018/02/dasa_how_states_use_data_report_v5.pdf

This includes a report of challenges government executives at the state level faced in using data that were similar to those faced at both the federal and local levels. The report also contains a series of recommendations, including the need for a more organized and centralized approach to data in the future. Key actions for state leaders include the development of “governance structures to guide data use and access while also prioritizing privacy” and the need to “take stock of their data systems and perform an inventory of data sets.”

The report found the following major challenges: 1) Staffing. Few state employees were experienced in both policy and data analytics. Many states reported that existing staff lacked skills in data analytics or the ability to interpret data findings to make policy recommendations. 2) Data accessibility. Many state agencies had archaic data systems, some developed in the 1980s, which made it very difficult to access and use data. 3) Data quality. Data quality issues impaired the analyses of data. Many state databases suffered from quality issues which made them difficult to use and interpret. 4) Data sharing. If a state agency wanted to make quality data accessible, a combination of problems including organizational culture, laws, or other factors often prohibited the data from being shared.

Despite government’s recent progress with using and communicating data and analytics, collecting policy relevant data, reducing delays, promoting ability to use data to inform action,  providing continuous outcome improvements, as well as technological developments that have increased data processing power and cut data handling costs, current government data and analytic practices are nowhere near as sophisticated as they could and should be. Many government data systems remain clunky and hard to use, while government’s analytic and evaluation capacity is woefully scarce.

Recommendation: 1) Build process mastery and innovation team; 2) Identify three problematic government processes that would benefit from “common infrastructure services everyone could access without reinventing the wheel every time,” as Amazon did to speed application development; 3) Launch operational excellence scrums in these areas to learn how to speed process improvements; 4) Support these process improvements with the governance structures they need to sustain continued progress. 5) Establish standards for data governance, provenance, and ethics, including who owns and gets access to data.

 

Best Practices in Open Data Initiatives – Montgomery County, Maryland 2013

https://www.montgomerycountymd.gov/olo/resources/files/2013-7bestpracticesinopendatainitiatives.pdf

An overview of Montgomery County’s own investigation into the best practices for local government pursuing open data policies and includes discussion and findings from their own interviews with data governance experts in local governments and notable organizations including the Sunlight Foundation.

Findings include: 1) The terms “open data” and “open government” have different meanings; 2) Other jurisdictions have implemented open data initiatives to promote innovation, to increase transparency, and to efficiently meet demands for government data; 3)President Obama’s recent open data policy initiatives have established new standards for generating and releasing government data, and are intended to help serve as a template for state- and local-level government data initiatives; 4) Leadership buy-in and strong project management can facilitate an effective open data initiative; 5) Geographically coded data that can be mapped can be very useful to application developers because the majority of apps that use open data let users see data on an interactive map; 6) Jurisdictions can help software developers and other stakeholders combine data from multiple jurisdictions by contributing datasets to regional data portals and by complying with established data standards for specific types of data; 7) In managing and prioritizing the release of data, other jurisdictions have found it important to both engage community stakeholders extensively in this process and consider the potential costs of releasing specific datasets; 8) Software developers represent key stakeholders of open data initiatives who can use open data to create innovative software tools or “apps”, and other jurisdictions have worked to encourage and support their involvement in a number of ways; 9)The potential uses of specific datasets are not always immediately apparent, and outside parties often use datasets in unexpected ways to provide benefits to the community; 10) While the success of open data initiatives depends in part on whether external stakeholders use the data, it is difficult for jurisdictions to quantify how stakeholders use data released through these initiatives; 11) Individuals involved in open data initiatives see the potential for the initiatives to help existing businesses grow and provide building blocks for new businesses.

 

Centre for Information Policy Leadership, “Data Governance for the Evolving Digital Market Place”, Centre for Information Policy Leadership, Hunton & Williams LLP, 2011. http://www.huntonfiles.com/files/webupload/CIPL_Centre_Accountability_Data_Governance_Paper_2011.pdf.

This paper argues that as a result of the proliferation of large-scale data analytics, new models governing data inferred from society will shift responsibility to the side of organizations deriving and creating value from that data.

It is noted that, with the reality of the challenge corporations face of enabling agile and innovative data use “In exchange for increased corporate responsibility, accountability [and the governance models it mandates, ed.] allows for more flexible use of data.”

Proposed to shift responsibility to the side of data-users, the accountability principle has been researched by a worldwide group of policymakers. Tailing the history of the accountability principle, the paper argues that it “(…) requires that companies implement programs that foster compliance with data protection principles and be able to describe how those programs provide the required protections for individuals.”

 

The DAMA Guide to the Data Management Body of Knowledge

The DAMA Guide to the Data Management Body of Knowledge, Edited by M. Brackett, S. Early and M. Mosley. Bradley Beach, NJ: Technics Publications LLC, 2009 (Available for purchase at https://www.dama.org).

The Data Management Association’s guide is a compilation of principals and best practices. It provides data management and IT professionals, executives, knowledge workers, educators and researchers with a framework to manage their data and mature their information infrastructure.

 

Database Management & Integration

https://www.govtech.com/library/papers/Database-Management-Integration-81804.html

State and local government agencies seek greater value from their data. Public officials and taxpayers want increased efficiency and better outcomes – and this often involves increased data sharing and analysis. Database management systems store, organize and manage this valuable employee and citizen information, and modern data platforms enable agencies to use this information in powerful new ways. Download this report to see the results from a recent Center for Digital Government survey to learn about the database management systems IT decision makers are using, their integration plans and the common challenges these agencies have encountered.

 

Data Governance in the Digital Age

https://www.cigionline.org/publications/data-governance-digital-age

Data has been hailed by some as “the new oil,” an analogy that captures the excitement and high expectations surrounding the data-driven economy. The success of the world’s most valuable companies (Apple, Google, Facebook and Microsoft) is now underpinned by a sophisticated capacity to collect, organize, control and commercialize stores of data and intellectual property. Big data and its application in artificial intelligence, for example, promises to transform the way we live and work — and will generate considerable wealth in the process. But data’s transformative nature also raises important questions around how the benefits are shared, privacy, public security, openness and democracy, and the institutions that will govern the data revolution. The recent Cambridge Analytica scandal has exposed the vulnerability of democracies to data strategies deployed on platforms such as Facebook to influence the outcomes of the Brexit referendum and the 2016 US presidential race. Any national data strategy will have to address both the economic and non-economic dimensions of harnessing big data. Balances will have to be struck between numerous goals. The essays in this collection, first published online in spring 2018, by leading scholars and practitioners, are grouped into five blocks: the rationale of a data strategy; the role of a data strategy for Canadian industries; balancing privacy and commercial values; domestic policy for data governance; and international policy considerations. An epilogue concludes with some key questions to consider around data governance in the digital age.

 

The essential elements for successful data governance

https://www.govtech.com/library/papers/The-essential-elements-for-successful-data-governance-107560.html

To develop a successful data governance strategy, you need the right foundation from the start. This white paper explores the critical components to build a successful data governance framework and foster a culture that will maximize data asset understanding, usage, and value across your organization.

 

Learn key principles and priorities in good data governance

https://www.govtech.com/library/papers/Learn-key-principles-and-priorities-in-good-data-governance-107557.html

This white paper provides helpful information for every step of the data governance journey, as it covers the “who, what, when, and why” of data governance. From what it is, to why it’s important, who should be involved, and when to start, it can clarify concepts and help define a strategy.

 

Lessons from Leading CDOs: A Framework for Better Civic Analytics, by Jane Wiseman. 2017.

https://datasmart.ash.harvard.edu/news/article/lessons-from-leading-cdos-966

The Chief Data Officer (CDO) can lead a city or state toward greater data-driven government. Leveraging data enables more responsive and rational allocation of government resources to address priority public needs. Data-driven executive leadership in government is relatively new, with just over a dozen cities and a handful of states having named a CDO as of late 2016. There is growing momentum and increasingly frequent news of the next government CDO appointment. While there is a growing proliferation of CDOs in government, there are few resources that describe the landscape, either for the benefit of the chief executive appointing a CDO or the new CDO taking office. This paper intends to help new entrants by documenting selected current practices, including advice shared by existing government CDOs, observations by the author, and analysis from government technology and analytics experts. A few key points for a new CDO to consider include:

 

1) Support from the chief executive sets the CDO up for success. Whether a CDO reports to the chief executive (mayor, governor, or county commissioner) or not, it is important to have the support of that chief executive and have the resources, credibility, and authority that go along with executive sponsorship.

2)Basic management skills can accelerate progress. Strong basic management and leadership skills, the ability to clearly articulate the mission and roadmap to achieving it, and the ability to hold staff accountable for results will accelerate success for a CDO. Standardizing tools and processes, including project management tools, will make the work more efficient. Balancing the demand for results with the need for foundational data stewardship demands leadership from a CDO and a delicate balance of people and technology skills.

3)Data stewardship can create the conditions for solid analytics. Data stewardship – comprising data governance and data infrastructure – lays the foundation on which analytics is built, and whether these activities are part of the CDO operation or not, they are essential to the success of any analytics program.

4)Setting priorities becomes an increasingly important and challenging task for a successful CDO. As the profile of the CDO grows and demand for services increases, it can be difficult to manage priorities and stay true to the mission. As one expert advised, CDOs should stay focused on important policy issues and operational improvements in government and avoid “data qua data” analytics.

 

Maricopa Integrated Health System Uses Data Governance to Align the Enterprise, Increase Productivity and Deliver on Its Mission

McKesson. Maricopa Integrated Health System Uses Data Governance to Align the Enterprise, Increase Productivity and Deliver on Its Mission.2015.

http://www.mckesson.com/healthcare-analytics/resources/case-studies/learn-how-to-use-data-governance-to-align-the-enterprise-and-increase-productivity/.

This case study details how Maricopa Integrated Health System, in south-central Arizona, uses data governance to align the enterprise and increase productivity.

 

Planning the Data-Driven City – by Laura Adler 2017

https://www.govtech.com/data/Planning-the-Data-Driven-City.html

An overview of San Francisco, Chicago, and New York’s open data pursuits reveal some best practices for data and tech strategies and different models.

  • Single agency or inter-departmental implementation? The achievement of broad plans requires the participation of many departments, and collaboration can often be difficult. Single-agency plans are easier to implement, but narrower in scope. 
  • Authorship and oversight? Leadership is key to accomplishing goals in city government, especially when those goals span multiple agencies and require substantial human and capital resources. A plan presented by the mayor and implemented by City Hall may thus be able to achieve goals more quickly. At the same time, City Hall might have limited resources to dedicate to data projects and is subject to transformation from administration to administration. A focused team within an operational agency, as in Chicago, or as a freestanding group might allow for more continuous attention.
  • Integrated or separate plans for infrastructure and data? Data has a critical role to play across all technology initiatives; similarly, infrastructure is the foundation on which data is built. By embracing both data and infrastructure, cities can ensure that these two areas develop in tandem but trying to tackle both at once may tax limited planning and implementation resource.
  • Timeline? Many great projects require long timelines, but city government operates according to election cycles. To ensure that the data plan reaches its goals, effective strategies often focus on the near term, in two- or three-year increments. 

Across diverse plans, several foundational best practices emerge. These are central to the achievement of any dedicated open data plan, but they provide critical support to goals ranging from growth of the tech sector to better broadband deployment. 

 

A data-smart city must engage in planning processes to identify, collect, integrate, and analyze its data resources. Whether planning for city data occurs in the context of open data or as part of broad technology strategies, cities must begin to look ahead and plan for the data needs of the future.

 

Resource Guide to Data Governance and Security

https://www.urban.org/research/publication/nnips-resource-guide-data-governance-and-security

Any organization that collects, analyzes, or disseminates data should establish formal systems to manage data responsibly, protect confidentiality, and document data files and procedures. In doing so, organizations will build a reputation for integrity and facilitate appropriate interpretation and data sharing, factors that contribute to an organization’s long-term sustainability.

To help groups improve their data policies and practices, this guide assembles lessons from the experiences of partners in the National Neighborhood Indicators Partnership network and similar organizations. The guide presents advice and annotated resources for the three parts of a data governance program: protecting privacy and human subjects, ensuring data security, and managing the data life cycle. While applicable for non-sensitive data, the guide is geared for managing confidential data, such as data used in integrated data systems or Pay-for-Success programs.

2016 Report, Ten Actions to Implement Big Data Initiatives: A Study of 65 Cities, Alfred Ho and Bo McCall http://businessofgovernment.org/sites/default/files/Ten%20Actions%20to%2...

Ho and McCall surveyed 65 cities to understand their use of big data and analytics. They found that 75 percent of the cities surveyed reported having undertaken big data initiatives, including increased used of analytics, better integration of data with budgeting, and using a team approach or multidepartment governance structures for their data initiatives. Their survey also found that many cities were creating chief data officer positions to lead these data initiatives. Cities were also increasingly providing citizen-friendly ways to visualize city and access data, as well as empowering citizens to conduct their own data inquiries and analysis of city-generated data. While Ho and McCall found that big data was being used in the cities they surveyed, and had much potential, a variety of issues involving data began to surface. The increase in the availability of data created new ethical and legal challenges in both the public and private sectors. These issues included: potential privacy and individual rights infringement, hidden inequity and discrimination in algorithm-driven decision making, and potential conflicts between efficiency, customization, and equal access to government services by all. Specific privacy issues include how data should be collected, stored, and analyzed, as well as how data should be shared with non-government entities.

 

Data Inventory Guide | GovExLabs

https://labs.centerforgov.org/data-governance/data-inventory/

How to conduct a data inventory, why to conduct one, insights from data inventory efforts, and resources from local government data inventory efforts

When it comes to inventorying, there’s no need to reinvent the wheel. A lot of cities have gone through the inventory process and publicly share their resources. The following list includes data inventory guides, templates, publishing plans, presentations, publicly released inventories, and workflow diagrams for completing inventories from cities across the United States. There are many ways to structure and scope a data inventory; the first step is deciding what works best for your city.

 

Step 1: Establish an Oversight Authority

Step 2: Determine the Data Inventory Scope and Plan

The ROI of Data Governance: Seven Ways Your Data Governance Program Can Help You Save Money

Thomas, G. The ROI of Data Governance: Seven Ways Your Data Governance Program Can Help You Save Money. 2009. http://www.cio.com.au/white paper/323775/the-roi-of-data-governance-seven- ways-your-data-governance-program-can-help-you-save-money/.

This white paper outlines seven ways data governance and stewardship programs can help manage costs and a mechanism for quantifying the return on investment for those contributions.

 

Seven Steps to Effective Data Governance for State and Local Government Agencies

Seven Steps to Effective Data Governance for State and Local Government Agencies. Information Builders. Last modified 2011. http://www.informationbuilders.com/about_us/whitepapers/download_form/4558.

This white paper explores why data governance is so important in the government sector, and provides seven steps for approaching data governance in your organization. It will also provide real world examples of how data governance was successfully applied in state and local government agencies.

Step 1: Prioritize Areas for Business Improvement  Step 2: Maximize Availability of Information Assets  Step 3: Create Roles, Responsibilities, and Rules  Step 4: Improve and Ensure Information Asset Integrity  Step 5: Establish Accountability Infrastructure  Step 6: Convert to a Master Data-Based Culture  Step 7: Develop a Feedback Mechanism for Process Improvement

 

Six Bumps on the Road to Successful Data Management

https://www.govtech.com/analytics/Six-Bumps-on-the-Road-to-Successful-Data-Management-.html

At the second annual Chicago Digital Government Summit this week, public-sector data experts shared common challenges that government should prepare for in creating and running data programs.

 

The State CIO Operating Model: A Playbook for Managing Change in a Sustainable Way

https://www.nascio.org/publications

This is the fourth in our NASCIO series “The CIO Operating System:  Managing Change in a Sustainable Way.”  It is also the culmination of the work from NASCIO’s project team and a partnership with Integris Applied, Inc., a corporate member of NASCIO, that began in January of 2018.  This is a playbook of eleven plays that any state or territory can utilize in order to move into a new operating model.  This operating model creates a highly disciplined state CIO organization that proactively engages with state agencies, understands current and emerging program and citizen needs, as well as maintains market awareness of current and emerging trends and offerings.  Moving into and maturing this model is essential for each state and territory to effectively map capability demand with capability supply.

 

State of Oregon Delivers Pioneering Public Records Management Solution

https://www.govtech.com/library/papers/State-of-Oregon-Delivers-Pioneering-Public-Records-Management-Solution-111867.html?promo_code=GOVTECH_web_library_list

With governments under pressure to manage today's explosion in information, meet greater public expectations and cut costs, the State of Oregon has implemented a cloud based EDRMs (electronic document and records management system) solution centering on Micro Focus Content Manager

 

Sustaining a Data Culture in City Hall: Lessons from Los Angeles and Boston – Gardner, B. 2019. City Analytics Network. Harvard Kennedy School for Democratic Governance and Innovation.

https://datasmart.ash.harvard.edu/news/article/sustaining-data-culture-city-hall-lessons-los-angeles-and-boston

Whether you’re already running a data literacy training, or you’re looking to start one, sustainability is a crucial consideration. A data culture implies sustained data literacy that permeates all levels of an organization, not just a “one and done” session. And beyond being literate in data, a true data culture affects organizational enthusiasm, improves service delivery, and empowers employees. If you haven’t started a training program yet, now is a great time to plan a sustainable model. And if you’ve already started, there are still many ways to weave sustainability into your current trainings. Below are several actionable ways to do this, featuring advice from data leaders in Los Angeles and Boston.

Lessons include: 1) focusing on personal connections and in person collaboration and trainings, 2) providing multiple options to connect staff to data literacy accommodations, 3) responding to needs of employees and provide useful, personalized help, 4) share the leadership, disperse ownership across departments to gain commitment long term to data literacy, 5) know the wants and needs of employees in order to create a data program they will actually want to participate in, 6) demonstrate usefulness in learning data skills, 7) prioritize staff by providing literacy trainings, 8)include staff from all levels, and 9)think long term sustainability in mapping out a plan.

**For cities that are interested in data literacy trainings, there are various free courses available from GovExQlik and the Data Literacy Project, and the National Neighborhood Indicators Partnership. Building, and sustaining, data literacy in cities will benefit both city hall and the community it’s working to serve.** (see tools).

 

US Ignite Smart City, Big Data Playbook

https://smartcitiesconnect.org/wp-content/uploads/tc/pdfs/USIgnite_Portland_Playbook_FINAL_9-26-2018.pdf

This playbook examines some of the initial use cases for data-driven initiatives, including what value they can deliver, and what pioneering communities have learned from early deployments. This guide offers information about early project successes, as well as details about new pilot programs now underway. The second half of the playbook also recommends specific tools and techniques for managing data, using real-world experiences to point the way toward smart city big data strategies for the future.

 

Conclusion:

The literature is rich regarding Data Governance Projects and Open Data Governance in terms of the best practices and resources available for cities to build these projects. Across many of these projects, cities pursue open data initiatives and data driven policy to increase transparency, engagement, and efficiency in government. Many resources are available to assist cities in starting a data governance project or open data project. Consensus across the research and experiences by cities overall is to have a champion; to build a data governance team and create governance structures to support sustainability of the program, including a possible chief data officer. Cities can further the sustainability of the program and increase buy in across departments by creating these structures, as well as through inclusion, education, and data training. Many cities support demonstrating how departments and staff will benefit from participation in data governance through staff training (i.e. resume building and job skills), the streamlining of data that they use, making data more useable for departments and for delivery of services, and showcasing to other departments these successful models that worked in other cities or in your own city to encourage participation. Further consensus is that data governance projects should take anywhere from 6 months – 3 years to implement, but cities should demonstrate ongoing efforts to continue to evolve their data processes and initiatives. Challenges identified include finding staff or training staff to understand both policy and data, the age of data systems, the quality of data, and the ability to share data based on quality, laws, or organizational culture.

 

 

 

You may also be interested in

Feedback