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ELP by Mind Map: ELP

1. T1: Leading Confidently with Data

1.1. Description: Leaders demand and receive data before making high-stakes decisions and establish routine practices that are sustained to solve complex problems.

1.2. OBJECTIVES (WHAT)

1.2.1. Model behaviors that value, practice, and encourage the use of data to inform the best policy decision, intervention, or solution

1.2.1.1. For New Mayors only

1.2.2. Develop a theory of change to improve the city’s ability to use data and create a data-driven culture, activities, outputs, outcomes, and goals

1.2.3. Refine and articulate problems connected with the mayor/chief executive's vision using analytics, describing how it impacts the City, and identifying the current state.

1.2.3.1. COACHING

1.2.3.2. DEEP DIVE

1.2.3.3. WORKSHOP

1.2.4. Define purpose and end state for improving the quality of high-value dataset[s] related to a particular focus area; Develop a plan to improve the quality and to revise existing data.

1.2.5. Determine the appropriate governance structure and data infrastructure to lead the city’s efforts to enhance data culture.

1.2.6. Create objectives related to the priority area and use the levers of change to inform key performance indicators.

1.2.7. Generate clear questions that will help guide data selection and analytic approach leveraging data to conduct a deeper analysis.

1.2.8. Activate data as a strategic asset and use data to make equitable policy decisions that promote transformational outcomes.

1.2.9. Build a compelling narrative that spotlights a priority and weakens misinterpretation of information to shift perspectives toward a shared idea.

1.2.10. Engage in the process of telling a variety of stories about City data ensuring it is accurate, complete, unbiased, and unadulterated information.

1.3. Goals/Impact

1.3.1. Mayors demand data when approached by internal and external actors to understand cross sector-issues and indicators and look holistically at data to make decisions.

1.3.2. Mayors dedicate time to strategizing and setting a shared vision for the city with data, and encourage the use of data, risk taking, and innovative solutions.

1.3.3. Mayors activate levers of change, including understanding and employing budget, policy, technology and communications to create cross-cutting city-wide change (e.g., civil service transformation).

1.3.4. Mayors center data in their management of the city, and city hall meetings encourage data use. Teams are encouraged to invest in new strategies and take risks.

1.3.5. Cities use data to understand and address their toughest problems, and track and measure impact on those issues

1.4. Keynote Address

1.4.1. Dr. Tamas Budavari

1.4.1.1. JHU Whiting School of Engineering

1.4.2. Sam Liccardo

1.4.2.1. Mayor or San Jose, CA

1.4.3. What is Data Driven Culture? Why do we need to adopt it and how do we sustain it?

1.4.3.1. Jamboard

1.4.4. Transforming City Services through Data Culture Shifts

1.4.4.1. Jamboard

1.4.5. Data Driven delivery approaches in Government

1.4.5.1. DA

1.4.6. Building a data informed organization in government

1.4.6.1. How to execute the leadership tasks associated with building a data-informed organization in the city, such as formulating a vision, setting an example, enforcing norms, and upholding values.

1.4.7. Innovative City Leadership

1.5. WORKSHOPS (HOW)

1.5.1. For New Mayors Only -- Explore how a new mayor turns a "that's not how we do things around here" city hall to one that is innovative, data-driven, and ultimately curious

1.5.2. Appreciative Inquiry for Strategic Planning

1.5.3. Aligning Performance and Priorities

1.5.4. Defining and Sharing your Leadership purpose: setting vision and direction for your city

1.5.5. Managing and Communicating through a crisis: leading in times of uncertainly or ambiguity

1.5.6. Prioritizing and tough decision making: Negotiating and taking risks

1.5.7. Diagnosing Complex, Persistent problems

1.5.8. Data manipulation for leaders

1.5.9. Addressing inequities in City Hall

1.5.10. COACHING

1.5.10.1. Foundations of Data sharing and governance.

1.5.10.2. Developing leadership and Institutional awareness

1.5.10.2.1. PD

1.5.10.3. Distinguishing Governing v Decision making

1.5.10.4. Continual conversations on delivering and capturing transformational results (What's the application?)

1.5.10.4.1. setting up a system that helps them capture information, changes, and successes/challenges

1.5.10.4.2. follow up and check in on specific committments

1.5.10.4.3. How did you change your practices this month?

1.6. PARTNERS (WHO)

1.6.1. BIT: Establishing a shared understanding/vision of evaluation (listed under speaker, but seemed important to elevate here)

1.6.2. BIT: Creating an evaluation agenda

1.6.3. BIT: Exploring problems using tools of evaluation

1.6.4. PD: Defining policy problem

1.6.5. PD: Digital Transformation

1.6.6. DA: Government Transformations

1.6.7. DA: Bogota’s new city data agency

1.6.8. PD: Evolution of best practice for city data (maturity model)

1.6.9. PD: Developing leadership/institutional awareness

1.6.10. DA: Building a digital Govern (Estonia)

1.6.11. DA: Data driven delivery approaches (sierra leone)

1.6.11.1. community engagement

1.7. DEEP DIVE

1.8. Impact

1.8.1. Leaders demand/receive data before making high-stakes decisions, and establish routine practices that are sustained to solve complex problems.

1.8.2. Mayors have the right data and evidence before allocating or shifting resources or making decisions.

2. T2: Investing in the Right People, Practices, and Platforms

2.1. Description: Mayors create an infrastructure consisting of skilled teams, routine practices, and the right platforms to launch city-wide data transformation.

2.2. Objectives

2.2.1. Interrupt inequitable data use and management practices as well as build habits that embed equity across the cities data structures and systems.

2.2.2. Assess current data culture, practices, and capacity to identify areas of strength and development and to plan for change.

2.2.3. Collectively survey city landscape and culture, define priority and focus areas, and build city wide momentum towards a specified broad goal.

2.2.4. Collect and review existing interventions to prioritize issue areas to explore options for testing and/or areas of inequity

2.2.5. Define purpose and end state for improving the quality of high-value dataset[s] related to a particular focus area; Develop a plan to improve the quality and to revise existing data.

2.2.6. Lead a data driven city that identifies talent and skills needed to transform data culture

2.2.7. Develop and implement a plan to gain buy-in from department heads, staff, internal/external partners input by engaging them in goal-setting and project planning discussions.

2.2.8. Assess the state of internal access to data related to a particular focus area, reviewing the availability of existing technology, infrastructure, and human capacity to leverage for this work.

2.2.9. Discover the current range of skills and technology that can be tapped for the development of analytic insights and train analysts on the production of insights using various techniques

2.2.10. Close gaps with existing resources and training by inventorying existing analytics staff (skills, time, performance), and data management and analytics tools (technology).

2.2.11. Identify a process to routinely share and/or publish analysis of progress on key priorities to internal / external stakeholders.

2.2.12. Lead organizational change: diagnosing resistance to change, recognizing levers to change practices, and removing obstacles to data-use.

2.2.13. Develop policies that sustain the structure and implement systems and processes city-wide through change management efforts.

2.2.14. Employ available and effective levers of change throughout the city to improve existing skills, processes, methods, performance standards, or conditions.

2.2.15. Assess and evaluate the progress of current capacity, investments, programming, or initiatives toward intended outcomes in the given priority area.

2.2.16. Refocus initiatives towards stated theory of change using the levers of change (e.g. regulatory, fiscal, communications, policy).

2.2.17. Invest in programs/initiatives with a proven track record of advancing levers and driving impact

2.2.18. Investigate progress toward systemic change incorporating staff and public input (e.g. feedback loops)

2.2.19. Generate clear questions that will help guide data selection and analytic approach leveraging data to conduct a deeper analysis.

2.2.20. Activate data as a strategic asset and use data to make equitable policy decisions that promote transformational outcomes.

2.2.21. City staff effectively maintains and routinely supplies data to support policy and budget requests and changes.

2.2.22. "Crafting specific narratives as it relates to individuals, organizations and/or projects; Craft and model stories that highlight vision and value, and generate buy-in across city departments and agencies

2.2.23. "

2.3. Goals

2.3.1. Cities have a designated leader and/or team responsible for implementing city-wide data governance practices and policies.

2.3.2. Cities consistently allot time and resources that support staff to collect, maintain, store, improve, and discuss data.

2.3.3. Cities have structured data governance routines (including development and maintenance of data governance plan, ongoing check-ins, etc.).

2.3.4. Cities collect and analyze disaggregated demographic data for impact.

2.3.5. Cities use data to drive transparency and operational excellence within each department, aligned with the city’s vision.

2.3.6. Cities use data to shape investments by prioritizing resources and intervening surgically

2.3.7. Cities use data to align investments with strategic priorities and promote equitable deployment of resources based on actual needs of communities

2.4. Keynote Address

2.4.1. Rosalyn Bliss (Mayor Grand Rapids MI)

2.4.2. Michael Bracken (Public Digital)

2.4.3. Michael Handsworth BIT)

2.4.4. Jeff Liebman (GPL)

2.4.5. Collective and Collaborative Data Tracking across multiple agencies: how multiple agencies work toward a common goal (story of success)

2.4.6. Building Stakeholders from within

2.4.7. Role of leadership: in driving performance, leading change, holding people accountable, and motivating collaborators.

2.4.8. Imagination: Spotting new trends and Technology

2.5. Workshops

2.5.1. Sustaining and empowering collectives

2.5.2. Career Bureaucrates: Getting Buyin

2.5.3. Tech for success and innovation

2.5.3.1. PD: Civic Technology

2.5.4. Leading through Budget constraints and shared resources

2.5.5. Navigating Resistance and change

2.5.6. Making Good ideas sticky and Diagnosing system Failures

2.5.7. BIT: Investing in Data Systems (Partnership with PD on high quality data services?)

2.5.7.1. PD: Importance of high quality data services

2.5.8. GPL: Procurement as a strategic function

2.5.9. Improving Resource Allocation and City services

2.5.9.1. DA: Improving City maintenance using real time data

2.5.9.2. DA: Improving Resource Allocation (Puerto Rico)

2.5.10. DA: Data Capacity review -- Reviewing data capacities

2.6. Partners

2.6.1. BIT: Passing an evaluation policy mechanism

2.6.2. BIT: Mini evaluation scoping workshop

2.6.3. BIT: Using evaluation results to inform programs

2.6.4. BIT: (Budgetary fear - don’t be afraid when an evaluation shows a less-than-stellar result)

2.6.5. BIT: Building an evaluation function (may also fit under pillar three)

2.6.6. PD: Product Management

2.6.7. DA: Monitor urban ops

2.6.8. GPL: Bringing procurement data into city leadership strategy meetings to drive improvement (particularly in contracting and resident-facing contracted services)

2.7. Deep Dive

2.7.1. Hr: Building Talent

2.7.2. PD: Building internal capacity

2.7.3. GPL: Problem based procurement

2.7.4. DA: Building capacity in City Teams

2.7.5. PD: Culture Infrastructure maps (reading/application)

2.7.6. Capitalize HR functions

2.8. KSA

2.8.1. K: COMPLETE KSA FOR EACH WORKSHOP

3. T3: Empowering People to be Part of the Solution

3.1. Description: Leaders create a cross-sector collective beyond city hall to engage people in helping to achieve common goals.

3.2. Objectives

3.2.1. Collectively survey city landscape and culture, define priority and focus areas, and build city wide momentum towards a specified broad goal.

3.2.2. Develop and implement a plan to gain buy-in from department heads, staff, internal/external partners input by engaging them in goal-setting and project planning discussions.

3.2.3. Identify a process to routinely share and/or publish analysis of progress on key priorities to internal / external stakeholders.

3.2.4. Lead organizational change: diagnosing resistance to change, recognizing levers to change practices, and removing obstacles to data-use.

3.2.5. Develop policies that sustain the structure and implement systems and processes city-wide through change management efforts.

3.2.6. Advocate for the development of mechanisms to continually monitor and evaluate city-wide change and accountability.

3.2.7. Invest in programs/initiatives with a proven track record of advancing levers and driving impact

3.2.8. Investigate progress toward systemic change incorporating staff and public input (e.g. feedback loops)

3.2.9. Activate data as a strategic asset and use data to make equitable policy decisions that promote transformational outcomes.

3.2.10. Build a compelling narrative that spotlights a priority and weakens misinterpretation of information to shift perspectives toward a shared idea.

3.2.11. Assemble an effective, readable, and accessible report on performance or change management or one that tracks the performance of a data driven decision.

3.2.12. Conduct outreach to potential partners to build understanding, awareness, and support around particular potential projects.

3.2.13. "Crafting specific narratives as it relates to individuals, organizations and/or projects.

3.2.14. "Crafting specific narratives as it relates to individuals, organizations and/or projects; Craft and model stories that spark collaboration and team alliance from staff and public.

3.2.15. Engage in the process of telling a variety of stories about City data ensuring it is accurate, complete, unbiased, and unadulterated information.

3.2.16. Provide feedback on the effectiveness of communication as it relates to the presentations and data visualizations on performance analytics (e.g. Stat slides, dashboards, reports, etc.).

3.3. Goals

3.3.1. Cities gain staff buy-in by engaging front line teams in setting priorities, testing strategies, and data use.

3.3.2. Cities foster trust and transparency by depending on data practices that are reliable, well maintained, and deeply connected to priorities and strategies.

3.3.3. Cities mobilize community members to solve problems and support efforts to educate, activate, and collaborate with the community to better understand and use city data to deepen community impact.

3.4. Keynote Address

3.4.1. Natalie Evans Harris (US Dept of Commerce)

3.4.2. Prof. Hahrie Han (JHU)

3.4.3. Citizen City engagement

3.4.3.1. GPL: Successful citizen-city engagement (particularly around affordable housing)

3.4.4. Engaging Citizens at the center of Design

3.4.5. Staff Driven Initiatives

3.4.6. First Open Data Governance Policies

3.4.6.1. DA: Open Government data policy

3.4.6.2. DA: Being the first (open data policies -- Montevideo)

3.5. Workshops

3.5.1. Problem Based Data Collectives

3.5.1.1. PD: Data Community of practice

3.5.1.2. BIT: Community of Practice

3.5.2. Out of the box: Partnership with government entities

3.5.3. Resident collaboration and feedback

3.5.3.1. GPL: Engaging the community in setting priorities

3.5.4. Communicating Change: Beyond PIOs and other channels

3.5.4.1. Bigger than Dashboards

3.5.4.2. BIT: Communicating evaluation results

3.5.4.3. DA: Visuals to communicate with residents

3.5.4.4. DA: Using Data and Evidence in Stories

3.5.5. Human angle of data

3.5.6. Managing Resident and vendor ecosystems

3.5.6.1. PD: Resident and Ecosystems management

3.5.6.2. BIT: Use existing external evidence to inform decision making

3.5.7. Public Policy Lifecycle??

3.5.7.1. BIT: Creating and passing/publishing a policy

3.5.8. PD: serving public through internet use

3.6. Partners

3.6.1. GPL: Entertaining vendor proposals and solutions

3.6.2. DA: Building Strong Data Systems (Pakistan)

3.6.3. PD: Data as a service infrastructure

3.6.4. PD: Push v Pull Model for providing Data

3.7. Deep Dives

4. Kickoff (PreGame)

4.1. Description: Red carpet event for Mayors and 2-3 people on their C Suites to get them motivated about their acceptance into the program and about the potential impact.

4.2. Objectives

4.2.1. Analyze the successes of cities with exceptional data-driven cultures in order to adopt central behaviors and themes.

4.2.2. Build and model the foundation for data-informed-decision making by using data to gain a holistic understanding of the facts contributing to a policy problem.

4.2.3. Model behaviors that value, practice, and encourage the use of data to inform the best policy decision, intervention, or solution

4.3. Goals: ....

4.3.1. why are we here, expectations and challenge

4.3.2. Relay 100 Cities goal

4.3.3. Host Partner Plenary - Cities rank by desire to work in that area

4.3.4. Networking - forge relationships with other mayors, cities, and regions

4.3.5. Meet with Mayoral/City Coach to start work,

4.3.6. Team Building

4.4. Modality: Hosted during City lab

4.5. Keynote Address

4.5.1. Mike Bloomberg: ...

4.5.2. Ron Daniels: ...

4.5.3. How individual urban level change has global effects

4.5.4. Building a legacy through sustained initiatives

4.6. Workshops

4.6.1. Why are we here: level setting and expectations

4.6.2. Preparing your team for the work ahead

4.6.3. Analyzing Success Models and Impact

4.6.4. Assessing current practices; Surveying the Landscape

4.6.5. Gathering Baseline QOL metrics

4.6.6. General Coaching Conversations

5. Closing Address

5.1. Keynote

5.1.1. Mike Bloomberg

5.1.2. President Joe Biden

5.1.3. Pete Buttigieg???

5.2. Goal

5.2.1. Cities access and track progress on standard of living metrics

5.3. Objectives

5.3.1. Identify a process to routinely share and/or publish analysis of progress on key priorities to internal / external stakeholders.

5.3.2. Advocate for the development of mechanisms to continually monitor and evaluate city-wide change and accountability.

5.3.3. Track effects of data informed decision making and realigned strategy and technique to the central theory of change.

5.3.4. Assemble an effective, readable, and accessible report on performance or change management or one that tracks the performance of a data driven decision.

5.3.5. "Crafting specific narratives as it relates to individuals, organizations and/or projects.

5.3.6. Engage in the process of telling a variety of stories about City data ensuring it is accurate, complete, unbiased, and unadulterated information.

5.3.7. Provide feedback on the effectiveness of communication as it relates to the presentations and data visualizations on performance analytics (e.g. Stat slides, dashboards, reports, etc.).

5.4. Workshops

5.4.1. Presentations of learning completed by all city groups including TA outcomes

5.4.2. Partner highlights of exceptional work products and progress, features cities and city teams

5.4.3. Resident or NGO highlights