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Insights

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The Insights page focuses on capturing how we were able to arrive at the current state that is described in the previous steps. The work of the Data Collaborative was messy and complicated — and we loved every moment of it. For that reason the insights all influenced and depended on other parts. The overall model for the last two years of the Data Collaborative relied on using Design Thinking to identify bright spots and opportunity areas. While we have tried our best to separate out the insights into cookie-cutter categories, it's important to know that all the insights were sourced from the synthesis of themes and patterns throughout the work. The purpose of this section is to pull out the most relevant learning for each section. We have tried to identify bodies of work as they relate to the Knowledge Management's layout, by step. Here are quick links to each step of the Knowledge Management:

Step 0: Background: Who Inspired Us? This section provides the reader an introduction and bridge to the bodies of work that influenced and shaped our theoretical approach to the work. They helped us in all phases of the work and really helped inform us.

Step 1: Aim: How did we evolve to achieve our Aim? This section is really focused on providing previous frameworks that we used in the Data Collaborative and where we are looking for the future. The purpose is to provide insight, by giving the raw framework and roadmaps the Data Collaborative team used throughout the design process.

Step 2: Concepts: How did we develop the concepts? This section, similar to the previous one, reflects the evolution of how we chose the core content the Data Collaborative focused on and worked to integrate it into the participating Partners’ routines within the community.

Step 3: People: What reflections do we have from the last three years? This section allows you to explore excerpts from the Data Collaborative Team, a highlight from a few Partners, and data from the surveys that have been provided throughout the years.

Step 4: Capacity Building and Step 5: Operations: What combination of capacity and operations did we need to carry out the work? These sections aggregate the Capacity Building and Operations based on the areas for which the Suite of Supports focuses. We try to reflect the evolution of each bucket. Here are the buckets we include:

Training
Coaching
Tools
Technical Assistance

The purpose of this section is to provide as much clarity and resources about what has informed the work we have created, as well as any future thinking we were able to achieve before transitioning the work.

Step 0: Background

The bodies of work below are popular frameworks, fields of study or organizations that have had success and solutions for the work that falls within the four pillars of the Data Collaborative and how to create a community that learns and improves together. We should highlight that we were not always able to replicate exactly what this inspiration called for and we stole and then created for our context based on how the community responded. It is important to note that at the center of our design process were the Data Collaborative Partners. We adapted and adopted the inspiration below to help us support our Partners.

Collective Impact Model

The Collective Impact model helped inspire the different roles and how the Data Collaborative community interacted and worked together. Its influence can be seen in the high level desires of the community.

Collective Impact (SSIR)Large-scale social change requires broad cross-sector coordination, not the isolated intervention of individual organizations.

Active Implementation Framework

The Active Implementation Framework was stolen and directly inserted into the Data Collaborative model. Pillar #3 is solely focused on Implementation Drivers. In addition, we have looked at NIRN to better understand what, how and when to introduce evidence-based practices (or innovations) to our work.

Asset Based Community Development

Asset Based Community Development was a lens that we focused on throughout the design process. The purpose was to constrain ourselves to what already exists within the community versus seeking something outside of it. The approach focuses on appreciative inquiry, chasing bright spots and leveraging existing resources to which the community has access and agency.

Carnegie Foundation for the Advancement of Teaching Frameworks

Carnegie Foundation has influenced and led to many ideas that are embedded in the Data Collaborative. Carnegie’s biggest influence is the Driver Diagrams and Fishbone Diagrams that are promoted by the organization. The Data Collaborative Team attended (and presented a poster at) its annual conference. A lot of the Team's mindsets have been impacted by how Carnegie promotes its work.

Our Ideas | Carnegie Foundation for the Advancement of TeachingUsing Improvement Science to Accelerate Learning and Address Problems of Practice Carnegie advocates for the use of improvement science to accelerate how a field learns

Design Thinking

Design Thinking is a human-centered approach that focuses on truly understanding the problem and then creating novel solutions when clarity is not available in the beginning. The practices and strategies of Design Thinking lend themselves well when working in adaptive challenges with groups of unique and diverse groups of people. A helpful by product of D. School’s work is Liberatory Design.

Stanford d.schoolWe are a hub for innovation, collaboration and creativity at Stanford. Our mission is to help people become everyday innovators, everywhere.

Secondary Resources Capture

The rest of this folder is really specific articles and disciplines that were suggested or discovered by the Data Collaborative Team.

This is a range of different materials that helped shape and influence design decisions and potential solutions to challenges that we experienced in the Data Collaborative.

Step 1: Aim & Step 2: Concepts

The information below is a representation of how we were able to develop our Aim and Concepts. Before we were able to evolve into a Driver Diagram we had to have a less integrated model and a lot more hypotheses. Overtime we were able to understand what worked and what really helped resolve pain points that the Data Collaborative community experienced. Following the bright spots and opportunity to bridge the gaps, we were able to develop a more dynamic model. The dynamic model allowed the opportunity to adjust very specific components of the model without disrupting all the work.

Read Charlotte KSM Crosswalk

The Read Charlotte KSM Crosswalk document reflects the first attempt to conceptualize the work the Data Collaborative community hoped to achieve. The document was created in collaboration with UPD consulting and Deepti.

The document defines the concept of Knowledge, Skills and Mindsets (KSMs) and how those are reflected in the development of the Suite of Supports. The first iteration of the Read Charlotte KSM document happened in-between Year 1 and Year 2. This can be found on the tab labeled First List of KSMs. This iteration was the first consolidation, eliminating more aspirational KSMs and prioritizing more foundational KSMs.

As represented in the visual above, the purpose of this was to understand and define the aspirational characteristics of an organization and its staff that accomplishes the Data Collaborative Aim.

Goals, Milestones, and Actions

GMA 2018-19 Actual

The Data Collaborative Team slowly started its transition from consultants to an in-house team. Through this growth, it was important to help define the scope of work and a system to continue to support it. This scope of work used the Read Charlotte KSM Crosswalk document as a foundation to map out the work and to understand what can be achieved within the Knowledge, Skills and Mindsets.

The Goal, Milestones and Actions (GMAs) reflects and defines the strategies that the Data Collaborative Team were implementing to achieve the Knowledge, Skills and Mindsets reflected in the KSM crosswalk document. The leverage created from this document was to better articulate how the work was done and what processes can be streamlined and replicated in the future. The learnings from this document were used to define a long-term oriented, open framework that is reflected with the Driver Diagram above. The Driver Diagram captures the core work that facilitated the KSMs that were most important and could be routinely accomplished through streamlined processes.

Driver Diagram with OKRs Cheat Sheet

The image was helpful to help unpack and identify how we want to look at the Driver Diagram. The purpose of the image is to overlay specific constraints throughout the diagram while making decisions. While the constraints were helpful to design within, constraints can always be changed.

Concepts Development

This folder contains all of the iterations and documents that helped us reach the current version of the Concepts model. In addition, this gives access to all of the Copy documents associated with the Partner Portal.

Potential Proposals

This folder contains all of the potential proposals that the Data Collaborative Staff created in the last 6-8 months of the Project's life cycle. The purpose of these proposals is to see how the Data Collaborative Team was looking at how the work serves the greater community in Charlotte-Mecklenburg County and where potential opportunities exist.

Step 3: People

This section reflects all of the unique perspectives that we have been able to capture from the various stakeholders involved in the Data Collaborative community. The purpose of this insight section is to give clarity around what we were able to learn and what we used as a source to inform the development and design of the community. It was important to provide the information in aggregate and allow for new conclusions to be made based on different vantage points within the work.

Data Collaborative Partners

(Direct Practitioners)

The approach in designing and facilitating the Data Collaborative community used a human-centered design approach. The Data Collaborative Partners were the humans placed in the center of our design practices. To capture the insights of Data Collaborative Partners, we have multiple sources to gain insight and appreciate the unique qualities of each organization. Sources include:

Surveys and Questionnaires.
In-person Perspective Activities.
Coaching Notes.

Data Collaborative Staffing

(Backbone Organization)

Within the Step 3: People section, each Data Collaborative Staff member has left a section that answers four questions:

What do you wish you knew from the beginning?
What body of work are you most proud of? Why?
Where would you push the work if the project was going to continue? Why?
What do you recommend when there’s resistance from Participants in adopting the work?

Each Team member tried to respond to the questions transparently and from their unique perspective.

Step 4: Capacity Building & Step 5: Operations

The bodies of work below correspond with the Bucket areas that are found in Step 4: Capacity Building and Step 5: Operations. The purpose of this section is to give clarity and reflect how the work has evolved over the past three years. Each bucket area has significantly changed and we continue to learn how to get better. In the areas where we have identified Change Ideas, you will see how some of the ideas made it to your products while others were left in the parking lot. It is important to say that not all of our Change Ideas made it to our products. We had design and creation constraints that we worked with during the process. Some ideas, although really strong, were not feasible with all of the priorities of creating and executing simultaneously.