Data Strategy Playbook in Action: From Assessment to Building a Roadmap

CareQuest Institute, a national nonprofit organization, improves lives by making a more accessible, equitable, and integrated oral health system across the U.S., especially for marginalized and underserved communities. To fulfill this mission, leveraging data effectively is crucial.

In this blog, we discuss the steps CareQuest took to maximize the value of their data, including:

• Identifying gaps through a strategic data assessment ↵
• Crafting a plan based on key elements of a data strategy ↵
• Culminating the data strategy initiatives into an executable data strategy roadmap ↵

 Identifying through a Strategic Roadmap

To expand their services and help more communities, CareQuest needed to understand:

• What was required — time and cost — to become technology independent from their affiliate company, DentaQuest.
• How a new data architecture would impact both internal and external initiatives.
• What to put in place — people and processes — to support their growing organization and its mission.

Why is a strategic data assessment important?
“It’s a way to identify the best path to provide business value. We have conversations with all stakeholders–business team, data team, IT, etc. — to understand their pain points, thread the needle through those challenges, and provide solutions that will enable them to do their job better using data.” – Rebecca Zeni, Data and Analytics Consultant

As part of the assessment we interviewed 24 stakeholders across the organization, evaluated their existing landscape, and reviewed data documentation to capture their current state and challenges to achieving their goals.

We asked questions such as:

• How are you currently moving data? Are there immediate concerns with this process?
• What operational/analytical tools are being used? How do you want to use them?
• How are manual processes creating bottlenecks?
• What needs to be moved from your current system to the new system, and why?

Crafting a Plan Based on Key Elements of a Data Strategy

Based on initial conversations and evaluations, we identified key areas CareQuest needed to address to meet their goals, including: 

Business Alignment

Early access to stakeholder insights earned executive support, organizational buy-in, and project momentum. It also allowed CareQuest to:

• Prioritize their data and analytics initiatives
• Understand business user objectives and technology needs to get their jobs done
• Make informed decisions about data organization, architecture, and data modeling
• Identify opportunities for low-hanging fruit that would provide immediate value and a basis for future iterations

Why is alignment with the business strategy the single most important part of building a data strategy?
“A common quote that we hear from companies is that they have lots of data but no insights. This is an indicator of misalignment between business strategy and data strategy. The biggest risk to any data or analytics project is that it is not aligned to the business objectives or business needs.” – Christina Salmi, Managing Director

Data Maturity

CareQuest exhibited characteristics within different analytics and data maturity levels, somewhere between ‘Reactive’ and ‘Defined’.​ We identified key areas for improvement to move to the next level of maturity, including:

• Building the infrastructure and team to promote data democratization
• Adding additional staffing positions to support growth
• Migration to the cloud and leveraging modern technologies
• Removing technological dependencies
• Developing tech stack to house a single source of truth with both a data lake and data warehouse

We use the Analytics8 Maturity Model to define where our clients are today and what it would take to move them forward on the scale.

Modern Data Architecture

To support their business objectives, we needed to design a future-proof, scalable solution for CareQuest that considered:

• The need for independent data management and security
• Different types of users — data analysts, data engineers, BI developers, etc. — and the unique data needs of each
• Automating data integration from disparate data sources and creating a single source of truth
• Promoting data democratization

We made recommendations specific to CareQuest’s current and future needs and suggested implementing a modern cloud technology and architecture that would support the range of their data management needs and planned growth in research and analysis, as well as support data ingestion for organizational initiatives.

The right-fit data architecture for CareQuest was Azure Data Lake as a persistent staging layer and for ad hoc data science and analysis requests, and a data warehouse in Snowflake for curated and modeled data, as well to support self-service analytics.

Team Structure and Skills

To support new processes and a new data architecture, we assessed the existing data analytics team’s skillset. Challenges included:

• Too many informal requests of data analysts and data engineers; not able to effectively prioritize
• Some data requests are asked for more than once
• The team is stretched thin
• Cleaning data when it is needed rather than when it comes in​
• Lack of collaboration organization wide

As we built the data strategy roadmap, we included recommendations for:

• Training and user enablement for new technology and self-serve analytics for everyone, not just the data analysts and engineers
• Data literacy for internal and external users, as well as the need for a data catalog, so that everyone in the organization understands what data is available and what it means
• Team roles and structure, especially as the company grows, to support data-driven decision making across the business

Data Governance Program

Throughout the assessment, we worked with CareQuest’s existing data governance committee to better understand the challenges, which included:

• Data was not always conformed and there were multiple definitions for the same terms throughout the organization
• There was a need for compliance with HIPAA and HI-TECH before they could be technologically independent
• It was unclear what data was available, or what metrics might be contained in a report​
• There wasn’t any standard documentation, and files were difficult to find

Data governance should be practical, maintainable, and proportional so that it leads to high quality data.

After evaluating their current state, we suggested key areas to get started with including:

• Identifying governance standards and processes
• Building a data catalog to address concerns over data literacy and improve data transparency

Culture Change and Adoption

With any change, it’s crucial that everyone involved understands what is happening, and how they play in a role in making it happen. Throughout the process, CareQuest communicated intentions and progress, which was key to their success.

Stakeholders in each department were accessible and played an active role throughout the process, making it easier to design a data strategy with user adoption in mind, cater to the needs of the business and prioritize data projects that were immediately valuable to the business, and build momentum around culture change across the organization. Business users began to perceive the possibilities of data differently and were prepared — and excited — for the upcoming changes.  ​

Culminating the Data Strategy Initiatives into an Executable Data Strategy Roadmap

With a clear understanding of the big picture and top areas of focus, we built a data strategy roadmap for CareQuest that included a backlog of 45 prioritized use cases — homing in on the low hanging fruit and urgent priorities such as technology independence, while also building a solid foundation to make progress toward — and prioritize — any new goals.

How do you work through prioritizing use cases when building out a data strategy roadmap?
“We use a business prioritization matrix based on technical feasibility and business value derived from the conversations we have with stakeholders. It’s important to remember that things can change along the way. CareQuest was a fantastic client to work with because not only were they active in giving us feedback on the use case prioritization, but they went the extra step and provided weighted  values. The communicated any changes early and often.” – Rebecca Zeni

A data strategy roadmap helps you prioritize your use cases, as well as any design, build, training, or re-engineering of a business process.

The data strategy roadmap had multiple phases with associated time, costs, and necessary resources for:

• Modern cloud technology and a new architecture stack
• A plan to migrate and ingest data sources
• Staffing and training recommendations
• Data governance program
• Consolidation to one analytics platform
• A plan to prioritize use cases with quick wins and foundational projects

Data Strategy Leads to Impactful Changes for CareQuest

Planning, communication, and executive buy-in and support all contributed to CareQuest’s success and quick progress in increasing analytic maturity. ​With renewed trust in and accessibility to the data, CareQuest is seeing progress toward their organizational goals.

Improved analytical capabilities to enhance and expand service offerings

CareQuest can rely on analytics to monitor and uncover correlations and indicators of medical and dental health for the communities they serve. They are also able to see gaps in oral health access and care for specific populations, such as veterans. This information helps them identify where and how they can improve their offerings.

Improved data democratization and self-service

CareQuest implemented a self-service dashboard that provides analytics to business users, who in turn share insights with the public for advocacy and educational purposes — further promoting CareQuest’s mission.

Established data governance practices

The development of a business glossary template and data catalog, along with updated security practices improved the role of data governance. Additionally, CareQuest’s external partners can feel confident with the robust security, confidentiality, and integrity of the data and analytics environment, creating more opportunities to collaborate with data and gain powerful insights which will improve people’s lives.

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