Analytics strategy and roadmap
Contact our teamDespite years of investment, many health systems still struggle to unlock the full potential of their data.
Complex technology ecosystems, fragmented analytics, and inconsistent data can stand between information and timely, trusted insights.
Chartis helps organizations build a high-performing analytics function with the governance, data management, delivery model, and roadmap needed to support better decisions across care quality, experience, performance, growth, access, and health equity.
Building a stronger foundation for analytics and AI
Health systems are navigating increasingly complex technology and data environments, with information spread across disparate systems, platforms, vendors, and analytics tools, making it difficult to establish consistent definitions, trust the data, and connect analytics resources to enterprise priorities.
AI makes addressing these challenges more urgent. Advanced analytics and AI depend on trusted data, clear governance, and well-defined measures. Health systems need a clear strategy and roadmap to strengthen that foundation, prioritize investments, and build analytics capabilities that can evolve as enterprise needs and technology advance.
Build an analytics program around how decisions get made
Chartis works across strategy, governance, data, technology, and operating models to build analytics capabilities around enterprise priorities, creating a foundation that can deliver value today and evolve to support future-state advanced analytics and AI.
- Align analytics to enterprise priorities: We help build cross-functional leadership alignment around the organization’s most important priorities, then define the analytics vision, priority decisions and use cases, and measures of success needed to focus investment where analytics can create the greatest value.
- Establish trusted data and governance: We create clear governance, decision rights, definitions, and data management practices that improve the quality, consistency, and usability of data across the organization.
- Build a scalable analytics operating model: We design how analytics work gets done from intake and prioritization to self-service, visualization, advanced analytics, collaboration, and change management, so resources are focused on the decisions that matter most.
- Create the roadmap for what comes next: We develop a future-state roadmap that sequences investments across people, process, data, and technology while creating a sustainable foundation for advanced analytics and AI.
The goal isn’t analytics maturity for its own sake. It’s building the leadership, governance, and operating model that connect analytics to enterprise priorities and turn insights into better decisions and measurable performance. For one client, enterprise leaders went from meeting fewer than half of their KPIs to meeting every one within 18 months. ”
Frequently asked questions
How should health systems structure their analytics operating model?
There isn’t one operating model that works for every health system. The right structure depends on the organization’s strategy, scale, capabilities, and needs. Centralized, decentralized, and federated models can all work when leaders create clear accountability and a practical way to prioritize, develop, deliver, and adopt analytics across business and technology teams.
How can a health system improve data quality and trust?
Consumers trust data when they understand what it means, where it came from, and who is accountable for it. That takes common definitions, clear ownership, governance, and stewardship, along with visibility into how data is collected, transferred, modeled, accessed, and secured. Getting those fundamentals right makes the data more useful across clinical, financial, operational, and administrative teams.
How should health systems measure the value of analytics and AI?
The value of analytics and AI should be measured by the decisions and outcomes they enable, not by the number of dashboards, reports, or tools an organization deploys. Health systems should define measures of success upfront and connect analytics and AI investments to measurable clinical, operational, financial, growth, access, workforce, and other enterprise outcomes.
From data to insights: How CentraCare is using analytics to drive organizational performance
One of Minnesota’s largest health systems needed to turn its rich data store into actionable insights, supporting the latest in clinical advancements and achievement of business and operating goals.
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