Director Of Enterprise Data, Analytics And Ai

Horizon Health•Paris, IL

About The Position

Horizon Health is a Critical Access, Rural Health Facility comprised of 25-inpatient beds located in Paris, IL & a multitude of outpatient clinic settings including Family Practice and Specialty Clinics in Paris and surrounding cities. We have been serving residents of Edgar County since 1968 though community education, emergency services, and outpatient care. As we continue to expand our services & locations, our community has grown far beyond Paris. Our rich history and strong community support pave the way for the future of healthcare as we serve you—our family, friends, and neighbors. Position Summary: The Director will create trusted, standardized information that strengthens decision-making across clinical care, operations, finance, revenue cycle, workforce, access, market strategy, and growth. This leader will establish enterprise data standards and sources of truth, participate in a cross-functional Data & AI Governance Council, help guide Horizon’s data strategy and analytics roadmap, and support AI governance, prioritization, implementation support, and value measurement. This is an enterprise leadership role—not simply a reporting or IT role. The successful candidate will be a builder who can work credibly across executive leadership, physicians, operations, finance, clinical teams, IT, and external partners.

Requirements

Master’s degree in a relevant field.
Experience in a hospital, health system, Critical Access Hospital, rural health organization, payer, or similarly complex healthcare environment.
Experience supporting EMR selection, implementation, optimization, or reporting strategy.
Experience with enterprise data architecture, business intelligence strategy, or data-platform design.
Experience with provider productivity, labor productivity, revenue-cycle, reimbursement, forecasting, pro forma, service-line, market-share, and referral analytics.
Experience establishing AI governance or implementing responsible AI programs.
Experience with CRM data governance or marketing/patient engagement analytics.

Responsibilities

Lead Horizon’s enterprise data strategy, governance framework, and multi-year roadmap.
Establish standards for data definitions, ownership, stewardship, data quality, reporting, and appropriate use.
Create and maintain a governed enterprise KPI catalog with approved definitions, methodologies, and authoritative data sources.
Lead the inventory and rationalization of current reports, dashboards, data sources, and analytic processes.
Participate in the Enterprise Data & AI Governance Council and coordinate data stewards and cross-functional leaders across operational, financial, clinical, and administrative domains.
Build a scalable decision-support and analytics capability that delivers timely, actionable insight to executive, operational, financial, and clinical leaders.
Establish a formal intake, prioritization, and service-level process for analytics and reporting requests.
Lead development of standardized operational KPI dashboards and executive reporting.
Strengthen analytics for provider productivity and access, labor productivity, revenue cycle, reimbursement, service-line planning, forecasting, pro formas, referral patterns, market share, and strategic growth.
Partner with IT to define a practical enterprise data architecture and phased roadmap for reporting, business intelligence, integration, and analytics capabilities.
Serve as a key leader in Horizon’s upcoming EMR transition by defining data, reporting, interoperability, governance, and embedded-AI requirements.
Guide data governance and analytical usability for future CRM implementation in partnership with Marketing/PR and operational leaders.
Help and implement Horizon’s AI strategy, governance framework, policy suite, and phased implementation roadmap.
Participate in the AI Governance Committee and establish processes for AI use-case intake, approval, monitoring, and value realization.
Maintain visibility into AI tools, vendors, use cases, risks, approvals, and outcomes.
Build the Enterprise Data, Analytics & AI function, assess current internal capabilities, and recommend the future-state team structure, staffing plan, and investment priorities.
Strengthen data literacy and disciplined use of information across leadership teams.