Post Health Plans May 27, 2026 Your data fabric investments are only as strong as your operating model Why data and analytics teams at health plans spend more time maintaining their data platform than enabling value for stakeholders DIY operating models, incomplete implementations, data fabric debt, and thin or revolving resources all prevent value creation. Most health plans already have data, analytics platforms, and data fabric components in place. When those tools are no longer meeting expectations, replacing them with best-in-class tools is important, but only part of the solution. The other challenge is the operational burden required to make the data fabric operate at a level that creates value for business stakeholders. Too often, plans find themselves spending heavily on platforms, integrations, and data infrastructure, only to add more resources for maintenance, reconciliation, configuration, and issue resolution. Business stakeholders are left asking a fair question: Why are we spending so much just to keep the machinery running without creating the value we expected? The real barrier to progress is not only a lack of best-in-class technology. It is the compounding complexity of operating those tools, fixing data issues, adapting to changing business needs, and keeping up with a regulatory landscape that never stops moving. And because upstream and downstream system demands never stop, the maintenance burden never ends. The impact becomes clear in the day-to-day reality of data and analytics teams. The operating model shift for data platform & analytics teams: maintenance vs. value creation A ‘Day-in-the-life’ for current operating models & tools With CareEvolution® Data Refinery Current operating model & tools8:30am — Pipeline triage Overnight jobs failed or partially processed Team digging through logs across multiple systems With CareEvolution data refinery8:30am — Pipelines run automatically Data ingestion + normalization handled in software No manual intervention required by your teams Current operating model & tools10:00am — New data source request “Can we onboard this new clinical feed?” Response: scope interfaces and variable costs define mappings estimate 4–12 weeks With CareEvolution data refinery10:00am — New data source Use pre-built connectors Onboarding measured in days—not months Predictable costs Current operating model & tools12:00pm — Data quality investigation Analytics team flags inconsistent results Platform team asked to: trace lineage debug mappings reconcile across sources With CareEvolution data refinery12:00pm — Trusted data Data quality already improved through normalized, standardized, and uplifted data Clear lineage and consistency across sources Quickly determine data source value Current operating model & tools2:00pm — Member matching issues Duplicate / mismatched records across systems Multiple EMPIs returning different results Which match is correct? Team begins researching With CareEvolution data refinery2:00pm — Unified member matching Identity resolved across all data flows No reconciliation between systems required Creates a secure privacy-preserving record link(PPRL) that can be leveraged for other internal use cases Current operating model & tools3:30pm — Integration maintenance Updating mappings for: EMR changes new codes format variations Often requires vendor or consultant support With CareEvolution data refinery3:30pm — Minimal maintenance No ongoing mapping work No dependency on consultants Benefit from broad industry data format updates Current operating model & tools4:30pm — Stakeholder friction “Why is this data wrong?” “Why does this take so long?” “Why are we paying for things that were never fully implemented?” Engineering stuck between: business expectations system limitations burnout and low morale With CareEvolution data refinery4:30pm — Productive engineering Team focused on: analytics platform innovation supporting business stakeholder outcomes simplified adoption of industry changes E.g. regulations, digital HEDIS, member and provider engagement Current operating model & tools ~80% of organizational spend and effort is on operational issues like: Data feed troubleshooting Data mapping updates Reconciliation work Operational firefighting With CareEvolution data refinery ~80% of organizational spend and effort freed up for value creation areas like: Analytics and AI enablement Risk adjustment & quality measure programs Business stakeholder outcomes Data as a competitive advantage Closing the operational gap Retooling alone doesn’t get the value health plan business stakeholders need. Health plan data and analytics teams need best-in-class tools and an operating model designed for value creation. CareEvolution’s Orchestrate Data Refinery automates ingestion, normalization, identity resolution, and ongoing integration maintenance—work that often consumes critical health plan resources that could be redeployed to support business stakeholders. Built on 20+ years of healthcare data expertise and proven across tens of billions of data events, our software and operating model handle the complexity so internal teams can focus on higher-value work: analytics, quality initiatives, innovation, and stakeholder outcomes. The result: Faster data source onboarding from months to days Trusted data and member matching Shifting your team’s identity to value creation partners In short: the faster you can get out of maintenance and break/fix mode, the faster your team can focus on the work that actually matters. And that starts with a partner who’s been solving this problem with software at scale for over 20 years.
