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Modern Data And Analytics Accelerator

Unleash Data and Insights to Power Your Digital Healthcare Strategy

 

Transform your data strategy with Productive Edge's Modern Data and Analytics Accelerator

Our 5-week solution provides a comprehensive approach to help healthcare organizations transition to a modern, flexible, and agile data foundation. Our experts will work with you to understand your business goals, assess current technology, and create a roadmap for a patient-centric and transformative journey. With our accelerator, you'll receive a tailored, execution-ready architecture that incorporates the latest data management practices and technologies. Develop a future-proof data foundation and achieve your digital healthcare ambitions with Productive Edge's Modern Data Accelerator.

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Outcome

Roadmap of Engagement Results

Access an execution-focused data and analytics blueprint outlining people, processes, and technology definitions. Receive an overall roadmap and suggested timeline required to build and sustain the modern data foundations that will power your digital healthcare strategy.

What to Expect

Our modern data and analytics strategy accelerator helps healthcare organizations develop a tailored data-driven strategy and roadmap that considers their objectives, culture, and priorities. This ensures sustainable change management and alignment across the organization.

Part 1: Define Digital Ambitions and Desired Outcomes

  • Analyze and document key analytics and AI use cases specific to healthcare.
  • Identify master data needs and target maturity for healthcare data.
  • Evaluate users, user access needs, roles, and entitlements within the healthcare context.

Part 2: Technology Execution Plan

  • Discuss internal data source integration, access, and systems relevant to healthcare.
  • Determine target DataOps process, roles, and responsibilities tailored for healthcare data management.
  • Define physical data lake components, system and versioning considerations, and the design and architecture required to support the overall approach in healthcare.

Part 3: Formalize Key Findings and Recommendations

  • Business goals and representative healthcare use cases.
  • Data analytics strategy that aligns key healthcare data sources to priority and provides a sample data pipeline.
  • Master data scope definition specific to healthcare.
  • Target modern data analytics architecture for ingesting raw healthcare data, rules around data cleansing, data curation, managing gold data, and querying through data virtualization.
  • Iterative data governance scope and roadmap tailored to healthcare needs.
  • Physical data lake design and architecture to adopt modern data architecture principles for healthcare.
  • User access, user access needs, roles, and entitlements within the healthcare sector.
  • DataOps process, roles, and responsibilities aligned with healthcare data management.

Case Study

Maximizing Productivity & Reducing Waste With Predictive Analytics

Read this case study to see how our customer deployed AI in its survey process to increase productivity, reduce downtime and maximize their financial goals.

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Ready to discuss your project?

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