© 2021 Health Catalyst
What’s Next for Data
Management and Healthcare?
Bryan Hinton – CTO, Health Catalyst
June 23, 2021

Learning and Evolving
2
ON THE GROUND
EXPERIENCE
INDUSTRY ANALYSTS SOLUTIONS FROM OTHER
INDUSTRIES

Learning and Evolving with Documented Results with Improvement
3
ON THE GROUND
EXPERIENCE
From 2018 to 2020 YTD
• 1,000 documented financial,
operational, and clinical
improvements
• Estimated economic valuation
of $407M

Learning and Evolving from Industry Analysts
INDUSTRY ANALYSTS
4

Learning and Evolving from Other Industries
OTHER INDUSTRIES
5

Macro Trends
• Cloud Adoption
• Importance of Data Orchestration
• Modernization of Data Engineering
• Data Product Thinking
• Augmented Intelligence
• Deeper Interoperability

Macro Trends – Cloud
Data and Analytics Is Moving to the Cloud
7

Macro Trends – Data Orchestration
• Historical constraint for data platforms was compute scalability.
• Billions of dollars of investment later and with many failures, we now have near-infinite scalable
cloud native data tools like Snowflake, Spark, and Blob Storage.
• A new constraint that will limit innovation and scale of data use in the enterprise is orchestration
and governance of data assets.
• The historical rise in investment in distributed compute technologies signaled that area as the
constraint to scaling the utilization of data.
• Now a similar rise in investment in data orchestration technologies signals the data
orchestration space as the emerging constraint to scaling the utilization of data.
8

Standard Industry Reference Architecture
9

Data Flow Graph of a Small EDW
10
This represents the complexity of orchestrating the data-flow
dependencies of less than 2% of the EDW.

Requirements for a Scalable Data Orchestration Platform
Includes:
• A metadata-driven/declarative approach
• Support for orchestrating multiple data transformation
technologies (not just SQL or just Python, etc.)
• Support for a single pane of glass for operationally
supporting hundreds of data pipelines regardless of
technology
• A data quality and testing framework available in multiple
technologies
• Built-in capabilities to capture metadata, data relationships,
and dependencies to optimize system performance and
provide data-catalog and discovery tools
• Reusable software packages in a package-management
framework
11
Will:
• Decrease cost to develop and maintain
data products
• Decrease cost to operate data platforms
• Enable operating at greater scale due to
increased automation
• Enable iterative adoption of new
technology
• Provide a foundation for data-product and
app-level innovation
A standard data orchestration platform is foundational to enabling reusable healthcare
data content because it standardizes the authoring and deployment of that content.

Macro Trends – Modernization of Data Engineering Practices
• The Data Engineering discipline has begun learning from and applying Software Engineering
concepts and practices
12

Macro Trends – Modernization of Data Engineering Practices
13
DataOps is One of The Most Talked-about Changes Coming to the Discipline of Data Engineering

Macro Trends – Data Product Thinking
• Product thinking replaces project thinking
• Project thinking leads to too many data assets in production without appropriate governance
(e.g., Are they being maintained? Are they being used? When should they be retired?). It’s not
scalable.
• Data product thinking often leads to greater self-service analytical enablement to address the
long queues for custom data requests (most of which are likely projects).
• Data products include:
– Defined business value, including scope and use cases
– Defined support model
– Data models
– Data pipelines
– Measure use
– Optional visualization assets
14

Healthcare Data Macro Trends – Augmented Intelligence
15
The New Healthcare.ai™
framework consists of 5
increasingly robust levels of
augmented intelligence (AI) that
significantly broaden the
application of AI in healthcare.
The framework:
• Expands the number of
potential AI users by applying
AI to the most common and
basic analytics uses cases
(Level 1)
• Expands the scope of
potential AI use cases by
identifying more complex,
system-wide, business-critical
issues (Level 2-5)

Healthcare Data Macro Trends – Data Aggregation and Interoperability
• The need to aggregate a great number of disparate data systems for value-based care and
population health use cases continues to grow.
• With that comes the need to normalize the data elements to enable greater data
interoperability.
• The VBC/pop health use cases coupled with the regulatory requirements around interoperability
have increased the importance of broad interoperability.
• FHIR is a key piece of the future interoperability story.
• Too little attention is being given to the difficult work of normalization to enable true
interoperability.
• Many advertised FHIR-based “interoperable” solutions are dramatically underinvested in data
normalization.
• Deeply integrated terminology capability needs to become as key to a modern healthcare data
platform as are ETL tools, data governance tools, and data science tools if we truly want to
unlock the potential locked up in healthcare data.
16

Poll Question (Multiple Answers Allowed)
1. Cloud Adoption
2. Importance of Data Orchestration
3. Modernization of Data Engineering
4. Data Product Thinking
5. Augmented Intelligence
6. Deeper Interoperability
17
Please select which follow-up webinars to deep dive on these technology trends you
would be most interested in.

Poll Question (Open ended)
18
What data technology trends are you seeing that weren’t captured in the webinar
today?

Trends
Questions?
• Cloud Adoption
• Importance of Data
Orchestration
• Modernization of Data
Engineering
• Data Product Thinking
• Augmented Intelligence
• Deeper Interoperability

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