A key feature of effective analytics infrastructure in healthcare is a metadata-driven architecture. In this article, three best practice scenarios are discussed: Automating ETL processes so data analysts have more time to listen and help end users Using a metadata repository to enhance data literacy among users and improve trust in data, thus enabling data governance policies Improving turnaround time for data analysts who support frontline staff who, in turn, monitor interventions based on evidence-based medicine that is constantly changing The article unravels the components of the metadata-driven architecture as part of an overall analytics platform. Learn the methodology for creating faster data results, generating speed to value, and realizing systemwide analytics adoption.

1. Aiding Analytics Adoption Via
Metadata-Driven Architecture:
If You Build It, They Will Come
̶ Sri Rajamani

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Metadata-Driven Architecture
The return on analytics investment depends on
systematic and scalable outcomes
achievement in clinical, financial, and
operational areas.
This presentation describes how a metadata-
driven architecture is pursued as the backbone
feature of enterprise data warehouse (EDW)
and analytics ecosystems that enables faster
analytics adoption among users.
First, a quick look at the definition of metadata-
driven architecture.

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Metadata-Driven Architecture
A good analogy for describing a metadata-
driven architecture is the manufacturing
process of die-cast products.
There are three key components: a reusable
mold or die, the machine, and the molten
metal. In order to produce the desired output,
the molten metal is passed through the
reusable mold assembled in the machine at
high pressure.
Similar to that, in a metadata-driven
architecture, we are building a metadata
repository (the reusable mold) for the EDW
and analytical applications.

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How to Approach a Metadata Repository
In a white paper by Dale Sanders, Executive
Vice President for Health Catalyst, he
described the metadata repository in a very
simple and pragmatic way, likening it to the
Yellow Pages for an EDW.
Every part of the data movement and
transformation is captured in metadata format
and stored in the repository.
The metadata repository should be a 50/50
combination of human-generated content
(commentary) and computer-generated
content (facts) that aids various types of users
to interact with it in a wiki-style interface.

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ETL Engine Basics
An ETL engine functions as the heart of
the metadata architecture.
The way we set it up at Health Catalyst,
the ETL engine reads the metadata
repositories (source and target), creates
necessary ETL logic, and moves data from
source to destination(s) into various
analytical models.
To provide a visual for the metadata-driven
architecture, the following slide shows how
we organize our data flow design.

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ETL Engine Basics
An example of a data flow
design that is supported by
the Health Catalyst
Analytics Platform.
This diagram depicts three
core components of a
metadata-driven
architecture (metadata
repository, ETL engine,
and data sources).

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Automating ETL Processes for Data Analysts
A common problem for a health
system analytics team is how
much time spent on hunting and
gathering data versus analyzing
and interpreting data.
Data analysts have core clinical,
financial, and operational know-
ledge, in combination with
technical skills, best put to use
when engaging with, listening to
and helping end users.

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Automating ETL Processes for Data Analysts
Gathering data, loading it, and
managing ETL processes are the low-
hanging fruits that can be automated
to free up data analysts’ time.
When data analysts spend less time
hunting and gathering data, they
engage more with end users, answer
their questions, and develop reports
and applications for adoption of
analytics across the user base.

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Automating ETL Processes for Data Analysts
A health system can consider a metadata-
driven architecture as a strategic approach
to automating ETL processes for analytics
and business intelligence solutions.
To achieve such automation, consider
some of these aspects:
Data analysts should spend minimum time in data
mapping. Consider the option of loading source data in its
native format (with minimal necessary cleansing,
transformation, summarization, and assignment of rules).
>

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Automating ETL Processes for Data Analysts
Identify ETL tools that abstract the majority of data-
loading scripts, but allow data analysts to create, review,
validate, and complete the data mapping using a simple
Graphical User Interface (GUI). At the end of this process,
there should be a metadata repository (recall the reusable
molds in the die-cast manufacturing process above).
>
Think about an ETL engine that can automatically read
metadata repositories and churn out ETL scripts for the
data loading process.
>
We recommend to health systems considering
transforming their existing data warehouse to consider
and evaluate a metadata-driven architecture approach.

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Enhance Data Literacy to Improve Data Trust
As an enabler for data governance
policies, a metadata repository
enhances data literacy among users,
which improves trust in data.
We embrace a simple and pragmatic
approach to data governance that has a
triple aim of ensuring data quality,
building data literacy, and maximizing
data utilization.
As we have observed in many
performance improvement initiatives,
lack of trust in the data among end
users is never a trivial problem

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Enhance Data Literacy to Improve Data Trust
Whenever certain results are presented,
users often question the underlying data.
While questioning data is healthy to
validate its integrity, one should be careful
not let it deviate from the goals and aims
of improvements.
One of the best approaches to improve
trust is to educate users about:
• Data quality
• Data consistency
• Data timeliness
A single metadata repository
for the EDW and analytics is
a strategic asset to educate
and improve data literacy
among users.

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Enhance Data Literacy to Improve Data Trust
Here are some ways a metadata repository
can help to improve data literacy.
Data dictionary: As various data sources are provisioned
in the EDW, business/technical definitions, source to
target lineage, PHI sensitivity, and other aspects of data
are captured and stored in metadata, providing a rich data
dictionary. With a simple GUI to expose the data
dictionary, various types of users can browse the
repository and find the answers to many questions about
data and its validity (where did it come from, when was it
loaded, what logic was applied, if any, where is it being
used, etc.).
>

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Enhance Data Literacy to Improve Data Trust
Here are some ways a metadata repository
can help to improve data literacy.
Understand various uses of the data: With the help of
the data dictionary, a data analyst can review, learn, and
validate how certain measures are defined/used across
various improvement programs. For example, a simple
search of ‘LOS’ in a metadata repository might return more
than one result with different definitions. While an LOS for
a surgery department might be different from an LOS for
ED, by tracking the lineage of each definition back to its
data model, data analysts are exposed to different use
cases and improvement projects that are using the EDW.
>

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Enhance Data Literacy to Improve Data Trust
Here are some ways a metadata repository
can help to improve data literacy.
Collaboration among users: Analysts working on
improvement projects unearth various issues about data
elements that might be critical for other users. Having a
metadata repository where users can contribute about
data quality issues can be a great collaboration platform
to improve data literacy.
>

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Improving Agility of Data Analysts
So far, we have looked at how a metadata-
driven architecture can automate ETL
processes and improve data literacy
among users. But can it also empower an
agile team to meet their Aims and Goals?
As health systems engage in outcomes
improvement projects, a permanent
cross functional agile team comprises
various roles (data analysts, SME,
physician champion, nurse lead,
source application admin, etc.).

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Improving Agility of Data Analysts
This permanent agile team is responsible for
defining aims and goals, building measures
to monitor interventions and enabling
adoption of best-known practice intervention
protocols in the health system’s workflow.
One of the expectations in this permanent
agile team is managing change requests.

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Improving Agility of Data Analysts
It is critical for data analysts to turnaround change
requests in hours rather than days or weeks. For
example, some of the change requests can be:
Adjusting the analytical measures that are being monitored
due to constant regulatory changes (from CMS).
>
Clinicians constantly changing interventions based on
evolving, evidence-based medicine. To provide the best
possible care to patients, related measures constantly
change, as well.
>
End users constantly requesting changes to the
visualization of analytics to make better use of them.
>

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Improving Agility of Data Analysts
How can a metadata-driven architecture
enable a data analyst to be productive
and turnaround change requests quickly?
To construct change requests, data
analysts can browse through the
metadata repository for needed data
elements and intervention measures, and
also be aware of any reported data
quality issues.

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Improving Agility of Data Analysts
If a data element needed for the change
request is not available in the EDW, data
provisioning from source systems can be
completed faster using an automated
ETL process through the metadata-
driven architecture.
Use case data model components
(cohort definitions, inclusion/exclusion
rules, metrics definitions etc.) can be
available in the metadata repository.

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Improving Agility of Data Analysts
Data analysts can review, modify, and re-
publish those components, per change
requests, back to the metadata repository.
Using metadata-driven architecture,
dashboards/reports can be automatically
refreshed to publish results to end users.
The graphic on the next slide shows how
a data analyst can take various use case
data model components available in the
metadata repository to work on specific
change request scenarios.

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Improving Agility of Data Analysts
How a data analyst can use various use case data model components available in the metadata
repository to work on specific change request scenarios.

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Faster Data Results in Faster Adoption
Organizations that Health Catalyst works
with across the country are embracing the
metadata-driven architecture for their
EDW and analytics platform and finding
great success.
Community Health Network (CHNw) is an
excellent example of how one healthcare
system successfully organized data from
multiple technologies, including several
EHRs and other unintegrated data
sources, using an EDW.
CHNw boosted operational efficiency by 70 percent and achieved its data integration goals
within 12 months.

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Faster Data Results in Faster Adoption
This kind of speed-to-value and adoption are
typical of possible outcomes for organizations
that implement the appropriate technology.
As described throughout, location, lineage,
and usage of data being surfaced for data
analysts enables early usage of the data
and broad distribution of the analytics.
Any health system embarking on a journey
in search of broader adoption and easier
digestion of data should consider a
metadata-driven architecture.

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For more information:
“This book is a fantastic piece of work”
– Robert Lindeman MD, FAAP, Chief Physician Quality Officer

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More about this topic
Link to original article for a more in-depth discussion.
Aiding Analytics Adoption Via Metadata-DrivenArchitecture: If You Build It, They Will Come
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Success Story: Community Health Network -
PDF download

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Sri Rajamani joined Health Catalyst in October 2014 as a Sr. Solutions Consultant. Prior to joining
HC, Sri worked with Global Consulting Firm, Cognizant Technology Solutions as a Manager-Biz. Dev,
focusing on Healthcare Information Management and Analytics. Sri has a Global Master’s in Business
Administration from SP Jain Center Of Management, Singapore.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com