The role of a data lake in healthcare analytics is essential in that it creates broad data access and usability across the enterprise. It has symbiotic relationships with an enterprise data warehouse and a data operating system. To avoid turning the data lake into a black lagoon, it should feature four specific zones that optimize the analytics experience for multiple user groups: 1. Raw data zone. 2. Refined data zone. 3. Trusted data zone. 4. Sandbox data zone. Each zone is defined by the level of trust in the resident data, the data structure and future purpose, and the user type. Understanding and creating zones in a data lake behooves leadership and management responsible for maximizing the return on this considerable investment of human, technical, and financial resources.

1. The Four Essential Zones of a
Healthcare Data Lake

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The Four Zones of a Healthcare Data Lake
Health Catalyst has published articles describing
early- and late-binding data warehouse
architectures, comparing data lakes to data
warehouses, and explaining how health systems
can leverage unique data lake functions within
their existing analytic platforms.
The evolving healthcare data environment
created the need for data lakes, but they are a
significant IT investment.
Understanding the relationship between an
enterprise data warehouse (EDW) and a data
lake, and its zones, is fundamental to investing
in the right technology with the appropriate
financial and human resources.

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Why a Data Lake Is Necessary
In healthcare today, outcomes improvement efforts are fueled by limited
information, primarily healthcare encounter data (Figure 1).
Figure 1: The human health data ecosystem is large, though we use
very little of it for improving outcomes.

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Why a Data Lake Is Necessary
To see more of the picture, bring it into focus,
and understand what really impacts outcomes,
we need genomic and familial data, outcomes
data, 7×24 biometric data, consumer data, and
socio-economic data.
The complete ecosystem of data necessary for
massive outcomes improvements will increase
the total amount of healthcare data tenfold.

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Why a Data Lake Is Necessary
According to a 2014 IDC report, the healthcare
digital universe is growing 48 percent per year.
In 2013, the industry generated 4.4 zettabytes
(1021 bytes) of data. By 2020, it will generate
44 zettabytes.
Unfortunately, this data volume would explode
the data warehouse of most organizations.
Fortunately, a data lake can handle this volume.

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The Benefits of a Data Lake
The benefits of a data lake as a supplement to an EDW are numerous in
terms of scale, schema, processing workloads, data accessibility, data
complexity, and data usability:
A data lake, typically designed using Apache Hadoop, is the preferred choice for larger
structured and unstructured datasets coming from multiple internal and external sources,
such as radiology, physician notes, and claims. This removes data silos.
A data lake doesn’t demand definitions on the data it ingests. The data can be refined once
the questions are known.
A data lake offers great flexibility on the tools and technology used to run queries. These
benefits are instrumental to socializing data access and developing a data-driven culture
across the organization.
A data lake is prepared for the future of healthcare data with the ability to integrate patient
data from implanted monitors and wearable fitness devices.

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The Data Lake’s Strength Leads to a Weakness
A data lake can scale to petabytes of information
of both structured and unstructured data and
can ingest data at a variety of speeds from batch
to real-time.
Unfortunately, these capabilities have led to a
negative side effect.
Gartner’s hype cycle for 2017 shows that data
lakes have passed the “peak of inflated
expectations” and have started the slide into the
“trough of disillusionment.”
This isn’t surprising. Often, an industry develops
a concept thinking it will solve world hunger,
then learns its real-life limitations.

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The Data Lake’s Strength Leads to a Weakness
Initially, data lakes were predicted to
solve all of healthcare’s outcomes
problems, but they have ended up just
collecting petabytes of data.
Now, data lake users see a lot of detritus
that can’t be used to build anything. The
data lake has become a data swamp.
Understanding and creating zones
within a data lake are the keys to
draining the swamp.

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The Four Zones of a Data Lake
Data lake zones form a structural governance
to the assets in the data lake.
To define zones, Zaloni excerpts content from
the ebook, “Big Data: Data Science and
Advanced Analytics.”
The book’s authors write that “zones allow the
logical and/or physical separation of data that
keeps the environment secure, organized,
and agile.”
Zones are physically created through
“exclusive servers or clusters,” or virtually
created through “the deliberate structuring of
directories and access privileges.”

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The Four Zones of a Data Lake
Healthcare analytics architectures need a
data lake to collect the sheer volume of raw
data that comes in from the various
transactional source systems used in
healthcare (e.g., EMR data, billing data,
costing data, ERP data, etc.).
Data then populates into various zones
within the data lake.
To effectively allocate resources for building
and managing the data lake, it helps to define
each zone, understand their relationships
with one another, know the types of data
stored in each zone, and identify each
zone’s typical user.

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The Four Zones of a Data Lake
Data lakes are divided into four zones (Figure 2).
Organizations may label
these zones differently
according to individual
or industry preference,
but their functions are
essentially the same.
Figure 2: Data lake zones

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The Four Zones of a Data Lake
The Raw Data Zone
In the raw zone data is moved in its native
format, without transformation or binding to
any business rules.
Often the only organization or structure
added in this layer is outlining what data
came from what source system.
Health Catalyst calls those areas in the
raw zone source marts. Though all data
starts in the raw zone, it’s too vast of a
landscape for less technical users.

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The Four Zones of a Data Lake
The Raw Data Zone
Typical users include ETL developers,
data stewards, data analysts, and data
scientists, who are defined by their ability
to derive new knowledge and insights
amid vast amounts of data.
This user base tends to be small and
spends a lot of time sifting through data,
then pushing it into other zones.

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The Four Zones of a Data Lake
The Trusted Data Zone
Source data is ingested into the EDW,
then used to build shared data marts in the
trusted data zone.
Terminology is standardized at this point
(e.g., RxNorm, SNOMED, etc.). The
trusted data zone holds data that serves
as universal truth across the organization.
A broader group of people has applied
extensive governance to this data, which
has more comprehensive definitions that
the entire organization can stand behind.

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The Four Zones of a Data Lake
The Trusted Data Zone
Trusted data could include building blocks,
such as the number of ED visits in a
certain period, inpatient admission rates
from one year to the next, or the number of
members in risk-based contracts.

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The Four Zones of a Data Lake
The Refined Data Zone
Meaning is applied to raw data so it can be
integrated into a common format and used by
specific lines of business.
Data in the refined zone is grouped into Subject
Area Marts (SAMs, often referred to as data marts).
A department manager looking for end-of-month
numbers would query a SAM rather than the EDW.
SAMs are the source of truth for specific domains.
They take subsets of data from the larger pool and
add value that’s meaningful to a finance, clinical,
operations, supply chain, or other areas.

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The Four Zones of a Data Lake
The Refined Data Zone
Refined data is used by a broad group of
people, but is not yet blessed by everyone in
the organization.
In other words, people beyond specific subject
areas may not be able to derive meaning from
refined data.
A SAM gets promoted to the trusted zone
when the definitions applied to its data
elements have broadened to a much larger
group of people.

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The Four Zones of a Data Lake
The Sandbox Data Zone
Anyone can decide to move data from
the raw, trusted, or refined zones into the
sandbox data zone.
Here, data from all of these zones can
be morphed for private use.
Once sandbox information has been
vetted, it is promoted for broader use
in the refined data zone.

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The Four Zones of a Data Lake
Zones and Their Data Definitions
For an example of the data type in each
zone, consider length of stay (LOS).
There are dozens of ways to define LOS
using ED presentation time, admit time,
registration time, cut time, post-
observation time, and discharge time.
The clinical definition of LOS for an
appendectomy may be from cut time to
discharge time, but the corporate
definition may be from admit time to
discharge time.

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The Four Zones of a Data Lake
Zones and Their Data Definitions
A SAM that focuses on appendectomy
might choose to use the clinical
definition, which doesn’t apply to the
global definition (i.e., the definition in the
trusted zone).
For an individual SAM definition of LOS
to be promoted to the trusted zone, it
needs to be vetted through a broader
group of people to confirm it has
universal application.

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The Four Zones of a Data Lake
Zones and Their Data Definitions
Directors who have financial responsibility
over a single line of business may need to
evaluate their department’s productivity.
They may need to see things a certain way,
such as excluding corporate overhead, over
which they have no control.
This is what makes the SAM more specific
to one area. The data definition has been
vetted and agreed to by a group of people,
though it has yet to reach global agreement.

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The Four Zones of a Data Lake
The Right Technology for the Right Zone
Different technology can run on top of
different zones in a data lake. The data lake
itself typically runs on Hadoop, which is
optimal for handling huge data volumes.
Relational Databases like SQL Server are
more user friendly and will provide data to a
larger user base.
SQL queries can run on top of Hadoop to
produce data marts and SAMs in the trusted
and refined zones.

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The Four Zones of a Data Lake
The Right Technology for the Right Zone
Hortonworks refers to a Connected Data
Architecture, in which “data pools need to
ensure that connected data can flow freely to
the place where it is optimal for the business
to get value from it.”
Zones may not live on the same data
technology. Much of the data will live in a data
lake, but more refined zones may have a
portion of their data that resides in an EDW or
smaller data marts.

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The Four Zones of a Data Lake
Data Lakes Are Integral to a Larger Operating System
Earlier, we said that huge data volumes have turned
data lakes into data swamps, which is remedied
through a larger healthcare analytics ecosystem.
Some, or all, of a data operating system can be
deployed over the top of any healthcare data lake.

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The Four Zones of a Data Lake
Data Lakes Are Integral to a Larger Operating System
The Health Catalyst® Data Operating System
(DOS™) (Figure 3 on next slide) can index, catalog,
analyze, and provide insights from the terabytes and
growing data assets in a health system and provide
health system leaders with the knowledge they need
to produce massive outcomes improvements:
• IT departments
• Clinicians
• population health managers
• financial leaders

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The Four Zones of a Data Lake
Data Lakes Are Integral to a Larger Operating System
Figure 3: The Health Catalyst Data Operating System.

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The Four Zones of a Data Lake
Data Lakes Are Integral to a Larger Operating System
DOS enables a data lake to be built with
the required governance and meaning
added to the data so it is easily
organized into the appropriate zones.
Data can then be used according to zone
by the various data consumers in a
health system.
DOS also allows data to be analyzed and
consumed by the Fabric Services layer to
accelerate the development of innovative
data-first applications.

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The Four Zones of a Data Lake
The volume of healthcare data is
mushrooming, and data architectures
need to get ahead of the growth.
Vast volumes of data will continue to flow
into the EDW.
A data lake is required to make data
accessible to a subset of ETL developers,
data stewards, data analysts, and data
scientists.
Data lakes allow data to be moved into
various zones for experimentation and
research, or for customization into shared
data marts and SAMs.

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The Four Zones of a Data Lake
To prevent data lakes from becoming mired
in the petabytes of data now swamping
healthcare, the new architecture presented
by the data operating system offers a
breakthrough in analytics engineering that
can renew the life of a data lake and
accommodate the big-bang growth of
healthcare data.

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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.
The Four Essential Zones of a Healthcare Data Lake
What Is a Healthcare Data Lake and Why Do You Need One? Imagine a Supermarket
Imran Qureshi, Chief Software Development Officer
Data Lake vs. Data Warehouse: Which is Right for Healthcare?
Jared Crapo, Sales, Senior VP
Data Warehouse Tools: Faster Time-to-Value for Your Healthcare Data Warehouse
Doug Adamson, Chief Technology Officer, VP
Comparing the Three Major Approaches to Healthcare Data Warehousing: A Deep Dive
Review (White Paper) Steve Barlow, Senior VP of Client Operations and Co-Founder
The Health Catalyst Data Operating System (DOS™) Solution
Health Catalyst Solution

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Joined Health Catalyst in February 2012. Prior to joining the Catalyst team, Bryan spent
six years with Intel and four years with the The Church of Jesus Christ of Latter-Day
Saints. While at Intel Bryan was on teams responsible for Intel's factory reporting systems
and equipment maintenance prediction.
At the LDS Church he led the .NET Development Center of Excellence and was responsible for the
Application Lifecycle Management (ALM) processes and tools used for development at the Church.
Bryan graduated from Brigham Young University with a degree in Computer Science.
Other Clinical Quality Improvement Resources
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Bryan Hinton