The Dangers of Data Shopping: The Mad Scramble for Information

The Dangers of Data Shopping:
The Mad Scramble for Information
– Steve Barlow

© 2014 Health Catalyst
www.healthcatalyst.com
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Data Shopping
To understand why data
shopping keeps many
healthcare organizations
from maximizing the value
of their data through
analytics, it helps to think
about your shopping options
in the real, offline world.

Data Shopping
One option is a strip mall with many independent stores.
If I want to purchase a pair of jeans, some boots, a water
storage system, and a backpack for a hike on Saturday, I
will likely need to visit four separate stores. The selection
may be limited, so I may have to accept what I can get.

Data Shopping
The second option is a large warehouse club where
everything is under one roof, often with ample choices,
allowing me to get exactly what I need in a single trip.
Since all of the merchandise has been vetted by a single
buying group, there is confidence in the quality of the
warehouse club and the products it sells.

Data Shopping
Many organizations have their
data stored like merchandise
in the strip mall, in siloed,
independent repositories
throughout the ecosystem.
Those repositories may
contain clinical, financial,
patient satisfaction, customer
relationship management, and
many other types of data.

Data Shopping
This lack of centralization
often leads to a phenomenon
known as “data shopping.”
Essentially, data shopping is
the practice of analysts and
knowledge workers searching
throughout the ecosystem to
obtain the information they
need to answer a pressing
business question.

Data Shopping
Ideally, analysts would access a
single source of truth, such as a
Late-Binding™ Enterprise Data
Warehouse (EDW), to mine the
data their analytics require.
Absent that, however, being the
resourceful types they are, they
will obtain it however they can.
This piecemeal approach to data
analysis can have challenging
downstream consequences on
the efficacy and consistency of
the analytics.

The Dangers of Enabling Data Shopping
There are dangers that result from
a data shopping approach.
One of the most significant is that
scattered data results in no single
source of the truth.
The data quality may be high, low,
or somewhere in between.
Additionally, the definitions of the
data from the different sources
often do not agree and are
inconsistent.

Just as important, the data
required for a particular analysis
may exist in both high and low
quality in different repositories.
As a result, the same analysis can
produce different results based on
where the data was sourced.
If the data used isn’t clean and
reliable, the organization risks
making poorly-informed decisions.

Here’s a real-life example.
Health Catalyst was working with
a healthcare provider that was
focused on reducing the rate of
elective labor inductions before
the baby achieved a gestational
age of 39 weeks.
Clinical evidence has established
that the risks and complications
related to induction of labor are
reduced significantly after the 39-
week mark.

A key to driving this clinical quality
improvement was knowing when
the 39 weeks had passed.
That was difficult to determine
because the data was captured in
14 different locations and 10
different formats.
Before we could establish the
baseline rate of elective labor
induction before 39 weeks, we
had to establish a single source of
truth in the EDW regarding when
the baby reached 39 weeks.

A second, related danger to
having poor data quality is it
becomes difficult to get clinicians
onboard with clinical quality
improvement programs.
If they don’t trust the data, they
won’t trust the conclusions.

This issue became evident when
Health Catalyst worked with
another provider on a population
health management (PHM)
program for diabetes care.
This was the first experience with
PHM for the physicians who
managed this population, so they
were wary about it.

It turned out they were right to be
wary.
When we showed them the
analytics, they immediately
pointed out flaws, such as a
patient who was not a diabetic or
a particular patient who had not
been in for a year.
We enlisted their help to clean up
the data and the program moved
forward.

When it came time to begin a
similar program for patients with
asthma, we didn’t bring data right
away; we started by asking them
questions.
But they told us we needed to
show them the data, because
they now had a level of trust in it
they hadn’t had before.

Knowledge workers, many with
advanced degrees in statistical
analysis, may spend the bulk of
their time on activities they were
not trained in, such as hunting for
and gathering data, scrubbing it,
and making it useful for analysis.
In the process, they become
producers of data rather than
consumers of it. By the time
they’re done, there’s little time left
for meaningful analysis.

Warning Signs of Data Shopping
So, how do you know if your organization
is in a data shopping mode?
There are obvious warning signs:
There are multiple data repositories
where a knowledge worker can go to get
answers to similar questions.
This is an indicator that the same data
elements are being captured, probably in
different ways, within different areas.

There is a growing number of
decentralized analysts or information
consumers within the organization. While
there will always be a need for analytics
within specific departments to support
operations, the organization should have
a core group who perform most of the
analytics across the enterprise.

The organization worked hard to hire
brilliant analysts with tremendous training
and experience, but those analysts are
spending most of their time hunting and
gathering data and making it consumable
rather than working their magic.

According to attendees of the Healthcare Analytics Summit,
40% of data analysts spend 80% of their time gathering
data, while 39% spend 60% of their time gathering data .

Organic Growth of Analytics
If data shopping causes so many
problems, why does it occur?
It results primarily from building
new, disparate systems to capture
data sets without having a data
governance plan in place.
Since information needs within a
healthcare organization surfaces
organically, analysts or knowledge
workers may attempt to answer
specific questions using whatever
data they can find.

The problem is, in the quest to
answer the immediate needs, little
thought is given to the structure of
the entire data ecosystem.
Data isn’t often thought of and
treated as a strategic asset that
needs to be managed carefully,
like human or financial capital.
Soon there are little pockets of
data everywhere, and those who
want to use it must go shopping.

Compounding this process is the
way many chief information officers
view their jobs vis-a-vis data.
Since the beginning of the
computer age, the bulk of CIO
budgets focused on capturing,
storing, and securing data.
A much smaller percentage is
dedicated to how the data will
actually be used.

EDW pioneer Ralph Kimball says
enlightened CIOs should allocate
as much of their budgets to getting
data out of their systems as they
are to getting it in.
Getting there, however, will require
healthcare organizations to move
from siloed data systems in favor of
a centralized approach that
incorporates comprehensive data
governance.

Solving the Data Shopping Dilemma
Some organizations are just getting
into analytics and don’t yet have
established patterns.
Others may be well into their
analytics efforts and now realize
they have a data shopping issue.
Either way, the advice to
avoid/solve it is the same.
It begins with having a plan that
starts with the end in mind.

Two key points to consider:
1. Creating a single source of truth
(such as an EDW) where
everyone in the organization can
go to obtain data that is clean,
accurate and consistent.
2. Devoting as much time and as
many resources into pulling out the
data as is spent on storing and
securing it.

This is all part of treating data as a
strategic asset rather than simply
capturing it and locking it away.
Establishing a single source of
truth is the first priority.
It will not only save analysts time
in finding and making the data
consumable; it will also ensure
that all knowledge workers are
starting from the same point and
using the same data.

Once the plan is in place and a
single source of truth has been
established, the next step is to
deploy a data governance structure
that focuses on the three pillars
of data governance:
1. Data Quality
2. Data utilization and access
3. Data literacy

For organizations that are already in
data shopping mode, it’s time to
invoke the first law of holes: when
you find yourself in one, stop digging!
Acknowledge the situation and start
moving toward establishing a single
source of truth and a data
governance structure, even if that
means stopping or reducing work
on analytics for a little while.

Don’t Head to the Mall
Data shopping occurs when there
are no better alternatives.
Rather than forcing knowledge
workers to search for data
throughout the enterprise like
holiday shoppers going from strip
mall to strip mall in search of the
perfect gift, bring it all together.

Don’t Head to the Mall
Build a centralized, single source
of truth, and create a centralized
core set of data consumers.
Make sure analysts are spending
the bulk of their time consuming
data and developing insights.
Make getting data out of the
systems is as easy as getting data
into those systems.
Establish a data-driven culture with
a data governance program
focusing on the three pillars.

More about this topic
Master Data Management in Healthcare: 3 Approaches
Brian Eliason, Jason Burke and Pete Hess
Problems With Relying on EHR Analytics For Your ACO: You Need a True Data Warehouse
Dr. David Burton – Senior Vice President, future product strategy
Clinical Data Management: 3 Improvement Strategies
Jane Felmlee
7 Essential Practices for Data Governance in Healthcare
Dale Sanders – Executive Vice President, Software
The 3 Challenges of Translational and Clinical Research Data Management and a Strategy
to Succeed – Eric Just and Sean Whitaker
Link to original article for a more in-depth discussion.
The Dangers of Data Shopping: The Mad Scramble for Information

For more information:

Steve Barlow is a co-founder of Health Catalyst and Executive Vice President of
Client Operations. He is a founding member and former chair of the Healthcare Data
Warehousing Association. He began his career in healthcare over 25 years ago at
Intermountain Healthcare where he was a member of the team who developed the
analytical capabilities that helped make Intermountain a nationally recognized leader
in outcomes improvements and cost optimization. Mr. Barlow earned a BS in Health
Promotion and Education from the University of Utah.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com

The Dangers of Data Shopping: The Mad Scramble for Information

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The Dangers of Data Shopping: The Mad Scramble for Information