Archived Publications Zach Gemignani: Delivering the Last Mile | Page 2

How Juice Analytics Makes Data Useful
There is no shortage of data in the healthcare market,
but there is a shortage of data being used to its full
potential. Gemignani points out, “The best data in the
world is useless if the everyday decision-maker can’t
understand and interact with it.” At Juice Analytics, the
team of visualization experts changes the way
organizations see, discuss, and use data. Gemignani
details four ways they make data more useful—
resolving the “last mile problem,” using data stories,
making training more efficient, and being data-driven.
Last Mile Problem
In studying industries that collect data, the team at Juice
Analytics found a lot of companies stuck in what they
call the “last mile problem,” where they don’t know
how to transform the data they have already gathered
into something of value. Gemignani explains, “If the
data doesn’t get into the hands of the people who are
on the frontlines of your organization in a way they can
understand it, then all of that gathered data is
essentially wasted.” Juice Analytics helps organizations
make that leap by taking their collected data, packaging
it up into an understandable form, and delivering it to
the decision-makers.
Gemignani emphasizes that the people typically
receiving the data are not usually data analysts, and that
they need to be able to step back from the data with a
clear understanding of what action they should take
based on what they’re seeing. “We try to lead the
non-analytical person to make the connection between
what insights or analysis they’re seeing in the data to
what action they should take to achieve a better
outcome for their organization,” Gemignani says.
Data Stories
There are a lot of ways to visualize data by using
dashboards or analytical tools, but Gemignani explains
that the key to making reporting systems useful is
through the use of what Juice Analytics calls “data
stories.” Juice Analytics applications use data stories to
walk people through data like they would walk through
a narrative flow, step-by-step on a journey as they
explore the data to understand what they should do
about it. Gemignani says that this differentiates their
technology from other tools. He adds, “First we think
about the end users—their jobs, how busy they are,
“The best data in the world is useless
if the everyday decision-maker can’t
understand and interact with it.”
and how comfortable they are with data. If you start
from your audience and you immediately think about
how you can help those people succeed in their jobs,
you’re going to build a better way of exploring, of giving
people data, and of giving them access to it in a way
that is intuitive and digestible.”
Creating Efficient Training
Juice Analytics’ smarter use of data helps organizations
provide more efficient training, which is a major concern
in healthcare. Gemignani explains that a key starting
point is to find the gaps in knowledge, where students
or staff are struggling, and help remediate those
situations by recommending the right kind of training
that will be helpful to those certain sets of people.
Juice Analytics also provides an analytical tool that helps
administrators have clear visibility into which students
have and have not completed their work and looks for
the most efficient way an organization can deliver
training. Gemignani says, “There are always
opportunities to think about which people are assigned
to which courses and what time and frequency is
necessary to achieve proficiency in the workforce. But
training is expensive, requires time, and takes people
off the job. Our analytical applications help balance
those two sides and deliver really efficient training
across an organization.”
Data-Driven Organizations
To Gemignani, hearing that an organization wants to be
data-driven is only a starting point. He reminds us that
actions speak louder than words, and that a lot of
organizations struggle to begin the journey to being
data-driven. Gemignani highlights three factors that
indicate whether or not an organization is data-driven:
• Does leadership demonstrate data-driven decision-
making?
• Have they chosen a narrow set of metrics
that are the key performance measures of the
organization and communicated that throughout
the organization?
• Are they building a workforce that has the skills and
capabilities to be data-driven themselves?
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