Training Industry Magazine - July/August 2020 - 44

FOR CONTENT DEVELOPMENT

BY SARAH DREYER, MEGHAN CIPPERLEY
AND MERAV YURAVLIVKER

It's Tuesday morning, and managers from
the federal government gather virtually
for an online data literacy course. Some
hail from human resources; others come
from policy teams. However, each of
them shares a similar objective for the
class: to learn how they can use data
more effectively in their roles. With that,
the instructor launches into material
covering the data analytics maturity
model, describing the stages an
organization passes through on its way
to being data driven.
The maturity model starts with
descriptive analytics, which can answer
questions about what has already
happened. For example, a customer
service center may track and report
on the number of calls received in
an hour, day or week. A learning and
development (L&D) organization may
track the number of enrolled students
or percentage of passing grades. We
are then responsible for taking this
information and using it in our decisionmaking. This type of analysis is valuable
and relatively easy to deploy. Nobody in
the class has any questions.
As we move up the continuum, the
instructor says, analytics become more
valuable yet more difficult. Diagnostic
analytics, for example, can explain

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why something happened and help
managers identify root causes. However,
more detailed data is required. In a
customer service center, L&D may tie
sudden spikes in call volume to events
such as recall announcements or
software updates. Participants start to
mumble about how difficult it is for them
to access data.
Once discussion moves into the realm of
data science - which includes predictive
analytics, prescriptive analytics and
artificial intelligence - the instructor
remarks that more than just detailed data
is required. The data must also be clean,
stable and supported by a coordinated
governance strategy. There must also
be an innovative environment, the right
tools and knowledgeable employees
within the organization. The benefits,
however, are great. You can leverage
data to predict what will happen in the
future, let data prescribe how to make
decisions, and do so continuously and at
scale. Participants rattle off great ideas
for utilizing data in these ways but don't
know where to start.
As the course unfolds, participants
are equipped with the knowledge and
skills necessary to start planning and
executing data projects. However,
chances are many of you currently feel

the same way these students did in the
first hour of the class.
The idea of applying analytics to learning
data isn't new, and the benefits of doing so
have been covered extensively. More and
more, instructional designers and content
creators are called upon to incorporate
analytics into their processes, particularly
relating to learner analysis and evaluation.
However, most learning professionals
don't see themselves as data scientists
and have not been exposed to extensive
data analysis.
Meanwhile, data is integral to the
role of an instructional designer.
When they design curriculum or pick
class activities to reinforce learning,
instructional designers are often doing
so using research-based best practices.
Likewise, they find the highest levels
of the Kirkpatrick Model of Evaluation
can only be measured with data-driven
assessment approaches.
There is little difference between learning
analytics and data analytics as a whole,
so there are lessons to be learned from
our data science brethren. Here are some
steps learning leaders can take to start
moving up the data analytics maturity
model without returning to school for a
degree in data science.
Training Industry Magazine - July/August 2020

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