A Consumer’s Guide to Understanding Data - Data Fluency: Empowering Your Organization with Effective Data Communication - page 55

Sadly, this graphic is the exception. Most data products are created by people
without the experience demonstrated here. (Hopefully Chapter 5 rectifies this
problem.) More often, data products have arbitrary visualization choices, an
unclear message, and data presentation without thoughtful design or concern
about the users' priorities. They leave the burden of understanding to the
audience. The remainder of this chapter is intended to give you guidance and
tools to find your way through the data as it is shared in many organizations.
You explore some barriers to using data products and then experience some
practical challenges that help you increase your data fluency.
BARRIERS TO uSING DATA PRODuCTS
Learning the language of a new industry can be difficult. Likewise, you often
encounter barriers to engaging with data products. These barriers include
jargon, not knowing where to start and what to focus on, and inconsistency.
Jargon
Unleveling. Fitnessgram testing. Scaffolding text-to-text connections. Dahlia
Lithwick, who was keenly interested in learning what her second grader was
doing in school so that she could provide support at home, described going
to a parent-teacher conference at her child's public school only to encounter
all these terms. The teachers didn't take time to explain the terms but assumed
that all the parents knew what they meant. When she looked around, all the
other parents were smiling and nodding in agreement, and she certainly
didn't want to appear to be uneducated or uninvested in her child's success.
Jargon can make a speaker sound smart and listeners feel dumb. As a consumer
of data, it is important to remember that most jargon represents simple and
often familiar concepts.
When learning a new language, it can be intimidating to encounter a native
speaker. ¿Dónde está la biblioteca? may uncork a fountain of Spanish that
you cannot begin to comprehend. It is the same in learning to speak data.
The following table presents a common jargon list used by experienced data
analysts, with an explanation of the relatively simple and familiar concepts
they represent.