Data Authors: Skilled Designers of Data Presentations - Data Fluency: Empowering Your Organization with Effective Data Communication - page 94

When the metric value
changes, it is clear what
caused the change
and/or what actions
should be taken

The data can be acquired
with modest effort from a
source that people trust.

Accessible,
credible data

Actionable

Good metrics

Transparent,
simple
calculation

Common
interpretation

People in the organization
recognize what the
metric means.

The method for generating
the metric is shared and
well understood

Figure 5-5: Choosing the right metric
Source: Juice analytics .

A lot of metrics fail in at least one of these dimensions. Following are a few
examples:

Common interpretation —We had a client who made a distinction
between “leads” and “prospects” in its marketing organization. Pros-
pects had theoretically expressed more interest in the service through
their actions. Unfortunately, the line between leads and prospects was
always hard to decipher and the definitions were hard to communicate.

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Actionable —Metrics are frequently too broad for the impact that a
particular group can have. Customer satisfaction is a popular dash-
board staple, but it is hard for most managers to see how they can
have a significant impact on the number.

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Accessible, credible data —Sometimes, the most valuable and obvi-
ous metrics are frustratingly hard to track. In the web analytics world,
unique visitors is important data to know, but user deletion of cookies
makes it hard to trust the data.

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Transparent, simple calculation —Top NFL agent Leigh Steinberg
says of the famous football quarterback ratings metric, “Other than
one attorney in our office, I am unaware of a single human being who
has the capacity to figure a quarterback rating.” I don't know what

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