Database Reference
In-Depth Information
The following sections provide details on each of these principles.
FINDING YOuR PuRPOSE AND MESSAGE
Data products must strive for simplicity—a capability to communicate the
most important data clearly, effectively, and directly. The message of a data
product is likely to change as the data changes, but finding a simple way to
present the data is a constant challenge for data product authors.
In the topic
Simple
(Twelve, 2013), authors Alan Siegel and Irene Etzkorn pro-
pose a framework for simplicity that applies as much to data products as it
does to their discussion of business writing, documents, and presentations.
For them, simplicity requires the author to:
Empathize
by perceiving others' needs and expectations.
⬛
Clarify
by making the offering easier to understand, use, and benefit.
⬛
Distill
by boiling down and customizing what's being offered to meet
needs.
⬛
Like Siegel and Etzkorn, you can put the audience at the forefront of your
efforts to design data products. You need to know what information is most
critical to your audiences' decision-making and the questions that they need
answered to be more successful. Data products—data-rich presentations,
reports, dashboards, and analytical tools—clarify by presenting data visually,
using the right chart or graphic for the job, and by using clear language and
thoughtful context to ensure understanding. Finally, data product authors need
to be extremely discriminating in what information they choose to focus on.
Only the right metrics and most actionable data should be featured to make
the most of your audiences' attention.
In this section, you learn the following:
How to create a balance between what the data is saying and the
needs of your audience
⬛
How to understand the perspective of your audiences and consider
what data will be most important to them
⬛
How to narrow your focus to deliver the most important information
⬛
The goal is to achieve a balance between the data, audience, and objective in
your data product. Your data product has a purpose—whether it is as broad as
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