Database Reference
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that is customized and scoped to their situation. Many interactive
applications can support this ability to filter to the relevant data.
Sharing support —Data should spur conversation. However, some
solutions for data products create an isolating environment. The data
product should make it easy to share, discuss, and capture insights—
whether the discussion happens online, offline, on a desktop, or on
mobile devices.
Quality visualization —It matters how data is visualized. Clean, clear
charts can make it easy for readers to quickly understand the data. The
default settings for data visualizations should adhere to the fundamen-
tals shared by well-known data visualization authors like Stephen Few
and Edward Tufte.
Fit workflows —Finally, we believe it is important that data products
integrate into how people do their jobs. If the consumer of data is
constantly on the run, bombarded by information of all types, an
effective data product will deliver simple, narrow content to this per-
son. If the consumer wants to drill deeply into the data to understand
underlying assumptions, the functionality should exist to allow for
this need.
“IT'S A POOR CRAFTSMAN WHO BLAMES HIS TOOLS”
The tools you have today probably are a suitable start for developing data
products. Better to start with the investments you've already made and
the technologies your team is comfortable with. Communicating data is
far more about the people creating the data products than the technology
tools they use.
Start by refining the skills of the individuals using these tools—even if they are
simply Microsoft Office products like PowerPoint and Excel. The design skills
and capabilities described earlier in this chapter and in Chapter 5 have little
to do with the specific technical solution. Furthermore, many tools—Excel in
particular—can be extremely capable with practice and creativity. For example,
we built the visualization in Figure 7-4 using Excel, inspired by a hand-crafted
visual shown on The New York Times website.
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