Database Reference
In-Depth Information
Often information gets included in data products for reasons that are superflu-
ous to the purpose, audience, and message—reasons that cater the product
to someone influential or use information that has been included historically.
The bar should be higher. Here are a few strategies to help narrow down to
the information that matters:
Find the core problem —Your data product should be more than a
lot of data on a screen or page. It should have a core theme based on
the essence of the problem. For example, a sales dashboard may be
designed around the question, “How can we more effectively move
leads through our pipeline?” Or a marketing dashboard may strive to
answer, “How can we optimize our marketing investments?” Finding
this core problem can give you the logic and argument for discarding
extraneous information.
Ask a better question —Data requirements can quickly turn into a
laundry list of unrelated metrics, dimensions, and half-baked analyses.
The root of this problem stems from asking, “What would you like to
know?” The follow-up question that narrows down the list of require-
ments is, “What would you do if you knew this information?” This ques-
tion separates the novel and whimsical desires from the important and
actionable information. The next section talks more about actionable
metrics and data products.
Push to the appendix —Sometimes, it is impossible to ignore the
requests for certain information to be included in a data product. Mul-
tiple audiences demanding a multitude of metrics. In these cases, it can
be helpful to create an appendix report that includes some of these
requests. Doing so can help keep the focus on the most critical data.
Separate reporting from exploration —Tools designed for reporting
need to be narrowly focused with a clear topic and address a limited
set of questions. Exploration or data analysis tools serve a different
purpose. There are many data products designed to give the users a
broad palette to explore a variety of data. It helps to understand which
of these goals you are setting out to serve.
The success of many data products is determined by an ability to distinguish
between useful, productive information and interesting but ultimately extrane-
ous information. In short, we echo the sentiment expressed by French author
Antoine de Saint-Exupéry, “Perfection is achieved, not when there is nothing
more to add, but when there is nothing left to take away.”
Search WWH ::




Custom Search