Database Reference
In-Depth Information
The 1920s and 1930s saw the advent of factories to efficiently mass produce
automobiles, and generations of insight inform our patterns of consumption
of this critical product. Almost 100 years later, a critical set of skills is now
needed to ensure individuals and organizations are thoughtful consumers of
data, and emerging skill sets are essential to produce data-based presentations
and actionable data products.
It is to the development of this critical set of skills—those of being informed,
capable consumers of data, and of being accomplished producers of data
presentations and products—that this topic is dedicated. Our goal is to help
individuals and organizations understand and develop data fluency, as we con-
tend it is the new language, the new highway, of commerce in the 21st century.
THE INFORMATION AGE: DRIVING THE
NEED FOR DATA FLUENCy
Fantastic advances in data storage capacity have fundamentally changed
the trade-offs we need to make regarding what to keep and what to delete.
Rather than having to carefully decide what elements of our digital reality to
capture and which to throw away, we can now keep everything. We can have it
all—and we do. According to independent research organization SINTEF (The
Foundation for Scientific and Industrial Research), 90 percent of all the data in
the world has been generated over the last two years. 1 With more Instagram
pictures, more tweets, more history of where customers go on the web, we
are rapidly growing the amount of data we can sift through.
In a sense, the raw materials for informed decision-making have never been
more plentiful. Yet the promise of data nirvana still seems far of. Students,
scholars, employees, and executives are often still making crude decisions
based on chance, gut, or whims of the crowd. In this era of data as the new
oil boom, where's the payoff? Are we making better decisions and are we
better able to understand our world? Are we driving cars or still riding horses
along the digital highway?
On the ground, in the organizations we've worked with at Juice Analytics,
people are often frustrated by their inability to effectively use data. They've
built data warehouses, invested in expensive business intelligence solutions,
and spent finite fiscal resources to hire data scientists. They've data-mined,
analyzed, defined key metrics, and created dashboards. Despite these efforts,
data is often under-used and misunderstood.
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