OpenText Data Digest Nov 20: The Last Mile of Big Data
For this week, we provide some examples of visualizations that crunch their fair share of Big Data on the back end but present it in a way that meets the Last Mile challenge.
In a recent study by the Digital Clarity Group, Thinking Small: Bringing the Power of Big Data to the Masses, our very own Allen Bonde (@abonde) from before he joined OpenText noted that the best opinions are formed and actions are taken within the “Last Mile” of Big Data.
By Last Mile, Allen means the most immediate information and data that is accessed or consumed. For application designers, meeting the Last Mile challenge requires understanding self-service use cases and leveraging tools that turn Big Data into “small data” that helps people perform specific tasks. Some of these use cases include insight, monitoring and targeting.
With the holidays coming up, we thought we’d look at dieting trends over the last few years. Health and science reporter Julia Belluz (@juliaoftoronto) assembled a review using Google Analytics based on most searched diets by year and metropolitan area.
Reaching back to 2005, the series of visualizations allows the viewer to see a slow yet steady spread of first the gluten-free and now the Paleolithic diets that command the news cycles and self-help bookshelves. Other diets covered include vegan eating, the low-carb diet, the South Beach Diet, and the Atkins Diet.
Parole Risk Assessment
A recent trend emerging in criminal sentencing is the notion of using predictive analytics and risk assessments to determine how likely a prisoner will commit the same crime in the future. Scores are determined by factors such as gender, county, age, current offense, number of prior arrests, and if multiple charges were filed.
The authors of the FiveThirtyEight study point out that more than 60 risk assessment tools are being used across the U.S. Although they vary widely, “in their simplest form, they are questionnaires — typically filled out by a jail staff member, probation officer or psychologist — that assign points to offenders based on anything from demographic factors to family background to criminal history. The resulting scores are based on statistical probabilities derived from previous offenders’ behavior. A low score designates an offender as ‘low risk’ and could result in lower bail, less prison time or less restrictive probation or parole terms; a high score can lead to tougher sentences or tighter monitoring.”
The simulation I quote is loosely based on the Ohio Risk Assessment System’s Re-Entry Tool, which is intended to assess the probability of prisoners reoffending after they are released from prison. The visualization was produced in collaboration with The Marshall Project (@MarshallProj), a nonprofit news organization that covers the criminal justice system.
Considering the U.S. Attorney General’s office has endorsed the idea of risk assessment, it’s likely that visualizations will be used in the future to manage criminal sentencing.
School for Startups
It’s not who you know, but where you go to college, that could determine the success of your startup, according to our last visualization. Our friends over at DataBucket built a series of visualizations based on data and a Crunchbase API in order to compare the top 5,000 most funded startups over the past 15 years and the education of each of their founders.
They found success has a pattern. Graduating from universities that are prestigious, on the West Coast, focused on engineering, and/or offer high-powered MBA programs helps increase your chances for smarter founders and benefactors with deep pockets.
“In terms of average amount of funding graduates from each school gets, Harvard, MIT, and Stanford get a standard amount of funding. Indian Institute of Technology has a disproportionately high average funding as well as a large number of founders,” the DataBucket authors comment. “Hanzhou Normal University and Zhejiang University of Technology are off the charts for average funding received. This is attributed completely to Jack Ma and Eddie Wu, [the] founders of Alibaba.”
Like what you see? Every Friday we share great data visualizations and embedded analytics. If you have a favorite or trending example, please comment below.
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