If you haven’t already heard of Chi Hack Night, you probably should. Originally known as Open Gov Hack Night, this weekly gathering of open data aficionados, data scientists and civic technology mavens is a fixture in the Chicago data ecosystem. The community shares a common mission “to build, share and learn about tools to create, support, and serve the public good.” When it started in 2012, the first meeting was an intimate affair attended by four people. Fast forward four years and renamed Chi Hack Night, the weekly event hosted by Derek...

At this point, we've all heard the news. Data is growing at an exponential rate. Data is the new oil. Data is a strategic advantage. Data is big. Data is small. Certainly Goldilocks believes her porridge data is jusssst right. Meh. Data is simply a means to an end. So, how does one go from "data is super awesome" to actually getting value from it? Well, it's a long and winding road, often called "a data project." I recently came across a nice article discussing the process of building a predictive application with machine...

[Editors note: As a bunch of data geeks, we always enjoy getting our hands dirty exploring interesting data. This is the third of a three-part series on data sets with a story to tell; check out part one and part two. Also, you can find the source data here.] When looking at how well NFL teams perform, we often talk about everything from offensive formations to coaching and personnel to properly inflated footballs. But what about external factors beyond a team’s control – are there ever any scenarios where the cards...

[Editors note: As a bunch of data geeks, we always enjoy getting our hands dirty exploring interesting data. This is the first of a three-part series on data sets with a story to tell. You can check out the source data for this here.] There’s a reason that practically everything that happens in a baseball game is meticulously tracked. Interesting baseball stories are often captured beautifully in data. For instance, a recent analysis has shown that hitters start losing their abilities as soon as their careers begin. Perhaps this is why it’s...

Data projects come in many shapes and sizes. From big data predictive analytics to small data spreadsheet projects, from building new open data applications to reconciling a couple of ERP tables in the accounting department. There's one feature all these projects have in common: they all take far too long. Efficiency matters – and for maximizing the value of data, it matters a lot.  The math on this is simple. The more rapidly an organization can perform data projects, the greater its capacity to leverage data resources, generate real value and gain competitive advantage. According to...

Much electronic ink has been spilled on the rise of the data scientist. They've been called sexy. They've been called unicorns. And, we clearly see a direct correlation between this recent adulation and the uptick in searches for "sexy unicorn". (At least, I hope that's the reason). As discussed previously, capturing a unicorn is great if you can find it, but it takes a village to get data stuff done in the enterprise.  Michale Mout once put together a Venn diagram based on the Wikipedia definition of data science: In the response to...

I had a chance to attend an excellent meetup of the Chicago City Data User Group last week, which focused on Chicago's contribution to the Code for America Summit: The Code for America Summit is THE annual convening for civic technology. This year’s summit (which took place in San Francisco in September) saw 800 government, tech, and civic engagement leaders from dozens of cities to talk about ways to make their cities work better. Civic-minded technologists, designers, community organizers, and entrepreneurs heard best practices, discussed emerging ideas, and showcased the latest tools...

In an excellent post, CIO Isaac Sacolick asks, what technologies work best for decentralized data scientists? It's a great question.  As he aptly describes it, working with data in a decentralized environment presents a challenging scenario: But what happens when these resources are scattered across multiple departments. One department may have an expert data scientist, another may have a small group doing internal reporting, and a third group might have outsourced its analytic function. If data scientists in the organization are decentralized with different goals, skills, and operating models, can IT still provide...

Recently, we've been seeing a lot of news about the promise of emerging applications for machine vision. Much of it's at the trial stage at this point, particularly with Google Glass and related projects.  For instance, the police in Dubai are testing facial recognition on the streets, ER doctors are exploring uses for quick access to critical medical records and Walgreens is experimenting with augmented reality in stores. Even the mantis shrimp is getting in on the action!  By mimicking their incredible eyes, researchers are developing a camera that makes it possible to see...