- Denver Broncos Testing In-Game Analytics — their newly hired director of analytics working with the coach. With Tanney nearby, Kubiak can receive a quick report on the statistical probabilities of almost any situation. Say that you have fourth-and-3 from the opponent’s 45-yard-line with four minutes to go. Do the large-sample-size percentages make the risk-reward ratio acceptable enough to go for it? Tanney’s analytics can provide insight to aid Kubiak’s decision-making. (via Flowing Data)
- Visual Review (GitHub) — Apache-licensed productive and human-friendly workflow for testing and reviewing your Web application’s layout for any regressions.
- Effective Altruism / Global AI (Vox) — fear of AI-run-amok (“existential risks”) contaminating a charity movement.
- The Dataflow Model (PDF) — Google Research paper presenting a model aimed at ease of use in building practical, massive-scale data processing pipelines.
Hacking performance across your organization.
I’ve given Web performance talks where I get to show one of my favorite slides with the impact of third-party dependencies on load time. It’s the perfect use case for “those marketing people,” who overload pages with the tracking pixels and tags that make page load time go south. This, of course, would fuel the late-night pub discussrion with fellow engineers about how much faster the Web would be if those marketing people would attend a basic Web performance 101 course.
I’ve also found myself discussing exactly this topic in a meeting. This time, however, I was the guy arguing to keep the tracking code, although I was well aware of the performance impact. So what happened?
Liza Kindred on the evolving role of data in fashion and the growing relationship between tech and fashion companies.
In this podcast episode, I talk with Liza Kindred, founder of Third Wave Fashion and author of the new free report “Fashioning Data: How fashion industry leaders innovate with data and what you can learn from what they know.” Kindred addresses the evolving role data and analytics are playing in the fashion industry, and the emerging connections between technology and fashion companies. “One of the things that fashion is doing better than maybe any other industry,” Kindred says, “is facilitating conversations with users.”
Gathering and analyzing user data creates opportunities for the fashion and tech industries alike. One example of this is the trend toward customization. Read more…
A practical example of how anomaly detection makes complex data problems easier to solve.
As new tools for distributed storage and analysis of big data are becoming more stable and widely known, there is a growing need for discovering best practices for analytics at this scale. One of the areas of widespread interest that crosses many verticals is anomaly detection.
At its best, anomaly detection is used to find unusual, rarely occurring events or data for which little is known in advance. Examples include changes in sensor data reported for a variety of parameters, suspicious behavior on secure websites, or unexpected changes in web traffic. In some cases, the data patterns being examined are simple and regular and, thus, fairly easy to model.
Anomaly detection approaches start with some essential but sometimes overlooked ideas about anomalies:
- Anomalies are defined not by their own characteristics but in contrast to what is normal.
- Before you can spot an anomaly, you first have to figure out what “normal” actually is.
This need to first discover what is considered “normal” may seem obvious, but it is not always obvious how to do it, especially in situations with complicated patterns of behavior. Best results are achieved when you use statistical methods to build an adaptive model of events in the system you are analyzing as a first step toward discovering anomalous behavior. Read more…