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Poll: Data Ownership and Wearable/ IoT Devices!

Who should have the data ownership for wearable/IoT devices?


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Can advanced analytics have a significant impact in the NFL?


Source: https://www.bostonglobe.com/sports/2016/01/09/can-advanced-analytics-have-significant-impact-nfl/UJYTVqUVXLRpCSz7AxKdfN/story.html

The Cleveland Browns went way outside the box last week, hiring Paul DePodesta away from the New York Mets to be their new “chief strategy officer.”

The team is 14-34 and about to hire its third head coach in three full seasons under owner Jimmy Haslam, but DePodesta’s hiring shows that Haslam is at least trying to commit to a more mathematical and stats-based approach to try to turn around the Browns’ fortunes. Despite firing the head coach and general manager last week, Haslam retained analytics expert Ken Kovash.

Everyone knows DePodesta’s prominence in “Moneyball” and how he is going to bring analytics to the Browns. But what exactly does that mean in a football context? Unlike baseball, football has several players who don’t produce statistics (offensive linemen), success is more team-based than individual, and the sample size of statistics is much smaller.

How can DePodesta’s background in identifying statistical inefficiencies help the Browns on the field? And how new is this approach, anyway?

“It’s really just an extension of what quality control coaches have been doing for years,” said Joe Banner, a former executive vice president with the Eagles (1995-2012) and CEO of the Browns (2012-14). “‘What’s the probability they’re going to blitz on third and 6?’ Every coach has probabilities for virtually every scenario you could come up with, and that’s sort of analytics.”

The most important aspect to remember is that unlike in baseball, where the scouting and stats-based communities tend to disagree on how to evaluate players, few in football believe that advanced stats are as important as good, old-fashioned scouting — game film, interviews, body type, motivation, medical checks, and more.

Banner says if you let analytics “be the engine that drives the machine, you’re in trouble.” Aaron Schatz, creator of the advanced stats website Football Outsiders, calls analytics “just a tool in the toolbox.” And no matter what the stats say, there will always be outliers.

The stats said that the Seahawks’ decision to throw the ball on the 1-yard line only had a 3.1 percent chance of being intercepted.


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Top 5 Reasons Why Analytics Projects Fail

Whether you are a seasoned analyst or a business executive with a significant investment in analytics, chances are you’ve seen the powerful impact of analytics as well as the failures. So what drives an analytics project’s failure or success?

Analytics projects fail when they produce no actionable insights. Even a seasoned analyst’s efforts to coax insights from the data can be futile unless a number of key factors come together to increase the odds of success.

Let’s talk about the top 5 reasons analytics fail.

  1. Starting with data instead of the question. The most common misunderstanding about analytics is that if you look at data hard enough, you will find insights. Staring at daily dashboards in the hope that insights will miraculously reveal themselves is often overwhelming, confusing and unsuccessful.Successful analytics start by identifying the question you’re trying to answer from the data. For example, if site conversion is an issue, instead of studying your website data hoping to find reasons for low conversion, narrow down your efforts to a specific question. In this case, it might be “How can we increase conversion from 23% to 26%?” This approach allows you to focus on finding actionable drivers of conversion that can have impact.
  1. An exploratory approach to analytics. Once you have identified the question you are trying to answer, do you explore all the data at hand in the hopes of finding insights or do you identify which data to study by using hypotheses as guard rails?The exploratory approach often fails to find any insights and if it does, is a lengthy process. On the other hand, using hypotheses to narrow down both the scope of the project and the data set needed, leads to the answers quickly. This process also generates secondary questions to ask data to further refine the insights.In our example, the hypotheses might involve certain pages or experiences thought to be driving lower conversion. These hypotheses are then used to identify the data needed to find segments of low conversion, and, once proven, address them.
Source: http://www.forbes.com/sites/piyankajain/2015/12/12/5-reasons-why-analytics-projects-fail/

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