Tag Archives: Analytics
There is widespread agreement that the Internet of Things will be a transformative factor in the business use of information. The prospect of billions of connected devices promises to transform home activities, transportation, industrial operations, and many other aspects of our lives.
The bad news about the IoT is that we have a lot of work to do before we are ready for it. We’ve got to up our games considerably with regard to data management and analytics if we’re going to capture, store, access and analyze all the IoT data that will be flowing around the Internet. The good news (in addition to its potential) is that most organizations have a few years to get better at these capabilities before the real onslaught hits. The sensor devices, IoT data standards, and data management platforms are still in their relatively early stages, and no customer, business partner, or CEO could reasonably expect that you could tame all that IoT data today.
Google today announced a significant enhancement to the developer tools for its Google Cast software, which lets people stream media from their PCs and mobile devices to TVs and speakers. The Google Cast software development kit (SDK) Developer Console now includes dedicated pages for analytics for apps that work with Google Cast.
The tool is a bit reminiscent of Google Analytics, which lets people see check website performance and usage. Developers can access it by clicking the View link under the Statistics column for a given app.
“The devices tab shows the number of Cast devices that have launched your application, the sessions tab shows the number of Cast sessions of your application, and the average playback tab shows the average length of media playback time per session for your application,” Google Cast software engineer Chris Dolan wrote in a blog post. Developers can break things down by geography and operating system and change the time range.
Predictive Analytics Innovation Summit, March 2-3, Melbourne, Australia
Big Data and Analytics Summit, March 2-3, Singapore
Gaming Analytics Summit, March 3-4, London, UK
European Smart Grid Cyber Security, March 7-8, London, UK
Big Data Paris, March 7-8, Paris, France
2nd Global Data Science Conference, March 7-9, Santa Clara, CA
Structure Data, March 9-10, San Francisco, CA
Internet of Things Forum, March 9-10, Cambridge, UK
10th Sloan MIT Sports Analytics Conference, March 11-12, Boston, MA
IEEE International Conference on Big Data Analysis (ICBDA 2016), March 12-14, Hangzhou, China
Gartner Business Intelligence & Analytics Summit, March 14-16, Grapevine, TX
Discovery Summit Europe, March 14-17, Amsterdam, The Netherlands
The Internet of Things Conference, March 14-17, Munich, Germany
Re:Work Connected City Summit, March 16-17, London, UK
HR and Workforce Analytics Summit, March 16-17, London, UK
Data Innovation Summit, March 22, Stockholm, Sweden
Industrial IoT, March 23-24, London, UK
2nd IEEE Conference on Big Data Computing Serivce and Applications, March 29-April 1, Oxford, UK
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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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Selected definitions for the term “Analytics” from literature:
Analytics is the method of logical analysis (while Analysis is the separation of a whole into its component parts).
Analytics is the discovery and communication of meaningful patterns in data.
Analytics has emerged as a catch-all term for a variety of different business intelligence (BI)- and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain and so on).
Analytics is defined as the scientific process of transforming data into insight for making better decisions.
How do you define “Analytics”? Email us and publish your idea!