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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.
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.
- 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.
- 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.
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!
Business intelligence (BI) and analytics are central to smart city initiatives. These solutions are what puts the “smart” in smart cities and cities need to have an independent assessment of which vendors can provide the best solution for their needs. To support this effort, IDC Government Insights announced the availability of a new report, IDC MarketScape: Worldwide Smart Cities Business Analytics Software 2015 Vendor Assessment. The scope of this report is limited to business intelligence and analytic tools and performance management and analytic applications. Vendors featured include IBM, Microsoft, Oracle, Salesforce, SAP, SAS, and Tableau.
This is the short list of global Smart City business analytics software vendors. The vendors studied for this IDC MarketScape are among the few business analytics vendors that have specific offerings geared towards Smart Cities and that are addressing the most important characteristics for smart cities.
There is a tight field of leaders. There is contention in the Leaders category based primarily on capabilities.
According to Ruthbea Yesner Clarke, Research Director for IDC’s global Smart Cities practice, “The vendors studied for this IDC MarketScape are among the few business analytics vendors that have specific offerings geared towards Smart Cities and are addressing the most important characteristics for smart cities. This report will help city decision-makers understand their options more fully.”
Those important characteristics include:
– Ease and speed of analysis/ self-service
– Strength of analytics
– Flexible delivery models
– Ability to share data
– Innovation and/ or Co-innovation
According to IDC, the amount of data that is created each year is expected to grow from 4.4 zettabytes in 2013 to 44 zettabytes – or 44 trillion gigabytes – in just five years, a growth of 40% per year. (Source: EMC Digital Universe Study, research and analysis by IDC, May 2014) Much of this growth is driven by connected devices and, more specifically, mobile connected devices (RFID, smart cards, body cams, GPS). Government organizations will need to analyze data created from government systems as well as from outside government. Social media, information from mobile apps and smartphones will become more and more useful to cities as they work on managing traffic, crime, events etc.