49ers Select New Technology For NFL Draft | Only A Game
podcast / article on SF 49ers use of stats and SAP’s HANA appliance in scouting, drafting. Continues to speak of NBA statistics and further use of HANA in sports business.
“49ers COO Paraag Marathe says piling up information is easy, but the challenge is making it user-friendly for coaches and general managers.
“And that’s something that’s sort of overlooked. People want to get reams of data and put together like this really robust analysis,” Marathe said. “But you know what? If it’s not communicated [or] it’s not simple and clear, it’s not going to be used.” …
“Does our simulation make sense in the context of that team’s roster, that team’s depth charts, in the context of that team’s salary cap? Will they be able to afford these kind of players, or are they going to make a different trade, are they going to make a different pick? That kind of real-time simulation is the real beauty of this virtual draft board. Otherwise, what you have is those big magnets.””
Get Innovation Right: Tap Into Women Over Forty | LinkedIn
“According to New York Times article Innovators Get Better with Age, research indicates that a 55-year-old and even a 65-year-old have more innovation potential than a 25-year-old. He also notes that the directors of the top five grossing films in 2012 were in their 40s and 50s. While Nobel Prize winners may make their break-through discoveries earlier in life – the average age is around 38 – typically it takes twenty years to socialize their ideas, meaning they don’t receive recognition for their achievements until around the age of 60.”
“Then there’s the research around entrepreneurs. “The average founder of a high-tech startup isn’t a whiz-kid graduate, but a mature 40-year-old engineer or business type with a spouse and kids who simply got tired of working for others,””
Does Your Architecture Pass the “So What” Test? | Doug Newdick’s Blog
Nice reminder and simple check from Doug Newdick. Reminds me of a quote I recently pulled from the New Yorker: “Harm averted is benefit unseen”. Be prepared to show / communicate value, even if it’s for harm averted.
“Does your architecture pass the “So What” test? Can you demonstrate the specific value that a particular architectural deliverable or activity will add? If not, why are you even bothering? In this case, as with justice, your activity must not just add value, it must be seen to add value.”
10 Breakthrough Technologies 2013 | MIT Technology Review
Good list from MIT Tech Review. Several I’ve discussed with a client as they relate to the future of healthcare, and therefore, society. [Deep learning, DNA sequencing, Additive Manufacturing, Robotics]
Great Innovators Think Laterally – Ian Gonsher and Deb Mills-Scofield – Harvard Business Review
“The creative process is just that: a process. Recognizing value that others have missed doesn’t require preternatural clairvoyance. A well-honed creative process enables us to intuitively recognize patterns and use those insights to make inductive predictions about divergent ideas, both vertically within categories, and horizontally across categories. By understanding the genealogy of innovation within a given category, we can imagine what might come next.
We need to break out of thinking that is solely based on what we know, what we assume, and what we’ve experienced. Many of us are so entrenched in our industries that we don’t know how to think laterally or horizontally. We usually go a mile deep but only an inch wide. We haven’t given our people and ourselves the time and opportunities to explore other industries, cultures designs, ways of being and doing, and other “adjacent possibilities.””
How Kaggle Is Changing How We Work – Thomas Goetz – The Atlantic
“Because here’s the thing: the Kaggle ranking has become an essential metric in the world of data science. Employers like American Express and the New York Times have begun listing a Kaggle rank as an essential qualification in their help wanted ads for data scientists. It’s not just a merit badge for the coders; it’s a more significant, more valuable, indicator of capability than our traditional benchmarks for proficiency or expertise. In other words, your Ivy League diploma and IBM resume don’t matter so much as my Kaggle score.”
Cartoons from the Issue of April 22nd, 2013 : The New Yorker
Perhaps I’ve spent too much time talking about trend convergences with a client, but this cartoon amused me.
Archives for April 2013
Link Collection — April 7, 2013
What’s Next in the Techonomy? — Hagel & Seely Brown
“In the last few decades, we have witnessed a steady doubling in the price performance of digital technologies. However, we are reaching a tipping point of this exponential growth, and it is unclear how the cumulative effects of technology will reshape our economy, political systems, and collective future. One thing is clear: in the hands of existing institutions-firms, schools, non-profits, civic institutions and governments-this awesome technology will achieve only a fraction of its potential.
Unfortunately, we haven’t seen the same exponential rate of change in institutions as we have in technology (Unlike computer chips, government and business structures don’t predictably get faster and less expensive). Managerial fiefdoms, rigid hierarchies and tightly scripted procedures remain from the industrial revolution era like vestigial structures; they were important at some point, but it’s unclear what purpose they serve now…”
How To Think Like An Engineer ⚙ Co.Labs ⚙ code + community
Good insights in here. If you already think this way, consider passing it along to folks who “don’t get your thought process”…
“Excelling in business today means knowing how to think through technological abstraction and ambiguity. Here, we listen in as engineers discuss this very skill–and decode their secrets for how to hone it.”
Big Data’s Promise and Limitations : The New Yorker
“Big Data can be especially helpful in systems that are consistent over time, with straightforward and well-characterized properties, little unpredictable variation, and relatively little underlying complexity.
But not every problem fits those criteria; unpredictability, complexity, and abrupt shifts over time can lead even the largest data astray. Big Data is a powerful tool for inferring correlations, not a magic wand for inferring causality.”