Daily Archives: April 2, 2015
Furious 7 has been one of the most awaited movies for the past two years. The release of the seventh installment from the hit franchise had been delayed due to the tragic death of one of the actors from Fast & Furious, Paul Walker.
The handsome actor who plays Brian died in a tragic car accident in 2013 which delayed the project for an year. Now that the movie is finally out, we are expecting a lot of from the movie.
Expectations on “Fast & Furious 7” is huge as it the final movie of Paul Walker, who is one of the lead actors of the film.
The movie has released today (2nd April) in many parts of the world. Furious 7 hit countries namely India, UAE, Austria, Australia, Bolivia, Chile, Colombia, Czech Republic, Denmark, Georgia, Greece, Hong Kong, Hungary, India, Iraq, Republic of Macedonia, Mexico, Malaysia, Peru, Portugal, Singapore, Slovenia, Slovakia, Thailand, and Ukraine today.
As it is the last movie of Paul Walker and is also buzzed to be the last in the franchise, people are excited to see the last ride of the 7 stars!
“Fast & Furious 7” is expected to break records at the box office. Pre-sales of the movie has been great with MovieTickets.com reporting that it got 34%more than “Fast & Furious 6”.
Fandango claims that the 7th installment could become the “biggest pre-seller for any April release in its history,” according to Variety.
The paper went on to say that “Furious 7″ would overtake “Captain America: The Winter Soldier” as the biggest April opening of all time (if it grosses $100 million on opening weekend). Now if the box office collections of Furious 7 goes to this, it could beat $97.4 million earned by “Fast and Furious 6.”
Coming to India, the movie has been creating a lot of buzz as the name is only enough for F&F fans! The Fast and Furious franchise started in 2001 and it has been more than a decade, but the craze of fans over the film is increasing.
Furious 7 is all the more important as we get to see Paul Walker take one last ride on the big screen.
Directed by James Wan, Furious 7 stars Vin Diesel, Paul Walker, Dwayne Johnson, Michelle Rodriguez, Jordana Brewster, Tyrese Gibson, Ludacris, Lucas Black and Jason Statham in the lead roles.
“In God we trust; all others must bring data.”
That famous line from statistician William Edwards Deming has become a mantra for data-driven companies, because it points to the promise of finding objective answers. But in practice, as every analyst knows, interpreting data is a messy, subjective business. Ask two data scientists to look into the same question, and you’re liable to get two completely different answers, even if they’re both working with the same dataset.
So much for objectivity.
But several academics argue there is a better way. What if data analysis were crowdsourced, with multiple analysts working on the same problem and with the same data? Sure, the result might be a range of answers, rather than just one. But it would also mean more confidence that the results weren’t being influenced by any single analyst’s biases. Raphael Silberzahn of IESE Business School, Eric Luis Uhlmann of INSEAD, Dan Martin of the University of Virginia, and Brian Nosek of the University of Virginia and Center for Open Science are pursuing several research projects that explore this idea. And a paper released earlier this year gives an indication of how it might work.
The researchers recruited 61 analysts (mostly academics) and asked them to assess whether soccer referees were more likely to give red cards to players with darker skin tones. The analysts split up into 29 teams, and were given a dataset that included numerous variables about both players and referees.
Each team devised their method for answering the question, and then shared that approach – but not any results – with the group. The result was a heated debate over which methods were defensible, and which were not. If you’re looking for a correlation between skin tone and red cards received, does it make sense to control for the position the player plays? What about the country their team is located in, or how many yellow cards they’ve received?
After receiving feedback from the group on their proposed method, the teams were able to tweak their approach if they wanted to, and then proceeded to the actual analysis. Then all the analyses were shared with the group and a debate took place over the results, which ones might have been influenced by outliers, and whether things would look differently if teams had taken different variables into account.
The results clearly illustrate why different analysts come to different conclusions about the same data. From 29 teams came 21 different sets of variables. Different teams also used different statistical models.
Not surprisingly, then, they came to different conclusions. 20 of the teams found a statistically significant relationship between a player’s skin color and the likelihood of receiving a red card. Nine teams found no significant relationship.
Had it just been a single team using a single method, they would have stopped at their result, declared a relationship between skin color and red cards (or not), and been done with it.
But with 29 slightly different results, the group could see clearly that their analyses hinged on difficult, somewhat subjective decisions about the best model to use and which variables should be included. There was another round of debate, after which “the analysts converged toward agreement that there is a small, statistically significant relationship between player skin tone and receiving red cards, the cause of which is unknown.” And although this paper can’t prove it, the authors suggest that taking the median result from the range might provide a less biased answer to the question.
Though most companies don’t have 60 analysts to throw at every problem, the same general approach to analysis could be used in smaller teams. For instance, rather than working together from the beginning of a project, two analysts could each propose a method or multiple methods, then compare notes. Then each one could go off and do her own analysis, and compare her results with her partner’s. In some cases, this could lead to the decision to trust one method over the other; in others, it could lead to the decision to average the results together when reporting back to the rest of the company.
“What this may help [to do] is to identify blind spots from management,” said Raphael Silberzahn, one of the initiators of the research. “By engaging in crowdsourcing inside the company we may balance the influence of different groups.”
Silberzahn and his colleagues are currently working on a second effort in the same vein: to crowdsource the analysis of how gender and status impact scientific debate. You can trust the results will be interesting, and thanks to the crowd, more likely to be right.
Trust in business leadership is at historic lows, according to surveys by Edelman and the World Economic Forum. One reason might be what INSEAD professors Gianpiero and Jennifer Petriglieri call the “dehumanization of leadership”- that is, our tendency to think of leaders as either instrumental (pursuing a particular business goal) or heroic (pursuing a unique vision). In short, we want super-machines or super-humans, or both, at the top of our organizations, and many CEOs strive to meet those expectations. They’ve been trained to hide vulnerabilities, to plan and stay the course, to minimize risk, and to be consistent, level-headed, and in complete control at all times. Inevitably, however, they fall short.
It’s therefore time for a different approach – one that allows leaders to fully acknowledge their humanity, thereby winning both the trust and respect of followers. This may seem like conventional wisdom, but it’s still not easy for leaders to do. Here are three specific suggestions:
Get emotional. The marriage researcher John M. Gottman has long contended that small moments of attachment and intimacy are vital to a healthy relationship. This is true in business, too. More than purpose or perks, employees value heartfelt moments of connection that meet their needs as social beings. I’ve forgotten many interactions with former bosses, but I will always remember the time that one of them began to cry in my presence during a time of immense investor pressure. That moment of vulnerability cemented my loyalty to him. Yes, you’re probably comfortable showing happiness or excitement. But, when it’s called for, you can also show disappointment, worry, and anger.
Be whimsical. Guy Laliberté, the CEO of Cirque de Soleil, once hired a clown to follow him around and mimic his mannerisms. Mark Bertolini, the CEO of Aetna, became an avid practitioner of meditation and yoga after a near-death experience and subsequently launched free classes for all employees. Wired recently described Microsoft CEO Satya Nadella’s leadership style as “warm, fuzzy, and slightly bonkers.” For him, and other future leaders, that should be a compliment. By exposing their idiosyncrasies, passions, and whims, bosses can make themselves more human. Consider what aspects of your life or personality might surprise or delight your employees and share that side of yourself.
Express doubt. Smart leaders know there is more than one right answer, and once they commit to a decision, they aren’t afraid to revisit and change it if necessary. Futurist Paul Saffo’s mantra is “strong opinions, weakly held” and it’s one that managers should also adopt, especially if they are trying to navigate complex and uncertain situations. Based on its recent CEO survey, strategy firm Wolff Olins concluded: “Employees, more than ever, are individualists. Leaders, in response, are learning to be less the visionary, less the sage, less the objective-setter, and more the shaper, the connector, the questioner.” So express doubt, ask for contrarian options, and be willing to change your mind, several times if necessary.
We might think we want our leaders to be machines or heroes. But it’s impossible to trust a person who is always rational, serious, and in control. If you’re a boss, have the courage to present yourself as a more complex being: as a sinner, not a saint; a fragile identity, not a robust platform; a lively question-mark, not a dead-certain exclamation point.