With the recent passing of the actor Leonard Nimoy, who played Spock in the 1960s TV series “Star Trek” and the subsequent movies, let’s consider his character’s behavior in contrast to some executives you may have worked for.
I’m going to break it to you gently. Despite all the advanced technology lining your pocket, car, home, workplace–and even the proverbial cloud floating virtually above your head–the world is a remarkably inefficient, wasteful place.
Your decisions are always about the future – what direction to take, where to invest, what course corrections to make, what markets to expand into, what and how much to produce, who to hire and where to put them. In other words, a forecast, the third of my four points, with the fourth being perhaps the most important of the lot - a confidence level or uncertainty measurement about that forecast, these last two coming from the realm of predictive analytics.
My favorite definition of ‘Dark Data’ comes from Matt Aslett of 451 Research:
“Data that was previously ignored because of technology limitations”
I’ve recently talked to several big, traditional organizations that are finding lots of dark data down in the cellar.
Using visualization and modelling to predict the weather.
For the first three weeks of January, I thought Chicagoland was certain to get a second straight reprieve from harsh winter. Every day, we were setting a record for the number of consecutive days without an inch or more of snow. And the temperatures hovered in the 20s, 30s and 40s – seemingly far warmer than historical Januarys.
Alas, winter has returned to the area with a vengeance over the last three weeks. While spared the 2+ foot blizzard that just hit the Northeast, we’ve had more than enough snow, ice, rain, wind and bitter cold of our own. And to think, there’s a least another month of the same …
Me? Twenty years ago, I was a statistical purist, an unabashed, hypothesis-driven fanatic – perhaps as much as the first author. In fact, I probably would have been considered a top-down analytics “planner.” I did, however, come to expand my horizon to a broader data science discipline that combines statistical orthodoxy with large N data, exploratory visualization and machine learning techniques – balancing an aggressive search for predictive relationships with the cross-validating protection of the no-pattern null hypothesis. So I guess I’ve now evolved into more a bottom-up data-driven “searcher.” Statistical science, though still a central tool, is only a part of a larger analytics portfolio.
For many, the sales planning season does not conjure up thoughts of joy and happiness. However, it is that time of year when sales leaders and sales operations must focus on how to ensure success in the coming fiscal year. For help, sales organizations should take a lesson from the jolly old guy in the red suit: Make a list and check it twice. The list below contains five critical elements that need to be on the planning list to make sure that the sales organization kicks off the new year aligned.
There are so many opportunities to apply analytics today- it’s like being a mosquito in a nudist colony.
There is a problem, however, that not everyone thinks or behaves like a mosquito. They do not always inherently sense opportunities – the opportunities to apply analytics.
Perhaps I stretch this mosquito analogy too far when I presume that many opportunities may have insect repellent applied to them. For example, let’s consider the high expectations of service at a five star hotel. Ever wait in a long line at your hotel check out during the morning rush with others checking out? It might not appear cost-justified to the hotel, but it may be a very valuable extra expense to add one or more front desk staff if you irritate an important and delayed social media influencer who will complain on Twitter or TripAdvisor.com. How would you know? It is an opportunity for an analyst’s experiment or survey. The insect repellant analogy implies that an analyst may not “sense” an opportunity.
Imagine a game show featuring three competing teams of contestants who are given a business problem involving choices. They get one week to design and test their hypotheses through experiments and return to the show with their answers. A panel of CEOs would judge the winning team.
Why not provide analysts, and the important role they perform, more visibility to the public? Make it fun. The popular TV show “The Big Bang Theory” highlights physicists. So why not have a TV game show for analysts and IT specialists to show off their investigative and discovery skills? We might call it “The Big Data Theory!”
The NBA professional scouts saw little in (Harvard University graduate and NBA star Jeremy) Lin. But a FedEx delivery truck driver, Ed Weiland, was paying attention. Weiland was a contributor to the website HoopsAnalyst.com blog.
Weiland predicted that Lin would be an exceptional talent based on the combination of two statistics: two-point field goal percentage and RSB40. The first stat is obvious, but the second completed the picture about Lin. RSB40 is a combination statistic measuring rebounds, steals and blocked shots (the RSB) per 40 minutes. Lin’s high index for these two stats revealed his dominance at both ends of the basketball court. Weiland’s prediction was initially ignored. Now he appears to be clairvoyant.
Who knew? Was it Ed Weiland? Or were the numbers already there, and Ed Weiland was listening?