Of Superorganisms and Super Intelligence

#intelligence #sales #sales efficiency #predictive analytics #realtime

Every morning in a red harvester ant colony begins the same way. Patroller ants peek out their heads with the sunrise, explore their landscapes and report back a course of action to the Foragers, who then begin the day’s work of collecting food for the colony.

From a wolf hunt to a football play to a sales deal, every endeavor requires information. Even bacteria must communicate to thrive.

Granted, a doughnut could be dropped beside a red harvester ant colony at midday, and the Foragers would literally walk over it, set on their goal of the old grass seeds they were pointed toward by the Patrollers that morning. Because while information is critical, they have no mechanism for updating information in real time.

Nature has seemingly decided that a 24-hour update cycle is good enough for an ant world, making for a clear diving line between superorganisms and super organizations.

Instant access to of-the-moment information, culled from startlingly fast-growing troves of data, is changing the world. It’s also upending the pace and intensity of competition in every industry, if not the ability for some companies to continue to compete at all.

For example, early this year, the City of Los Angeles approved an Open Data Initiative that made publically available, for the first time, hundreds of datasets, from building permits and traffic information to the breakdowns of budgets across the city.

Instantly, far greater transparency and accountability were imposed on city representatives and their offices, and opportunities for innovation became possible. Within months, the navigation app Waze announced that, using the datasets, it would begin sharing information with drivers about road closures, construction and blocked streets — and updating that information every two minutes.

Across the business universe, another example is Amazon’s “anticipatory shipping.” So confident is the company in its predictive analyses — fed by uniquely vast data sets — that it’s begun shipping some items before they’ve been ordered, according to the Wall Street Journal.

In a business environment in which customer purchases can be reduced to one controlled detail among many, surviving, if not thriving, requires the collection and analysis of far more data than ever before, and a way to make those findings instantly available to the people who can use them to act, or change course.

Because without real-time analytics and updates, they could fail to recognize even the most tempting, sugar-coated opportunity looming over them.

The Magic of Certainty

#forecast #risk #predictive #analytics

At Collective[i] we spend a lot of time thinking about forecasts. The point of predictive analytics is to push the invisible needle between guessing and certainty as solidly toward the latter as possible.

Experience is invaluable in every profession, and in Sales it’s been really all that professionals have had to go on. But leveraging hard-earned wisdom to advantage, and trusting one’s gut where data-backed analytics can instead illuminate a reality, are very different things.

Given our obsession with creating certainty from data, we read with interest a recent New York Times Magazine article about Roomy Khan, a wildly successful stock trader who was found guilty of trading on inside information and last year completed a one-year prison term.

Her downfall, she explained, was a yearning for certainty.

After Khan showed the writer, Anita Raghavan, stocks she still pays attention to —“otherwise my mind would be mush” — and deals she would have made that would have turned a profit, Raghavan asked why Khan had resorted to insider trading when there was “ample money to be made through good old-fashioned stock analysis.”

“Do you think I would bet $1 million on my analysis?” Khan answered. “But if someone told me Caterpillar is going to miss its quarter, I would bet $1 million. Insider trading introduces certainty.”

To Khan, Raghavan added, “That certainty felt like magic.”

Turns out that when certainty comes legally, and backed by the work of award-winning data scientists, none of the magic is lost.

A Brief History of Potential

#networks #bigdata #evolution #human potential #technology #sales #analytics #automation

“What’s lost when we replace a person with software or a machine?” a New York Times slideshow recently asked, presenting a “brief history of failure” and counting humans alongside the longbow, airships and the Dvorak keyboard.

The Times’ framing of the question — not what changes, but what’s lost — happily works against the thesis of humans as failures. And still the premise is notable for so neatly summarizing a real fear: our obsolesce at the hands of our inventions.

What’s lost? What was lost when people no longer had to rub sticks to make fire? What was lost when refrigeration entered our homes?

Marx and Engels theorized more than 150 years ago that certain basic needs must be met before humans can “make history.” I.e., you don’t get a space program, or the Mona Lisa, if you have to spend your afternoon hunting down your dinner.

A perfect modern example of this idea is Charity: Water. People obviously need clean water, but the full story the organization tells is that when clean water is accessible, children are able to go to school and families are healthy and newly ambitious. Donating to help build a well isn’t about giving children, families and communities water — it’s about giving them a future.

Business technology follows the same formula.

A 1985 ad for the Apple Macintosh XL bragged that what a graphic design service could do in a week, you could now do yourself, over lunch.

The insinuation wasn’t that could take a few days off, given the time you’d saved, but that — with a beautifully designed presentation suddenly in hand — you were suddenly in a position to start exciting things happen.

What was lost, we could ask, when our ability to share documents expanded beyond mail carriers?

MakerBot has showcased a mechanical hand that resulted from a collaboration between a prop maker in Washington State and a master carpenter in South Africa who’d lost four fingers in a work accident. Sharing files online, and each with a 3D printer, the men were able to reduce the prototyping process from weeks to literally minutes and ultimately perfect a solution for families that otherwise couldn’t afford to buy a prosthetic for a child in need of one, never mind replace it every year, or more frequently, as the child grew.

Today, our advancements revolve around data. The more intelligently we use data to understand the big and small stories playing out around us, the better we can influence everything from climate change to how diseases travel to how adjusting traffic signals can reduce driving congestion.

And truly, the most advanced approach to understanding data is networks.

It’s with a network that LinkedIn has evolved the recruiting process from searching the world for the best applicant, or narrowing down possibly thousands of applications, to pinpointing the perhaps three perfect people for a job, whether they applied or not. (So in the time that you would have still been searching, your new hire could be getting to work.)

It’s thanks to networks that fraud is more quickly caught by PayPal than by banks (its network understands patterns so well that inconsistencies jump out); eBay is a less risky transaction than a classifieds sale (its network establishes accountability); and Google won out over the once-leading Altavista (Google searches networks of pages, while Altavista searched individual instances of terms).

Collective[i] is similarly transforming how people work — starting with Sales —
by using the larger intelligence of a network to instantly provide answers that people might otherwise spend days or weeks or months trying to figure out alone.

It’s just one more example of humans learning to minimize a drab, necessary preamble so we can more quickly get to the potential-rich part.

Of course, for all that technology is capable of, what it’s incapable of is all that’s distinctly human — not the ability to adjust the thermostat at night, but spirit, soul, compassion, inquisitiveness, brilliance and love. Or as the Times article put it, “subtleties.”

What happens when software or machines take over the tedious or dirty or dangerous jobs that humans have long performed without passion or creativity, so that we can advance to the part where our very humanness can have impact?

We think the answer is innovation, inspiration, exploration, greatness and beauty. In a word: brilliance.