Samer Takriti, at the Watson Research Center
Still, Takriti confesses that he's nervous. His assignment is to
translate the complexity of highly intelligent knowledge workers into
the same types of equations and algorithms that are used to fine-tune
shipping or predict the life span and production of a mainframe
computer. With time, he and his team hope to build detailed models for
each worker, each one complete with a person's quirks, daily commute,
and allies, perhaps even enemies. These models might one day include
whether the workers eat beef or pork, how seriously they take the
Sabbath, whether a bee sting or a peanut sauce could lay them low. No
doubt, some of them thrive even in the filthy air in Beijing or Mexico
City, while others wheeze. If so, the models would eventually include
this detail, among countless others. The idea is to build richly
textured models that behave in their symbolic realm just like their
flesh-and-blood counterparts. Then planners can manipulate them,
looking for the most efficient combinations.
Takriti's team is hardly starting from scratch. IBM has long been a
leader in converting all kinds of complex systems into numbers. Right
after World War II, Big Blue used a new science called Operations
Research to construct a mathematical model of the company's industrial
supply chain. It included its costs and capabilities, as well as
limitations, or constraints. Once the supply chain existed as numbers,
engineers could experiment with it—optimizing it—and later incorporate
the improvements in the real-life version. This drove efficiency and
lowered costs. It was wonderful for manufacturing. But now, as IBM has
shifted its focus to services, the corporate supply chain is made up
less of machine parts than of people—Takriti and some 300,000 of his
colleagues. His job, quite simply, is to start optimizing his
To put together these profiles, Takriti requires mountains of facts
about each employee. He has unleashed some 40 PhDs, from data miners
and statisticians to anthropologists, to comb through workers' data.
Personnel files, which include annual evaluations, are off-limits at
IBM. But practically every other bit of data is fair game. Sifting
through résumés and project records, the team can assemble a profile of
each worker's skills and experience. Online calendars show how
employees use their time and who they meet with. By tracking the use of
cell phones and handheld computers, Takriti's researchers may be able
to map the workers' movements. Call records and e-mails define the
social networks of each consultant. Whom do they copy on their e-mails?
Do they send blind copies to certain people?
These hidden messages could point to the growth of informal networks
within the company. They may show that a midlevel manager is quietly
leading an important group of colleagues—and that his boss is out of
the loop. Eventually, say experts, e-mail analysis may single out
workers whose behavior places them outside the known networks. Are
these outliers depressed, about to jump ship, consorting with the
competition? In companies around the world, the Numerati will be
hunting for statistical clues.
Even without reading all the e-mails, managers can automatically
spot the most common words that circulate within each group of workers.
This permits them to establish the nature of each relationship. They
can also see how communications shift with time. Two workers may
discuss software programming Tuesday through Friday but spend much of
their time on Monday sending e-mails about the past weekend's football
games. "The next big step," says Kathleen M. Carley, a lead researcher
in social networks at Carnegie Mellon University, "is to take tools
like this and tie them to scheduling and productivity programs."
Takriti's scheme is even more ambitious. He is not given to bold
forecasts. But if his system is successful, here's how it will work:
Picture an IBM manager who gets an assignment to send a team of five to
set up a call center in Manila. She sits down at the computer and fills
out a form. It's almost like booking a vacation online. She puts in the
dates and clicks on menus to describe the job and the skills needed.
Perhaps she stipulates the ideal budget range. The results come back,
recommending a particular team. All the skills are represented. Maybe
three of the five people have a history of working together smoothly.
They all have passports and live near airports with direct flights to
Manila. One of them even speaks Tagalog.
Everything looks fine, except for one line that's highlighted in
red. The budget. It's $40,000 over! The manager sees that the computer
architect on the team is a veritable luminary, a guy who gets written
up in the trade press. Sure, he's a 98.7% fit for the job, but he costs
$1,000 an hour. It's as if she shopped for a weekend getaway in Paris
and wound up with a penthouse suite at the Ritz.
DO THE MATH
Hmmm. The manager asks the system for a cheaper architect. New
options come back. One is a new 29-year-old consultant based in India
who costs only $85 per hour. That would certainly patch the hole in the
budget. Unfortunately, he's only a 69% fit for the job. Still, he can
handle it, according to the computer, if he gets two weeks of training.
Can the job be delayed?
This is management in a world run by Numerati. As IBM sees it, the
company has little choice. The workforce is too big, the world too vast
and complicated for managers to get a grip on their workers the
old-fashioned way—by talking to people who know people who know people.
Word of mouth is too foggy and slow for the global economy. Personal
connections are too constricted. Managers need the zip of automation to
unearth a consultant in New Delhi, just the way a generation ago they
located a shipment of condensers in Chicago. For this to work, the
consultant—just like the condensers—must be represented as a series of
Eventually, companies could take this knowledge much further, using
the numbers, in a sense, to clone us. Imagine, says Aleksandra
Mojsilovic, one of Takriti's close colleagues, that the company has a
superior worker named Joe Smith. Management could really benefit from
two or three others just like him, or even a dozen. Once the company
has built rich mathematical profiles of Smith and his fellow workers,
it might be possible to identify at least a few of the experiences or
routines that make Joe Smith so good. "If you had the full employment
history, you could even compute the steps to become a Joe Smith," she
says. "I'm not saying you can recreate a scientist, or a painter, or a
musician," Mojsilovic adds. "But there are a lot of job roles that are
really commodities." And if people turn out to be poorly designed for
these jobs, they'll be reconfigured, first mathematically and then in
Sound scary? It may depend on where you're perched on the food
chain. Remember the $1,000-per-hour consultant who almost got
dispatched to the Philippines? He didn't end up going, and instead, in
IBM's scheme, he remained "on the bench." Takriti smiles. "That's what
we call it," he says. "I think the term comes from sports." The
question, of course, is how long IBM wants to have that high-priced
talent gathering splinters. If there isn't any work to justify his
immense talents, shouldn't they put him on something else, just to keep
Not necessarily, says Takriti. Job satisfaction is one of the
automatic system's constraints. If workers get angry or bored to tears,
their productivity is bound to plummet. The computer keeps this in mind
(in a manner of speaking). As you might expect, it deals very gently
with superstars. Since they make lots of money for the company during
short bursts of activity, they get plenty of time on the bench. But
grunt workers in this hierarchy get far less consideration. They're
calculated as commodities. Their skills are "fungible." This means
these workers are virtually indistinguishable from others, whether
they're in India or Uruguay. They contribute little to profits. It
pains Takriti to say this, because humans are not machines. They have
varying skills and potential to grow. He appreciates this. But looking
at it mathematically, he says, the company should keep its commodity
workers laboring as close as possible to 100% of the time. Not much
kickback time on the bench for them.
Where is this all leading? I pose the question one afternoon to
Pierre Haren. A PhD from Massachusetts Institute of Technology and a
prominent member of the Numerati, he's the founder and chief executive
of ILOG. It's a French company that uses operations research to
fine-tune industrial systems, charting, for example, the most efficient
delivery routes for Coors beer. ILOG makes allowances for all kinds of
constraints. For example, a few years ago, the Singapore government
wanted to avoid diplomatic spats at its new airport. So officials asked
ILOG to synchronize the flow of passengers, making sure that those from
mainland China wouldn't cross paths with travelers from Taiwan. Haren
speaks in a strong French accent. We're talking in the lobby of a
Midtown hotel in New York, and he has to yell to make himself heard
over a particularly loud fountain.
Haren says the efforts under way at places like IBM will not only
break down each worker into sets of skills and knowledge. The same
systems will also divide their days and weeks into small periods of
time—hours, half-hours, eventually even minutes. At the same time, the
jobs that have to be done, whether it's building a software program or
designing an airliner, are also broken down into tiny steps. In this
sense, Haren might as well be describing the industrial engineering
that led to assembly lines a century ago. Big jobs are parsed into
thousands of tasks and divided among many workers. But the work Haren
is discussing is not done by hand, hydraulic presses, or even robots.
It flows from the brain. The labor is defined by knowledge and ideas.
As he sees it, that expertise will be tapped minute by minute across
the world. This job sharing is already starting to happen, as companies
break up projects and move big pieces of them offshore. But once the
workers are represented as mathematical models, it will be far easier
to break down their days into billable minutes and send their smarts to
fulfill jobs all over the world.
Consider IBM's superstar consultant. He's roused off the bench,
whether he's on a ski lift at St. Moritz or leading a seminar at
Armonk, N.Y. He reaches into his pocket and sees a message asking for
10 minutes of his precious time. He might know just the right
algorithm, or perhaps a contact or a customer. Maybe he sends back word
that he's busy. (He's a star, after all.) But if he takes part, he
assumes his place in what Haren calls a virtual assembly line. "This is
the equivalent of the industrial revolution for white-collar workers,"
It's getting late in Takriti's office. I can see that he's concerned
about my line of questioning. This virtual assembly line sounds
menacing. The surveillance has more than a whiff of Big Brother. For
those of us who aren't $1,000-per-hour consultants, life bound to a
mathematical model is sounding like abject data serfdom.
Here's Takriti's counterargument. As the tools he's building make
workers more productive, the market will reward them. We already use
math programs to plot our trips and look for dates. Why not use them to
map our careers—and negotiate for better pay? (Takriti, it turns out
months later, masters these market dynamics: He was able to shop his
gilded Numerati credentials to several Web companies and banks, and
finally leaves IBM in late 2007 for a post as a top mathematician at
Goldman Sachs. Work on the modeling project continues apace, says IBM.)
All sorts of workers will be able to calculate their own worth with
more precision. Let's say analytical tools show that a consultant's
value to the company topped $2 million one year. Shouldn't she have
access to that number and be free to use it as a negotiating tool? In a
workplace defined by metrics, even those of us who like to think that
we're beyond measurement will face growing pressure to build our case
with numbers of our own.
Adapted from The Numerati by Stephen Baker, copyright © 2008. Reprinted by permission of Houghton Mifflin Harcourt Publishing. All rights reserved.