Page 4
At the outset, we’ll acknowledge that these are still
early days for big data, which is evolving as a business
concept in tandem with the underlying technologies.
Nonetheless, we can identify big data’s key elements.
First, companies can now collect data across
business units and, increasingly, even from partners
and customers (some of this is truly big, some
more granular and complex). Second, a fexible
infrastructure can integrate information and scale up
efectively to meet the surge.
Finally, experiments, algorithms, and analytics can
make sense of all this information.
We also can identify organizations
that are making data a core element
of strategy. In the discussion that
follows and elsewhere in this issue,
we have assembled case studies of
early movers in the big data realm.
In addition, we’d suggest that
executives look to history for clues
about what’s coming next. Earlier
waves of technology adoption,
for example, show that productivity surges not
only because companies adopt new technologies
but also, more critically, because they can adapt
their management practices and change their
organizations to maximize the potential. We examined
the possible impact of big data across a number of
industries and found that while it will be important
in every sector and function, some industries will
realize benefts sooner because they are more ready
to capitalize on data or have strong market incentives
to do so (see sidebar: “Parsing the benefts: Not all
industries are created equal”).
The era of big data could also yield new management
principles. In the early days of professionalized
corporate management, leaders discovered that
minimum efcient scale was a key determinant of
competitive success.
Likewise, future competitive benefts may accrue to
companies that can not only capture more and better
data but also use that data efectively at scale. We
hope that by refecting on such issues and the fve
questions that follow, executives will be better able
to recognize how big data could up-end assumptions
behind their strategies, as well as the speed and scope
of the change that’s now under way.
1 What happens in a world of radical
transparency, with data widely available?
As information becomes more readily accessible
across sectors, it can threaten companies that have
relied on proprietary data as a competitive asset.
The real-estate industry, for example, trades on
information asymmetries, such as privileged access
to transaction data and tightly held knowledge of
the bid and ask behavior of buyers. Both require
signifcant expense and efort to acquire. In recent
years, however, online specialists in real-estate data
and analytics have started
to bypass agents, permitting
buyers and sellers to exchange
perspectives on the value of
properties and creating parallel
sources for real-estate data.
Beyond real estate, cost and
pricing data are becoming more
accessible across a spectrum
of industries. Another swipe at
proprietary information is the
assembly by some companies of readily available
satellite imagery that, when processed and analyzed,
contains clues about competitors’ physical facilities.
These satellite sleuths glean insights into expansion
plans or business constraints as revealed by facility
capacity, shipping movements, and the like.
One big challenge is the fact that the mountains
of data many companies are amassing often lurk
in departmental “silos,” such as R&D, engineering,
manufacturing, or service operations—impeding
timely exploitation.
Information hoarding within business units can also
be a problem; many fnancial institutions, for example,
sufer from their own failure to share data among
diverse lines of business, such as fnancial markets,
money management, and lending. Often, that
prevents these companies from forming a coherent
view of individual customers or understanding links
among fnancial markets.
Some manufacturers are attempting to pry open
these departmental enclaves; they are integrating
data from multiple systems, inviting collaboration
among formerly walled-of functional units, and
even seeking information from external suppliers
and customers to co-create products. In advanced-
manufacturing sectors, such as automotive, for
example, suppliers from around the world make
A next-generation retailer
will be able to track the
behavior of individual
customers from Internet
click streams, update their
preferences, and model their
likely behavior in real time.