8 Special reportThe data economy The EconomistFebruary 22nd 2020
1G
eeks arenot known for being poets. But sometimes even they
have a way with words, for example when trying to describe the
main challenge of dealing with data. It is the search, they say, for “a
single version of the truth”.
This also nicely describes what has been the goal of corporate
information technology since it emerged 60 years ago. And the ad-
age encapsulates the main tension for businesses in the data econ-
omy: finding digital truth—that is, identifying and combining
data that accurately represent reality—is becoming more difficult
and more important. More difficult because data and their sources
are multiplying. And more important because firms need to get
their data house in order to benefit from ai, which they must to
stay competitive. aiboosts revenues and profits, according to a re-
cent survey by McKinsey, a consultancy (see chart on next page).
Happily, technology is coming to the rescue. Data-handling
software and cloud computing are increasingly enabling what
George Gilbert, an investor and veteran observer of the itindustry,
calls the “ai-ssembly line”—in reference to what happened a hun-
dred years ago, when electricity replaced steam as the main source
of power in factories. Before, machines had to be grouped closelyaround the power source—a steam engine. Electricity then al-
lowed power to be distributed to where it was needed, which made
assembly lines feasible. What is happening now, however, is actu-
ally the inverse: the machines of the digital age—a firm’s business
applications and software to build these—are virtually regrouping
around a new power source: central digital repositories known as
“data warehouses” or “data lakes”. In time this may allow compa-
nies to build entire digital twins of themselves.
Finding digital truth is hard because the data come from many
sources and in a staggering variety of formats—which makes them
hard to integrate. Even simple things such as a customer’s name
can be defined and stored in many different ways. Companies can
have thousands of software applications, each with its own data-
base. Failed attempts to consolidate or link these digital reposi-
tories have cost armies of chief information officers their jobs.
Integrating data was already a major problem when itexisted
mainly to keep track of a firm’s “transactions”, such as processing
an order or managing the supply chain. It has only become more
difficult since. In the 1990s firms started using their data to work
out how they have been doing, something called “analytics”. A de-
cade ago, they turned to mining their data to make predictions
about their business, an approach first dubbed “big data” and now
ai. Today a firm’s data are often not just spread across many local
databases, but live in different cloud services and stream in from
third parties and connected devices.
It is the data warehouses and data lakes that are now making it
easier to use the digital stuff. They differ in the way they structure
information—the first takes a more rigid approach than the sec-
ond, although the differences are getting smaller—both can now
live in the cloud. This makes them not only cheaper to manage, butThe new AI-ssembly line
Integrating data is getting harder, but also more importantBusiness