The Economist USA - 22.02.2020

The EconomistFebruary 22nd 2020 Special reportThe data economy 9

2 they can more easily be fed with data from many different sources
and used by many different users. One such is made by Snowflake,
another startup, which has turned its data warehouse into what it
calls a “data platform” that can stretch across different computing
clouds. Big cloud providers such as Amazon Web Services and Mi-
crosoft Azure offer similar products.
A second improvement is specialised databases, which take
care of certain types of data. Since data often no longer come in the
form of static blocks, but rather real-time digital streams, they
have to be treated differently, explains Jay Kreps, the chief execu-
tive of a startup appropriately named Confluent. It sells cloud ser-
vices based on Apache Kafka, an open-source program, which ana-
lyse these streams and dump them into data lakes. Bosch, a
German conglomerate, uses Confluent to gather and mine data
from power tools to manage repair services and construction sites.
Yet it is a third group of software and services that turns all this
into Mr Gilbert’s “ai-ssembly line”. Some of these tools prepare
data for crunching, others make it easy to design and train an aial-
gorithm, deploy it in an application to automate decisions and
continuously improve it. Enel, a utility, has used such tools to de-
velop a service that helps it identify the power thieves it needs to
go after first. Shell, an oil company, has designed algorithms that
ensure that its thousands of spare parts are always available
around the world. And Kiva, a non-profit lender, has built a data
warehouse with Snowflake that allows it to make better decisions
about who should receive its loans.
Many other firms were not so lucky, forgetting that technology
is always only part of the solution. Motivated by studies that found
that aiboosts profits and, in some cases, panicked by the pos-
sibility of being disrupted by a startup, some tried to cobble to-
gether an ai-assembly line themselves, but failed. They did not
have the right type of developers and data scientists—or did not
want to pay their exorbitant salaries. This has created an opening
for itvendors to sell more or less pre-packaged versions of ai-
assembly lines, but each coming at it from a different direction.

Meanwhile, at the refinery

Take incumbents first, which are trying to build on their strengths.

In the case of the granddaddy, ibm, this is services. It helps firms

build what Arvind Krishna, soon its new boss, calls a “data plane”, a

collection of programs to develop aiapplications. It has also be-

come a data refiner itself: for example, it collects and sells granular

weather data that insurers can use to calculate rates, and utilities

to predict where power cuts may occur. And it offers a range of ai

services, including visual recognition and translation, that other

firms can plug into their products.

Oracle, the world’s leading vendor of re-

lational databases, still the workhorses of

corporate it, aims to extend that position

by providing what it calls an “autonomous

database”. This type of service combines

and automates all sorts of digital reposi-

tories, plus bits of ai, so customers do not

have to put together all these programs

themselves. “It’s many data engines in a

single engine,” explains Paul Sonderegger,

the firm’s senior data strategist, adding

that such integration will be key to increas-

ing a firm’s “data productivity—increasing

the dollar output per data input”.

As for younger itfirms, they are in-

creasingly offering to help firms to get their

digital ducks lined up, too. Salesforce,

which grew up as a web-based service to

manage customer relations, has spent bil-

lions in the past two years to develop its own aitechnology, called

Einstein, and acquire two big-data companies, MuleSoft and Tab-

leau. The idea, says Bret Taylor, Salesforce’s president and chief op-

erating officer, is to allow firms to consolidate and link their data

so they can have a “single view of their customers”. This makes it

easier for firms to anticipate what their customers will do, perso-

nalise offers and always recognise them, whether they show up in

a retail store or online.

Then there is a host of smaller firms. Databricks has put togeth-

er an aiplatform, complete with tools to cleanse data, build algo-

rithms and deploy them. c3.ai offers some-

thing similar, but mainly aims to help big

firms through their digital transformation.

Qlik is known for analytics and data visual-

isation, but has recently moved into ai.

But despite such tools, many aiprojects

still disappoint, says Debra Logan of

Gartner, a market-research firm. One big

problem is data silos which reflect a firm’s

internal boundaries. Different depart-

ments within a company, afraid of relin-

quishing power, are loth to share their data

or change what they collect and how (making the point that data

structures are often just thinly veiled power structures). This has

kept many firms from developing a coherent “data strategy” that

would ensure they actually collect and analyse the information

they need to achieve their business goals.

To overcome such digital divisions, some companies have

made organisational changes. A growing number have appointed

a “chief data officer” who can knock heads together to ensure that

the itdepartment and business units work together, which they

must to build anything resembling an ai-assembly line. Yet

changes at the top, as well as in technology, are not worth much, if

the rest of the company is not ready. “Poor data literacy” is the sec-

ond biggest barrier to corporate data projects, preceded only by

“cultural challenges to accept change”, according to a recent survey

by Gartner. Changing this does not mean that all employees have

to become data scientists, but that they have a basic grasp of what

data can be used for and what not, says Mike Potter, the chief tech-

nology officer of Qlik.

Data, he argues, are never neutral and must always be ques-

tioned: they may be collected for political reasons or in a way that

hides things. “We all think that data are so objective,” he says, “but

they are actually as interpretable as Shakespeare.” Despite all the

tech, there may never be a single version of the truth. 7

The bottom line

Average cost decrease and revenue increase from AI adoption, % of respondents* reporting

Source: McKinsey & Company *Surveyed November 2019

HR

Risk

Strategy and corporate finance

Service operations

Manufacturing

Supply-chain management

Product and service development

Marketing and sales

-75 -50 -25 0 25 50 75 100

More than 20% 10-19% Less than 10% Less than 5% 6-10% More than 10%

0

Costs decreasing Revenues increasing

“ We think data

are objective, but

they are actually

as interpretable

as Shakespeare”