58 Business The EconomistSeptember 7th 2019
216.2bn man-hours globally. A survey by
McKinsey last year estimated that aiana-
lytics could add around $13trn, or 16%, to
annual global gdp by 2030. Retail and lo-
gistics stand to gain most (see chart 2).
Data analytics have a long way to go be-
fore they live up to these expectations. Ex-
tracting and analysing data from countless
sources and connected devices—the “In-
ternet of Things”—is difficult and costly.
Although most firms boast of having con-
jured up ai “platforms”, few of these meet
the usual definition of that term, typically
reserved for things like Apple’s and Goo-
gle’s smartphone operating systems,
which allow developers to build compati-
ble apps easily.
An ai platform would automatically
translate raw data into an algorithm-
friendly format and offer a set of software-
design tools that even people with limited
coding skills could use. Many companies,
including Palantir, the biggest unicorn in
the data-analytics herd, sell high-end cus-
tomised services—equivalent to building
an operating system from scratch for every
client. Cloud-computing giants such as
Amazon Web Services, Microsoft Azure
and Google Cloud offer standardised pro-
ducts for their corporate customers but, as
Jim Hare of Gartner explains, these are con-
siderably less sophisticated and lock users
into their networks.The enterprising Mr Siebel
Enter c3.ai, founded to help utilities man-
age electric grids, a complex problem that
involves collecting and processing data
from many sources. After its near-bank-
ruptcy, advances in machine learning, sen-
sors and data connectivity gave it a new
lease of life—and allowed it to repackage
its products for a range of industries. Cru-
cially for corporate clients, c3’s approach
grew out of Mr Siebel’s experience with en-
terprise software. He wanted to make data
analytics hassle-free for corporate clients,without sacrificing sophistication.
3 m, an American conglomerate, em-
ploys c3 software to pick out potentially
contentious invoices to pre-empt com-
plaints. The United States Air Force uses it
to work out which parts of an aircraft are
likely to fail soon. c3 is helping Baker
Hughes to develop analytics tools for the
oil-and-gas industry (General Electric, the
oil-services firm’s parent company, has
struggled to perfect an analytics platform
of its own, called Predix).
c3’s chief rival in building a bona fide ai
platform is not Big Tech or the very biggest
data-analytics unicorns. It is a company
called Databricks. It was founded in 2013 by
computer wizards who developed Apache
Spark, an open-source program which can
handle reams of data from sensors and oth-
er connected devices in real time. Data-
bricks expanded Spark to handle more data
types. It sells its services chiefly to startups
(such as Hotels.com, a travel site) and me-
dia companies (Viacom). It says it will gen-
erate $200m in revenue this year and was
valued at $2.8bn when it last raised capital
in February.
Though c3’s and Databricks’ niches do
not overlap much at the moment, they maydo in the future. Their approaches differ,
too, reflecting their roots. Databricks, born
of abstruse computer science, helps clients
deploy open-source tools effectively. Like
most enterprise-software firms, c3 sells
proprietary applications.
It is unclear which one will prevail; at
the moment the two firms are neck-and-
neck. In the near term, the market is big
enough for both—and more. In the longer
run, someone will come up with ai-assist-
ed data analytics that are no more taxing
than using a spreadsheet. It could be c3 or
Databricks, or smaller rivals like Dataiku
from New York or Domino Data Lab in San
Francisco, which are also busily erecting ai
platforms. The field’s other unicorns are
unlikely to give up trying. And incumbent
tech titans like Amazon, Google and Micro-
soft want to dominate all sorts of software,
including advanced data analytics.
Mr Siebel would be the first to admit
that this scramble is likely to claim victims.
But it certainly bodes well for buyers of
data-analytics software, which is likely to
become as familiar to corporate it depart-
ments in the 2020s as customer-relations
programs are today.^7Dataset in motionSource: McKinseyPotential annual efficiency gains from artificial intelligence*, worldwide, by industry*Estimate based on 18 existing techniques220 25 30 35 40 45 50 55 600100200300400500600700
RetailTravelTransport and logisticsTransport and logisticsAutomotive and assemblyAutomotive and assembly
High techOil and gas
ChemicalsChemicals
Aerospace
and defenceConsumer
packaged goodsConsumer
packaged goodsHealth-care systems
and servicesHealth-care systems
and services
Public and
social sectorsPublic and
social sectors
BankingBankingBasic materialsBasic materialsAdvanced
electronics/
semiconductorsAdvanced
electronics/
semiconductors InsuranceInsurance
TelecommunicationsTelecommunications
Pharmaceuticals and
medical productsPharmaceuticals and
medical productsAgricultureMedia and
entertainmentMedia and
entertainmentGains from AI as a share of total from data analytics, %Gains from AI, $bnAnalyse thisSource: PitchBook *Data analytics account for part of businessSelected valuations of data-analytics companies
$bn1010203040502009 11 13 15 17 19PalantirUiPath*Snowflake*Rubrik*Horizon Robotics*DatabricksW
hen jan paul bach moved his busi-
ness, which makes ceramic-heating
kit, from Berlin to Brandenburg 13 years ago
he never thought about politics. Abundant
land near Werneuchen, a city of 9,000, al-
lowed Bach rcto build two new production
lines. Today it has 50 employees and an
overflowing order book from clients across
the globe. And Mr Bach has become dis-
tracted by the rise of the xenophobic Alter-
native for Germany (afd). He is now
hesitating about building another much-
needed line. He occasionally thinks about
relocating the business altogether.
The strong gains by the afdin elections
on September 1st in the eastern states of
Brandenburg (where its vote almost dou-
bled to 24%) and Saxony (where it tripled to
28%) is worrying the export-driven compa-
nies of Deutschland ag. The bosses of the
bda, an association of German entrepre-
neurs, and of the bdi, which groups Ger-
man industry, released statements signal-
ling their concern about the result. In Mr
Bach’s district the afd was the strongest
party. International clients and distribu-
tors are asking Mr Bach if Brandenburg has
become a no-go area. “They think Nazis areBERLIN
German business worries about the
rise of right-wing populistsPolitical riskDeutschland AG v
AfD