The EconomistFebruary 22nd 2020 Special reportThe data economy 72 of a typical bit and how both will change in the years to come. To-
day the bit is often still created by a human clicking on a website or
tapping on a smartphone. Tomorrow it will more often than not be
generated by machines, collectively called the “Internet of Things”
(iot): cranes, cars, washing machines, eyeglasses and so on. And
these devices will not only serve as sensors, but act on the world in
which they are embedded.
Ericsson, a maker of network gear, predicts that the number of
iotdevices will reach 25bn by 2025, up from 11bn in 2019. Such an
estimate may sound self-serving, but this explosion is the likely
outcome of a big shift in how data is collected. Currently, many de-
vices are tethered by cable. Increasingly, they will be connected
wirelessly. 5g, the next generation of mobile technology, is de-
signed to support 1m connections per square kilometre, meaning
that in Manhattan alone there could be 60m connections. Ericsson
estimates that mobile networks will carry 160 exabytes of data
globally each month by 2025, four times the current amount.
The destination of your average bit is changing, too. Historical-
ly, most digital information stayed home, on the device where it
was created. Now, more and more data flow into the big computing
factories operated by aws, but also its main competitors, Microsoft
Azure, Alibaba Cloud and Google Cloud. These are, in most cases,
the only places so far with enough computing power to train algo-
rithms that can, for instance, quickly detect credit-card fraud or
predict when a machine needs a check-up, says Bill Vass, who runs
aws’s storage business—the world’s biggest. He declines to say
how big, only that it is 14 times bigger than that of aws’s closest
competitor, which would be Azure (see chart on previous page).
What Mr Vass also prefers not to say, is that awsand other big
cloud-computing providers are striving mightily to deepen this
centralisation. awsprovides customers with free or cheap soft-
ware that makes it easy to connect and manage iotdevices. It of-
fers no fewer than 14 ways to get data into its cloud, including sev-
eral services to do this via the internet, but also offline methods,
such as lorries packed with digital storage
which can hold up to 100 petabytes to ferry
around data (one of which Mr Jassy wel-
comed on stage during his keynote speech
in 2016).
The reason for this approach is no se-
cret. Data attract more data, because differ-
ent sets are most profitably mined togeth-
er—a phenomenon known as “data
gravity”. And once a firm’s important data
are in the cloud, it will move more of its
business applications to the computing
skies, generating ever more revenue for
cloud-computing providers. Cloud provid-
ers also offer an increasingly rich palette of
services which allow customers to mine
their data for insights.
Yet such centralisation comes with
costs. One is the steep fees firms have to pay
when they want to move data to other
clouds. More important, concentrating
data in big centres could also become more
costly for the environment. Sending data to
a central location consumes energy. And
once there, the temptation is great to keep
crunching them. According to Openai, a
startup-cum-think-tank, the computing
power used in cutting-edge ai projects
started to explode in 2012. Before that it
closely tracked Moore’s law, which holds
that the processing power of chips doubles
roughly every two years; since then, de-
mand has doubled every 3.4 months.
Happily, a counter-movement has al-
ready started—toward the computing
“edge”, where data are generated. It is not
just servers in big data centres that are get-
ting more powerful, but also smaller local
centres and connected devices themselves,
thus allowing data to be analysed closer to
the source. What is more, software now ex-
ists to move computing power around to
where it works best, which can be on or near iotdevices.
Applications such as self-driving cars need very fast-reacting
connections and cannot afford the risk of being disconnected, so
computing needs to happen in nearby data centres or even in the
car itself. And in some cases the data flows are simply too large to
be sent to the cloud, as with the traffic lights in Las Vegas, which to-
gether generate 60 terabytes a day (a tenth of the amount Facebook
collects in a day).
How far will the pendulum swing back? The answer depends on
whom you ask. The edge is important, concedes Matt Wood, who is
in charge of aiat aws, but “at some point you need to aggregate
your data together so that you can train your models”. Sam George,
who leads Azure’s iotbusiness, expects computing to be equally
spread between the cloud and its edge. And Simon Crosby, the
chief technologist at swim.ai, while admitting that his firm’s ap-
proach “does not apply everywhere”, argues that too much data are
generated at the edge to send to the cloud, and there will never be
enough data scientists to help train all the models centrally.
Even so, this counter-movement may not go far enough. Given
the incentives, big cloud providers will still be tempted to collect
too much data and crunch them. One day soon, debates may rage
over whether data generation should be taxed, if the world does
not want to drown in the digital sea. 7One day soon,
debates may rage
over whether
data generation
should be taxed