Big data getting bigger: What the Internet of Things means for data
Big data getting bigger
What the Internet of Things means for data*
Written by the Economist Intelligence Unit
The mobile connection
he amount of information flowing across networks has mushroomed
in recent years, and its varieties multiplied, thanks to the growth of
social media, peer-to-peer websites, mobile Internet use and other
modes of digital communication. Data is now termed “big” not only due
to its enormous quantities and multiplicity of types (photos and video,
for example, in addition to conventional spreadsheet data). “Big” also
refers to the potential opportunities for organisations that can mine the
data mountains and extract the insights they contain.
The advent of the Internet of Things (IoT) means that big data is likely
to get a lot bigger. The IoT links wireless networks of tagged objects
as diverse as automotive components, clothing, appliances, medical
products and packaging. Data volumes handled by networks and servers
are certain to expand enormously as the IoT grows. The potential value
to public and commercial organisations able to analyse and act upon
the data is considerable. Unlocking such value, however, requires that a
range of technology and non-technology issues are addressed.
The Internet of Things is already with us due to the technologies
we now carry, maintains David Carrera, a professor at the
Barcelona Supercomputing Centre. “Every smartphone is a collection
of sensors, continually connected to the Internet, reporting interesting
information,” he notes. Such devices, connected to sensors in all manner
of objects, are creating a flood of data which begs to be interpreted.
Sensors and monitoring systems, to be sure, have existed for decades.
The difference now is that data can be transported from a broader
range of device types and locations than before, due to the increasing
prevalence of mobile and wireless technologies.
At the back-end, crunching and analysing the volumes of data generated
by such devices has only recently become possible—and affordable.
“Historically information has been discarded rather than analysed,
principally because there were not the tools available to analyse it in
a cost-effective manner,” says Philip Howard, a data expert at Bloor
Research, a UK-based analyst firm.
As the prices of data storage and transport, sensors, devices and
analytics technologies fall, the threshold for IoT adoption by
organisations is also falling, and the range of possible applications is
broadening. Not only can sensors be attached to increasing numbers of
physical objects, but historical data also can be analysed in new ways. In
Boston, for example, city authorities are using sensors, video cameras
and GPS (global positioning system) in taxis to report potholes in roads.
While innovative, such examples may quickly become old hat as both the
public and private sectors become smarter in how they interpret and
then use such information. The IoT, for example, underpins the capacity
of “smart grids” which distribute electricity according to demand, or
smartcard-controlled taps to manage water distribution in drought
areas. In a “smart” home, “learning” thermostats can upload data about
how specific rooms heat and cool; the results can then be fed into and
controlled by smartphone apps.
sp o ns o red b y :
*This and other articles about the challenges and
opportunities of mobility, sponsored by EE, can be found at
A question of reliability
For the IoT to deliver on its promise, all the links in the technology
chain need to deliver and collate data in a sufficiently timely manner,
to ensure sufficient data “latency”. This puts particular pressure on the
transmission network, which needs not only to ensure that sensor data
can get through, but also that a response can be delivered to be acted on
within the necessary time frame.
Meanwhile, analytics tools need must be able to “ingest” the data and
deliver actionable insights—the timeliness of which depends on the
storage, software and data architecture. Philip Howard explains: “If you
have to index the data as it is loaded, this will significantly slow down
the loading process and it will add to the size of the database, not to
mention adding to administrative costs.”
These factors require architectural decisions that take all elements of
the chain into account. Some data processing could take place on a
local device or server before key data elements are uploaded to a central
server. For example, a vehicle number plate recognition sensor could
process the number plate at the sensor. Such decisions require a tradeoff in terms of processing and power requirements at the device level,
within the network or during server processing and storage.
Understanding such trade-offs holds the key to linking big data with the
IoT to deliver maximum benefit, believes Niall Murphy, founder and CEO
of Internet of Things software company EVRYTHNG: “The challenge is to
develop the competencies and systems to use data in real time, to make
engagements and applications smarter.”
Questions of data ownership are also yet be ironed out. Who owns the
data generated by an electrical smart meter installed in a house, for
instance? Issues regarding the security, privacy and ownership of data
“are major challenges that need to be addressed before the IoT becomes
widely adopted,” believes Mr Carrera.
The success of the IoT is predicated on reducing the friction between the
data created and our ability to make sense of it, both in technological
and non-technological terms. Both the IoT and big data are likely to
be waypoints on a larger journey towards sensory, reactive, “smart”
environments. The journey will take time, and there will be numerous
stoppages along the way. But as the issues outlined in this article are
untangled, “big” may not be sufficient to describe the data-intensive
world that emerges.