CTO Straight Talk - Issue 1 - 14

Separating the Signal from the Noise
These principles apply not only to consumers,
but also to enterprises. The Internet of Things
promises to improve the internal operations
of enterprises everywhere, cutting waste
and improving productivity in numerous
activities, from inventory management to
supply chain logistics to customer relations.
Just as with consumers, however, enterprises
will not benefit from the true potential of
increased connectivity if their workers are
overwhelmed by data. If the data collected
and transmitted by the Internet of Things is
not relevant, if it is not provided in a timely
fashion, if the analysis does not suggest ways
to improve a work activity or process, then
enterprises will not benefit from the Internet
of Experiences. If employees, managers,
and senior executives in enterprises big and
small, private and public, don't have positive
experiences with this abundance of data, they
won't take advantage of it.
Mike Cavaretta, the Technical Leader for
Predictive Analytics and Data Mining at Ford
Research and Innovation Center, highlights
this importance of separating the signal from
the noise: "I don't think the problem is the
storage of the data. It's ingesting the data, the
analysis of the data, and the understanding
of the data. Machines don't drown in data;
people drown in data."
Cavaretta is in a unique position to assess
the impact of the current data deluge, and of
the coming data tsunami unleashed by the
Internet of Things, on both consumers and
enterprises. Ford's Fusion Energi car model
already generates about 25 gigabytes of data
every hour, which is used to improve fuel
efficiency and reduce emissions. But Ford's
research labs are experimenting with vehicles
that produce 250 gigabytes of data per hour.
And the number of cars connected to the

Internet worldwide will grow to 152 million in
2020, up from 23 million today, according to
IHS Automotive. That will be a small slice of
the embedded systems market that IDC says
will generate 4.4 trillion gigabytes of data
worldwide in 2020.
Staying afloat in all this data becomes
even more difficult if you attempt to increase
relevance and value by integrating data from
different sources-a task that's particularly
challenging for businesses. Says Cavaretta:
"As you take more and more data sets and
mash them together, you find that the value
of the data can go up quite significantly. But
it's a huge perennial challenge to look across
different and disparate data sets. Big data
technologies such as Hadoop, however, allow
you to put everything in one large repository."
And there's value to be found in data from
outside the enterprise and its ecosystem:
"One data source that is really important is
government, and more generally, the open
data movement. There's a lot of value here,"
says Cavaretta.
Data merging and mashing, while increasing
the value of the analysis, raise another issue:
My data does not always talk to your data. Yes,
they both consist of ones and zeros, but they
are formatted differently and may be using
different codes, names, and labels to describe
the same thing. Semantics in general and the
Semantic Web-a framework of common data
formats that allows data to be shared and
reused across Web pages and applications - in
particular promise to overcome this difficulty.
"I would like to see these technologies take
off," says Cavaretta. "Google Trends is a great
example of how semantic technology can
rationalize things across different domains."
Steven Gustafson, the Manager of the
Knowledge Discovery Lab at GE Global
Research, established his lab several years
ago when he realized what semantics can
continued on page 17...

CTO Straight Talk | 14
CTO Straight Talk - Issue 1

Table of Contents for the Digital Edition of CTO Straight Talk - Issue 1
