Much of the data classified as big data will originate from machines and will come into data analysis engines through Internet of Things (IoT) sensors, according to a 2010
McKinsey & Company article. In addition, software automation will increase;
robots in warehouses will run themselves; containers on docks will self-police their
contents; and trams and automobiles will operate without drivers. The potential
for converged automation and intelligence is enormous, and the market opportunity
is daunting.

It’s no surprise that major corporations like General Electric
are building up capacity and capability for an “Industrial Internet”
of sensors. GE invested one billion dollars into big data software and expertise to position itself
for the deluge of digital data that will emanate from sensors and other digital
devices embedded in machines such as GE’s jet engines, turbines, trains, and
hospital MRI equipment. In 2011, this was a $94 billion market for GE.

In 2013, Google launched its driverless cars, which offer many potential time-saving
and convenience benefits, including the ability for senior citizens to use
their cars longer because they won’t have to operate them.

Companies with supply chains are using containers with sensors
to monitor container temperatures for goods in shipment and to monitor container
break-ins for potential terrorist or highjacking attempts. The state of these
containers, no matter which dock around the world they sit in, is always visible
on monitors at the corporation’s HQ.

Logistics companies can monitor their in-field execution even
better by placing sensors in trucks. The sensors track locations and report maintenance
issues (e.g., the braking system needs repair soon) and driving habits (e.g.,
optimal fuel conservation speeds are not being maintained on the highway). These
applications translate into more effective ways of doing business, and they ultimately
deliver value to consumers.

But as this new machine-driven intelligence comes online,
new risks are created that challenge the traditional thinking of IT risk management
plans. Three major questions emerge:

  • What happens if the mechanized automation
    or sensors fail?
  • Is there a potential for greater
    security threats now that mechanized field operations flow over the Internet?
  • Are there new information privacy
    risks?

Answers to these questions are in the formative stages, but
that doesn’t mean IT shouldn’t be thinking about them. Here are three key steps
that your organization can start taking now.

1:
Revisit your disaster recovery plan

Because disasters seldom happen, organizations don’t generally
have their disaster recovery plans
as ever-present, frontline tasks. However, if you’re charged with bringing on
big data that includes automation and industrial sensors, tuning up your disaster
recovery plan should be part of the
process. If a sensor or the automation fails, do you have manual and/or other
system processes that can stand in? If not, what is the extent of your legal
liability?

2:
Check your edge security

How secure is your network at the edge, where the outside
sensors operate and control? Do you have edge management policies and technologies
in place to produce the level of security you need? If there is a security
breach, what are your risk mitigation strategies?

3:
Review your data privacy responsibilities

Industrial-level sensors can provide other people with additional
entry points into homes and businesses, where a vast amount of information can
be collected. Your organization may issue annual privacy statements to
consumers on the safeguards and information sharing practices that are applied
to their data, but consider whether it needs to do more for information that
can be accessed via in-home sensors.

Conclusion

IT can choose to avoid these steps for now, or it can assume
a proactive role in policy formation and in engaging others in the organization
who have a stake in the process (e.g., business leaders, regulators, corporate
counsel).

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