Big data promises speed, breadth, and access to a wide
variety of internal and external data sources, criteria that are often anathema
to information security policies and practices. Furthermore, with big data
analysis still in its infancy, many companies entrust their data stores to a
variety of third parties, from technical specialists to data scientists. Here
are a few suggestions for keeping your big data initiatives secure.

Data equals dollars

As you write large checks for the various software,
hardware, and services associated with big data initiatives, it’s easy to think
of the data itself as one of the least valuable aspects of the process. After
all, you already “own” it. Data are merely bits and bytes that are
easily transferred versus fancy database applications running on expensive
hardware used by data scientists commanding six-figure salaries. However, data
are usually the most valuable aspects of a big data project. These data may
contain proprietary insights into your customer base or an intimate look at the
financial health of your company.

You probably wouldn’t leave that expensive new disk
enclosure powering your analytics server sitting unattended outside your
building, so make sure you’re not doing the same with your data, by crafting
policies and procedures that protect and safeguard this valuable asset.

Vet your vendors

With even the best data protections in place, if your
vendors haphazardly exchange sensitive data with other third parties, or
mismanage this expensive asset, your protections are for naught. Rather than
trusting verbal assurances and handshakes, insist that vendors agree to
standards through associated penalties for violating those standards. Ask to
see things like training manuals and data security compliance statistics before
handing over sensitive data. If you’re in a rush or unable to vet a vendor
appropriately, create sample data that contain the same fields as the “real”
dataset, and a variety of data that are realistic but do not contain
actual data or identifiers. These can be used to create and vet the analytical
model, which can then be run against the true data in more controlled
circumstances.


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Outcomes are just as important

Just as important as the raw data that feed your analytics
engine are the outcomes. At its best, big data provides actionable decisions or
information that no other company has. You might identify a market or
environmental factor that can give you an edge on your competition or catch a
glimmer of future market conditions. Ensure that the outcomes of your big data
initiative are protected and tracked just as carefully as the raw data that
created them.

Speed kills

With the power of big data becoming evident, the processes
and tools surrounding big data have been built for speed rather than security.
For a fledgling big data initiative, it might be tempting to fire up the latest
open source analytic engines, load up a few tables of financial or customer
data, and see what comes out before worrying about trivialities like data
security, or even proper security and basic access management.

Put some basic policies and procedures in place around these
new tools, and think through on who will have access to which data and what the
financial impact will be if those data are broadly disclosed.

Bottom line

With big data security, there’s no need to spread fear and
panic, but presenting sound security as basic stewardship of high-value company
assets is a reasoned approach that everyone can get behind, even among cries
that big data should have been deployed yesterday.

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