Big Data Analytics by Ohlhorst Frank

Author:Ohlhorst, Frank [Ohlhorst, Frank]
Language: eng
Format: epub
Published: 2012-10-26T04:48:50+00:00

S E C U R I T Y , C O M P L I A N C E , A U D I T I N G , A N D P R O T E C T I O N ¥

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There is no easy answer to that dilemma, and it becomes a case of

choosing the lesser of two evils. If the data have intrinsic value for

analytics, they must be kept, but that does not mean they need to be

kept on a system that is connected to the Internet or other systems.

The data can be archived, retrieved for processing, and then returned

to the archive.

CLASSIFYING DATA

Protecting data becomes much easier if the data are classified—that is,

the data should be divided into appropriate groupings for management

purposes. A classification system does not have to be very sophisticated

or complicated to enable the security process, and it can be limited to

a few different groups or categories to keep things simple for proces-

sing and monitoring.

With data classification in mind, it is essential to realize that all data are not created equal. For example, Internal e-mails between two

colleagues should not be secured or treated the same way as financial

reports, human resources (HR)information, or customer data.

Understanding the classifications and the value of the data sets is

not a one-task job; the life-cycle management of data may need to be

shared by several departments or teams in an enterprise. For example,

you may want to divide the responsibilities among technical, security,

and business organizations. Although it may sound complex, it really

isn’t all that hard to educate the various corporate shareholders to

understand the value of data and where their responsibilities lie.

Classification can become a powerful tool for determining the sen-

sitivity of data. A simple approach may just include classifications such as financial, HR, sales, inventory, and communications, each of which is

self-explanatory and offers insight into the sensitivity of the data.

Once organizations better understand their data, they can take

important steps to segregate the information, which will make the

deployment of security measures like encryption and monitoring more

manageable. The more data are placed into silos at higher levels, the easier it becomes to protect and control them. Smaller sample sizes are easier to protect and can be monitored separately for specific necessary controls.

c07

22 October 2012; 17:58:55

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