Big Data. Big Decisions
InformationWeek
Special Coverage Series


A Strategy to Protect Unstructured Data

You've got data everywhere. We've got a plan to help you find and control it.

InformationWeek Green - September 20, 2010 InformationWeek Green
Download the entire Sept. 20, 2010, issue of InformationWeek, distributed in an all-digital format as part of our Green Initiative
(Registration required.)
We will plant a tree
for each of the first 5,000 downloads.

Protect Unstructured Data

IT organizations are well aware that sensitive information resides in corporate databases, but unstructured data--e-mail, Office documents, and other content types--can be just as valuable and need protection. The challenge for IT is that unstructured data is growing at a breakneck pace--a compound annual growth rate of 61%, according to IDC, almost three times the growth rate of structured data. It's also scattered throughout the enterprise: in folders on file servers, on laptops, and tucked inside USB drives. You need a strategy for securing it.

Start by understanding the types of content in your company, and the value it has to the business. If your company handles credit cards, then you automatically think of PCI. Your nightmare is credit card numbers sitting on a file server for anyone to find. If you're in the medical field, HIPAA and patient records are a top concern. Other important data types are customer and employee personal information, intellectual property, and operational data.

These groupings are broad but give you enough to build on. The main idea is to understand the types of data and how you will respond once each type is discovered. Once you compile a basic list, work with representatives from IT, legal, compliance, HR, finance, and business development. They will identify data you've forgotten or didn't know about.

Next, map your data types to a classification and handling policy that outlines how groups of data should be managed. The most common mistake we see when IT groups write these policies is specifying exactly how data should be protected. That approach is inefficient and causes more work for you later. Instead, provide a range of acceptable measures rather than mandates. For example, if your company prefers that data in transit be encrypted using SSLv2, but it also will accept the use of TLS 2.0, put both options in your policy. This makes the policy much more flexible for those implementing the protection. That's critical, because if they can't work with you, they'll work around you.

One last note on data classification policies: They often fail because all documents are tagged as confidential, devaluing the policy. Your classification system should differentiate between valuable information that carries a high level of risk and other information that may be sensitive but carries less risk if exposed or lost.

Searching For Unstructured Data

The next step is finding the data. This can be tricky. You know where it should be stored, but because information is so portable, it has a habit of turning up in unexpected places.

To read the rest of the article,
Download the September 20, 2010 issue of InformationWeek


Protecting Unstructured Data

Become an InformationWeek Analytics subscriber: $99 per person per month, multiseat discounts available.

Subscribe and get our full on protecting unstructured data free for a limited time.This report includes 14 pages of action-orientated analysis, packed with 5 charts. What you'll find:
  • How to set up a data classification and handling policy
  • Tips on searching unstructured data sources

Get This And All Our Reports



Related Reading


More Insights




Currently we allow the following HTML tags in comments:

Single tags

These tags can be used alone and don't need an ending tag.

<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags

These require an ending tag - e.g. <i>italic text</i>

<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

BYTE encourages readers to engage in spirited, healthy debate, including taking us to task. However, BYTE moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing/SPAM. BYTE further reserves the right to disable the profile of any commenter participating in said activities.

Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.

Follow InformationWeek

By The Numbers

What Are Your Primary Concerns About Using Big Data Software?

Base: 417 respondents at organizations using or planning to deploy data analytics, BI or statistical analysis software
Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 541 business technology professionals, October 2012

What Do You Think?

What's your attitude about SQL analysis on top of Hadoop?
We want fast, standard SQL analysis capabilities on Hadoop ASAP
Hadoop is for unstructured data; SQL is for relational databases
We'll give SQL on Hadoop a try, but relational DBs will remain the mainstay
Given strong SQL support on Hadoop, we'd nix the data warehouse
We're not interested in Hadoop
No opinion



Related Content

From Our Sponsor

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Business leaders often need a visual snapshot of data to quickly grasp and use it. This paper identifies five challenges in presenting data and how visual analytics can resolve them. Solutions are suggested to overcome the challenges of: speed, data clarity, data quality, displaying meaningful results, and dealing with outliers.

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Today's competitive advantage requires a deeper understanding of your business, your market and your customers. As an IT executive, you can drive that knowledge transformation. In this white paper, learn how to make decisions as a strategic business leader and three steps to begin an analytics initiative within your enterprise.

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

High-performance data visualization turns sophisticated analyses into meaningful graphics, leading to faster and smarter decision making. In this white paper, learn how visual analytics can transform big data, with additional features such as real-time functionality, mobile compatibility, robust applications for technical groups and accessibility for nontechnical users.

Big Data: Lessons from the Leaders

Big Data: Lessons from the Leaders

Financial performance, competitive advantage, operational efficiency, strategic decision making - every business goal can extract value from big data, and the time for doubt or inaction has long passed. In this Economist Intelligence Unit report, in-depth interviews with data pioneers reveal the link between the effective use of big data and the bottom line among other results.

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Which came first, the data or the decision? This white paper makes the case for having a decision in mind, then tailoring big data's volume, variety and velocity to achieve business results such as overcoming customer dissatisfaction or creating well-informed strategies in real time.

Informationweek Reports

Research: The Big Data Management Challenge

Research: The Big Data Management Challenge

The challenge of big data is real, but most organizations don't differentiate 'big data' from traditional data, and nearly 90% of respondents to our survey use conventional databases as the primary means of handling data. We'll help you understand what constitutes big data (it's not just size) and the numerous management challenges it poses.