Security Products
Showing results for 
Search instead for 
Do you mean 

Big Data Security Analytics Part 6: 3 Keys to Success

Kerry_Matre ‎05-29-2014 09:30 AM - edited ‎06-09-2015 11:27 AM

The first step in successfully solving the big data security problem is to approach the challenge with your eyes open and a realistic set of expectations. You cannot load all your data into a pot, have it boil for a while and expect it will report back with Edward Snowden in handcuffs. Work to solve all the tactical challenges along the way and you will find the strategic value. 

Master the basics. Deploying a security analytics solution is not the first step in an effective security program. The first step is asset management. An organization must know what they have and assess the risk associated with each asset in order to prioritize investments and protections appropriately. Organizations must have a well architected perimeter and network defense system to protect these assets as well as a SIEM to correlate the events and provide them to an analyst under one pane of glass. Analysts must be trained and the processes and procedures must be in place to handle the events that do arise. Most organizations have not achieved these basics and should not yet consider big data. When this security monitoring foundation is in place and effective, then an organization can begin integrating with other data analytics tools that can enhance security and reduce risks to an organization. 

Leverage tools though-out the organization. Data analytic tools are not specific to security and can be utilized by other pieces of a company. IT operations can use the solutions to identify automation opportunities. Marketing can use them to assess customer habits and target campaigns. Finance can use the tools to show historical trends and better predict future outcomes. By utilizing tools across an organization it will reduce costs to one business unit and will allow for potential cross-sharing of data from the segments of business and create a better picture of the company as a whole.

Continuously review and assess the system. Garbage in = garbage out. Stale or inaccurate data will negate any benefits of the solution and cause the project to fail. As new feeds are added assess the integrity of the source data. Set a schedule to regularly check existing data for staleness and utility and remove useless or incorrect data. Changes to data feeds (databases, etc.) should be reviewed and coordinated so that the changes do not impact the quality or flow of data to the data analysis systems. Having these review processes in place will reduce the likelihood of system downtime and dirty data.


Ultimately commit to analytics as a core competency across your entire business. Put in place analytical decision supports to demonstrate the value at all levels of your business. Success comes from a cultural commitment to a smarter analytics-enabled business.


Click here to learn more about HP HAVEn.


Thank you Chris Calvert for contributing this content.



About the Author


Leave a Comment

We encourage you to share your comments on this post. Comments are moderated and will be reviewed
and posted as promptly as possible during regular business hours

To ensure your comment is published, be sure to follow the Community Guidelines.

Be sure to enter a unique name. You can't reuse a name that's already in use.
Be sure to enter a unique email address. You can't reuse an email address that's already in use.
Type the characters you see in the picture above.Type the words you hear.
1-3 December 2015
Discover 2015 London
Discover 2015 in London, the ultimate showcase technology event for business and IT professionals to learn, connect, and grow.
Read more
November 2015
Software Online Expert Days
Join us online to talk directly with our Software experts.
Read more
View all