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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.



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