If You Get Your Security Right, Big Data Can Work For You

Large organisations across the globe are working to develop and deploy Big Data analytical services, alongside their established business intelligence infrastructure; and the momentum is growing, with recognition of the competitive advantage for companies who successfully harness it versus those who delay.

Very large volumes of data – think terabytes and petabytes of information – are not a new occurrence, but the rise of e-commerce and social media, the global distribution of machine intelligence business networks and personal electronic devices, and the exponential growth of commercial and scientific sensor networks, are making them commonplace. There are now many organisations with volumes of data that exceed the ability of conventional methods to organise, search and analyse in meaningful time intervals.

One reason these data sets are so large is their unprecedented growth rate. In a recent Harvard Business Review article1, Andrew McAfee and Erik Brynjolfsson report that:
· As of 2012, approximately 2.5 exabytes of data are created every day, a number that is expected to double roughly every 40 months.
· More data now crosses the internet each second than was stored in the entire internet just 20 years ago.
· It is estimated that Wal-Mart collects 2.5 petabytes of customer transaction data every hour.

Big Data includes a wide and growing range of data types, many of them new: text messages, social media posts, e-commerce click streams, GPS location traces, machine logs and sensor measurements. Structured, unstructured or semi-structured, much of this data is incompatible with the relational database repositories at the heart of most business intelligence facilities.

Big Data promises major benefits to organisations, but with these benefits comes huge potential risk. Managing massive amounts of sensitive customer, employee, and corporate data increases the chances, and scale, of potential data breaches. Sensitive data can be exposed as well as violate compliance and data security regulations, and moving data across borders can break data residency laws. So, finding a solution to secure sensitive data, yet enable analytics for meaningful insights, is necessary for any Big Data initiative.

Storing massive amounts of data within Big Data warehouses, such as Hadoop, increases the risk of an internal or external data breach. As companies can cost effectively save every type of data analysis, these multiple data sources can expose private, as well as sensitive, corporate information – and must be protected.

Without protection solutions that eliminate security risks, and support compliance with international regulations, organisations will inevitably face highly damaging breaches.

To be responsive, companies need comprehensive data protection to allow them the secure use and analysis of sensitive information. Without protection applied at the data level, organisations can’t safely move forward with Big Data initiatives – and that can damage their ability to compete.
Because this phenomenon is still relatively young, few standards exist to ensure that these new systems and analytical activities they support are successfully integrated into the existing policy and frameworks. One of those critical policy domains – data security – has the potential to arrest many of these developments, and block the realisation of their business benefits, if not adequately addressed. So, finding a solution to secure sensitive data, yet enable secure analytics for meaningful insights, is necessary for any Big Data initiative.

In order to get the most out Big Data, organisations need data protection solutions that are high strength and low impact. What’s needed to ensure the viability of Big Data is a data-centric solution that:
· Protects sensitive data wherever it is stored, moved or used, with no exposure between storage, transmission and processing.
· Enables compliance with global data security privacy and data residency mandates.
· Integrates quickly and affordably with existing infrastructure and adapts flexibly to new analytical applications and data sources.
· Allows quick policy based retrieval of original data values by properly authorised and authenticated users and applications.
· Impose no significant overhead on analytical performance.
· Preserves the formats and referential integrity of protected data, so that existing analytics and ad hoc queries don’t need to change.

The unique values and capabilities of a data-centric solution mean that data protection can be rapidly implemented to reduce security and compliance risks.

Solution Benefits
· Proactively protect any data source at the point of creation, before it even enters into a Big Data warehouse.
· Scalable and seamless data security across the entire enterprise.
· Provide secure access for data analytics internally, externally, and in the cloud.
· Ensure the ability to analyse protected data, while keeping data secure against attack!
· Securely analyse global data while complying with international regulations.
· Rapidly integrate high-performance data security into existing IT environments.
· Dramatically reduce maintenance by eliminating the need to constantly back up encryption key stores.
· Grow Big Data security with true horizontal scaling – responsive to business demand growth.
· Flexibly adapt data security to the fast-growing ecosystem of the newest tools and technologies.
· Integrate with tools that run across the enterprise, in z/OS mainframes, Linux, UNIX, and Win.

When harnessed correctly, Big Data can offer numerous opportunities to drive business innovation and, by arming yourself with the right security, you can fully benefit from its potential.
 

1 Big Data: The Management Revolution, by Andrew McAfee and Erik Brynjolfsson, Harvard Business Review, October 2012