When I first heard the term "Big Data" a few years ago, I immediately thought it was some industry "jargon" and didn't pay much attention to it. In fact, the more I heard the subject of Big Data being raised, I would equate it to a comedic sketch conjured up on a Seinfeld episode – imagining it as some fictitious product fabricated by George Costanza, sold by Vandelay Industries. Nevertheless, though I didn't understand all the noise surrounding the Big Data topic, I eventually became intrigued, and just like the big project assigned by Mr. Wilhelm to George, I was aimed at figuring out the meaning of Big Data – even if I had to go all the way downtown!
It's Not a Show About Nothing!
Big data is a buzzword, or catch-phrase, used to describe a massive volume of both structured and unstructured data that is so large that it's difficult to process using traditional database and software techniques.Over the past several months, I have actually started to pay closer attention to companies in the Big Data space and have quickly come to realize the potential impact they can have within Financial Services, Healthcare and other verticals. In fact, a few of these companies are positioning themselves extremely well to help Compliance organizations optimize their current Compliance Monitoring, Surveillance and Reporting tools, increasing the overall effectiveness and efficiency of the various "scenarios" executed by these environments.
Given the massive amounts of data that needs to be accessed, managed and leveraged within organizations, Compliance Departments are seeking broader "what-if" capabilities to augment and enhance their current production and sandbox environments. Analysts desire an environment where they can quickly and easily incorporate additional data sources and attributes to find new patterns and practices of behaviors within their existing scenarios.
The current processes utilized within standard monitoring scenarios rely on structured data models that require months to modify and extend to support unproven data requirements. The high cost to onboard new data limits the analyst's ability to test new, unproven hypotheses.
• Scalable Environments
A new "what-if" environment should enable analysts to efficiently and effectively test new hypotheses and find hidden patterns. This is where the Big Data companies are seeking to help organizations. Using a scalable "graph analytics" approach, analysts should be able to identify and onboard new data sources in a straightforward and rapid fashion, enabling real-time, interactive analysis. As part of my Big Data knowledge quest, I learned that graphs are gaining a foothold in the Internet world, given that their data is full of relationships and connections. However, enterprise risk functions are not truly leveraging the power of graphs just yet. Think about the power behind this technology; if organizations were to leverage graphs and look at data, relationships and connections this way, this could impact the manner in which we detect fraud, money laundering, front-running, trading on material non-public information, etc. The possibilities are truly endless!
That risk management stuff you wrote for me is killer… It's gold, Jerry, gold.
• Graph Analytics at Work – Finding Needles in a Haystack
Many Big Data problems are about searching for things you know you want to find. It's challenging because the volumes of data make it like searching for a needle in a haystack. However, a needle and a piece of hay, though similar, do not look exactly alike…
Discovery problems are about finding what you don't know. Imagine trying to find a needle in a stack of needles-that's even harder. How can you find the right needle if you don't know what it looks like? How can you discover something new if you don't know what you're looking for? In order to find the unknown, you often have to know the right question to ask. It takes time and effort to ask every question and you keep learning as you continue to ask questions.
At the end of the day, this is an essential component to an organization's overall risk management strategy. Our ability to challenge our scenarios and learn to separate good behaviors from bad behaviors will ultimately impact our ability to pinpoint, measure and effectively mitigate our risk.
And You Want To Be My Latex Salesman
As referenced in my previous article – GRC - "Governance Risk and Chaos?," it's critical that organizations understand the Big Data vendor landscape and have the ability to assess the most viable players in this space. With several Big Data companies emerging, how does one go about choosing the right support partner?
• How Can Compliance Risk Concepts Help?
Helping organizations build a business case to support a Big Data implementation strategy is a new and critical component to the CRC support model. We believe the use of graph analytics will ultimately help organizations turn "noise" into meaningful and impactful information, enabling a robust and dynamic Compliance Risk Management process.
As of part of our growth strategy, CRC recently partnered with YarcData. The YarcData team has years of experience in data management and a reputation for hardware performance and reliability that stretches back decades. YarcData provides the highest performance processing capabilities and visionary data management resources. Together with the talented team at YarcData, we believe that we can offer organizations compelling argument that supports the build-out of these capabilities to bring greater efficiency, clarity and understanding of enterprise regulatory and compliance related risks.
We are very excited about our partnership with YarcData and the value proposition that both organizations bring to our customers and prospects!