The smart thing to do: practical applications of monetizing big data in finance
Conversely, by graphing all the aspects of the datacenter down to individual switches, companies not only see how something’s affected by a change but also the best time to make it to minimize downtime. The same datacenter relationship graph can be deployed for triage analysis, which further demonstrates the compounding benefits of the approach. In the latter example, organizations can elucidate how to mitigate any issues involving the datacenter and the various objects contained in its graph. “If something breaks, you can now use the same graph to say this is the collateral damage and these are the people I need to get involved to get the business back up and running,” Reed says. “Then I can start doing my root cause analysis to prevent it from happening again. And by the way, I’m going to use the same graph to do that too.”
Market intelligence categories
According to Reed, business intelligence for finance involves four categories: client intelligence, market intelligence, operational intelligence, and risk and reputational intelligence. “They all share common things and common relationships between things; they just use those in a different way,” he says. The insider trading graph addresses risk and reputational intelligence and can be repurposed as an employee entity relationship graph to provide client intelligence. The datacenter graph delivers operational intelligence, while a similar graph yields understanding of the market forces influencing that vertical. The Global Information Classification Standard (GICS) is one of the most commonly used tools to analyze those forces. Reed explains that modeling its data on a semantic graph is influential for ascertaining “how you can traverse it. A lot of companies spend a lot of time trying to figure out, for example, how do I look at supply and demand to understand credit risk across GICS categories. Again, that’s all about relationships.” Once organizations have invested in semantic graph architecture, they can also facilitate cluster analysis to illustrate the density of relevant financial concepts. For instance, cluster analysis can depict collateral concentrations of specific customers or financial organizations according to factors such as entities and region.
Due to the numerous regulatory entities and punitive measures for non-compliance, financial companies all but require the uniform consistency of centralized data governance. By harmonizing their data (regardless of source or location) in an RDF graph, they can readily implement such governance with a top-down approach. Governance dictated by business unit, application or enterprise function is simply a way to increase the number of silo Hadoop implementations. When attempting to demonstrate provenance for multiple regulatory entities and purposes, local governance aggravates what’s already a difficult task. In contrast, the holistic governance of Reed’s method heightens regulatory compliance capabilities while demonstrating data lineage across the enterprise. Thus, organizations save money on potential non-compliance penalties and perform better with the plethora of use cases graphs facilitate. “The beauty of [centralized] governance in place is that you can start applying this new technology to any existing silo problems and improve it incrementally over time while you deal with these new data fronts with the right underlying architectural approach,” Reed says.
Data as an enterprise asset
Every organization looking to profit from data-driven practices must invest accordingly. The nature of that investment, however, can determine how successfully and efficiently a company monetizes its big data. In finance, organizations have a number of ways in which they can justify what is essentially the same investment for an interminable number of use cases across Reed’s four dimensions of BI. The use cases explained in this article are all variations of the enterprise knowledge graph concept, which is partly so named because of the infinite number of applications it supports. Still, the way to avoid non-compliance fines, inefficient operations and unjustifiable returns on data expenses is to spend wisely.
Reed says, “For me, there’s an intuitive sense once you get into the space where you start realizing data is an enterprise asset, a concrete asset. And, I think a lot of people are there. When you start thinking about assets, it’s far more intuitive to say why wouldn’t I invest in that asset once and leverage it as many times as I could, versus having my organization create it for themselves by process on demand. That makes no sense whatsoever.”
By modeling all the different functions, infrastructure, applications and departments involved in a datacenter in a relationship graph, organizations can understand the effect of each of those objects to streamline operations.