Rod Ebrahimi has a great piece in Forbes identifying three big Silicon Valley trends that will reshape the Financial Services industry. And while I think he’s right that increased transparency, more automation, and improved access to capital are, indeed, changing the way the Financial Services world operates, I can’t help but think he left out the biggest trend in Financial Services:
Open-source innovation in Big Data.
In his defense, Ebrahimi was focused more on front-office innovations in retail banking, like how much information banks share with Main Street customers like you and me. And perhaps he would consider open source “old news” in Financial Services, anyway. After all, Financial Services was one of the first big industries to embrace Linux and other open-source technologies.
Much of that early adoption, however, came down to open source’s price tag. As Anthony Golia, executive director of enterprise computing at Morgan Stanley, has declared, “We use [Linux] because it performs well on inexpensive, commodity hardware. That continues to be true and that continues to be a reason we use it.”
That’s great, as lowering costs while maintaining or improving performance is critical to being able to offer end-customers better service at lower prices. But while open source may have started out as a cheap imitation of proprietary technology, it’s now leading the charge on innovation, fed not by gargantuan R&D budgets but instead by open collaboration around common code.
That’s how innovation happens in 2013.
This is particularly needed in Financial Services, which is seeking a reprieve from long years shackled to relational database technology. As my colleague and vice president of Sales, EMEA, Joe Morrissey puts it,
Traditionally, investment banks - as all other organisations large and small - have relied on relational databases, with their rigid and tabular structures to store data. However, it is now becoming clear that these relational data stores are finding it difficult to cope with the enormous increase in data volumes and throughput that are now commonplace. Scaling relational databases is often prohibitively expensive due to the nature of their design.
This has led to many financial services firms reconsidering their default position of a relational model for their database architecture. Instead they are seeking alternatives that not only provide the performance and scalability at lower cost, but also introduce other benefits such as flexibility and agility.
Yes, there’s an element of cost savings driving this. As he goes on to suggest, “NoSQL databases are designed from the outset to offer massive scale-out capability on commodity and virtualised platforms. This is distinct from how relational databases are usually scaled through the utilisation of increasingly large and expensive servers.” In the absence of capital expenditures for software and low-cost, commodity software, open source can dramatically lower the cost profile of operating a Financial Services company.
But it’s deeper than this. Financial Services firms are increasingly determined to put Big Data to work, which requires a healthy dose of open source, as virtually all of the most popular Big Data technology is open source. Maybe all of it.
Big Data is what enables those banks to heavily customize their offerings to fit individual needs of their customers, just as Sears has done in retail using Hadoop and NoSQL technologies. It’s also how Financial Services companies increasingly process high-volume data feeds to glean insights that give it competitive advantage in trading, not to mention storing huge quantities of data in open-source “data hubs”, which data can then be easily analyzed or repurposed for later use.
In sum, where Ebrahimi sees three big trends in Financial Services being driven by Silicon Valley, I see four, and that fourth one - open-source innovation in how data is stored and processed - is the biggest of all. Open source is what largely makes it possible for Financial Services firms to emulate Silicon Valley, which is built on open source itself.