Big Data Analytics in Financial Services Organizations: A Q&A with John Guevara of Actian

Originally published February 3, 2015

This BeyeNETWORK article features Ron Powell’s interview with John Guevara, vice president for financial services at Actian. They discuss big data analytic strategies within financial services organizations.
What are the forces acting on financial services and the challenges these organizations are facing today?

John Guevara: Fundamentally, financial services is going through a significant transformation. It’s a fully fragmented industry. There are markets throughout the world with varying currencies and regions. It’s highly distributed, highly digital and highly regulated. There is a new need for transparency. These forces are effectively driving financial services to transform themselves in order to adapt.

It’s really all about change today in financial services. Do you think the financial services industry has different data and analytic needs than other industries? What is unique? What is the same?

John Guevara: In some ways, they’re unique and in some ways they’re not. Looking at consumer banking, consumer cards and consumer lending, they’re dealing with similar data types as a retail company or any other B2C type of organization where they want to understand and know the consumer – a full 360-degree view of that particular individual so that they can properly engage that individual, make good recommendations and offers, align products, and understand and effectively manage that relationship through its life cycle with a focus on retaining that customer. In some ways, the data that they need is the data that tells them the transactions they have with these customers, the data that tells them more about the consumer – similar data that any other B2C type organization would want.

I think where some of the uniqueness comes in with financial services is that they also are the keepers of very sensitive data – an individual’s private, personal financial information. So while they deal with similar data, while they try to analyze that data and engage with consumers in a similar fashion as a retailer, they also have to be very, very mindful of the privacy and security of the data they operate on. And they have to strike a delicate balance between really engaging personally with that consumer and potentially making a consumer feel uncomfortable. So that’s an interesting challenge that financial services has to face that’s somewhat unique to them.

Privacy is always a big issue with how much data you really have on a customer. Could you give us some big data analytics examples of the ways the financial services industry has been impacted like in banking, equity markets, and insurance?

John Guevara: The truth is that big data in and of itself in financial services is really now starting to enter phase two. What we’ve seen in phase one in the last four to five years was a lot of research, and then that research and that testing evolved into some interesting and specific use cases. So we’ve seen the application of big data analytics for fraud detection, now being able to incorporate new data sets into viewing interactions in order to identify cases of fraud, fraud patterns, and potential fraud rings that exist out there that might not otherwise have been visible. We’ve seen it applied for things like visualizing the full trade life cycle – how a trade makes its way through the markets. We’ve even seen it used for archive.
So across the board we’ve seen some unique initial use cases, but we’re really starting to enter into the second phase of big data analytics where it’s now being applied closer to the more operational uses – the true mission-critical cases – or at least there’s the desire to do so.

What are your thoughts on next-generation technologies like Hadoop in the financial services space? How successful are these organizations at utilizing Hadoop?

John Guevara: I think that we have seen phase one of the adoption of big data. There's an interesting research paper done by an independent authority featuring a study on 16 big data projects across ten major banks where some of the use cases I named previously were identified. What was interesting is that Hadoop was central to every single one of those projects. I think, in terms of big data technologies, Hadoop is coming. It’s inevitable. There’s a gravitational pull to Hadoop. Hadoop is almost becoming somewhat like an operating system where applications and use cases are being built on top of it. So it’s becoming a foundational technology around which a full ecosystem will form.

Well, John, that mirrors our experience as well. We’re seeing that over 90% of our audience is using Hadoop in some capacity right now to gain an analytics advantage. You know financial services companies are often known as the trailblazers in technology. What is the next wave of use cases where big data technology will be applied in the industry?

John Guevara: The next wave, the way that I see it, I believe that big data will become more and more woven into the operation – the day-to-day core functions – of these financial institutions. So kind of progressing from these somewhat pocket-focused fixed use cases where it was effectively trialed and used for a very finite use case. We will see Hadoop becoming more of a player, enabling portfolio managers to make better recommendations based on a wider data set. I believe that the next wave is really making it more critical – more core – to the functions.

What’s also interesting and may have been overlooked is that we’re seeing that the end users want access to the data. We almost see this phase two starting off with how to get users access to the data that has now been moved over to Hadoop. So that’s part of what I see as the next wave – getting all the business users access to the data that now sits within big data technology and how can those technologies be applied into more core functions.

Well, that makes sense that an end user would want to access that data with the tools they already have as well as gaining additional tools, but if they could use it just as another data source, that would make it far easier for them to access it and consume it.

John, what would you say to an organization that’s sitting back right now that really is not embracing big data analytics, staying with their old legacy methods. What do you believe the cost is to them for doing nothing, but just staying with what they have?

John Guevara: Well, I believe the cost to that organization is potentially becoming irrelevant. You have to do something. It’s a movement. It’s happening. You can’t stop this train. There has to be a process of evaluating and incorporating next-generation technology to continue to compete and succeed. So I’d say it’s absolutely necessary that organizations evaluate, incorporate and weave next-generation technology into their architectures. The cost of doing nothing is potentially catastrophic.

That makes a lot of sense, especially if all of your competition is doing it. From a solutions perspective, could you talk about some Actian’s offerings for the financial services industry?

John Guevara: At our core, Actian sells a platform that can help companies connect to any data, anywhere – whether it’s internal or external – and then effectively merge, enrich and prepare that data. Within the same platform, we offer these organizations the ability to derive insights from it. Folks now just want, at the very least, to be able to analyze all of this data. So our platform can begin by offering just SQL access to any of this data, and then build up into more sophisticated techniques like machine-learning or predictive analysis. And we do this at any scale and at tremendous performance in order to basically shorten the cycle times to insight from capturing data to deriving value from that data.

And then from a platform perspective, we can do this in or out of Hadoop. Hadoop gives us a landscape where there are immense amounts of data. And if I apply this to financial services, that changes the game in how to conduct fraud analysis, how do you consider all of the data and all of the different touch points that could potentially point you to a potential fraud incident. The same applies to surveillance with cyber threats and attacks, which are some of the hottest topics within financial services. What’s really becoming more obvious is that there’s a good chance that these banks, these institutions, these insurance companies have already been impacted by a cyber attack. How long will it take them to understand that they’ve already had a breach in security, and how can they prevent it from happening in the future.

There are a number of ways to apply the platform into different use cases within financial services. However, fundamentally it’s always about getting access to the data, analyzing the data and deriving an insight from that information as fast as possible to affect the business.

John, typically how quickly would a financial institution be able to come up to speed and utilize the platform? What’s the implementation cycle for getting started?

John Guevara: It’s actually pretty easy to get started. One of the fundamental tools we use is SQL. It’s one of the capabilities in our platform. That’s a skill set that is incredibly abundant in the industry and has been used for decades. So we start by offering what they’re already used to and then we effectively now apply it into these big data environments that were otherwise difficult to get started with. So get the data into the environment, apply the Actian platform, and then you’re already starting to do what you know how to do but now on a much richer data set. The time to get going with our platform and derive value is rather quick – much quicker than going with the big data technologies alone.

How would you suggest that someone begin?

John Guevara: I would suggest that they get started by contacting Actian. We can get started by establishing an environment with Hadoop, applying the Actian platform, start loading up some data and deriving business-changing insights.

Thank you, John, for discussing with us how Actian is addressing the big data analytics needs in financial services organizations.

  • Ron PowellRon Powell
    Ron, an independent analyst and consultant, has an extensive technology background in business intelligence, analytics and data warehousing. In 2005, Ron founded the BeyeNETWORK, which was acquired by Tech Target in 2010.  Prior to the founding of the BeyeNETWORK, Ron was cofounder, publisher and editorial director of DM Review (now Information Management). Ron also has a wealth of consulting expertise in business intelligence, business management and marketing. He may be contacted by email at

    More articles and Ron's blog can be found in his BeyeNETWORK expert channel. Be sure to visit today!

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