Meet…Jayaram Iyer

Eagle’s newest data management engineering leader brings over two decades of experience from Amazon, Microsoft, and other leading technology companies. Jay Iyer shares what drew him to Eagle, as well as his views on opportunities for financial services companies to best leverage the cloud and the vast amounts of data available at their fingertips.

You’re joining Eagle after having worked at some of the most well known technology companies in the world. Reflecting on your background, could you discuss how your background will influence your work here at Eagle?

Most recently, I served as the Engineering Leader of Amazon’s Alexa Communications platform. I also spent time as Head of Engineering for AWS DynamoDB Storage services, and before that, led Amazon’s Listings and Catalog team. I also spent a considerable part of my career at Microsoft and served as Director of the company’s Caradigm healthcare-informatics joint venture. All of this, in different ways, will influence my work at Eagle.

My work at AWS DynamoDB, for instance, entailed helping stand up and manage a petabyte-scale, low latency, multi-tenant no-sql database in the cloud. It can handle literally millions of transactions per second, so when we talk about scalability, this is what we have in mind. But this experience with distributed systems and high-volume data processing is critical to what we’re doing at Eagle.

I think my experience around data management and data lakes will also prove valuable. At Microsoft, to name another example, we developed and brought to market the Caradigm Data Lake and Analytics platform. This is a highly scalable, fault-tolerant, big data aggregation and analytics engine. It’s integrated with the Hadoop open-source ecosystem; incorporates advanced security features such as encryption; and leverages a full-stack, read-write, apps-development platform with open REST-based APIs. So this experience is very relevant to what I’ll be working on at Eagle.And finally, the Machine Learning experience from my work on the Alexa Comms platform, most recently, and on Amazon’s Marketplace Listings Platform before that, will be critical.

That’s a good segue to discuss what drew you to the financial services industry, and Eagle, in particular.

There were several different factors. On the technology side, though, what really appealed to me was the tremendous opportunity to continue my work around data and analytics. In my previous roles at Amazon and Microsoft, we were building data lakes before the term had even been coined; and we were working on real-time analytics and dashboards ten to fifteen years ago. Breaking into new frontiers is incredibly exciting—while most financial services companies are still navigating how to leverage these applications, Eagle is already well positioned to drive adoption and help define the future of the industry.

Having worked at two of the largest cloud companies in the world, it’s probably a bit shocking when you hear that some organizations in financial services still hold reservations about transitioning to the cloud. What do you think is the catalyst that will finally spur further cloud adoption among asset managers and financial institutions?

It’s interesting, because I was on the other side of the equation when I was at AWS. In building out the cloud infrastructure, we were constantly talking with clients about their specific needs, their concerns, and how they used or planned to use the technology. Through this dialogue, going back to the early days, we gained significant insight into how various industries utilize the cloud. But in many ways, the financial services industry is the hardest one to crack.

It’s a very private industry, for one. Firms are generally protective of any possible edge they can gain, be it through technology or any other means. The demands on data are also quite intense. This is driven by the exhaustive regulation, client reporting, and front-office demands, so you have a number of different constituencies who all use data very differently. And then there are all of the security aspects. Just as these institutions have a fiduciary duty in managing capital, they also have to protect client data. So these factors, together, help explain why the financial services sector has been more hesitant to embrace the cloud.

But there has been a major shift that has taken place over the past two years. It’s no longer a question about whether or not financial services companies will shift to the cloud; it’s now about the pace at which they can make the transition, and how they intend to do so, whether it’s through the public cloud, a private cloud, or a hybrid strategy.

These transitions are never easy. But having been on the other side, I’m familiar with the process and the key issues to keep in mind, and can recognize that they’re almost always worth it. I’m a bit biased, but the flexibility, resiliency, and agility of the cloud can really provide a big advantage—especially today. Eagle has always held a leadership position when it comes to the cloud, so I’m looking forward to collaborating with the team and working to advance Eagle’s capabilities in this area.

You discussed some of the challenges that are specific to finance, but what are some of the more universal challenges when it comes to data and the cloud?

Every industry is going through the challenge of increasing data volumes and varieties of data and, then, finding ways to secure and manage this data, while putting it to use in powerful and innovative ways.

I think data integration is one of the biggest challenges. In some industries, the prevailing platforms have to be compatible with potentially hundreds of different systems. It creates considerable challenges to manage and utilize this kind of disparate data.

So what are the solutions? It seems like there have been a number of developments in recent years that may apply.

Absolutely. Data lakes, for instance, provide more of a “free form” way to delve into this information than the traditional “data warehouse.” You can ask one question that then leads to five more questions, and you can begin to pull new insights in real time. Data warehouses, alternatively, are designed for users to ask a very specific question, and you can’t really explore the data because you’re very limited in terms of the questions you can ask..

One of the enabling factors has been the cloud and the fact that you can now store petabytes of data at a very low cost, allowing organizations to maintain massive amounts of data without necessarily knowing how it will be used. I think the scalability of these solutions is a true game changer, particularly since there are no constraints in terms of the types of data, the volumes or, now, even the costs.

Another development is that the Machine Learning, or ML, landscape has changed significantly over the past four or five years. It used to be that you required a PhD in data science to leverage these capabilities. We’re now in a world in which we have a whole array of tools and technology available so engineers can very quickly apply a machine learning algorithm to a data set and start driving results.

We’re seeing this evolve to the point that self-service capabilities will soon be available to those who don’t even have an IT background. Again, five years ago, you’d have business users provide the IT team with their desired analytics and specified requirements and it would take six months to build the capability and generate insights. Pretty soon, CEOs, COOs, CIOs and other business users will have these tools at their disposal without the need for IT intervention.

Specific to financial services, how else do you see the landscape evolving in the coming years?

I think automation will continue to grow and allow investment firms, in particular, to focus on their core competencies. Look at the data management function. Across the “data journey” – from aggregating the data to building and refining the models on top of it – there are a lot of efficiencies that can be introduced through automation.

I think we’re also seeing a paradigm shift as it relates to real-time streaming data. Traditionally, in asset management, portfolio managers or executives are basing their decisions on data that, at best, was compiled at the end of the previous business day. When you think about how quickly the markets are moving, this isn’t good enough. In fact, day-old data isn’t really useful when you have to make decisions in the moment. You need to know how much cash is available at a specific moment in time; you need to know where currency exchange rates stand, again in real time. So we’re pushing the envelope on this front now, because it’s so critically important to the business.

You mentioned that there were several factors that appealed to you in joining Eagle. Would you care to discuss any other draws?

On a personal level, the fact that I’ll be leading efforts in India was a huge draw for me. I’m initially starting at Eagle’s headquarters in Wellesley, Massachusetts, but I’ll be relocating to the Chennai office in the spring. This will be a bit of a homecoming for me, as I’ve been away for about 20 years, so it’ll be nice to be back home. I’m definitely looking forward to being closer to my family.

Another factor, was just the commitment Eagle is making in building a world-class engineering and innovation center in India. It’s really humbling to lead that effort. One distinction that I would highlight, too, is that Eagle already has a meaningful presence, both in Chennai and across India. This is a very strategic decision. It’s based the company’s experience in India, and premised on attracting true world-class talent. So the opportunity to spearhead this effort and recruit and work alongside the best engineers in the world was something I couldn’t pass up.

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