VMblog recently connected with Scott Gnau, VP of Data Platforms at InterSystems where we learned more about InterSystems IRIS data platform, solving challenges related to data management, and tips for capital markets firms undergoing digital transformation.

VMblog:  What are the biggest challenges related to data management for capital markets firms and how does InterSystems IRIS address these challenges?

Scott Gnau:  Some of the largest data management challenges that capital markets organizations face are access to, and interoperability of accurate and timely data. Trading tools rely on massive amounts of data from millions of sources, varying in structure, to complete transactions successfully and to drive reporting, compliance and analytics applications. Increasingly large volumes of data are assembled, integrated, and processed in real time to not only drive the transactional business, but also to provide analysts, traders, and brokers with a full view of their operational landscape. Even the smallest delays in data latency, gaps in processing efficiency, inaccurate or incorrect information will skew results and cause incorrect outcomes and increased latency. We call this "unhealthy data."

By having access to a solution such as InterSystems IRIS® data platform that can clean and normalize data easily while eliminating silos, capital market organizations can power their applications with the latest insights quickly and accurately.

VMblog:  Why is a transactional and analytical database like InterSystems IRIS important for financial applications?

Gnau:  Today's finance industry is moving at an accelerated rate in which immense amounts of data are being created every day and accurate decisions must be made in a matter of micro-seconds. For mission-critical applications such as financial trading platforms, having a transactional and analytical database in place that supports agility, scalability, security, and speed are vital to deploying a high-performing application that can handle these vast amounts of data.

The notion of simplicity driving efficiency applies here. In many instances, InterSystems IRIS is able to manage applications inside a single data platform as opposed to legacy architectures that are deployed with multiple solution engines including persistent databases, document databases, in memory databases, cache distribution engines and streaming solutions. InterSystems IRIS, as a multi-model storage engine integrated with an enterprise cache and integration engine, can deliver the performance and scale to replace multiple solutions. This means overall data latency and solution supportability are improved out of the box.

With its proven speed, scalability, and reliability, InterSystems IRIS continues to power a wide range of mission-critical, high-throughput applications throughout the front, middle, and back-office for leading global banks. If organizations don't have the correct infrastructure in place, they put themselves at risk of mishandling customer demand. This can include anything from increased downtime during updates and maintenance or worse, an outage caused by unprecedented spikes in usage.

VMblog:  What tips do you have for capital markets firms undergoing digital transformation?  What capabilities should they prioritize in this process?

Gnau:  As the finance industry continues to digitally transform, developers must take the lead in driving innovation by making healthy data a priority, having the infrastructure in place to avoid costly outages, and implementing necessary internal workflows to efficiently build and deploy next generation applications.

DevOps will also play an increasingly large role. To successfully meet the real-time demands of the finance industry, development teams must adopt a DevOps mentality that enables them to be agile and flexible in their work, and to build, test, and deploy innovations quickly, with zero downtime.

VMblog:  How are current market volatility and remote work settings forcing new trends in data infrastructure management for capital markets firms?

Gnau:  As global markets face high levels of volatility and trading volumes due to COVID-19, financial services organizations have had to adjust quickly and digitally transform at record speeds. This, on top of an increasingly dispersed workforce, has made accurate, reliable, and real-time data sharing more important than ever before. For analysts, advisors, brokers, traders, and others in the industry, failing to accurately keep up with incoming data can lead to miscommunication, downtime, and significant financial loss. Organizations are finding that data platform infrastructure is critical as extreme volatility will stress complex solutions and bottlenecks can create outages at precisely the wrong time. They've also found this transition to be much easier if they were able to adopt and deploy new tools that enhanced collaboration and on-demand services during these turbulent times.

VMblog:  New applications help enterprises sustain and grow their businesses, but how can organizations ensure that they're fully leveraging all of this data being produced?

Gnau:  When it comes to a company's data, many organizations are only focused on the outcome and what they can glean immediately from their data. While the immediate output is important, it's also vital for businesses to understand that data and the ongoing enrichment of that data is also an asset and should be treated as such. So for companies who are deploying new applications, my recommendation would be to adopt the mindset that data is valuable in the long term as well, and take the appropriate steps to maintain and protect it.

VMblog:  With the amount of data available only set to increase, how do you think the data management space will evolve in the future?  What types of questions do businesses need to ask about their data strategy in order to optimize it going forward?

Gnau:  With the increasing amounts of diverse data streaming in from a myriad of sources, organizations will need to continue to think about recasting their data management toolset to address this growing complexity - it's no longer a "one size fits all" environment. Organizations should reevaluate their storage capabilities to make room for this surge in data, look into connectivity and traceability tools, and strategically think about the different kinds of solutions that need to come together to optimize the value of their incoming data.

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