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    How Data Optimization Simplifies Workflow for Hedge Funds

    Hedge fund managers now have access to more sophisticated investment data analysis tools, allowing them to move beyond a simplistic, static view of their portfolio performance.

    1. What does data optimization mean for a hedge fund manager?

    Robert Ismailov: During the past 20 years, the amount of data and its complexity have skyrocketed. Today, hedge fund managers receive data from various sources and in multiple formats. They need to be able to efficiently collect and interpret their information to gain insights that can enhance decisions. For them, data optimization means automating their processes and removing manual inputs to be more productive, make faster decisions, reduce the chance of errors and reduce operational and execution risk. By optimizing their data, they can also leverage analytics to improve processes and make better business decisions.

    2. Why is it important? What are the opportunity costs to not effectively managing data?

    Ismailov: Managers use their data in many ways. It could be used for trading or risk management, but it could also be used for regulatory or investor reporting. If they don’t have timely and accurate data there can be multiple opportunity costs, including the inability to spot shifts in investment trends and loss of competitive edge that can impact alpha generation and ultimately lead to business and reputational risk. Productivity is also a key consideration. If people are spending time stitching data together, manipulating and organizing it, data operations become a productivity drag, often for the highest value drivers of an organization, which can be a big challenge.

    3. Historically, how have hedge funds managed their data? Why is that no longer effective today?

    Ismailov: As mentioned, hedge fund managers collect and aggregate data from many different sources. While many of them have sophisticated data management capabilities, a surprising number are still relying on manual processes and tools to manage their data. These managers, for example, may accept multiple files via raw data extracts which they then must transform to their specifications and upload to their systems. In some cases, managers are still using spreadsheets to organize their information. And they do this across numerous vendors and relationships. It is a very manual and time intensive process.

    Michael Siswanto: That’s really changing today. Our clients want their data on a more frequent basis, in near real-time – the days of sending a file once a day are long gone. Not only do they want it more frequently, they also want it to be accurate, so our focus has been on how we can leverage better tools and better ways of sharing information with clients, and work with them to streamline data delivery in a way that most benefits them.

    4. What is Northern Trust’s approach to helping their clients manage data? What specific tools are available?

    Siswanto: As I said, we’re looking at better tools and ways to help our clients manage their data. For example, we use a cloud-based platform, Snowflake, to enable data sharing in a much more efficient manner. Snowflake, as the common platform between our systems and our clients’ systems, allows clients to layer their own data into the cloud seamlessly. Whether their data is hosted in Azure, AWS or Google Cloud, we're able to meet them where they are via data sharing directly into their Snowflake account. The cloud has been a powerful tool for streamlining and optimizing data delivery workflow.

    Ismailov: Beyond data sharing, we’ve also been developing tools and interfaces to help our clients manage their day-to-day processes. One of those is our reconciliation dashboard. We have many clients that manage their reconciliation process by taking our raw data and manually doing their own reconciliations using spreadsheets. While leveraging Excel may be an option for a smaller firm with less data, this approach is less feasible for larger organizations with much higher volumes. For reconciliations that we perform on behalf of our clients, the client experience is critical. As a result, we leverage a robust reconciliation engine and provide clients with access via an interface that they can leverage throughout the day.

    Siswanto: We designed our reconciliation platform to be a single pane of glass for clients to view their data. So, whether it's coming in from SWIFT, their brokers or their counterparties, we can reconcile with Northern Trust’s investment book of record (IBOR) data. With our Omnium Reconciliation platform, clients have full transparency at their fingertips for reconciling their data.

    5. What capabilities are on the forefront for tackling data challenges?

    Ismailov: Data analysis is a key challenge for clients, so we have been leveraging business intelligence (BI) tools, such as Microsoft PowerBI, that can bring more sophistication to analytics. We also have a path forward with the cloud which not only assists with making data extraction efficient, but also provides managers the ability to tell a story using their data. Having a data management strategy isn’t enough, you need to stay one step ahead, especially with the growth of artificial intelligence and machine learning.

    Siswanto: Today, data is gold, and being able to unlock that data is key. What you couldn't do before in a traditional data center, you can do now with the cloud. As a result, we've been investing heavily in our software and our capabilities and expanding our technology. Moving to the cloud allows a lot of self-service as well, both for our internal teams and our clients. The insights within Snowflake itself will allow managers to review their data in a more efficient manner.

    6. What will the “Future State” of data optimization look like for asset managers, and how can they prepare for a shifting landscape?

    Siswanto: One aspect of the future state is cloud adoption, removing the extra complexity of data exchange and review. Machine learning will also figure in more heavily as algorithms get better. As we refine the rules and identify patterns, we’re able to continuously improve the machine learning model. Other more advanced tooling may include natural language querying, where a manager can ask a question such as “What are my top five exposures to China?” and know immediately what positions they hold. It ultimately comes down to looking at ways to optimize the data so that asset managers can be more efficient.

    Ismailov: The technology landscape and tools that the industry will be using are going to change very quickly over the next few years. Hedge fund managers will need to prepare for the shifting landscape by ensuring their data management systems are optimized for full productivity. In addition, they’ll need to learn new tools as well as think about new systems and have better data proficiency. They should carefully choose the partners they work with, because they don’t want a provider who will sell them software and not support their ongoing needs. At Northern Trust Hedge Fund Services, our focus is on helping our clients be in the best position for the constantly evolving data environment.

    Michael Siswanto

    Head of Asset Servicing Technology, Omnium Platform

    Robert Ismailov

    Head of Information Delivery, Hedge Fund Services

     


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