A Data Strategy to Future-Proof Investment Operations

This article first featured in Markets Media.

How are firms changing their data management strategies to improve investment operations and what is the increasingly relevant role of Artificial Intelligence (AI)?

In a recent Markets Media webinar, Duncan Cooper, Chief Data Officer at Northern Trust Asset Servicing, Miguel Castaneda, Partner at private markets advisory firm Alpha Alternatives (formerly Lionpoint Group), and Tom McHugh, CEO and Co-founder of FINBOURNE Technology, shared their perspectives.

Over the last 10 years, new technologies have allowed firms to capture greater volumes of data with increasing granularity. The types of data firms are dealing with is also largely unstructured and from a wide variety of sources, yet it still requires the same level of governance and control.

What are the key drivers instigating improvements in investment operations and the implementation of data strategies? 

The amount of data has grown exponentially in all organizations and firms are now dealing with more diverse and more complex data. This is driving a change in the way firms think about and interact with data. Data silos have become more prominent and there is an ever-greater need for data across teams. Clients need a better way to communicate across functional silos to do their day-to-day work.

“One of the current challenges that we’ve seen is a lot of clients have legacy file exchange methods that are either SFDP or flat file uploads and they come in different formats and cadences, whether it be real time, daily, weekly, quarterly,” said Miguel Castaneda at Alpha Alternatives (formerly Lionpoint Group). “When an issue comes up there is a lot of emailing back and forth, resending and reloading. That causes delays across all the teams in the front middle and back office that need access to that data,” Castaneda said.

Where should a firm start with establishing a modern data strategy?

The first step  is to conduct a thorough audit of where they are now and where they would like to go so that they can be strategic about what their data infrastructure should look like.

Understanding how much data they are dealing with, where the data is going and the rate of decay or entropy of that data is a really good place to start.

Duncan Cooper, Northern Trust

According to Tom McHugh at FINBOURNE, firms often lack a clear understanding of the data terminology. Firms should first get a handle on what terms such as data lakes and lake houses really mean, and then look at their processing on top of it.

It boils down to three things: what are you going to use it for, who cares about it, and who is responsible? Who really owns it and who cares if it is not right? If they can solve that little panacea, everything else tends to be just technology that works around it.

Tom McHugh, FINBOURNE

The panelists were clear that firms should focus first on the process and the business need rather than just looking at the shiniest new tools. “What money will they save, what potential revenue could they open up, what risks could they mitigate? How will they get a better understanding of their data within the organization to be able to understand where potential risks may be or how they can optimize the business?” Cooper said.

It is incumbent on good technology vendors to not necessarily sell what they have, but actually look at what solutions people need. That is easier said than done, but it breeds better outcomes.

Tom McHugh, FINBOURNE

What is the view on realistic uptake and the application of AI in data management within investment operations today?

Artificial Intelligence is seeing a lot of hype for its ability to process data at scale, but it is not yet widely used in financial services. “People want AI in their technologies, but do not always understand the costs involved,” said Castaneda. It is not just the AI models and compute power, but all the upfront energy that goes into creating a data strategy or a data environment to enable AI.

Firms should approach AI incrementally, and not just in terms of technology, but also in terms of people, process and data.

Miguel Castaneda, Alpha Alternatives (formerly Lionpoint Group)

Firms have been very cautious with using AI, according to McHugh. People are using AI to provide meeting summaries and as an email assistant. But in the rest of financial services, people are wondering if they have the right to train the model on data they are looking at. It is unclear who owns the product of that. There is no real standard on digital rights for market data, for reference data and for the customer’s data. And firms have not yet put in the necessary safety rails for people to be comfortable using AI, McHugh said.

If AI can help in the workflow and give a helpful suggestion or automate a task in an overall workflow, where a human is still in the loop, that is a good use case for now, said McHugh. The technology will evolve and become a more expected feature, but firms need to make sure they have the right governance and security in place to know what happens once they enable AI.

Discover more by watching the full recording of our recent webinar with Markets Media here.