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How to future-proof your data management strategy

3 questions for an expert

Brent Knapp, Data Management Specialist at SimCorp, explores why many forward-looking firms are rethinking their data management strategies and embracing more holistic approaches as part of a wider transformation journey.

Brent Knapp

Data Management Specialist at SimCorp

Q1: What do you see as the key challenges in your clients’ data management setup today?

There are several significant challenges, particularly if you look at legacy enterprise data management systems. Primarily, clients want to improve their ability to scale quickly. They want to add new instrument types, vendor feeds, data domains and to find people who know how to manage those new domains.

A good example of this is the rapid rise of ESG investments. Firms know they must act quickly to develop compelling ESG products and services, but the capacity of investment managers to process multiple, non-standardised data sets and make them coherent is quickly becoming unsustainable.

The landscape is evolving quickly, and organizations are constantly trying to keep up by adding people or adding hardware. In many cases, it's about better processes and more effective technology, which is why I think we see so many people starting to adopt Data as a Service.

Q2: Why should firms look at more holistic approaches to manage market and reference data?

The easiest answer is that it gives them the ability to focus on results and be much more proactive with their data.

Our clients are thinking more about who's consuming their data and why. They’re exploring whether there are better sources to use, rather than having people combing through data looking for errors, omissions, and exceptions. Having a complete view of their data management and operations allows the teams to add more value to the organization.

Holistic approaches, such as managed services, remove a lot of the noise for those asset managers by taking on the process and the technology. At the same time, fund operators get a scalable model that covers all their needs across data advisory, change management and business operations. Investment managers can then get valuable insights on how that data is being used and consumed.

Q3: What advice would you give to organizations thinking about adopting Data as a Service (DaaS) for market and reference data management?

The first thing is to think about where you might be in two to three years versus where you were two to three years ago.

These data domains are changing rapidly, and end-clients are demanding more transparency and more information. Think about the effort that it takes for you to onboard new sources or bring in the people that have the subject matter expertise to add value around new data sets.

When you start to explore this, you will quickly realize that there's a lot of value to a fully managed service. That’s because you can remove some of the processes and data admin that don't necessarily add tremendous value to your bottom line - but are necessary.

For that reason, employing someone who can do this at scale is becoming more and more popular. It's more of a configuration than a customization these days, which drastically differs from what historical platforms and technology allowed us to do.

You still get data that is fit for purpose and ready to use, but at the same time, you don’t have to worry about the organizational responsibilities tied to hardware, software, upgrades and continuity. Keeping all of these parts in sync and updated to stay on top of the demands of your clients is invaluable.

Remember the pace of change is only accelerating and, without the right foundation, firms’ wider digital agenda is likely to get stuck. Today’s data management capabilities may be advanced but deploying them and managing those capabilities is very difficult. Services are evolving rapidly and have allowed firms to become much more outcome oriented.

Change is often difficult to navigate and can be daunting, but the cost of doing nothing and maintaining the status quo is far high. An outdated data management model that does not support your long-term growth ambitions, could be catastrophic for the success of the business. That is why embracing innovation must be a top priority for asset managers that want to stay relevant.

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