Is your asset management firm’s data management strategy on solid ground?

With efficient, effective, and, most importantly, digital-first data management processes, your firm can be more flexible, more responsive to change, and better-placed to seize new opportunities. 

Conversely, if your firm’s data is siloed and still largely reliant on time-consuming, mistake-prone, manual processes, you risk — as Accenture bluntly put it — setting your firm up for failure. 

So what does a robust, modern data management strategy look like? 

And how do you get started putting one in place?

 

Striving for growth in a difficult environment

Ross Tremblay, a senior manager in Accenture’s asset management practice, argues that the single biggest challenge asset managers face today is growth. And this is all down to rising costs, shrinking margins, and ever more complex regulation. 

On the revenue side,’ Tremblay explains, ‘I think we all know about expense ratio squeezes…

In 2019, asset management firms were already under intense financial pressure from passive funds that were outperforming them while charging lower fees. 

Then, the two big Cs that have defined the 2020s so far — the Covid-19 pandemic and the cost of living crisis — came along, driving up operational costs even further and reshaping investors’ attitudes to risk

At the same time, regulators are also becoming more demanding. 

New rules put in place to support sustainable investing and changes to PRIIPs key investor documents — to name two examples in a sea of many — are highly technical and require firms to get to grips with huge volumes of data.

This is before firms begin to factor in what’s in the regulatory pipeline, such as the ongoing Alternative Investment Fund Managers directive review, and inevitable divergence between the UK and EU. 

Unsurprisingly, in the first quarter of 2022, firms brought in around 66% less revenue than they did in the first quarter of 2021. And even big players like Blackrock and JP Morgan have been feeling the pinch.

 

Data as an asset

In an environment where expense ratios are shrinking for everyone, the obvious way forward is to look for opportunities to stand out from your competitors.  

According to a 2021 Accenture study, there are three main ways asset managers can do this: cutting costs, simplifying compliance, and creating new revenue streams. A robust data management strategy is key to identifying these opportunities and exploiting them successfully. 

By some estimates, inefficient data processes collectively cost asset managers in excess of $1 billion (around £830 million) a year

And that’s excluding the risk of regulatory fines due to reporting mistakes. In 2019, for instance, the Financial Conduct Authority fined a single firm £27.6 million for reporting failures. 

Using technology can significantly reduce regulatory risks. Fundipedia’s First Responder feature, for example, alerts predefined user/s when there are data discrepancies allowing them to be rectified before reaching investors. Similarly, our reporting templates update automatically whenever there are regulatory changes, so your reports are always accurate and compliant.

Better data management doesn’t just drastically lower costs and reduce the risk of expensive regulatory fines. It also boosts revenue. 

To begin with, data is a vital decision-making tool. And, as Tremblay observes, ‘there’s a high correlation between the quality of your data and the success of an initiative.

Secondly, as McKinsey note, data can strengthen client relationships and loyalty, whether it’s through personalised research or optimising a product, enabling it to better meet customer’s needs. 

Your data can also become a valuable product in its own right. 

‘...software,’ Tremblay explains, can be ‘...a key piece of [asset managers’] revenue model… whether that is … products that complement their investment management, or whether it’s exploiting the potential of… proprietary software… or going out and buying a software company.

Investment bank BNY Mellon, for instance, launched its own data and analytics service in 2020. 

 

How to build a future-proof data management strategy: our 3 top tips

Better data can drastically improve asset management firm’s decision-making, relationships with customers, compliance, and, ultimately, the bottom line. 

But how do you make sure your data is always complete and accurate in the first place, and that your firm can easily handle greater volumes of it as you grow?

Here are our three top tips for building a data management strategy that stands the test of time. 

1. Have clear goals

A common mistake when implementing a data strategy is trying to do too much too soon. ‘A lot of times,‘ says Tremblay, ‘large transformational programmes can start focusing too much on patching up different problems in different areas of the organisation.’

Modern enterprise software is highly customisable, and, as such, it is easy to get carried away. The result is an ever expanding list of requirements that risk derailing the project before it’s even started. For this reason, it’s critical to have a clear vision. 

This should be a mix of short-term goals — quick wins that build confidence, strengthen the business case, and increase the momentum behind the project — and longer-term objectives. 

Crucially, goals should be measurable and time-bound. This will enable the project team to define what success should look like and track progress. 

2. Choose your technology partners with care

This seems obvious, but 45% of asset managers don’t have a formal process in place to evaluate the business and operational potential of new technologies. As a result, they risk missing out on opportunities, and investing in technologies that might turn out to be a poor fit. 

Tremblay stresses that asset managers should look at technology with an open mind and consider all options when building out their model for what they’d like their ideal data scenario to be. 

To this, we’d add that, while they might look limited on paper, specialist vendors can be more effective than vendors with a broader offering. A vendor that does one thing and does it well can bring in-depth knowledge, expertise, and capabilities. Generalist software vendors won’t — and can’t — have in-house specialists that cover all bases. 

3. Build up a team of data scientists

As data continues to grow in importance, so will the need to have people on staff with the skills to work with that data. In five years’ time, data science is expected to be one of the most — if not the most — in-demand profession across industries. 

But you shouldn’t just be looking to hire a centralised data management team. You should have people who can interpret and use data across your entire organisation. 

Whether it’s transaction execution, collateral management, through fund accounting… expertise should be accessible,’ Tremblay says. Otherwise, you’re simply taking your silos digital. 

When key teams have their own data scientists, they can be proactive, instead of adding more and more data to a centralised queue that eventually turns into a bottleneck. 

 

Asset managers can no longer succeed unless they embrace data

By 2025, the world will have access to a whopping 180 zettabytes of data. To put that in perspective, if you were to store it on CDs, it would take you 1.8 billion years to download everything, and your stack would be long enough to circle the Earth 222 times. 

For asset managers, it’s going to get harder and harder to operate efficiently if this data isn’t utilised effectively. 

A robust data management strategy will enable asset managers to make better-informed decisions, helping firms to significantly cut administrative costs and stay compliant. Perhaps most importantly, a robust data management strategy holds the key to understanding what customers want and expect from asset managers now and in the future.

At Fundipedia, we’ve built a powerful, comprehensive, user-friendly platform that collects, verifies, and organises all your data in one place. 

Let’s show you how we can help you build an efficient, effective, and modern data management strategy.

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