Why wealth and asset managers are transforming their data approaches

By implementing strategies that harness the power of data, wealth and asset managers can improve performance and efficiency.

In brief

• Firms are transforming their use of data, focusing on the provision of trusted data to improve performance and efficiency.
  
• Key strategies include implementing new technologies and operating models and using pilot projects to pave the way for broader enhancements.
  
• Benefits can include a far stronger competitive position for the long term, as well as short-term gains.
  

The wealth and asset management sector is profitable and thriving but, as with most industries, it’s increasingly competitive and shaped by changes in technology. As a result, advantages of scale, winner-takes-all scenarios and pressures on costs can all pose challenges, as can the increasing emphasis on alternative assets, and areas such as regulatory requirements and environmental, social and governance (ESG) integration.

In such a fast-moving and complex environment, data plays a growing and critical role. This applies both to data generated within companies, and to that available from third-party providers, and in relation to numerous areas – from investment decision-making to marketing and compliance.

The good news is that the range of tools and services available has never been greater, and there are clear paths to using them effectively. EY teams have worked with clients to improve the quality of their data and trust in it, make better use of it at speed, and employ cloud-based data services to enhance performance and profitability.

Yet, while many management teams know that both their quality and use of data could be improved, they can also find that inertia, perceptions of complexity and other barriers impede progress. For example, when we surveyed a wide range of data-related senior executives, including Chief Data Officers, Chief Technology Officers, IT Directors and Chief Operating Officers across wealth and asset management firms, 74% said their data was improving, but 79% still felt that poor data quality was a pain point, while 45% cited an immature data culture as a pain point.

Determining the right strategy and implementing it effectively, with the right support and engagement, is therefore vital. It can bring not only short-term wins, but also long-term benefits, positioning a company for success as its industry and technology continue to evolve at speed.

Laying the foundations for success

In our experience, a key driver of success in firms’ data transformation efforts is the right tone and sponsorship from the top, and the resulting support from across the organization. Indeed, we often warn of the opposite: a “them-and-us” approach, where data is cast as only the responsibility of IT or data teams.

In light of this, a shared understanding of the advantages of a transformed approach to data, right up to and including the C-suite, is vital. As well as ensuring that resources are available, this can help ensure the right teamwork and culture are in place. Indeed, when we conducted research in collaboration with University of Oxford’s Saïd Business School, we found that transformation programs were 2.6 times as likely to succeed when the human aspects were right. The full research can be found here.

In particular, leadership teams must have a clear vision of the strategic benefits they’re pursuing. In the wealth and asset management sector, we see six areas that are common priorities for transformation, with data playing a crucial role in each:

• Client-centric operating models
• Digitized distribution
• A reimagined investment proposition
• A focus on growth areas
• Transformation of business models
• Leveraging of inorganic growth opportunities 

But while companies need speed and agility from their data in these areas, and a strong sense of confidence in it, these elements are too often missing or inadequate. In our survey of the wealth and asset management sector, 45% of respondents felt they lacked a trusted data layer in their company. When asked “How happy are business users with the state of data in your firm?” the average score was 4.8 out of 10, while for “How difficult is it to respond to new data requests from users?” the average score was 5.1.

Viable options for progress

Thankfully, there are proven options for progress that can deliver tangible benefits and cultural change. Naturally, firms need tailored approaches reflecting their specific circumstances and ambitions, but it is worth outlining three standout strategies that we frequently recommend:

1. Modernizing data operating models using managed data service providers and potentially a data mesh.
   
   
2. Implementing innovations, such as live data shares and self-service data product marketplaces.
   
   
3. Creating a pilot project to unlock paths to broader transformation.
   
   

Modernizing data operating models using managed data service providers and creating a firm-wide data mesh

Data transformation operating models can bring big benefits to businesses in terms of performance and preparedness for the future, while also reducing costs substantially. Two key strategies are particularly notable in this field, and typically help facilitate each other: using managed data services and creating a firm-wide data mesh.

Managed data services can help firms transform their sourcing and management of particular data domains, such as instrument or benchmark data. Indeed, in our survey of executives, data management was the most widely cited area for potential outsourcing among asset managers, with 48% considering it.

There are a range of benefits. Most obviously, outsourcing unburdens the internal data function. However, managed data services can also provide trusted data at a lower cost of ownership.

Furthermore, and at a broader level, they can be invaluable in creating a wider transformation to a firm-wide data mesh. This approach decentralizes data management, allowing organizations to share, access and manage data at scale, while being agnostic to specific technologies. Benefits include more agility, better data for decision-making, improved data quality, and more cross-functional collaboration. It has four guiding principles:

1. Domain ownership: Operational data ownership should be moved to domain teams, away from the central data team.
   
   
2. Data as a product: Data should be provided to, and meet the needs of, users beyond the domain team.
   
   
3. Self-service data platform: A dedicated data team provides a domain-agnostic data platform, enabling domain teams to seamlessly use data from other teams.
   
   
4. Federated governance: The organizational rules and practices to facilitate an effective data ecosystem and ensure adherence to industry regulations.

Perhaps unsurprisingly, data mesh requires organizational, cultural, architectural, technical, operational, and infrastructural changes.