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After years of growth driven by market performance, the wealth and asset management (WAM) industry appears to be facing significant headwinds. Higher interest rates, declining assets under management, increased competition and shifts in customer demand mean that revenues and margins are under increasing pressure. However, amid the gloom, there is a silver lining. Through artificial intelligence/machine learning (AI and ML) and cloud-enabled ecosystems, managers are finding ways to cut operating costs, supercharge the customer experience and add value to the bottom line.
Given significant innovation in the data and analytics industry, some wealth and asset managers see an opportunity to scale product development, boost efficiency, increase revenues and better serve customers. However, in many cases, there has been a significant gap between ambition and reality. While many firms have taken steps forward, only a few have leveraged more from data through cloud-based technology. Indeed, 75% of respondents to a recent EY survey cited data as a barrier to innovation, amid siloed assets and legacy systems.
The challenges of past approaches
The challenges wealth managers face in acquiring, managing and applying their data resources are grounded both in past approaches (characterised by data silos, slow time to market, high costs, challenges in accessing data and unfederated governance) and the complexity in transitioning to the operating models of the future. Take, for example, the many ways organisations are exploring how gen AI can support new back-office processes in document summarisation and analysis with incredible ease and efficiency.
“Many financial services organisations have invested in cutting-edge data platform technologies but are only using them as traditional data warehouses, leaving huge scope to drive more value from their investments,” said Matthew Goldsmith, Data and Analytics Leader at EY.
To empower the next generation of AI and ML and invest in data capabilities that drive advantage, firms need a more fundamental transformation. This would mean moving from basic data use cases to advanced functionalities such as live data sharing, which alone has the potential to cut outsourcing costs by as much as 60%. To achieve this aim, migration to cloud-enabled data platforms should be a priority. These will enable innovations such as data marketplaces and native apps, which can catalyse revenue uplifts of as much as 50%, accelerate time to market, and reduce operating costs by as much as 75%—as well as massively reduce risks such as data leakage.
Unlocking benefits
In almost all cases, the key to unlocking benefits in cloud data transformation is effective management and interrogation of internal and external data. This requires a single integrated view of the data landscape, for example, across investment data, client data and finance data. But while most firms are targeting a more holistic approach to data, many feel they are lagging. In EY’s survey, not a single participant was positive about all aspects of their data management, with 80% percent citing poor quality data as a specific pain point.
“We aren’t a million miles away from the quality of data we are after,” said the chief data officer at a global asset manager. “But there remain so many points of failure that you wouldn’t bet your life on it.” Rating their “data happiness” across a range of parameters, no asset manager scored their experience higher than seven on a scale of 1 to 10.
In the face of these challenges, some chief data officers are convinced that a more decentralised approach and cutting-edge data platform would be vital to achieving value at scale. Through proprietary technology such as the Snowflake Financial Services Data Cloud, users can simplify data management processes in an integrated environment that enables seamless data sharing without replication and support cutting-edge AI and ML applications and use cases.
Differentiation through advanced data and analytics embracing AI and ML
Wealth and asset managers are increasingly convinced that data solutions offering AI and ML functionality could be a significant driver of performance, and there is growing demand to find solutions that can be rolled out at scale. This means optimising data usage across numerous tasks and processes and scaling AI and ML initiatives—from investment decision-making and modelling to execution, risk management and customer services.
Firms making the most of their data resources have often predicated their transitions on two enabling levers: a data mesh framework—enabling decentralised data ownership—and migration of data assets to the cloud. This facilitates the processing of heavier workloads, innovations in data science, access to ecosystems, and advanced client-facing services. Snowflake provides a fully managed cloud-native platform service that can support data management, back-office operations, data engineering, trade processing and a range of activities through the trade lifecycle. This kind of transition is already catching on: 37% of asset managers expect to outsource in the next two years, according to EY’s survey.
One pervasive challenge across the industry is slow progress in benefiting from cloud-based platform services. In that context, the next stage for many will be to scale and deepen their commitments, opening the door to live data sharing for better data collaboration, analytics-based application development, and ultra-streamlined partner relationships.
Data science and collaboration
To learn how financial services industry leaders are leveraging the cloud, Snowflake surveyed 300 C-suite leaders and managers to gauge attitudes about cloud data transitions. The results of the survey revealed that the two features driving the most interest are the application of data science workloads and how users can collaborate on a single data platform. Platforms tailored to integrate and support the applications that data scientists use regularly enable users to get the most from data exploration, model development and model distribution. Data scientists can engage in multi-language code development for ML workflows with access to open source libraries, tools and frameworks. These create a powerful boost to decision-making, whether to rapidly investigate a new sector or company, analyse consumer dynamics or better understand client behaviours.
Meanwhile, managed service platforms can help data science users collaborate by providing a centralised platform to share live, ready-to-query data across clouds and regions in a governed and secure manner within the parameters of the firm’s systems and across its networks. “Since it’s virtually impossible for any single organisation to produce all the data needed to uncover global, market, competitive, consumer and societal trends, organisations are embracing data collaboration,” said Rinesh Patel, Global Head of Financial Services at Snowflake. “The opportunity to securely share and access governed data, tools, applications, other technologies and data services—while preserving privacy—creates a near-endless combination of strategies and solutions to advance any organisation’s business.”
Snowflake’s platform enables dynamic data sharing without copying or moving data. The technology is a significant departure from established approaches, in which data is often shared through simple copying of files in FTP/cloud buckets or through APIs, ELT pipelines or data marts. These methods can be effective but can also lead to gaps and delays in copying and moving data, as well as being costly to maintain and potentially insecure. For that reason, many leading wealth and asset managers are moving to data sharing in a single location, and in the process reducing costs and creating secure, flexible ecosystems across data languages.
“We were early cloud adopters. By transferring our on-premises servers to the cloud, we replicated a lot of our old problems without improving them,” said Barney Eccleson, Head of Data Engineering at St. James’s Place Wealth Management. “We adopted Snowflake’s Financial Services Data Cloud because it solved our three major problems around cost, scale and the ability to get our data to the people who needed it.”
Snowflake partners with industry-leading solution providers
Snowflake benefits from partnerships with leading WAM platforms. The combination creates a nexus effect through which managers can oversee almost all data needs in one place. These WAM platforms leverage Snowflake’s solutions to allow companies to combine their user’s data and third-party data to quickly create custom applications and dashboards. Equally, because many wealth managers work with more than one cloud provider (often AWS and Azure), Snowflake’s platform enables data integration across all those relationships.
Another Snowflake feature attracting attention is Snowflake Marketplace, a type of app store designed to make the discovery and sharing of data easier. Data marketplaces are evolving as strong value-adds in the cloud environment due to their ability to provide users with query-ready data access. A key benefit is enhancing internal data with external data, enabling wealth managers to run deeper analytics and create richer insights. Moreover, Snowflake Marketplace allows consumers to simplify their data experiences, reducing time to implementation with seamless access to data sets in a single location. Data providers such as S&P and FactSet are working with Snowflake to enable wealth managers to easily access, analyse and apply the data solutions they need.
One feature pioneered by Snowflake is an extension of the marketplace concept to the Snowflake Native App Framework. With development and testing environments built in, this helps users create data applications while sharing data and business logic with other Snowflake accounts. The apps can then be distributed and monetised through Snowflake Marketplace. In one example, self-service data visualisations from the Alpha Data Platform, powered by Snowflake, surface new insights to help investment professionals trade smarter and respond faster to investors, regulators and internal stakeholders. Delivering a single, current source of verified data promotes collaboration between risk, portfolio, compliance and trading teams. Near real-time access to large volumes of financial data accelerates time to insight at a lower cost.
The use case challenge
Given the ability of platform solutions to acquire, manage, and analyse massive data sets from across the wealth management ecosystem, firms have all the tools they need to create more efficient operating models. By sharing data in near real time, they can build customer-centric solutions that highlight their market reach, insight and expertise. However, to get there, a priority should be to identify use cases that will create impact and encourage buy-in across the organisation.
“History tells us that building data platforms with long lead times to business value are doomed to failure—the critical ‘get right’ when prioritising use cases is identifying which deliver early and incremental value,” said EY’s Goldsmith. “This builds business confidence and lays the foundation on which all other use cases can be delivered.”
Use cases where the Snowflake platform can unlock value include scenarios in which significant volumes of data flow between organisations; for example, in outsourced services. Another fast-expanding business line that presents a data challenge is ESG, where wealth managers need to integrate and manage numerous data formats and sources. Snowflake is working with EY to help managers strategise how to build optimised ESG platforms, supporting activities from product development to reporting.
In parallel, EY has invested in building out a range of Snowflake Accelerators, helping wealth managers make the best possible use of the platform in activities such as data ingestion, data migration, and the ability to build a common ESG data model aligned with mandatory ESG reporting and disclosure frameworks, including TCFD (Task Force on Climate-related Financial Disclosures) and the EU’s SFDR (Sustainable Finance Disclosures Regulation).
Looking ahead: Data-driven organisations will win
Snowflake and EY’s experience working with wealth managers to transform their data capabilities reveals benefits that go beyond more effective operations. Organisations that have successfully implemented data-driven business models find that they are more likely to win market share and deliver products and services faster. These organizations, are able to offer a stronger customer experience, incur lower client management costs, and remove legacy barriers to client servicing.
More than six in ten respondents to Snowflake’s survey see the endpoint of their investments as the creation of a fully managed cloud data platform solution to support their business initiatives. However, to get there, careful planning is required. Indeed, before starting the transition, businesses need a strategic lens on both the processes and enablers that will support their ambitions.
For many, this strategic approach will be accompanied by selective onboarding of data models in specific business areas. EY, for example, has created an ESG common data model that is fully integrated into Snowflake assets, alongside a data ingestion framework and regulatory compliance workflow. The result is an optimal tool to help organisations transform their data into a competitive advantage to achieve their goals.
This publication contains information in summary form and is therefore intended for general guidance only. It is not intended to be a substitute for detailed research or the exercise of professional judgment. Member firms of the global EY organization cannot accept responsibility for loss to any person relying on this article.