In today's digital age, data has become the lifeblood of the securities services industry. Companies have tons and tons of data, but the challenge isn’t about data collection, it’s about data management and generating insights.
“In a world of data abundance, the ones with meaningful data will succeed as data becomes a value-adding asset,” says Madhukar Makhija, Global Head of Data, AI and Analytics, Securities Services, HSBC.
For decades, asset managers and institutional investors have been crunching numbers to gain insights into market trends and investment opportunities. But what's changing now is the sheer scale and complexity of the data available across the lifecycle of fund and investment management. Having disparate internal and third-party systems that produce data in varied formats and structures, has made in-house data management more and more challenging – and costly.
“To fully capitalize on potential value of data, the key lies in having high-quality standardized data within a scalable, modern cloud-native architecture that can integrate with transaction processing systems, decision support systems and artificial intelligence applications to make it actionable,” says Madhukar.
“This must be complemented with human oversight and governance, and is vital for driving business growth, managing risks and achieving operational efficiencies in securities services business,” Madhukar adds.
Challenges in data management
As the financial sector is experiencing an unprecedented data boom, the global datasphere is expected to grow at an even faster pace from 33 zettabytes in 2018 to 180 zettabytes by 2025, with financial services being one of the key contributors, according to a report by IDC.
Whether it is generating standard and regulatory reports, using robotic process automation (RPA) for capturing corporate actions, leveraging analytics for risk management, or using machine learning for predicting settlement failures, data is becoming crucial prerequisite.
Product reference data, FX rates, data on portfolio positions, benchmarks, transactions and ESG data come from multiple sources and need to go through a series of operationally labor-intensive activities such as cleansing, standardization, mapping, and transformation, before they can be fed into investment and portfolio management platforms, used for analysis and reporting, or further processing. The high cost of developing and maintaining comprehensive data management solutions with integrated governance controls, while retaining specialized talent, is making it challenging for asset owners and managers to build or scale in-house data management platforms.
As a result, the middle and back offices of institutional investors perform complex, data-intensive and time-consuming processes for many of their operational or analytical needs. HSBC generates over 3,000 manual reports daily for its fund admin and middle-office clients because most data management platforms used by clients lack vital capabilities for customization.
Across the investment industry, technology and financial services providers are collaborating to offer data management as a service, so clients can not only have access to standardized data offered by one service provider but also have the capability to manage third-party and their own data, based on their own specific needs.
Why data management as a service
Post-trade securities services involve settlement, clearing and recording of securities transactions. Data management as a service is designed to meet the requirements of building or extending in-house data management systems to equip institutions with capabilities to manage their own data across these activities.
According to a report by Research and Markets, Data as a Service (DaaS) market size is estimated at US$20.74 billion in 2024 and is expected to reach US$51.60 billion by 2029, growing at a CAGR of 20% during the forecast period (2024-2029).
With the advent of scalable cloud applications, DaaS is becoming one of the most rapidly growing segments of product solutions. The primary value proposition is the ability to transform raw data into a standardized data that can be used for reporting, analytics, operational needs or simply performing due diligence and reconciliations. The process involves organizing data to eliminate redundancies, reduce ambiguity and represent each dataset accurately and in a most meaningful form.
“With advent of cloud technology, Data Management as a Service (DMaaS) is rapidly emerging as a crucial enabler for institutional investors to not only tackle challenges around scalability but also to seamlessly aggregate and consolidate data from multiple service providers. It is also becoming an essential tool to tailor data storage and custom structural requirements catering to their unique needs, keeping their data infrastructure and operations teams appropriately lean,” Madhukar explains.
“DMaaS can enable asset owners to consolidate asset servicing data and gain real-time access to their valuations. It can further aid operations to stitch the valuations and market reference data on securities from various 3rd party providers, and generate customized reporting in a unified platform,” Madhukar adds.
An effective data management platform must be built upon a foundation of essential core components. This begins with capabilities that allow robust and omnichannel data collection through standard real-time, API-driven architecture along with the ability to configure business metadata and governance controls. A data-cataloguing interface, complemented with features to standardize, integrate, and transform data, is necessary to allow users to represent each dataset accurately and in a most meaningful form. A strong master data management with centralized reference data further enables users to easily query data across accessible assets while maintaining oversight into how data is ingested and distributed. Finally, the platform must include configurable access management, fortified with encryption and governance controls to safeguard and protect sensitive financial and personal data.
Combined with subscription capabilities, the platform should allow flexible distribution and omni-channel integration into operational workflows while maintaining access controls and enhancing governance. These components work together to create an efficient yet resilient data management ecosystem.
“It should further help to elevate transparency for stakeholders across financial ecosystems, enabling both clients and regulators with integrated visibility directly from trusted data sources minimizing manual intervention,” Madhukar adds.
Solid experience in data management
Technology and financial services providers are rapidly evolving solutions that offer data management as a service, allowing clients to have access to unified data through a single channel along with ability to integrate data from third parties based on specific needs. The automated collection of data from necessary data sources with on-demand data management tools are instrumental in democratizing data and making it as an asset.
Over the years, HSBC has built a track record of helping asset owners, managers, banks and broker-dealers in managing complex global data across the securities services lifecycle, from traditional assets to digital assets. Furthermore, HSBC has a unique expertise in managing diverse regulatory and data compliance regimes and has recently been named as the Best in Asset Servicing by The Asset.
“In recent years, with rapid growth in portfolio sizes and soaring data volume, and with evolving complex regulatory requirements, it is important to our clients that they better manage their costs on data management across the investment value chain where there is a lack of data standardization and centralization. At HSBC, we are investing and scaling up our data management solutions with services that help our clients manage these challenges more effectively,” says Fiona Horsewill, Global Head of Securities Services at HSBC.
With an emphasis on scalability, security and cost efficiency, HSBC’s data solutions are designed to meet both strategic and operational needs of financial services today by transforming complex raw data into standardized, meaningful semantic datasets and making them available in real-time with on-demand data transformation, querying and reporting capabilities. These solutions can potentially reduce data management overheads, essentially helping financial institutions to save efforts and allocate resources to strategic initiatives and equipping them with capabilities to enhance their decision making.
“Data management as a service should enable asset owners, fund managers and other stakeholders to gain access to their post trade data centrally at scale, reducing the need to source it individually from multiple investment and market platforms. Our platform-driven model should significantly reduce the marginal costs of managing data and bring greater efficiencies enabling our clients to focus their resources on driving business growth,” Fiona adds.
