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Solving the Enterprise Data Challenge for Institutional Investors
Institutional investors are evolving under pressure from a range of emergent externalities that are prompting many to assume new roles and processes, some of which have traditionally been the preserve of asset managers. Enabled by data-led technology, they are making more direct investments and managing their portfolios in-house. At the same time, they are streamlining…
How Banks Harness the Value of Cloud for Scale and Agility
Financial services are recognizing the benefits that data-led processes can bring and are undergoing digital transformations that are putting data at the heart of their operations and decisions-making processes. They are seizing on data as an asset whose value can be unlocked through technology to increase revenue and reduce costs across the entire enterprise. With…
The Business Case for Master Data Management Transition Within Financial Institutions
Master data management ensures the creation of a “single source of truth” of information for banks and financial institutions that not only provides that the data remains intact but also makes it available and useable across the entire enterprise. Once the information foundation of golden data is established with MDM, the data can be used…
Driving Data Adoption Throughout an Organization via Self-Service
Financial institutions and corporations across the board are seeing value in the data sets they generate through their business activities. But harnessing this data to provide valuable insights for internal business teams can be a challenge. One approach is to adopt a self-serve data delivery model that empowers consumers and ensures they get access to…
Creating an Enterprise-Wide Data Fabric to Underpin Digital Transformation in Capital Markets
As they seek to adopt a data-driven approach to their business operations across the enterprise, capital markets firms need to put in place a common data fabric that embeds their single view of the truth, and to underpin analytics, reporting and regulatory processes. But legacy data systems often are not fit for purpose; often fragmented…
B2B Data Marketplaces and Beyond
Financial institutions and corporations often generate huge quantities of data as a by product of their core activities. This data – details of historical transactions, customer interactions and metadata for referring to instruments, counterparties or entities – can provide valuable insights for industry participants, and form the basis of a meaningful data sales business. But…
Sanctions Screening for Indirect Investments – The Buy Side’s New Compliance Challenge
The global political climate over the past few years has sparked a jump in the use of sanctions to attempt to influence the behaviour of players in the geopolitical landscape. While sell-side firms are familiar with sanctions and have long been required to monitor the securities they trade, own or recommend to clients to ensure…
Practical Data Strategies for meeting ESG Obligations in Financial Services
The ESG investing landscape is poised to become more defined, as competing definitions, standards and regulatory initiatives start to converge. The impact of ESG will be felt far and wide across the financial services community, which will face practical challenges in developing and implementing an ESG strategy that is both effective and avoids box-ticking –…
Embracing Automation and Collaboration Tools to Inject Reference Data into the Trade Lifecycle
Digital transformation in the financial services sector has raised many questions around data, including the cost and volume of reference data required by each financial institution. Firms need flexible access to the reference data required to ensure workflows can proceed without interruption. ‘One-size-fits-all’ bulk data licensing models are increasingly less fit for purpose. Emerging solutions…
Can ‘Observational Learning’ Help Improve Data Quality?
Data quality is a pre-requisite for financial institutions seeking to automate their operations. But given the huge volumes of transaction data, often in a wide array of formats, it is very difficult to achieve. Can newer AI-based techniques such as observational learning actually help or are they just hype? This white paper explores: What observational…