Data analytics in investment banking

Top shifts in the global IB&CM data landscape

Capital markets trends and recent technology advances are providing IB&CM institutions of all sizes with an increasing range of options to respond to the disruption. Big data in banking and financial services now counts cloud-based data technologies, artificial intelligence, and cognitive tools among the innovations delivering a profound impact within the sector.

Additionally, four major data shifts are redefining data analytics in investment banking, including:

1. Regulatory expectations for data management
   The regulatory environment has undergone significant changes with the plateauing of net-new regulations and corresponding increase in demand for more granular, traceable, and frequently collected data for statutory and regulatory reports.
2. Transition from chief data officer (CDO) setup to CDO operations
   As regulatory expectations have evolved over the past 10 years, the role of the CDO has matured from “set up and govern” to “delivering data operations.” New data requirements and challenges have compelled CDOs to centralize operational data management activities while federating responsibility to enhance data controls.
3. Innovation’s impact on data supply chain and CDO tooling
   Existing CDO tools are being modernized to enable more effective governance and management. Tools are being implemented to enhance capabilities in areas such as data governance, metadata management, data lineage, data quality, issue management, master and reference data management, data modeling, and reporting of data operations and monitoring.
4. Changing end-user expectations
   Expectations from end users are evolving, from availability of user-friendly technology to adoption of technology that delivers business value. Ease of access to real-time data with trusted quality on a consistent basis is required. Moreover, end users also are seeking a unified enterprise interface that serves multiple needs, and that integrates access to repositories containing data definitions, lineage, data requirements, and system inventories.

Key pillars of data analytics in investment banking at global institutions

In response to these four shifts, IB&CM institutions have set up scaled, multifunctional, and multiyear data programs. While each institution tailors its program to reflect inherent maturity of data management, four foundational pillars are crucial to address the changes necessitated by disruption.

Governance and accountability
Define an operating model that facilitates program governance and accountability at an enterprise level. The model must be supported by policies and standards that are tailored to institution-specific maturity and enable close coordination between key stakeholders.

Internal controls and reporting enhancements
Establish a sustained and reliable front-to-back control environment with the objective to reduce reporting adjustments, facilitate operational enhancements, and reduce the use of end-user computing (EUC) applications during the report preparation life cycle.

Data and quality management
Develop robust platforms to manage critical data elements, implement data quality rules, expand data tracing, simplify data lineage, and manage and resolve issues.

Business and data architecture
Reduce complexity of architecture while meeting regulatory requirements with granular data. Programs also seek to implement straight-through processing at the data origination and aggregation layers of their data supply chains.

Looking forward

While the disruptors continue to evolve, a tailored and timely response can help IB&CM institutions to capitalize on related opportunities and create competitive advantages. Modernizing and effectively scaling data capabilities can be accelerated with foundational components in place, including:

Suite of data elements for regulatory and financial reporting
A list of critical data elements with predefined linkages to product and line-of business-specific metadata that are vital for reporting needs (regulatory and statutory), facilitates standardization of data sourcing, and use across lines of business.

Standardized framework for validating data quality
A preconfigured and scalable platform that can test system functionality and quality of data required for regulatory reporting, including rules that cover various asset-class and reporting requirements.

Metadata management platform-as-a-service
A standards-based, integrated enterprise repository platform that supports scaled and speedy data governance and metadata management that caters to end-user expectations.

These accelerators are designed to help you rapidly expand your institution’s capabilities and capacity to help you adapt quickly in this fast-changing environment. To effectively respond to the market forces disrupting the industry, organizations should consider the right technologies to implement at their organization in order to align to the new data shifts and elevate data management and modernization.