The Challenge

A multinational financial services firm (80,000+ employees) sought strategic support to implement a data modernization program centered on semantic solutions to simplify its enterprise risk management ecosystem. The firm faced several challenges in managing risks across the organization. First, data originated from over 20 systems with different terminology and classifications, making it difficult to connect related information. This resulted in weeks-long efforts to aggregate information for regulators and gaps in key information required to maintain risk levels that fit the firm’s appetite. Second, this data needed to be centralized for easy access and consumption by various stakeholders and applications across the firm, but was siloed in process-specific applications. Finally, the complexity of the data created a need for a more intuitive and efficient way for employees to interact with and understand this information. These challenges first required a solution that could align terminology and classification, centralize data, enhance usability, and make the information easily accessible for various purposes across the organization. The effort then shifted toward scaling and formalizing the program to extend semantic capabilities to new domains and areas of the organization.

The Solution

The firm identified a semantic layer and corresponding solutions as a key enabler for risk management and partnered with EK to develop a strategy and implement the structures and technology required to support several use cases, including data-centric risk assessment, recommendation services, enhanced search, and talk to your data. To address the firm’s risk management and semantics challenges, EK developed semantic data capabilities and frameworks, including:

1. Standardized Categorization Structure:

Recognizing the need for a common language across the organization, EK created a standardized terminology structure by identifying the most used topical areas and vocabularies in need of standardization. One example of this categorization structure in action included free-text risk descriptions that EK shortened and standardized by leveraging an AI pipeline with humans in the loop. Another example involved the consolidation of product taxonomies that EK concatenated into a cohesive, central Product Taxonomy for use across domains. EK and the firm achieved common categorization across representations of business units, products, and legal entities among others.

This centralized, evolving framework provides a consistent way to categorize and classify data, ensuring interoperability across different systems and use cases. By implementing this structure within the risk assessment application and encouraging its adoption by other data providers, EK ensured that all parties were using the same terminology when handling related datasets. This improved data consistency and accuracy, facilitating data sharing and analysis. This framework is standards-based and can be expanded with additional use cases and can be implemented in additional systems, as it is an extensible source for descriptive metadata that applies across the firm. Additionally, the framework drives auto-tagging and metadata inference that reduce the manual burden on users for data entry.

2. Ontology and Knowledge Graph:
To enhance data connectivity, EK developed a domain-specific ontology and knowledge graph through net-new design, reuse of existing structures, and uplifts to meet semantic standards. This knowledge graph serves as a central repository for all risk-related data, capturing the complex relationships between data points and providing a holistic view of the risk landscape. The knowledge graph enables users to easily explore and understand key data relationships via a graphical user interface layer and acts as a central repository for risk data that can be published to and consumed from as an authoritative source of truth. This solution provides a foundation for advanced analytics, reporting, and decision-making while also accommodating future growth and evolving data needs.

3. Consumer-Grade Capabilities:
EK implemented a suite of consumer-grade capabilities designed to make interacting with data more intuitive by mirroring experiences end users have every day when utilizing tools such as Google and LinkedIn. Consumer-grade capabilities include an advanced search functionality that allows users to quickly find the information they need, as well as recommendation engines that proactively suggest relevant data and insights. EK also leveraged data analysis techniques to develop features like notification services, alerting users to critical changes or potential risks. These capabilities empower users to interact with data more effectively, improving productivity and decision-making.

4. Semantic Architecture Development:
EK collaborated with the firm to enhance the existing data management architecture. This involved building a scalable semantic layer ecosystem with extensive services to connect, enrich, and serve data for diverse use cases.
As part of the Semantic Architecture, EK developed an innovative approach for sources to publish their data into the graph using common models delivered, allowing for rapid expansion of the ecosystem without requiring all applications needing to be graph proficient. This approach ensures seamless integration and interoperability across the organization’s knowledge management infrastructure, maximizing the value of their data assets and promoting a data-driven culture.

5. Program Operationalization:
In parallel to Semantic Capability development, EK also built a dedicated data team to define best practices for data quality, standardization, and enhancements to strengthen firm-wide risk management. EK additionally supported the firm in training and enablement of new team members to run the data program. The program expanded to include new domains, further developed the ontology and knowledge graph with standardized onboarding processes for new data providers, and implemented knowledge panels to surface key information in an intuitive, easily digestible format.

This multifaceted approach for a data-centric program is now used as the blueprint for transforming the firm’s data ecosystems and is accelerated based on data, system, and operation model foundations established through the risk domain.

The EK Difference

With a team of experienced professionals spanning diverse disciplines—including taxonomy design, ontology development, knowledge graph implementation, semantic layer architecture, machine learning, and search—EK brought a holistic set of expertise to the engagement. This allowed for the development of a robust and scalable solution tailored to the firm’s specific needs. EK’s commitment to ongoing governance and maintenance ensured that the implemented solutions would continue to deliver value and adapt to evolving requirements, setting the firm up for long-term success.