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Industry Expertise

Semantic Arts has successfully completed projects across dozens of industries. We know that almost all information-intensive sectors share common challenges of data integration, system interoperability, enterprise search, and flexible query across data repositories. Below are a few areas where we have specific experience demonstrating the value of data-centric standards and architecture.

Life Science

Semantic Arts has expertise in ontology design and knowledge graph implementation for many life science and pharmaceutical clients. We work in collaboration with subject experts to organize, integrate, and analyze complex biological data from genomic sequencing, biomedical research, decision support systems, and clinical trials. We help clients integrate these diverse data sources by providing a common framework for retrieving, representing, and linking data.

Click on our Pharma Quick Start to learn more.

Pharma Quick Start

See how the life science industry benefits from data-centric adoption:

Case study: Amgen

Financial Services

We have extensive experience with the full spectrum of information requirements across the financial services sector. Our ontology and semantic modeling initiatives have centered on reference data change management, data lineage, traceability, and records management. We are engaged in efforts related to trade surveillance, regulatory compliance, global financial crime, operational risk, and post-trade processing.

See how the financial services industry benefits from data-centric adoption:

Case study: Morgan Stanley

Manufacturing

The manufacturing companies in our portfolio have adopted data-centric to optimize processes, ensure quality, and support innovation. We help in modeling complex supply chains by representing the relationships between suppliers, manufacturers, distributors, and customers. This real-time visibility into operations supports demand forecasting, inventory management, predictive maintenance, and product lifecycle management.

See how the manufacturing industry benefits from data-centric adoption:

Case study: Schneider Electric

Publishers/Data Providers

Many of the common challenges of our publishing clients have been solved by adopting data-centric principles. The primary value has been harnessing the explosion of metadata by representing and linking information, managing their data pipelines, and combining data from multiple domains. We understand the challenge of integrating licensed, government, and internally generated content to produce new data products.

Case studies: S&P Global Commodity Insights and LexisNexis

Defense and Intelligence

Our professional consultants have hands-on expertise in harmonizing, correlating, and integrating content from disparate data sources. Our clients use data-centric to identify patterns, trends, and anomalies to facilitate scenario analysis and threat assessment. We are devoting significant resources to understanding cybersecurity and vulnerability management where the challenge of analyzing large volumes of structured and unstructured data is essential.

Government and Non-Profit

We have performed many design and implementation projects for state and federal government agencies as well as non-profit organizations. Our approach provides a structured framework for integrating data from a variety of systems and agency departments. By leveraging data-centric principles, governments and non-profit organizations can address runaway complexity, improve operational efficiency, and better serve their members and constituents.

Case study: International Monetary Fund

Use Cases (Where to Start)

Semantic Arts has been engaged in guiding firms into their data-centric transformation for over 20 years. Most of the successful initiatives revolve around three core themes that are uniquely tailored to take advantage of the capabilities of semantic standards and knowledge graph technology.

Data Integration

Using meaning and identity resolution to harmonize data across repositories

Flexible Analysis

Engineering data to enable the user to better understand complex relationships

Risk Mitigation

To ask scenario-based questions to prevent unwanted outcomes

There are many specific use cases that exist within these three core themes. The ones that hold great promise for companies embarking on the data-centric journey are outlined below. We recommend the following as good places to start because they are valuable, hard to obtain any other way, and can be leveraged for onward value.

1—Data Integration (semantic data hub)

Over 70% of all knowledge graph applications relate to data harmonization and integration across repositories. This is where most of the data-centric value exists and is the leverage point for many onward applications. Valuable integration use cases to start with include:

Typical integration use cases

Data Catalog

Organize information in a structured manner which allows you to navigate across data sets, understand relationships, and make sense of vast amounts of data. Find data when you need it—complete with definitions, quality requirements, and ownership details.

Process Automation

Apply semantics to model processes and understand the workflows within your enterprise. The ontology captures dependencies and relationships between processes and events that can be adjusted based on changing circumstances. The common semantic framework is particularly good for mapping and harmonizing data definitions and models.

Critical Systems Analysis

Uncover the inner workings of important systems by breaking them down into their components and analyzing how they interact. This offers visibility into the web of relationships and dependencies needed to identify risks, comply with regulations, and perform resiliency planning.

Cost Allocation

By organizing cost data into a structured graph, you can trace the flow of expenses and understand dependencies based on actual usage regardless of how the data is renamed or transformed. Semantics provides you with a powerful framework for allocating expenses, examining resource usage, or improving budget forecasting.

Digital Transformation

Build structured representations of physical and digital entities to enable you to monitor, simulate, and optimize performance in real time. You gain a 360-degree view of operations to make decisions from predicting equipment failure and optimizing resource allocation to simulating ‘what-if’ scenarios.

Data Quality

Standardized data models with precisely defined concepts ensure consistency of how data is represented. By organizing metadata, lineage, and quality metrics into a structured graph, organizations gain a complete view of data assets to identify anomalies and deviations from expected data patterns. Ensure that data complies with regulatory requirements, privacy regulations, and organizational standards.

Lineage

By defining precise meaning via the ontology and linking it to the standard identifier, you can track the origin, transformation, and flow of data. You will always know what the data means, where it goes, and how it is transformed. You gain transparency into your data processes to mitigate risks and make more informed decisions with clarity and confidence.

Ethical AI

Data-centric architecture provides a framework for promoting transparency, accountability, and trustworthiness in AI applications. By representing information about data sources, model decisions, and training processes, stakeholders can understand how AI decisions are made, assess potential risks, and pinpoint areas where biases might be introduced.

2—Flexible Analysis (relationship management)

Semantic technology is expressive and allows the user to ask business questions without the need to restructure predefined tables and columns that characterize relational databases. It is the ability to query data and explore relationships that enables analysts to test hypotheses and generate new insights. Analytical use cases that provide leverage include:

Typical flexible analysis use cases

Customer 360

By organizing data about customer demographics, behavior, transactions, and news into a graph, organizations can gain a holistic view of customers’ behavior and history. This enables them to personalize marketing efforts, improve customer service, identify hidden connections, and flag risky behaviors.

Predictive Modeling

Use the knowledge graph to uncover hidden patterns, connections, and insights that might not be apparent in traditional datasets. This enables you to build more accurate predictive models, anticipate future trends, and make informed decisions based on a deeper understanding of the underlying data relationships.

Supply Chain

With a data-centric approach, firms can visualize the web of relationships between suppliers, manufacturers, distributors, and customers. This enables visibility into inventory levels, streamlines logistics, identifies bottlenecks, and helps analysts model the relationships between operational components.

Competitive Analysis

Graphs link what you know about competitors, including their products, pricing, partners, and target markets, to your own sales and product data. This enables you to compare offerings side by side, find gaps and overlaps, and see which of your customers a competitor’s move might affect.

Entity Relationships

Resolving entities and their relationships across diverse data sources is a foundational use case for graph technology. By analyzing the connections between entities, organizations can identify degrees of centrality, capture contextual information, and uncover patterns of interactions that help firms better navigate complex systems.

Profitability Analysis

Graphs provide a mechanism for organizing and connecting various data points related to revenue, costs, market trends, and operational factors. This enables you to identify key drivers of profitability, uncover hidden patterns, and gain insights into areas where improvements can be made.

Recommendation Engines

By leveraging the network of interconnected data based on user preferences and behavior profiles, organizations can personalize recommendations that drive engagement, loyalty, and revenue. Find hidden potential opportunities and deliver tailored suggestions based on the unique preferences of your customers.

3—Risk Mitigation (control environment)

Knowledge graphs and ontologies serve as a pivot point between communities, systems, schemas, and data models. This allows organizations to model, simulate, and monitor risk factors including financial, operational, compliance, and cybersecurity risks in a structured and interconnected manner.

Typical risk mitigation use cases

Regulatory Compliance

By defining common terms, relationships, and classifications, organizations promote a shared understanding of regulatory requirements across stakeholders. Trace regulatory dependencies across your organization to ensure understanding of legal requirements as well as the ability to generate audit trails and streamline regulatory reporting.

Fraud Detection

Ontologies provide clear definitions of fraud concepts and ensure consistency in how data is interpreted and analyzed. The graph allows for visualization of patterns and relationships between seemingly unrelated data elements to help analysts detect suspicious behavior and protect against financial loss.

Operational Risk

Knowledge graphs supported by ontologies provide a structured framework for risk modeling to help organizations assess the cumulative impact of risks at the enterprise level. Identify critical risk exposures, support scenario analysis, and respond to operational risk events as they occur.

Entitlement Control

By organizing users, roles, and permissions in a structured graph, organizations can create granular access control policies that specify who can access what resources under which conditions. Enforcement can be automated across systems and applications.

Cybersecurity

Create a detailed blueprint of your digital infrastructure with all devices, applications, users, and network connections organized into a structured graph. By leveraging these capabilities, organizations can enhance their cyber resilience, detect anomalies, monitor compliance, and protect their digital assets from unauthorized access.

Privacy Assurance

Firms can trace the flow of data across systems regardless of how often the data is modified or transformed to ensure compliance with regulatory requirements. By modeling privacy requirements, constraints, and objectives, organizations can ensure that privacy is considered at every stage of the development lifecycle.

Case study: Verizon