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.
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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.
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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.
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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.
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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.
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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.
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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.
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 Query
Engineering data to enable the user to better understand complex relationships
Risk Management
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.
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.
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, optimizing resource allocation to simulating ‘what-if’ scenarios.
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 ‘what-if’ scenarios.
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.
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.
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.
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 Query 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.
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.
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.
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.curate predictive models, anticipate future trends and make informed decisions based on a deeper understanding of the underlying data relationships.
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.
Competitive Analysis
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.
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.
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.
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.
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.
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.
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.
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.
