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Why Enterprise AI Fails (And How to Fix It)
Articles, webinars and a readiness diagnostic.
Articles, webinars and a readiness diagnostic.
We helped a global fashion group create a shared and trusted foundation for enterprise knowledge. Through semantic modelling and ontology engineering, we connected business terminology with the systems, data and metadata it described, creating reusable semantic assets that could support governance, engineering, analytics and future technology change.
Business glossary and semantic model development
Ontology engineering
Data landscape and metadata mapping
Data catalogue proof of concept
Standards, training and handover support
A connected body of business terms, definitions and relationships
Semantic models linked to wider data-management information
Reusable semantic assets
Practical standards and training enabling adoption
A basis for future data discovery, knowledge graph and AI use cases
Operating across a vast network of physical stores, ecommerce channels, international supply chain and global markets creates a complex business and data landscape.
Our client wanted to establish consistent meaning for important concepts across it’s decentralised environment. Domain knowledge was distributed across teams, while terminology, definitions and relationships needed to be structured and governed consistently.
The requirement was not simply to create a glossary or a standalone set of models. The semantic assets needed to connect business meaning with the wider data landscape and become usable across data governance, engineering, analytics and technology environments.
This project was first step of our client’s longer-term to establish reusable semantic foundations that could support discovery, governance and future technology change.
Building the foundations for enterprise knowledge requires more than semantic modelling in isolation. The work needed to connect business understanding with data structures, metadata, lineage and data quality, while considering how those assets would be governed, engineered and maintained.
That is where we work best: connecting data, governance, architecture, engineering and enablement to solve a real business need. Combined with the team’s understanding of the technology landscape, we were able to develop the models and explore how they could connect within a wider digital ecosystem.
We worked with our client’s data, governance and business teams to establish shared terminology and translate it into governed semantic models.
The work captured business terms, definitions, domains, processes and relationships. These were developed into ontology assets that represented not only individual concepts, but also the connections between them.
Using Graph.Build, we created and developed the semantic models underpinning this structure. This combined the organisation’s domain knowledge with Ortecha’s semantic modelling and ontology engineering expertise.
The result was a structured representation of enterprise knowledge that could connect business meaning with systems, data structures and reporting assets.
Semantic models become more valuable when they are linked to the physical and logical data environments they describe.
We used our Ortecha Trust framework to organise these connections across four areas: Define, Locate, Trace and Measure. The framework brought together glossary content, business processes, applications, data structures, reports, lineage and data-quality information within an integrated project workbook.
This created a clearer link between the organisation’s semantic layer and its data estate. It also established a structure through which governance and engineering teams could connect business context with metadata, lineage and data quality.
The project combined our data-management and semantic engineering expertise with technology platforms suited to different parts of the work.
Graph.Build supported the creation and development of the semantic models. We also used Ab Initio to deliver a data catalogue proof of concept, exploring how business glossary, metadata and semantic information could be exposed through a governance platform.
The semantic assets needed to work across different governance and engineering tools, so the models were designed to consider central governance and remain reusable across multiple platforms.
Developing the ontology was only one part of establishing a sustainable semantic capability. The organisation also needed the standards, processes and knowledge required to maintain and extend it.
We produced training materials for the client’s global data product managers and data engineers, explaining how the semantic foundations connected with their roles and wider data-management activity.
We created standards, handover support and practical recommendations for scaling the work, giving the teams a clearer basis for governing, maintaining and developing the semantic assets beyond the engagement.
The project created an integrated body of business terminology, definitions and relationships, supported by semantic models and connected data-management information.
We established the foundations for a broader enterprise knowledge layer. It gave the organisation a structured way to preserve shared business meaning across governance, engineering and technology environments, connecting glossary content with metadata, lineage and data-quality information.
The semantic assets were designed as reusable assets to suit as the organisation’s platforms and technical architecture evolved. Standards, training and handover support also gave internal teams a basis for maintaining and extending the work.
The project showed leadership how the ontology could support data discovery, glossary-to-data connections, conversational AI and future knowledge graph capabilities.
Enterprise knowledge cannot be used at scale when it’s spread across documents, systems and individual teams. It needs to be made explicit, structured and connected to the data it describes.
Ontologies and semantic models provide that structure. They capture not only what business concepts mean, but how those concepts relate across domains, data and technology. This creates a shared context that can be governed once and reused across different platforms and projects.
That foundation becomes increasingly important as organisations build new data platforms, knowledge graphs and AI capabilities. These technologies can process information at machine speed, but they still need reliable context to interpret what the data represents and how it connects to the business.
By building that context into reusable semantic assets, organisations are better placed to expand their technology capabilities without recreating business meaning for every new system or use case.
A global fashion and design brand with an extensive supply chain, physical retail and ecommerce operation. Its scale and decentralised structure meant important business and data knowledge was distributed across teams and domains.
Founding Partner
Partner, Head of Data & AI Engineering
Principal Consultant
Principal Consultant
“What made this engagement so rewarding was the scale and complexity of the retail environment. Shared meaning has to hold across brands, markets, physical stores, ecommerce and the technology landscape behind them. We helped turn that knowledge into governed, reusable semantic assets, giving the organisation a stronger foundation for data discovery and engineering today, and the machine-readable context needed for future platforms, automation and AI.”
– David Lee-Smith, Principal Consultant, Ortecha
