# Neuroplastiq and common data models, explained Source: https://neuroplastiq.ai/knowledge. Content owner: Gareth Sutton. Last reviewed 4 November 2026. ## Summary Neuroplastiq is a system for developing a common data model from fragmented source systems, with AI-powered suggestions and human review. It substantially accelerates creating your common data model to speed up your AI transformation. ## Key facts - Company: Neuroplastiq - Product category: Common data model development software - Founders: Elliot Rock and Gareth Sutton - Website: https://neuroplastiq.ai - Contact: enquiry@neuroplastiq.ai ## How Neuroplastiq works 1. **Connect your sources.** Point Neuroplastiq at the source systems you want to understand. 2. **Review the suggestions.** See proposed definitions, attributes, relationships and mappings, with the reasoning behind them. 3. **Build your common data model.** Neuroplastiq forms the canonical and semantic models, allowing your team to refine as required. 4. **Connect your platforms.** Leverage the common data model to integrate systems, populate the data warehouse and stream data. 5. **Keep it up to date continuously.** When sources change, your team reviews the proposed updates before they reach the model. ## Who it is for Technical leaders in scale-ups and mid-market organisations with complex source systems and limited data engineering capacity. Their engineers are capable; they simply have more systems to understand than time to understand them. - Data teams consolidating systems after an acquisition, merger or platform change. - Technology partners and data consultancies assessing whether Neuroplastiq fits their clients' programs. ## What Neuroplastiq does not do - Neuroplastiq does not apply changes to your common data model without a person approving them. - Neuroplastiq does not replace your ingestion, transformation or warehouse tools. It works alongside them. - Neuroplastiq does not decide business meaning for you. It proposes definitions for your team to inspect, refine and approve. ## Security and deployment - Suggestions do not change your common data model. A person implements or dismisses each recommendation, and the model only changes when approved. - Recommendations show their reasoning, so reviewers can check the evidence before acting. - Questions that need business judgement are flagged as needing review rather than decided automatically. - We walk through deployment options, what Neuroplastiq can access and how AI is used, for the release being demonstrated, during your demo. ## Glossary - **Common data model:** A shared definition of data across an organisation's systems: names, attributes, characteristics, relationships and business meaning. - **Canonical data model:** Another name for a common data model. Integration teams often use "canonical" for the shared model that every system maps to. - **Data dictionary:** A reference that documents the terms and fields in a data model. It describes the model; it is not the model itself. - **Semantic layer:** The layer that makes agreed business meaning available to analytics and AI tools, so they use the same definitions as the people who agreed them. - **Source system:** An application or database where data is created or held, such as a CRM, billing platform, wallet or marketplace system. - **Schema drift:** Changes to the structure or meaning of source data over time, such as new, renamed or repurposed fields. - **Data lineage:** A record of where data came from and how it moved or changed on the way to where it is used. ## Use cases - **A model for a new warehouse:** Start a warehouse build from a reviewed common data model instead of a blank page and a round of discovery workshops. - **Consolidating systems after an acquisition or migration:** See where two businesses describe the same customers, products and transactions differently, and agree one model. - **Giving AI tools business context:** Agents and AI assistants need agreed definitions to produce reliable answers. The common data model and semantic layer provide them. - **Keeping a model current when sources change:** Review proposed updates when source systems change, rather than discovering the change when a report breaks. ## FAQ ### Does Neuroplastiq replace dbt? No. Neuroplastiq works alongside dbt, your ingestion tools and your warehouse layers. It helps your team agree what the data means; your existing tools keep doing the transformation work. ### Does Neuroplastiq move data? Neuroplastiq focuses on understanding source systems and building the common data model. Moving data is usually handled well by the ingestion and transformation tools you already run. We walk through how Neuroplastiq connects to your sources and warehouse in the demo. ### How are suggestions reviewed? Each recommendation shows why it was made and the suggested action. A person implements or dismisses it. Items that need a business decision are flagged as needing review, and the model only changes when someone approves the change. ### Which platforms are supported? Coverage depends on your source systems and warehouse. Tell us what you run when you request a demo, and we will confirm what the current release supports for your stack. ### How do we evaluate fit? Book a demo. We show Neuroplastiq proposing a common data model from sample sources, then discuss your systems, your analytics and AI goals, and whether the product fits. ### What is a common data model? A common data model gives your organisation a shared definition of data across its systems: names, attributes, characteristics, relationships and business meaning. It provides a foundation for system integration, data analytics and AI transformation. Without that shared understanding, teams spend more time reconciling differences and have less confidence in the results. ### How does Neuroplastiq help me build a common data model? Neuroplastiq provides a structured starting point: a proposed common data model that your team can inspect, refine and approve. This reduces manual discovery and gives people something concrete to review together. The sooner you agree what your data means, the sooner you can deliver integrations, analytics and agentic workflows. ## Guides - [What is Neuroplastiq?](https://neuroplastiq.ai/what-is-neuroplastiq): Neuroplastiq is a system for developing a common data model from fragmented source systems, with AI-powered suggestions and human review. - [How does Neuroplastiq work?](https://neuroplastiq.ai/how-neuroplastiq-works): Neuroplastiq works in five steps: connect your sources, review the suggestions, build your common data model, connect your platforms and keep it up to date. - [What is a canonical data model?](https://neuroplastiq.ai/canonical-data-models): A canonical data model, also called a common data model, is a shared definition of data across an organisation's systems. Here is how it works, with an example. - [What is semantic schema analysis?](https://neuroplastiq.ai/semantic-schema-analysis): Semantic schema analysis compares source schemas by business meaning, not field names alone, to propose how they align in a common data model. - [How do industry-standard data models relate to a common data model?](https://neuroplastiq.ai/industry-standard-data-models): Industry standards such as ISO 20022, FHIR and GS1 describe data for a specific purpose. They work best as outputs derived from your own common data model. - [How does Neuroplastiq connect to source systems and platforms?](https://neuroplastiq.ai/platform-connectors): How source connectivity, warehouse output and downstream tools relate to a common data model, and what to ask about connector coverage. - [What is schema drift, and how do you manage it?](https://neuroplastiq.ai/schema-drift): Schema drift is change in the structure or meaning of source data over time. Here is how it affects a common data model, with an example of reviewing a change. - [What makes data AI-ready?](https://neuroplastiq.ai/ai-ready-data-foundations): AI-ready data has agreed definitions, known sources and current structure. A common data model and semantic layer give AI tools that business context.