Nearly everyone wants to get started with artificial intelligence, and many companies are wondering if their data is ready for AI. The answer can be found in data capability modelling. Data capability modelling is a comprehensive analysis of how an organization collects, stores, governs, and uses data. It helps companies understand what is possible with their current data structure and what could be possible with an updated, well-structured, modern data environment. From there, data capability modelling defines a future state that supports data-driven decisions, faster data access, more effective use of data resources, and the capability to add data-dependent systems, such as AI.
According to the Project Management Institute, 70-80% of organizations that start developing AI applications will fail, and poor-quality data is a main reason why. Successful AI initiatives must use high-quality data for model training, through deployment, and during operation. Yet, many companies do not prioritize the health of their data. They operate in data silos, with different systems in different departments, each with unique data requirements. Until recently, most organizations never considered their level of data maturity. Nevertheless, AI has proven to be a reason to care about data. With data capability modelling, organizations reach a higher level of data maturity and are prepared for AI.
Most companies have collected and stored a lot of data. But without structure, that data stays hidden inside systems or under legacy processes. Over the years, many companies have developed ad-hoc workarounds rather than solve a core issue with the data architecture. Data capability modelling uncovers these core issues and provides actionable solutions.
What is data capability modelling?
Data capability modelling is a structured method for evaluating how data flows through an organization’s various systems and how well that method supports business goals. It shows what data exists, where it is stored, and how it’s used. It also shows what’s getting in the way. The result is a clear picture of the organization’s current maturity and a realistic path forward. The process and its recommendations can be broken into three parts: Discovery, use case analysis, and gap assessment.
Discovery
Data capability modelling starts with discovery. Engineers working on the project conduct interviews and review documentation to understand the pain points in the current data flow and discover how data is used. They then discuss strategic business goals. During this phase, software and data engineers learn the details of existing systems, their operators, and how they create and consume data.
As engineers develop their data capability modelling report, they begin mapping the system to their established business goals. The result is a shared understanding of the current state and its challenges. Reports on legacy systems usually include a recommendation to modernize. They also suggest a use case to demonstrate the benefits of upgrading the data architecture.
Use case analysis
During data capability modelling, engineers identify a use case that demonstrates a functional environment if the organization’s data matures. They outline the architecture of the new system and discuss appropriate governance. Once everyone understands what is possible, they can begin discussing plans to improve the flow of data throughout the organization.
Gap assessment
After data capability modelling is complete, the combined team identifies gaps in the plan and prioritizes improvement projects based on their feasibility. Data capability modelling encourages opportunities to deliver quick, early wins, and more use cases that meet the criteria are added to the plan. By the end, the organization understands the strengths and weaknesses of their data. They also have a blueprint for modernization and data maturity. Finally, the organization also has a solid foundation to scale, automate, and adopt AI with confidence.
The benefits of data capability modelling
One of the advantages of data capability modelling is that it often reveals unknown data. For example, the Finance Department might run separate reports or have unique processes. Data capability modelling uncovers these siloed systems and traces the root of the problem to the core of the data architecture. It also leads to faster resources for the data pipeline, a standardized data schema, and a single source of truth for the entire organization.
By engaging in data capability modelling, organizations can learn how to move away from fragmented systems. Data becomes a shared resource, which improves the quality of the data and increases the organization’s level of data maturity during its digital transformation.
Maturity along a digitalization journey
Another benefit of data capability modelling is that it provides a snapshot of an organization’s digital maturity. Additionally, data capability modelling demonstrates what has been done and what remains.
The modelling project also paints a broader picture of how data is stored, accessed, governed, and shared. Furthermore, it shows how much time and effort each department is spending to make data usable, which often signals low maturity. The five phases below represent common phases along a digitalization journey. Each phase builds on the previous phase and adds a level of data and digital maturity. By the time an organization reaches the final phase of their journey, data has become an advantage rather than a barrier. This is the model that data capability modelling helps you understand and act on.
Organizations go through these five phases of data maturity:
- Foundation: During the first phase of the digitalization journey, organizations organize and centralize scattered data. Departments begin working from the same unified data source instead of multiple separate repositories.
- Processing: Next, organizations begin structuring, cleaning, and sanitizing their data. Data also becomes easier to manage and work with.
- Integration: When organizations connect disparaging systems and remove data silos, they can begin to use analytics across the organization.
- Enablement: The organization is finally able to use their data during the enablement phase.
- Intelligence: Finally, in the intelligence phase, organizations can operationalize AI and automation, make data-driven decisions in real time, and scale efficiently.
Inside data capability monitoring
Data capability modelling is structured as a stepped approach, with each stage building toward a new plan for data architecture.
Discovery and alignment
The process begins with gathering context. This includes stakeholder interviews, workshops, and system reviews to understand the current state, desired outcomes, and known barriers. During this phase, the organization learns how data supports (or fails to support) business processes.
Deep dive into architecture and data flows
With the initial stage complete, the engineering team maps the data ecosystem in detail. That includes ingestion pipelines, transformation logic, reporting layers, governance mechanisms, and access patterns. This stage often reveals process or data duplication, lack of ownership for undocumented data dependencies, and mismatches between policy and execution.
Assessment of governance and AI readiness
The modelling process also examines how data is governed and whether the organization has the data architecture to support AI and machine learning. In situations where the data architecture is not up to task, leaders must decide about the future of AI within their organization. Finally, findings are translated into a delivery roadmap with defined milestones. The roadmap links each recommendation to a business case. As a result, stakeholders understand the purpose and effect of each action. It becomes a practical guide for advancing data maturity.
Accelerating AI readiness for a global identity verification company
Recently, a global leader in digital identity verification and fraud prevention wanted to introduce AI-powered analytics into their production systems. The company had operations in Europe, Africa, the Americas, and the Asian Pacific regions. While they already had a central analytics platform, data ingestion and access control were inconsistent across services. Each region maintained separate systems, rule engines, data pipelines, and user interfaces. Clients could configure verification flows and fraud scoring thresholds, but there was no scalable way to measure whether these configurations were working as intended.
The company asked Intellias for a data capability modelling study. Our engineers assessed the client’s data architecture to identify inconsistencies and determine if they were ready for AI. The project focused on building a data pipeline capable of generating AI-driven configuration recommendations. Using 90-day rolling datasets, our engineers applied SHAP values to explain the contribution of each verification rule to pass rate outcomes.
To create operational value, we also introduced dashboards with role-based access control, enabling near real-time performance tracking and better reporting. An AI-powered rule scoring engine provided peer-based recommendations and showed which verification settings worked best for specific customer profiles, industries, and timeframes. The system also recorded configuration history and created a user feedback loop for continuous improvement.
The results were immediate. Because of the data capability study, the company could configure updates 41% faster, achieve a 27% higher pass rate, and increase adoption 89%. The client could now explain how AI reached its recommendations, improving transparency and trust. The architecture changes and governance framework established through data capability modelling created a sustainable environment for ongoing AI use.
Toward future capabilities
For organizations exploring AI, data capability modelling provides a structured, evidence-based way to ensure your data is ready for the task and comprehensive recommendations for modernization. By treating data as a strategic asset and using capability modelling to map the journey to AI readiness, organizations create a foundation that supports both the immediate advantages from AI and future-proofs the organization for what comes next.
In the era of AI agents, is your data ready? From establishing governance to cloud migration, Intellias engineers make high-quality data work for you. Contact one of our data capability engineers for a full assessment of your data.
