MD101 Glossary

Cost Management refers to the business strategy and capability that empowers teams to understand, forecast, and optimise resource usage, driving visibility, accountability, and alignment between financial efficiency and product outcomes.

Cross-Domain Data Sharing is the intentional exchange of data across different business units or domains, designed to unlock new value, power collaboration, and create connected user experiences while respecting ownership, trust, and governance. Such cross-domain interfaces are enabled by standardised contracts and APIs, or data products.

A Data API is a user-facing access layer that provides curated, purpose-driven and governed data assets enabling data consumers to access, query, and integrate data in real time or on demand. This provides the capability to reuse data with clear contracts, easy discoverability and high performance to ensure that data is accessible, consistent, and aligns with business needs.

Data Analytics is the process of examining data to uncover patterns, trends, and insights. It supports decision-making by transforming raw data into clear, actionable answers.

A data catalog helps users discover, understand, and trust the data available across the organisation. It supports self-service access, encourages responsible reuse, and builds shared context thus making collaboration and governance more effective across teams.

Data Contracts are clear agreements that set expectations between teams about the data they share. They define the structure, meaning, and quality of shared data: what the data looks like, what it means, and how reliable it will be. They make it easier for users to trust, build on, and depend on data without constant rework or surprises. Data Contracts, therefore, enable users to treat data as a reliable product, reduce breakages, and create accountability across teams.

Data Discovery enables teams to explore what data exists, assess its quality and context, and make informed decisions faster. It’s a core capability for self-serve analytics, empowering users to unlock hidden value and reduce dependency on central data teams.

Data Drift refers to unexpected changes in the structure or distribution of data over time, which can silently degrade models and downstream systems. Detecting and managing drift early is key to maintaining reliable, production-grade data workflows and features.

Data engineering is the discipline of engineering that moves, cleans, and prepares data behind the scenes, so that data applications and consumer-facing applications have what it needs to run smart, reliable features. It's not just building and maintaining pipelines but also about enabling agility, scalability, and clean data experiences that enable users to leverage data for their business needs.

Data Fabric is a data architecture that integrates and connects data across all environments (cloud, on-premises, and hybrid) through a unified, automated layer. It focuses on creating a centralised data access layer that adapts to changes without requiring manual data movement or extensive restructuring. Data Fabric depends on centralisation as an approach to simplify data access, eliminate silos across teams, automate integration, and provide consistent data governance.

Data Governance is the framework of policies, roles, and processes that ensure data is accurate, secure, and responsibly used across the organisation. It enables teams to build confidently with compliance, consistency, and control.

Data Granularity refers to the level of detail or resolution at which data is captured, stored, and surfaced within a product, directly impacting user experience, feature precision, system performance, and decision-making flexibility. For end-users, the right granularity means getting just enough detail to answer questions effectively, without being overwhelmed or missing key insights.

Data Ingestion is the process of collecting and importing data so it can be used for insights, features, or decision-making. It’s the first step in the data journey that powers everything downstream and forms the foundation for reliable, scalable data products.

Data integration is the behind-the-scenes flow that brings data from different systems into your product, ensuring every aspect works together without any friction for the user. This helps make the process flow feel invisible with no messy formats, no missing fields, just the right data showing up where and when it’s needed, ready to power features, decisions, and insights.

Data Lineage is a live map that visually and programmatically traces how data flows and transforms across systems to enable users to trust, debug, govern, and optimise data usage. It is designed with users in mind connecting technical traceability with business understanding and accelerating confident, compliant, and insight-driven decisions.

A Data Marketplace provides a platform that enables governed discovery, access, and exchange of data assets and data products across internal teams or external partners, driving reuse, innovation, and faster time-to-insight. It is designed to serve distinct user personas (like analysts, engineers, business users) with features that help with ‘search,’ ‘access control,’ ‘usage analytics,’ and monetisation to optimise data quality, trust, and ease of use to maximise adoption and driving value from data.

Data masking is a process comprising tools and solutions to hide sensitive data in your data product, so teams can work with realistic values without exposing private info. This is about enabling safe development, testing, or sharing, while keeping compliance and user privacy intact.

Data Mesh is a data distribution design or blueprint. On implementation, it enables a way of organising data ownership around business teams, treating data like a product that’s built for others to use. It helps users get reliable, high-quality data faster by pushing responsibility closer to where the knowledge lives, instead of bottlenecking through a central team.

Data Modeling is the process of structuring and organising raw data into clear, meaningful forms that reflect real-world concepts: forming the foundation of a data product. It defines how data is related, interpreted, and queried, enabling the product to deliver insights that are relevant, scalable, and user-ready. A well-crafted data model makes the product intuitive to consume, easier to maintain, and more responsive to changing needs.

Data monetisation refers to the strategies of turning data into tangible business value through value-added services, decision-enabling tools, or enhanced customer experiences, either as standalone offerings or embedded features. It focuses on identifying high-leverage data assets and packaging them into scalable solutions (e.g. analytics features, intelligent automation, benchmarking tools) that align with customer needs, deliver measurable outcomes, and support business goals while ensuring governance.

Data Observability is the practice of monitoring the reliability, quality, and timeliness of data across its lifecycle. It enables teams to detect schema changes, freshness delays, or anomalies before they impact users.

Data onboarding is part of your product journey that helps users bring offline or external data into the system, automatically matching, mapping, cleaning, and validating it so it’s ready for insights and features. This helps build an efficient, frictionless, error-free, self-serve user journey, ensuring your data aligns with the product objectives for a quick time-to-value, thereby improving user experience.

DataOps is the set of practices and tools that streamline how data flows across a platform/product, from ingestion to delivery, so teams can build and ship reliable, data-driven features faster. The primary purpose is to treat data pipelines like a product infrastructure: automating testing, monitoring, and deployment to improve agility, reduce breakages, and create a smoother experience for both builders and end users.