MD101 Glossary

Data Product Optimisation is the ongoing process of improving a data product’s usability, adoption, performance, and impact based on real user feedback and usage data. It involves enhancement sprints like refining queries on the product, reducing output latency, enhancing data product documentation, and tuning outputs to better serve the evolving needs of business users. The goal is to maximise value delivery while minimising friction for users.

A Data Product Orchestrator coordinates the components, workflows, and dependencies that power a data product (logic, resources, validation, delivery, etc.). It ensures each part of the product runs in the right sequence, at the right time, with the right context. By managing complexity behind the scenes, the orchestrator (as enabled by self-serve platforms) enables reliable, scalable, and responsive data product experiences.

A Data Product Owner is accountable for the quality and success of specific data product(s). They define and prioritize the product backlog, make trade-offs on features and timelines, and ensure the data product delivers value to its intended users. Working closely with technical and business teams, they serve as the single point of truth for what the product does and why, bridging execution with purpose. Unlike the Data Product Manager, who focuses more broadly on strategy, roadmap, and stakeholder alignment across multiple products or initiatives, the Owner is deeply embedded in day-to-day delivery and tactical decision-making for one product.

Data Product Performance refers to how reliably, quickly, and accurately a data product delivers value to its users. It encompasses system speed, freshness of data, uptime, error rates, and usability under real-world workloads. Strong performance ensures trust, drives adoption, and supports the product's role in critical decision-making.

A Data Product Platform is the foundational system that enables teams to design, build, deploy, and manage data products at scale. It provides the infrastructure, tools, standards, and governance required to streamline the entire data product lifecycle (as specified by the Data Developer Platform Standard). By abstracting technical complexity, it empowers teams to focus on delivering high-quality, user-ready data products with speed and consistency.

Data Product ROI (Return on Investment) measures the value generated by a data product relative to the cost of building and maintaining it. It considers impact on revenue, cost savings, productivity gains, and strategic outcomes like faster decision-making or improved customer experience. Demonstrating ROI helps align data efforts with business goals and justify continued investment.

Data Product Scaling is the process of extending a data product’s adoption, reach, reliability, and impact as demand grows across more users, use cases, or domains. It involves strengthening performance, automating operations, ensuring governance holds at scale, and evolving interfaces to stay intuitive. Scaling isn’t just technical, it's about preserving product value as usage and adoption increases.

A Data Product Specification outlines what a data product is expected to deliver, how it behaves, and how it integrates with its ecosystem. It typically includes schema definitions, SLAs, access policies, update frequency, lineage, and intended use cases. The specification acts as a shared contract between producers and consumers, ensuring clarity, consistency, and alignment throughout the data product’s lifecycle.

Data Product TCO (Total Cost of Ownership) captures the full lifecycle cost of designing, developing, deploying, and evolving a data product: including infrastructure, tooling, development effort, maintenance, support, and governance. It helps teams make informed decisions about trade-offs, resource allocation, and scalability by revealing the real cost behind delivering sustained value.

Data Provenance tracks where data comes from, how it changes, and where it goes while creating a transparent history across the product lifecycle. It gives users the context they need to trust outputs, debug issues, and make informed decisions. Provenance makes every data point traceable and meaningful.

Data quality is about how reliable, usable, and relevant data is within the product/ user experience, which directly impacts how well product features perform and how much users can trust what they see or do. Ensuring data quality means designing for accuracy, completeness, consistency, and timeliness, so that every feature powered by data works as intended and delivers clear, trustworthy value to the user.

Data readiness is the state of having data that's clean, structured, and accessible enough to power product features, workflows, and decision-making effectively. This state focuses on ensuring the right data is available at the right time and in the right shape, so teams can confidently build, launch, and scale data-driven features without delays or rework

Data Science is the discipline of using statistical methods, machine learning, and programming to extract insights and build intelligent systems from data. It combines domain knowledge, experimentation, and technical skills to solve complex problems and enable smarter decisions.

Data security refers to how your product protects sensitive information from unauthorised access, so users can trust their data is safe by design. This means building in protection across every touchpoint: permissions, encryption, audits, without slowing down user experience or flexibility.

A Data Sharing Agreement defines how data can be accessed, used, and governed across teams or organisations. It aligns expectations around rights, responsibilities, and compliance.

A Data Sharing Agreement defines how data can be accessed, used, and governed across teams or organisations. It aligns expectations around rights, responsibilities, and compliance.

Data Synchronisation ensures that data remains aligned, consistent, and fresh across systems feeding into or consuming from a data product. It supports real-time or scheduled updates so users always see the latest, most trustworthy information.

Data tokenisation is the process of replacing sensitive data with non-sensitive, unique tokens, so products can use or share data safely without exposing the actual values. This is a crucial way to enable secure features like personalisation, analytics, or integrations, while reducing compliance risks and building user trust by ensuring privacy is baked into the product by design.

Data transformation is how raw data is cleaned, reshaped, or enriched inside the product, so it’s usable and relevant to the features that depend on it. This process focuses on ensuing the data is structured to serve real user needs, whether that’s powering a dashboard, driving a recommendation, or supporting a business rule, without requiring users to wrangle it themselves.

Data trust score refers to simple, user-facing indicator of how reliable and usable a dataset is. This enables users to quickly identify if the data is fit for their use case. A data trust score helps simplify complex qulsity signals like freshness, completeness, lineage and usage into clear and actionable scores that helps make confident decisions with responsible data use across the product.Data trust score refers to simple, user-facing indicator of how reliable and usable a dataset is. This enables users to quickly identify if the data is fit for their use case. A data trust score helps simplify complex qulsity signals like freshness, completeness, lineage and usage into clear and actionable scores that helps make confident decisions with responsible data use across the product.