MD101 Glossary

Domain-Oriented Data Ownership gives responsibility for data to the teams who create and use it, empowering them to govern, maintain, and improve the quality of their own data. Such distributed ownership ensures data is handled by teams with the most context (closest to data usage), leading to more accurate, reliable, and user-centric insights, while maintaining alignment with broader organisational standards.

Audit Logs primarily provide transparency, support compliance, and help diagnose issues by offering a clear trail of user and system actions. They help users understand and trust how systems are used.

Access Control means ensuring the right users have the right level of access at the right time. It impacts user trust, compliance, and operational efficiency.

A/B Testing is a comparative method for decision-making that lets teams validate changes by comparing user outcomes across variants. It’s not just about optimisation but learning what works for users in the real world before scaling.

Headless BI decouples the data layer from the presentation layer, so your data product can serve insights directly into user experiences without relying on traditional dashboards.This helps power personalised, contextual insights right where users need them, in apps, workflows, or notifications, without forcing them to “go look at the data.”

Growth metrics are the signals your data product tracks to show how usage, adoption, and business value are trending over time. These aren’t just vanity KPIs but are actionable insights that guide roadmap decisions, optimise features, and highlight what's truly driving impact.

Granular access controls let your data product define who can see or do what at a very detailed level, down to specific columns, records, or actions. It enables fine-tuned user experiences where security, compliance, and personalisation work together, without locking down innovation.

A graph database enables your data product to model relationships between data points like people, devices, or events, so that connections become first-class citizens. This powers smarter features such as recommendations, fraud detection, or network analysis by mapping how entities are linked in real time.

A generalised data model is a reusable, modular structure your data product uses to represent entities like users, assets, or transactions, regardless of source system. It replaces rigid, source-specific schemas with flexible, product-friendly models that scale across use cases and enable faster feature development.

First-party data is the information your data product collects directly from users through interactions, behaviours, or transactions. It’s considered the most reliable and privacy-compliant data, powering features like personalisation and customer analytics, without the dependency on third-party sources.

FinOps refers to the practice of tracking, managing, and optimising the cost of data infrastructure, so your data product runs efficiently without blowing the budget. These processes enable usage-based insights and accountability, helping teams balance performance with cost and make informed decisions about data architecture.

Federated data access refers to how your data product can connect to data across multiple sources without having to move or duplicate it. Thus, instead of relying on a centralised storage, it queries the data where it lives to ensure faster and stable access with improved security and minimal movement, eliminating complexities of integration-heavy models.

A feature store refers to a central hub in your data product where engineered features are stored, versioned, and reused across ML workflows. It streamlines model development by making high-quality, production-ready features accessible so teams don’t waste time reinventing or re-validating data pipelines.

Feature engineering is the process of shaping raw data into meaningful inputs that your data product can leverage to power predictions, decisions, or personalisations. This involves designing features that make machine learning models more accurate and explainable, so product outcomes align closely with user needs.

Event streaming is how your data product continuously processes real-time data as it flows in, so insights, alerts, or actions happen instantly. This empowers use cases like live dashboards, fraud detection, or logistics tracking, keeping experiences up-to-the-moment.

Event-driven architecture allows your data product react in real time to events like a new user sign-up or a device alert by triggering automatic responses. Such architectures enable fast, scalable experiences that feel alive, responsive, and efficient without polling or delays.

ELT (Extract, Load, Transform) is the process that pulls in raw data from multiple sources, loads it into storage first, and then applies necessary transformations to make it usable for downstream analysis. ELT in the data product realm (unlike traditional ETL) focuses on efficiency by transforming data only when needed after it's loaded into the system, reducing unnecessary data movement. ELT ensures that data remains clean, accurate, and ready for use in real-time.

ETL (Extract, Transform, Load) is the process your data product employs to pull in only the raw data demanded by downstream use cases, clean and reformat it, and load it into storage, so it is ready for use. ETL within the bounds of Data Products is more efficient given that it doesn’t necessitate migration of all data, but only the source data that directly maps to the end-use case. ETL with data products is configured to play out reliably and at scale in the background, so users always work with clean, trustworthy data.

End-to-end encryption ensures data is protected at every step, from the moment it enters your product to when it's stored or shared. This encryption method guarantees users that their data is secure by default, and not something they have to configure or worry about.

Embedded analytics is when insights, dashboards, or visualisations are built right into the data product experience, not tacked on as a separate tool. It helps users understand what’s happening as they work, without switching context, so data feels like a natural, seamless part of their workflow.

Edge analytics is when your data product analyses data close to where it is created, like on a device or sensor, so users get faster, real-time insights without relying on the cloud. This powers responsive features with low latency, even in limited-connectivity environments.

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 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 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.

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 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.

A demand forecasting model is a feature or engine in your product that predicts future customer or resource needs, so users can plan smarter and act early. It looks at surfacing predictions in the right place, at the right moment, with the right level of explainability, such that users can trust and act on the insight without digging into the math.

Decision intelligence refers to how a product helps users make smarter, faster choices: by combining data, AI, and business context into clear, guided recommendations. This involves building decision flows, what-if scenarios, and intuitive visualisations that turn complex data into confident, low-friction actions for the end user.

Data usage analytics tracks how data is accessed, used, and shared across your product, providing visibility into what’s valuable, what’s ignored, and how data drives outcomes. This helps teams prioritize improvements, refine data experiences, and make smarter roadmap decisions: by surfacing patterns like most-used datasets, common queries, or drop-offs in data workflows.

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.

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 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 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 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.

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.

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 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.

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.

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.

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.

Business Intelligence (BI) is about turning raw data into trusted, actionable insights. These are designed to serve diverse business users by optimising clarity, speed, and contextual relevance in decision-making. This translates to having dashboards, reports, and tools at the time of need to understand what’s happening and what to do next.

AutoML refers to an abstraction layer over machine learning that empowers non-experts to build, deploy, and iterate on ML models by automating complex tasks, delivering faster insights and enabling broader AI adoption across different domains.

AI Agents are intelligent and autonomous systems that interact with users to help make accurate and quick decisions to meet the users’ goals by acting on behalf of the users. These are designed with usability in mind to reduce the complexities of tasks and improve user journeys, empowering businesses scale, adapt, and cater to user needs efficiently.

AIOps refers to an approach that applies machine learning to IT operations, with the goal of improving incident detection, root cause analysis, and automation. Viewed through a product thinking lens, it’s a capability designed to deliver continuous value by reducing operational noise, shortening downtime, and enabling intelligent decision-making across IT systems. AIOps refers to an approach that applies machine learning to IT operations, with the goal of improving incident detection, root cause analysis, and automation. Viewed through a product thinking lens, it’s a capability designed to deliver continuous value by reducing operational noise, shortening downtime, and enabling intelligent decision-making across IT systems.

Data Product Accessibility ensures that data products are easy to discover, understand, use, and adopt long-term; regardless of a user’s technical background, role, or tools. It includes intuitive interfaces, clear documentation, consistent semantics, and appropriate access controls. Making data products accessible drives adoption, reduces support burden, and empowers more people to generate value from data.

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 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 Monitoring is the continuous tracking of a data product’s key indicators (such as availability, latency, data freshness, volume, and error rates) to ensure it functions as expected. Advanced data product monitoring triggers proactive alerts to specified upstream and downstream channels when SLOs or thresholds are breached. It helps teams to be prepared for bugs, maintain quality SLOs, prevent downstream failures, and preserve user trust. Data Product Monitoring focuses on known risks, complementing broader observability efforts.

Data Product Observability means to monitor, understand, and trace the internal state and behaviour of a data product, either in real-time or in spurts depending on the product's use case. It includes visibility into data freshness, lineage, quality, usage patterns, and system health, enabling teams to detect issues early, troubleshoot faster, and maintain trust. Observability turns a data product from a black box into a transparent, dependable asset.

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.

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.

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.

Data Product Documentation is the structured, user-friendly guide that explains what a data product is, what problem it solves, how to use it, and how it works under the hood. It covers everything from definitions and data sources to schemas, SLAs, ownership, and update cadence: ensuring that users can trust, adopt, and build on the data product with confidence. Good documentation reduces support load, speeds up onboarding, and drives product adoption.

Data Product Monetisation is the practice of generating revenue from data by packaging it into usable, valuable products (such as dashboards, APIs, insights, or models) that solve real customer problems. It goes beyond internal analytics by treating data as a marketable asset, with clear value propositions, pricing strategies, and measurable ROI. Success relies on understanding user needs, usage patterns, and delivering data in formats customers are willing to pay for.

A Data Product Marketplace is a curated environment where internal or external users can browse, compare, and access data products based on quality, relevance, and usage needs. It promotes transparency, self-service, and monetisation by showcasing data products with clear value propositions, pricing (if applicable), SLAs, and documentation; turning dormant data into discoverable, consumable assets.

A Data Product Catalog is a centralized, searchable inventory with decentralized accessibility. It projects all available data products within an organisation with key details like purpose, ownership, quality, access instructions, usage metrics, and documentation. Such granular data and visibility across the "vertical slice" of the data product (instead of isolated data assets) helps users discover, evaluate, debug, and request the right data products quickly. It’s a cornerstone for driving adoption, trust, and self-service across data consumers.

Data Product KPIs are key performance indicators that track the performance, adoption, and business impact of a data product. They are crucial to understand the product's relevance in the user "market" and accordingly serve more in alignment with changing user behaviour. These may include metrics like data freshness, uptime, user engagement, query volume, time-to-insight, or ROI contribution. Data Product KPIs guide prioritisation, signal product health, and align stakeholders around value delivery and continuous improvement.

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.

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 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.

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.

A Data Product Manager is responsible for shaping, delivering, and evolving data products that drive business value. They translate user needs into product requirements, align cross-functional teams, and oversee the full data product lifecycle — from discovery and design to deployment and iteration. With a blend of data fluency and product strategy, they ensure the product is useful, usable, and continuously improving.

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 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 as a Product treats data as a valuable, customer-focused offering: designed, developed, and maintained to meet specific user needs. This approach ensures that data is reliable, discoverable, and easy to use, with clear ownership, continuous improvements, and a focus on delivering measurable value to the business or end-users.

Distributed Data Processing involves spreading data tasks across multiple systems to handle large volumes efficiently. By breaking down processing into smaller, parallel tasks, it ensures faster, more scalable data workflows. For data product teams, this means delivering data insights faster without being bottlenecked by single system limitations.

Decentralised Data Governance shifts control and accountability from a central team to the domain teams closest to the data who are the end users of data or data consumers and understand the day-to-day governance protocols of the domain data better. Instead of one-size-fits-all rules, each team defines and enforces policies that work for their context, while still aligning with shared standards. It scales governance without becoming a bottleneck.

Data Virtualisation is a layered technology that enables users to access, query, and use data across multiple systems without physically moving or duplicating it. A data virtualisation layer is a unified virtual layer over various data sources enabling faster data delivery, simplified integration, and minimal duplication. This allows teams to work with up-to-date data in real-time without waiting for pipelines or managing complex ETL processes.

Data Vault is a data modeling technique built for flexibility, scalability, and historical tracking in large, evolving data environments. It organises data into Hubs (core business entities), Links (relationships), and Satellites (context and history), making it easier to adapt to change without breaking downstream systems. This structure supports reliable analytics, easier auditing, and long-term maintainability, especially in enterprise-scale data platforms.

Data Product Metrics are the measurable signals showing whether a data product delivers value to users and the business. They track adoption, reliability, usability, and outcomes, helping teams iterate based on real-world impact, not assumptions. Data Product metrics ensure that data products are not just deployed but improved based on how they are adopted and helping users get things done.

Data Product Management starts with understanding user needs: the real questions, decisions, and pain points they face. It guides a data product through its full lifecycle: design, develop, deploy, and evolve. It ensures that data products are useful, usable, and continuously aligned with user needs and business goals. For end users of data, it means the data tools, applications, and data they rely on are thoughtfully built, well-maintained, and always improving, not just published and left behind.

A data product is an integrated and self-contained combination of data, metadata, semantics, and templates. It includes access and logic-certified implementation for tackling specific data and analytics scenarios and reuse. A data product must be consumption-ready (trusted by consumers), up-to-date (by engineering teams), and approved for use (governed). Data products enable various D&A use cases, such as data sharing, monetisation, analytics, and application integration.For users, it means getting trustworthy data they can actually use, without chasing engineers or second-guessing definitions. (Source: Gartner, Modern Data)

A Data Platform is a set of tools, services, and interfaces that make it easy for both data and business teams to collect, exchange, store, manage, and use data. A good data platform hides technical complexity, so users can focus on building products, insights, and experiences instead of fighting infrastructure.

Data Ownership means making specific teams clearly responsible for meeting the end-users' quality requirements and ensuring the data they produce becomes usable (accessible, understandable, and trustworthy). It gives users confidence that someone is actively maintaining the data, not just producing it, so they can depend on it to affect real business decisions without fearing the consequences of unvalidated data.

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 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.

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.

Contract-Driven Development is the practice of defining clear, upfront agreements (contracts) between data producers and consumers. It ensures teams can work independently, reduces integration risks, and treats data interfaces as stable, reliable products.

Consumer-Grade UX means delivering a user experience for data tools that matches the simplicity, speed, and intuitiveness users expect from everyday consumer apps. It's about removing friction, making complex tasks feel easy, and driving adoption through thoughtful design.

Composability is the ability to build flexible, modular data solutions by assembling independent parts. It enables teams to move faster, adapt to change, and deliver experiences tailored to specific and dynamic user needs: treating data capabilities as building blocks rather than fixed systems.Composability is the ability to build flexible, modular data solutions by assembling independent parts. It enables teams to move faster, adapt to change, and deliver experiences tailored to specific and dynamic user needs: treating data capabilities as building blocks rather than fixed systems.

A Cloud Data Platform is a foundational solution that simplifies how users store, access, and work with data at scale. It's strength is in abstracting infrastructure complexity, accelerate data-driven products, and enable teams to focus on delivering insights and innovation. Some examples of such platforms would include Snowflake, Databricks, DataOS, and dbt.

Build vs. Buy is the strategic decision of whether to create a data solution in-house or adopt a readdy-to-use external product. It's not just about cost, it’s about aligning with user needs, speed to value, long-term ownership, and the ability to differentiate through data capabilities.

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.

The Data Product Lifecycle captures the maturation of a data product. It is a cyclic journey from identifying user needs and modelling specific goal-oriented solutions to building, deploying, and continuously evolving them for higher adoption and relevance. Each stage (Design, Develop, Deploy, and Evolve) ensures the product remains relevant, reliable, and valuable. A well-managed lifecycle aligns teams, shortens feedback loops, and sustains long-term impact.