MD101 Glossary

Data Orchestration is the automated coordination of data workflows that power a use case, ensuring data flows, transforms, and arrives where it should and in time. It manages dependencies, retries, and sequencing behind the scenes so user-facing products remain timely, stable, and performant.

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 Pipelines are structured systems that extract, transform, and deliver data from source to destination based on downstream needs. They automate data flow enabling consistency, freshness, and usability across features.

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 Privacy is the practice of protecting sensitive or personal information across the data product lifecycle. It ensures compliance with laws, ethics, and domain-specific standards while building user trust.

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)

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.

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 Deployment is the process of releasing a data product into a live environment where it can be accessed, used, and deliver value. It includes configuring access, validating performance, integrating with consuming systems, and ensuring reliability from day one. Deployment here isn’t about moving code but about preparing the product to succeed in real-world use.

Data Product Design is the process of shaping a data product through early prototypes that reflect real-world needs and usage patterns. It focuses on clarifying who the product is for, what decisions it supports, and how data should be structured and delivered to be immediately useful. Good design reduces friction, builds trust, and ensures the product is usable before it is even built.

Data Product Development is the process of turning a well-defined data product concept into a working solution including data modeling, pipeline engineering, access controls, testing, and integration. It focuses on building the right backend and interface so that the product delivers value reliably, securely, and at scale. Development is where design choices meet technical execution, and where usability, performance, and trust are built in.

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.

A Data Product Ecosystem is the interconnected network of data products, platforms, processes, teams, and governance that work together to deliver trusted, reusable data across an organisation. It is a clear map of infrastructure that supports data flow to the standards, tools, and roles that ensure consistency, and value delivery. A healthy ecosystem allows data products to evolve, interoperate, and scale efficiently without duplication or friction.

Data Product Evolution is the continuous process of improving a data product based on how it’s used, what users need next, and how the business context changes. It includes refining outputs, updating logic, enhancing performance, or rethinking interfaces: always with the goal of keeping the product relevant, valuable, and easy to trust over time. Feedback loops from users and systems guide where and how it evolves.

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.

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.

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 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.

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.

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 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.

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.