How to Build Data Product Ecosystems

Many smart minds have offered definitions of what a data product is. But definitions alone don’t build products. Let’s talk about how to build one. Below is a high-level checklist that covers steps from concept to delivery: 1. Define the Purpose - What problem is the data product solving? - Who are the consumers? - What decisions or actions will it enable? - Who is funding product development? - Who is funding product maintenance? 2. Identify the Data - Catalog all relevant data sources: structured, unstructured, semi-structured, streaming. - Consider data that are results of transformations, derivations, and other data manipulations. - Do not forget that data input may be metadata (e.g., data profiles or quality or observability metrics). 3. Establish Semantics & Metadata - Define metadata needed to understand the data (e.g., business terms, metrics). - Define metadata to guide appropriate use of the data product (e.g., privacy, sensitivity). - Define metadata describing data sources and lineage. 4. Design Processing Logic Within the Data Product - Build logic to transform, validate, and enrich raw data. - Implement error-handling mechanisms. - Define business logic for access control. 5. Build Interfaces - Decide how consumers will interact with the product: typically APIs for software and UIs for humans. - Design logic for how the data product receives and interprets requests. - Design logic for how the data product responds to requests and delivers data. - Design logic for how the data product communicates errors to both software and human consumers. 6. Implement Governance & Security - Design access controls in accordance with data privacy regulations. - Ensure compliance with internal and external standards. 7. Test & Validate - Verify data accuracy, performance, scalability - Test user experience. - Simulate edge cases and error scenarios. 8. Monitor & Evolve - Set up observability rules for product usage, performance, and data quality. - Continuously improve based on feedback and evolving needs. Bottom line: a data product isn’t just a dataset—it’s a living, evolving asset that combines data, metadata, processing, and interfaces. And it must deliver real business value.

“Your GTM Isn’t a Product—It’s a Platform.” a $21M CEO asked me: “how did Snowflake grow from zero to $2B+ in revenue in one of the most crowded categories?” my response? “they didn’t just build a product. they built a GTM system that scaled with every stage of growth.” most companies stall after finding early traction— 📌 they scale revenue, but not operations 📌 they hit product-market fit but don’t evolve 📌 they rely on one channel, one persona, or one hero rep but the best companies don’t just grow. they transform—from product to platform. and they do it with a go-to-market system. when GTM is a system, it evolves across stages: problem → product → platform so how did Snowflake do it? 1️⃣ predictable demand generation → how do we create pipeline at every stage of growth? 🟠 at problem-market fit: ✅ messaging focused on separation of storage & compute ✅ technical founders led early education + sales ✅ first customers were data engineers & architects 🟡 at product-market fit: ✅ launched an enterprise sales engine ✅ paid + partner channels activated ✅ early wins in finance and healthcare verticals 🟢 at platform-market fit: ✅ category creation: “The Data Cloud” ✅ multi-cloud strategy + marketplace fueled demand ✅ C-level, IT, and data teams engaged in the same ecosystem 🚀 Snowflake didn’t chase channels. they aligned GTM with product maturity. 2️⃣ seamless pipeline conversion → how do we turn interest into enterprise deals? ✅ sales process aligned to data transformation roadmap ✅ layered in vertical use cases + security/compliance value ✅ sales + SE + customer success teams worked in pods ✅ weekly forecast + usage reviews to spot and accelerate deals 🚀 every pipeline stage mapped to buyer readiness, not internal quotas. 3️⃣ revenue retention & expansion → how do we grow customer value over time? ✅ usage-based pricing → aligned value to cost ✅ net revenue retention (NRR) > 130% ✅ platform expansion: analytics → governance → apps ✅ integrations + marketplace drove stickiness 🚀 they didn’t just retain customers—they expanded into entire ecosystems. final thoughts 📌 if your GTM strategy doesn’t evolve with your product—you’ll stall. 📌 if you treat GTM as a one-time play—you’ll never become a platform. 📌 if you don’t invest in the system behind the growth—your wins won’t scale. Snowflake didn’t win because of one product. they won because their GTM system evolved at every stage. so i’ll ask you: 👉 is your GTM built to evolve—or are you still selling like it’s day one? let’s discuss 👇 — love, sangram p.s. follow Sangram Vajre to learn how to scale your GTM from product to platform with GTM O.S. #gotomarket #gtm #growth #b2b #sales #marketing #snowflake #platform #nrr #categorycreation

Treating data as a product is a necessity these days but the main question is: How do you operationalize it without adding more tools, more silos, and more manual work? There has been some confusion and process gaps around it especially when you're working with Databricks Unity Catalog. From contract to catalog, it's important for us to treat the data journey as a single process. Here, I'd like to talk about a practical user flow that organisations should adopt to create governed, discoverable, and mature data products using UC and contract-first approach. But before I begin with the flow, it's important to make sure that: ✅ Producers clearly define what they're offering (table schema, metadata, policies); ✅ Consumers know what to expect (quality, access, usage); ✅ Governance and lifecycle management are enforced automatically. That's why to do this, I'd like to divide the architecture into 3 parts: 👉 Data Contract Layer: To define expectations and ownership; 👉 UC Service Layer: API-driven layer to enforce contracts as code; 👉 UC Layer: Acting as Data & Governance plane. ☘️ The Ideal flow: 🙋 Step 1: Producer would define the schema of the table (columns, dtypes, descriptions) including ownership, purpose and intended use. 👨💻 Step 2: Producer would add table descriptions, table tags, column-level tags (e.g., PII, sensitive) and domain ownership rules. 🏌♂️ Step 3: Behind the scenes, the API service would trigger the table creation process in the right catalog/schema. Metadata would also be registered. 🥷 Step 4: Producer would include policies like: Who can see what? Which columns require masking? What's visible for which role? etc.. 😷 Step 5: Row/column filters and masking logic would be applied to the table. ⚡ Step 6: Once the table is live, validation would kick-in that would include schema checks, contract compliance, etc. 💡 Step 7: Just-in-Time Access would ensure consumers don't get access by default. Instead, access would be granted on demand based on Attribute Based Access Control (ABAC). The process, again, would be managed by APIs and no ad-hoc grants via UI. 👍 Step 8-9: All access and permission changes would be audited and stored. As soon as the consumer requests access to the table, SELECT permission would be granted based on approvals ensuring right data usage and compliance. 🔔 Step 10-11: Upon consumer request and based on the metrics provided, a Lakehouse Monitoring would be hooked-in to the table to monitor freshness, completeness, and anomalies. Alerts would also be configured to notify consumers proactively. ☑️ Step 12: The Lakehouse monitoring dashboard attached to the table would be shared with the stakeholders. 🚀 What do you get⁉️ -A fully governed & discoverable data product. -Lifecycle polices enforced for both producer and consumer. -Decoupled producer and consumer responsibilities. -Quality monitoring observability built-in. #Databricks #UnityCatalog #DataGovernance #DataContract #DataProducts

𝗙𝗿𝗼𝗺 𝗥𝗼𝘄𝘀 𝘁𝗼 𝗥𝗲𝘃𝗲𝗻𝘂𝗲: 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗧𝗵𝗿𝗲𝗲 𝗔𝗰𝘁𝘀 Most enterprises don’t fail at collecting data. They fail at turning it into impact. Confusion between data sets, data models, and data products is one of the biggest hidden taxes on transformation programs. Let’s break it down. 𝗧𝗵𝗲 𝗜𝗻𝗴𝗿𝗲𝗱𝗶𝗲𝗻𝘁𝘀, 𝗥𝗲𝗰𝗶𝗽𝗲, 𝗮𝗻𝗱 𝗦𝗮𝘂𝗰𝗲 𝗼𝗳 𝗗𝗮𝘁𝗮 Data Set (The Ingredient): Rows, columns, logs, and transactions. They provide visibility but are meaningless without context. Data Model (The Recipe): Structures data into meaning - predicting churn, segmenting customers, optimizing supply chains. Intelligence, but abstract unless operationalized. Data Product (The Sauce): What users consume - a pricing dashboard, fraud detection tool, or recommendation engine. It drives action by solving business problems. Taking an example of revenue growth management - The data set has outlet details, shipments, price lists, and promotions. The model translates this into elasticity curves, promo effectiveness, and pack architecture. The product delivers actionable guidance: which packs to push, discounts to drop, promotions to double down on. 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝟭: 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗨𝗻𝗹𝗼𝗰𝗸𝘀 𝗜𝗺𝗽𝗮𝗰𝘁 Data products need dedicated owners like product managers who bridge business and technical teams. They validate use cases, ensure business alignment, and champion adoption. Ownership accelerates decisions and keeps products impactful. 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝟮: 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝗲𝗿-𝗖𝗼𝗻𝘀𝘂𝗺𝗲𝗿 𝗠𝗼𝗱𝗲𝗹 Treat data products like commercial offerings. Producers focus on quality, documentation, and compliance; consumers discover and use products independently. Catalogs, self-service tools, and governance enable delivery at business velocity without sacrificing standards. 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝟯: 𝗖𝗿𝗼𝘀𝘀-𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗲𝗮𝗺𝘀 𝗳𝗼𝗿 𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆 Components like models, platforms, and APIs often sit in siloed teams. Leading companies form cross-functional teams that own data products end-to-end, reducing friction, accelerating innovation, and balancing enterprise consistency with business agility. 𝗧𝗵𝗲 𝗧𝗿𝘂𝗲 𝗨𝗻𝗹𝗼𝗰𝗸 When raw data, robust models, impactful products, and analytics align, data stops being a cost center and becomes a growth engine. What’s your view? Does your organization clearly differentiate between data sets, models, products, and analytics? Where are the biggest gaps or opportunities today? #DataStrategy #DataProducts #AI #Analytics #Transformation

𝗗𝗮𝘁𝗮 𝗠𝗲𝘀𝗵 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 - In Data Mesh architecture, moving away from centralized, monolithic data platforms towards a distributed, domain-oriented, self-serve design.  𝗞𝗲𝘆 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀: 𝟭. 𝗗𝗼𝗺𝗮𝗶𝗻-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗗𝗲𝗰𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗗𝗮𝘁𝗮 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗮𝗻𝗱 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲:   - Organizes data around business domains   - Each domain owns its data and is responsible for serving it as a product 𝟮. 𝗗𝗮𝘁𝗮 𝗮𝘀 𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁:   - Treats data as a first-class product   - Focuses on the needs of data consumers   - Emphasizes data quality, documentation, and ease of use 𝟯. 𝗦𝗲𝗹𝗳-𝗦𝗲𝗿𝘃𝗲 𝗗𝗮𝘁𝗮 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝘀 𝗮 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺:   - Provides standardized tools and platforms for domains to use   - Enables domains to autonomously create and serve their data products 𝟰. 𝗙𝗲𝗱𝗲𝗿𝗮𝘁𝗲𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲:   - Establishes global standards and policies   - Allows for local decision making within domains   - Ensures interoperability and compliance across the mesh  𝗞𝗲𝘆 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀: 𝟭. 𝗗𝗼𝗺𝗮𝗶𝗻 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀:   - Owned and managed by domain teams   - Includes raw data, transformed data, and data APIs   - Accompanied by metadata, quality metrics, and documentation 𝟮. 𝗗𝗮𝘁𝗮 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝘀 𝗮 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺:   - Provides tools for data storage, processing, and serving   - Offers standardized observability and governance capabilities   - Enables seamless integration between domains 𝟯. 𝗠𝗲𝘀𝗵 𝗧𝗼𝗽𝗼𝗹𝗼𝗴𝘆:   - Interconnected network of domain data products   - Allows for discovery and consumption of data across domains 𝟰. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗟𝗮𝘆𝗲𝗿:   - Enforces global policies and standards   - Provides mechanisms for data discovery and lineage tracking  𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 𝘁𝗼 𝗢𝘁𝗵𝗲𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀: 𝟭. 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲/𝗟𝗮𝗸𝗲:   - Data Mesh decentralizes data ownership vs. centralized approach   - Emphasizes domain expertise over centralized data team   - More flexible and scalable for large organizations 𝟮. 𝗟𝗮𝗺𝗯𝗱𝗮/𝗞𝗮𝗽𝗽𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀:   - Data Mesh focuses on organizational and ownership aspects vs. technical processing patterns   - Can incorporate Lambda/Kappa principles within domain data products if needed   - Emphasizes data as a product rather than just data processing

How to architect a Medallion pattern INSIDE a Data Mesh (without making a mess)  In my last post, we established a hard truth: trying to build one massive, centralized "Enterprise-Wide Medallion" pipeline completely breaks the autonomy of a Data Mesh. It creates a massive bottleneck. So, how do we get the engineering discipline of Bronze/Silver/Gold without losing the decentralized power of the Mesh? We abandon the macro-pipeline and adopt the "Micro-Medallion" pattern. Here is the 5-rule playbook for exactly how to execute it: 1. Shift from Macro to "Micro-Medallions" Instead of building a massive, enterprise-wide Bronze-to-Gold pipeline, Medallion scales down to the domain level. Each business domain (e.g., Finance, Supply Chain) owns its localized pipelines. Medallion simply becomes the standard internal data processing engine of the domain. 2. Bronze & Silver = Strictly Private In software engineering, you don't expose your internal application database. The same applies here. A domain’s Bronze (raw events) and Silver (conformed) layers are strictly private implementation details. This encapsulation gives domains the absolute autonomy to refactor logic and fix data quality issues without breaking downstream consumers. 3. The Gold Layer = The Data Product This is the paradigm shift. In a Mesh, your Gold layer is no longer just an aggregated BI table; it is the outward-facing Data Product. It is the formal, governed asset exposed to the rest of the enterprise, secured by strict Data Contracts (schema stability, SLAs, data quality metrics). 4.The Cross-Domain Handshake How do domains interact without creating a tangled web of spaghetti pipelines? Strict read-patterns. If Domain B needs data from Domain A, it subscribes to Domain A's Gold layer (the Data Product) and ingests it as a read-only, immutable source into its own Bronze layer. This enforces clean lineage and complete decoupling. 5. "Medallion-in-a-Box" Infrastructure To prevent a fragmented tech stack, the central Data Platform team provides "Medallion-in-a-box" IaC (Infrastructure as Code) templates. Domains can spin up standardized environments instantly, enforcing global governance while preserving their autonomy. The Takeaway: Data Mesh dictates who owns the data and how it is shared. Medallion architecture dictates how that data is engineered under the hood. Decentralization without standardized engineering patterns isn't a Mesh—it's a mess. Are you using Micro-Medallions in your architecture yet? Let me know in the comments! #DataMesh #MedallionArchitecture #DataEngineering #DataProducts #DataArchitecture

The value of the 𝐧𝐞𝐭𝐰𝐨𝐫�� 𝐞𝐟𝐟𝐞𝐜𝐭 must be noted right from the very beginning of the Data Product Journey: 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧 of the Data Product Strategy. The framework, the 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐀𝐦𝐩𝐥𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐌𝐚𝐭𝐫𝐢𝐱, helps stakeholders understand the trajectory of data products within an organization over phases of data product development and evolution of cross-domain collaboration. It visualizes how the 𝐯𝐚𝐥𝐮𝐞 𝐨𝐟 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐬 ♚ 𝐬𝐜𝐚𝐥𝐞𝐬 𝐚𝐬 𝐭𝐡𝐞𝐢𝐫 𝐢𝐧𝐭𝐞𝐫𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐞𝐝𝐧𝐞𝐬𝐬 𝐚𝐧𝐝 𝐧𝐞𝐭𝐰𝐨𝐫𝐤 𝐞𝐟𝐟𝐞𝐜𝐭𝐬 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞. The Four Quadrants ♛♜♝♞ -------------------------- 1️⃣ 𝐁𝐨𝐭𝐭𝐨𝐦-𝐋𝐞𝐟𝐭: 𝐈𝐬𝐨𝐥𝐚𝐭𝐞𝐝 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬 (Low Connection, Low Value) Data products that operate in silos—for example, narrowly scoped models or single-case dashboards. While these Data Products address specific business needs, their value is limited because they are disconnected from the broader ecosystem. Domains often start here, but staying in this quadrant signals inefficiency and missed opportunities for compounding value. The goal should be to evolve beyond isolated products. 2️⃣ 𝐁𝐨𝐭𝐭𝐨𝐦-𝐑𝐢𝐠𝐡𝐭: 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐞𝐝 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬 (Many Connections, Moderate Value) Data products begin to form connections. Pipelines are linked, datasets feed multiple downstream applications, and products become shared resources across teams. The network starts taking shape, enabling cross-functional visibility and collaboration. While connected products deliver moderate value, they often lack the coordination and intentionality required to unlock high synergy. This is the launchpad for scalable data ecosystems. 3️⃣ 𝐓𝐨𝐩-𝐋𝐞𝐟𝐭: 𝐏𝐫𝐞-𝐒𝐲𝐧𝐞𝐫𝐠𝐲 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬 (High Connections poised to reap the value of Network Effect) Data products are primed for exponential growth in value. They are highly interconnected, and the groundwork for a network effect is laid. At this stage, teams might be experimenting with integrations and aligning metrics but haven’t yet harnessed the multiplier effect. 4️⃣ 𝐓𝐨𝐩-𝐑𝐢𝐠𝐡𝐭: 𝐇𝐢𝐠𝐡-𝐒𝐲𝐧𝐞𝐫𝐠𝐲 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬 (High Connections, High Value) The pinnacle of the matrix, where interconnectedness leads to compounding/exponential returns. Data products become the lifeblood of decision-making, driving personalization and real-time optimization. At this stage, data products amplify each other’s value, creating a self-sustaining feedback loop. Achieving this requires robust data platforms, clear product ownership, and a product mindset that views data as a strategic business asset. Which stage are you in right now, and what's your strategy for progressing to the next>> #dataproducts #datastreategy #datastack

Meshing Your Data Products In chapter 8 of “Implementing Data Mesh,” Eric Broda and I explore how to utilize multiple data products within a Data Mesh by registering, connecting, and meshing them. The registration process involves the storing data contracts in source control, which are then assembled and published to a data marketplace by the Data Product Owner (DPO). This registration ensures that data products are discoverable, usable, and meet quality standards. The process, often automated via CI/CD pipelines, involves collaboration between data engineers, architects, and the DPO to refine and finalize data contracts before initiating registration. This structured approach facilitates the seamless integration and lifecycle management of data products within the ecosystem. Connecting data products within a Data Mesh unlocks significant value, such as consistent data quality and service levels. By meshing multiple data products, like combining precipitation data from different geographic regions, you can consolidate information into a single, comprehensive data product with unified SLAs. This approach also enhances data lineage, making it easier to track data transformations and meet regulatory requirements. Additionally, Data Mesh enables efficient notification systems, alerting users and applications to data issues promptly. This proactive communication ensures data reliability and trust, ultimately enhancing the overall data management and user experience within the mesh. Find out more on the O'Reilly learning platform at: https://lnkd.in/gD9DE43B cc O'Reilly, Aaron Black, Shira Evans, Scott Hirleman, Ole Olesen-Bagneux, Colleen Tartow, Ph.D., Max Schultze, Karin Håkansson, John Y Miller #DataMesh #ImplementingDataMesh #DataProduct

Teams often struggle with turning data into something that actually delivers value. That’s where a Data Product Architecture changes the game. Instead of scattered pipelines and dashboards, this approach treats data like a product - designed, managed, and continuously improved. Here’s how it comes together end-to-end: Acquire → Integrate → Organize → Analyze → Deliver It starts with data sources, moves through integration pipelines, and gets structured on data management platforms. From there, analytics and data science extract insights that are ready to be consumed. But the real shift happens in the middle layer 👇 Data Product Management Layer This is where data becomes usable and scalable: • Standardized delivery templates ensure consistency • Delivery methods define how data reaches users (APIs, dashboards, streams) • Delivery channels distribute data across systems and teams • Performance tracking ensures the product actually delivers business impact Surrounding everything are the pillars most teams underestimate: • Data Observability → Know when things break before users do • Metadata Management → Understand what your data actually means • Data Governance → Ensure trust, security, and compliance The result? Not just dashboards. Not just pipelines. But reliable, scalable data products that teams can actually depend on. That’s the difference between data that exists… and data that drives decisions. #DataEngineering #DataArchitecture #DataProducts #Analytics #AI #DataStrategy