Best Practices for Product Data Management

I've always championed a product-based approach to data management. A decade ago, this perspective was often dismissed, as products were merely seen as buttons on a digital interface. However, that view is shifting as AI initiatives now place data at the forefront. The cornerstone of data product design is user centricity. Today's users could be agents or humans, but the principle remains the same. Do you have clear user profiles? Are you analyzing queries in your environment like we do with clickstream data on websites? How can you improve data navigation? Is your data model intuitive? Are you providing the right aggregations for frequently used queries? Do you have a searchable catalog that helps users find data? Are your data models designed for easier visualization? There are many simple questions to consider. My first step, even as an executive, is to examine the data model and see if I can write a query on the first day. If you're not SQL-savvy, ask your analyst to show you one of theirs. If table names require a PhD to decipher (like a schema name such as ABCD12345), it's clear that the data isn't user-friendly; it’s designed as a technical output. We can all work towards making data more accessible. It just takes a bit of observation, active listening, and thoughtful analysis.

👥 A customer asked a question I hear often when starting data governance: 𝐇𝐨𝐰 𝐬𝐡𝐨𝐮𝐥𝐝 𝐰𝐞 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐞 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞 𝐭𝐚𝐛𝐥𝐞𝐬, 𝐝𝐛𝐭 𝐦𝐨𝐝𝐞𝐥𝐬, 𝐚𝐧𝐝 𝐓𝐚𝐛𝐥𝐞𝐚𝐮 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 — 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫 𝐨𝐫 𝐬𝐞𝐩𝐚𝐫𝐚𝐭𝐞? 𝐒𝐡𝐨𝐮𝐥𝐝 𝐰𝐞 𝐜𝐚𝐥𝐥 𝐭𝐡𝐞𝐦 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐬? We recommended them to use our Collections features, but it's more about the approach rather than the product feature itself. Organizing your data assets into collections or folders isn’t just about tidiness — it’s a foundation for discoverability, access control, lineage tracking, and ultimately driving trust in the data. 📈 Should You Call Them “Data Products”? Yes — if you’re driving toward a product-thinking approach to data. It encourages thinking beyond just “tables” toward complete deliverables (i.e., dbt models). Many companies now define “Data Products” as collections of datasets, business logic, and outputs designed to serve a specific analytical or operational need. Examples: Customer 360 Data Product Subscription Revenue Metrics Product Marketing Funnel Data Product 💡 Collection Naming is Important for Long-term Usability. We recommend following the business domains and use cases. Here are some good patterns to follow: 📂 <Domain> / <Use Case> (i.e., Sales / Forecasting) 📂 <Data Product> (i.e., Customer 360 Data Product) 📂 <Business Unit> / <Process> (i.e., Finance / Closing Process) 📂 <Source System> / <Transformation Stage> (i.e., Stripe / Cleaned Data) 🔒 Tie to Governance: Ownership and Access When creating collections, define & understand the following: - Owners: Each collection or data product has a person/team responsible - Access policies: Collections drive RBAC (role-based access control) or policies - Lineage & Documentation: One place to trace what downstream assets depend on which source As a TLDR; here are the top best practices for collections & curation of data: ✅ Organize around business domains or data products, not tools. ✅ Mix Snowflake tables, dbt models, dashboards in the same collection. ✅ Use consistent, scalable names like <Domain> / <Use Case>. ✅ Treat collections as governable objects — assign owners and access. ✅ Avoid tool-based silos unless for backend-only purposes. Anything I missed? I might need to write a blog post on this 😄 #datagovernance #dataproducts #datamesh #analytics

The FAIR principles—Findable, Accessible, Interoperable, Reusable—originated in 2016 as guidelines for managing scientific research data. Below is how FAIR translates into practical data product design: every data product, regardless of purpose, must adhere to these principles. Findable: Consumers must be able to locate the product in a product catalog or product registry. There should be an inventory of data products, and each product must include metadata describing its purpose, content, and context. Accessible:  Each data product needs a stable, standards-based address (such as an API endpoint or URI) so that humans and software can reliably access it. At the same time, access controls, governance rules, and compliance requirements should be embedded into the product and not added as an afterthought. Interoperable:  A data product must be able to connect with other data, software, and data products. This requires shared definitions, consistent formats, and adherence to enterprise standards. Reusable:  Data products must be thoroughly tested and quality-assured to ensure reliable processing and results. Documented data lineage instills trust in the data itself, allowing it to be confidently reused across multiple use cases. At the same time, a true data product should be independently deployable. It must be free from dependencies on other products, and decoupled from other software components.

It feels great to launch a new data product, but don't forget about the work that follows afterward! Here are steps that will help to keep it relevant for a long time: 1. 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗣𝗲𝗿𝗶𝗼𝗱𝗶𝗰 𝗥𝗲𝘃𝗶𝗲𝘄𝘀: Business goals and data needs change over time. Establish a routine for reviewing your data product’s usage and relevance. Is it still meeting the needs of your users? 2. 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸: Create channels for ongoing feedback and encourage users to report issues or suggest improvements. 3. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁𝘀: Use feedback and review outcomes to make relevant improvements. This could mean refining visualizations, adding new data points, or optimizing performance. Most data products are never truly finished. 4. 𝗘𝗱𝘂𝗰𝗮𝘁𝗲 𝗮𝗻𝗱 𝗘𝗻𝗮𝗯𝗹𝗲 𝗨𝘀𝗲𝗿𝘀: Offer training sessions for new features or changes. Enable users to fully utilize the data product, ensuring it remains a valuable tool that gets regularly used. 5. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗖𝗵𝗮𝗻𝗴𝗲𝘀: Keep a changelog or documentation of updates and modifications. This transparency helps manage expectations and provides a history of the product’s progression. 6. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Continuously monitor the data product’s performance and reliability to ensure it functions well under changing conditions. Identify and address issues before they impact your stakeholders. 7. 𝗧𝗮𝗿𝗴𝗲𝘁 𝗡𝗲𝘄 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀: Regularly check for opportunities to expand your data product's functionality or apply it to new business use cases. Staying proactive and anticipating needs will keep your work results relevant for a long time.     8. 𝗞𝗻𝗼𝘄 𝗪𝗵𝗲𝗻 𝘁𝗼 𝗦𝗮𝘆 𝗚𝗼𝗼𝗱𝗯𝘆𝗲: Not all data products are meant to last forever. Recognize when a product no longer serves its purpose and plan for its retirement or replacement. This decision ensures resources are focused on tools that continue to deliver value to the business. Handling the post-launch lifecycle is an important task. Continuous improvement and alignment with changing needs will ensure your data products stay relevant for the business. What’s your experience with maintaining data products post-launch? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find this post useful ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field #dataanalytics #datascience #dataproducts #productmanagement #careergrowth

In one sense, our industry’s quest for data products spans three decades. That's because they promise to solve the triad of user complaints, which we hear repeatedly in client engagements:   "I can't find relevant data." "If I find it, I can't access it." "If I access it, I don't trust it." These persistent challenges underscore why data product management is gaining traction.   Forward-thinking organizations are adopting product management principles to transform one-off, artisanal data practices into scalable, business-driven processes. The Data Product Lifecycle offers a structured way to manage data assets with the same rigor as traditional products: 1️⃣ Product Definition: Identify target customers and define their requirements to ensure products meet business needs. 2️⃣ Product Development: Build the architecture and select the tools necessary to create robust, scalable data products. 3️⃣ Product Packaging: Enrich data products with business and technical metadata to improve discoverability and usability. 4️⃣ Product Governance: Establish roles, reviews, and certification processes to guarantee data quality, consistency, and compliance. 5️⃣ Product Provisioning: Publish data products in a centralized marketplace to ensure seamless access and discoverability. 6️⃣ Lifecycle Management: Continuously monitor, track, iterate, optimize, and, when necessary, retire data products to maintain their relevance and value. This disciplined approach not only addresses long-standing issues in data management but also paves the way for the next frontier: data monetization. With a solid foundation in data product management, organizations can more effectively package and sell data products to external customers or optimize internal processes for financial gains.   The potential is immense, but it requires a strong alignment between data governance, technical expertise, and business strategy. 📊 Download [The Data Product Revolution] to explore the trends shaping data products—and how they’re setting the stage for monetization opportunities: https://lnkd.in/ezA3JMCz #DataProducts #DataManagement #DataMonetization #DataGovernance

😬 Many companies rush to adopt AI-driven solutions but fail to address the fundamental issue of data management first. Few organizations conduct proper data audits, leaving them in the dark about: 🤔 Where their data is stored (on-prem, cloud, hybrid environments, etc.). 🤔 Who owns the data (departments, vendors, or even external partners). 🤔 Which data needs to be archived or destroyed (outdated or redundant data that unnecessarily increases storage costs). 🤔 What new data should be collected to better inform decisions and create valuable AI-driven products. Ignoring these steps leads to inefficiencies, higher costs, and poor outcomes when implementing AI. Data storage isn't free, and bad or incomplete data makes AI models useless. Companies must treat data as a business-critical asset, knowing it’s the foundation for meaningful analysis and innovation. To address these gaps, companies can take the following steps: ✅ Conduct Data Audits Across Departments 💡 Create data and system audit checklists for every centralized and decentralized business unit. (Identify what data each department collects, where it’s stored, and who has access to it.) ✅ Evaluate the lifecycle of your data; what should be archived, what should be deleted, and what is still valuable? ✅ Align Data Collection with Business Goals Analyze business metrics and prioritize the questions you want answered. For example: 💡 Increase employee retention? Collect and store working condition surveys, exit interview data, and performance metrics to establish a baseline and identify trends. ✅ Build a Centralized Data Inventory and Ownership Map 💡 Use tools like data catalogs or metadata management systems to centralize your data inventory. 💡 Assign clear ownership to datasets so it’s easier to track responsibilities and prevent siloed information. ✅ Audit Tools, Systems, and Processes 💡 Review the tools and platforms your organization uses. Are they integrated? Are they redundant? 💡 Audit automation systems, CRMs, and databases to ensure they’re being used efficiently and securely. ✅ Establish Data Governance Policies 💡 Create guidelines for data collection, access, storage, and destruction. 💡 Ensure compliance with data privacy laws such as GDPR, CCPA, etc. 💡 Regularly review and update these policies as business needs and regulations evolve. ✅ Invest in Data Quality Before AI 💡 Use data cleaning tools to remove duplicates, handle missing values, and standardize formats. 💡 Test for biases in your datasets to ensure fairness when creating AI models. Businesses that understand their data can create smarter AI products, streamline operations, and ultimately drive better outcomes. Repost ♻️ #learningwithjelly #datagovernance #dataaudits #data #ai

Before going off and building data products, you need to make sure they are built on strong foundations Even teams/ people who agree with this often don’t action it So what do you need? 1️⃣ 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 – Don’t build products for the sake of it. Understand the business goals, the data strategy (if there is one), and the needs/ use cases of your business stakeholders. Your data products need to solve real problems and strategic alignment is the only way to ensure that 2️⃣ 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 & 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 – Does the data team have the foundations to underpin any product development? Consider how the (1) enterprise architecture, (2) data models, (3) solutions architecture, and (4) engineering are set up and how they should feed into product development. This makes data products (and delivery) more efficient and scalable 3️⃣ 𝗗𝗮𝘁𝗮 & 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 – Governance isn’t a regulatory need, it is key to success. Governance defines ownership and ensures alignment between the product/ data ownership and the underlying foundations. This also ensures the business knows who to go to when they have questions/ feedback 4️⃣𝗗𝗮𝘁𝗮 ����𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 – Strong data management ensures quality, reliability, and consistency in the data feeding your product. Think master data, observability, and standardisation. My favourite analogy for data foundations is the process of build a house. Without the foundations in place, even the nicest-looking house will sink into the ground or fall apart: ✍️ Strategic alignment = the architect designing the house 🛠️ Infrastructure = the pipes, wiring, and insulation 👀 Governance = assigning tasks to the right professionals ⚙️ Data management = using the right tools and quality materials for a solid build Just like a house, your data product will fail without the right foundations! Check out my article last week (in the comments) for more on this and the data supply chain process #DataStrategy #DataProducts #DataEcosystem #DylanDecodes

Are you trying to fix every piece of your product data at once? That’s your WORST mistake: I see it all the time. Companies come to us drowning in messy data. Spreadsheets everywhere. Disconnected systems. Inconsistent taxonomies. They think they need to perfect everything immediately. But Derek Sewell at dataX.ai said it best: You don’t have to boil the ocean. DATA MANAGEMENT IS A JOURNEY, NOT A ONE-AND-DONE PROJECT When your company is in motion, starting from scratch, it’s overwhelming. You have to sort out who owns what. You have to decide what data matters most. Even after investing in a top-tier DAM, PIM, or CMS, the work never ends. Maintenance is continuous. Derek shows clients that you should focus on your top performing SKUs first. Get their images, descriptions, dimensions—everything—just right. This isn’t about fixing every product immediately. It’s about prioritizing what drives revenue. It's not easy, but it's wholly worth it. CREATE A PRIORITIZATION PLAN This is what to do: 1. Identity critical data –Find where your product information lives –Pull out the SKUs that really move your business forward 2. Develop a roadmap: –Prioritize improvements for those top performing SKUs –Set up processes for continuous validation and updates You'll save valuable time, boost sales, and free your team from endless, inefficient maintenance. Too many companies get overwhelmed, thinking they'll never get their data in order. You just need a plan that prioritizes what will impact your business the most, then apply that plan to everything else. Don't think of your data as a burden. Remember it’s your most valuable asset when  managed correctly. ____________________________ I'm on a mission to help e-commerce leaders sell more. Follow along as I share what I'm hearing from around our industry.