Updated: November 20, 2025- 17 min read
Product data management (PDM) is the process of collecting, organizing, storing, and sharing data within a product-led organization.
“Without data, you’re just another person with an opinion.”
There’s just no way around it anymore! If you want to work in product, you need to be comfortable with data. There’s barely a modern industry out there that doesn’t rely on it. It’s especially important for data product managers, technical product managers, and nowadays, AI product managers.
In this guide, we’ll go over:
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What is Product Data Management (PDM)?
Product data management (PDM) is the discipline of defining, governing, versioning, and distributing the product’s data (events, schemas, metadata, and artifacts) so every team uses a single, trusted source of truth. It sits under Product lifecycle management but focuses specifically on the flow of product data across systems and teams, ensuring changes are controlled, auditable, and aligned to business and product goals.
In real life, PDM is less a tool and more a working agreement between product, engineering, data, and compliance. It answers four practical questions every day: What is the gold source of each dataset, who owns it, how does it change, and how do downstream systems adapt without breaking?
That shows up as data contracts for telemetry and APIs, schema registries with versioning, lineage that traces features to dashboards and ML models, and access rules that meet privacy and audit needs.
When you ship a new product-led onboarding flow, PDM defines the event spec, validates it, backfills history, and communicates the deprecation plan so analytics, finance, and support don’t wake up to broken reports.
PDM is not version control for code, though it links to it.
It is not PIM or MDM, though it collaborates with them
It is broader than a dashboard or a warehouse table.
Good PDM is measured by fewer data incidents, faster time from change to insight, clear ownership, and predictable downstream behavior. Think OKRs like “reduce schema-change MTTR” or “increase certified datasets used in decisions.”
In short, expert teams treat PDM as part architecture, part governance, and part product craft. It’s a calm system that lets you evolve the product and its data at the same time, without surprises.
Product Data Management vs. Product Lifecycle Management
Product data management process manages the product’s data and documentation so every team works from one source of truth. Product lifecycle management (PLM) governs the entire journey of a product (from idea to retirement), coordinating people, process, and tools. PDM sits inside PLM as a core pillar.
In practice, PLM is the operating system for how a company plans, builds, ships, supports, and eventually sunsets a product. PDM is the discipline that keeps the information behind those moves clean, current, and consistent.
If PLM decides what changes and when, PDM makes sure the specs, events, files, and references that describe those changes are correct and easy to use. The two work together: PLM drives decisions and sequencing, and PDM removes friction by giving everyone reliable data to act on.
A quick way to tell them apart:
Scope: PLM spans the whole critical user journey; PDM focuses on the data and docs that describe the product.
Day to day: PLM handles roadmaps, approvals, releases, and end-of-life plans. PDM handles versioning of specs, event definitions, design files, and the “single source of truth.”
Owners: PLM is led cross-functionally by product, engineering, design, and go-to-market. PDM is owned by product and data partners who steward the product’s information.
Tooling: PLM runs on roadmaps, change reviews, and release workflows. PDM runs on catalogs, repositories, and lightweight rules for naming, versioning, and access.
Outcomes: PLM delivers clear decisions and predictable product launch strategies. PDM delivers trustworthy data and fewer surprises downstream.
Think of PLM as the script for the movie, and PDM as continuity. It keeps every scene consistent, so the story holds together from the first pitch to the final credits.
How do Product Managers Use Data?
So do you need to be a complete data scientist to be a data or data-driven product manager? No. But you still have to build data-driven products.
1. A/B Testing
A/B Testing is a very important and commonly used way of gathering data for a new feature. product managers usually have good instincts, but the best way to tell if a feature will work one way or another is to perform A/B tests.
There are many common pitfalls here, and most of them involve the mishandling of data. You need to know what questions you’re asking, what data you want to gather, and what you’ll do with it. It could be as simple as “did more people click the button when it was blue, or when it was red?” Or “if we layout our homepage this way will more users finish the onboarding process?” Depending on your product, it could be infinitely more complex.
2. OKRs and KPIs
As a product manager, you’ll also be in charge of setting the objectives and key results (OKRs) for your product. They must be in line with your company’s business goals, and an understanding of data is absolutely essential for translating these goals into your own.
For example, your company’s main business goal for Q1 might be to expand into a developing market. What does that mean for your product? You need to decide which metrics you’ll use to measure your product’s impact in that new market.
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3. Stakeholder communication
Stakeholder management is the activity of interacting and addressing all stakeholders involved in product management. Numbers are a universal language, and there’s no easier way to communicate with stakeholders than by using numbers.
As we mentioned before, without data you’re just a person with another opinion, and in a stakeholder meeting there might be a lot of different opinions. If you can use numbers to back up your position, you’ll have more authority. When a stakeholder requests a feature, you can use objective facts to prove why it won’t work, etc.
4. Usage tracking
You need to know how people are using your product. A good data-driven product manager should be able to tell how many features are using feature X compared to feature Y, how many people complete the full onboarding process, etc. Having this kind of information helps you to find areas where users aren’t necessarily doing what you want them to do. This way, you can make improvements and keep your churn rates low.
5. Data infrastructure for AI tools and agents
For effective AI product management, the value of your insights depends on how well your data is structured. Poorly organized data leads to inaccurate outputs, while a clear schema, consistent taxonomies, and defined ownership make it possible for AI agents to work effectively.
Product Managers should treat Product Data Management as the foundation for AI-driven workflows — from intelligent assistants that surface insights to agents that monitor metrics and trigger actions automatically. Structured, governed data enables these systems to interpret information correctly, automate repetitive tasks, and support experimentation at scale.
Enterprise Product Data Management: How Top Tech Companies Use Data
When talking about it in theory, it’s true that data can seem like a dry topic. So when you’re learning about it, it’s important to focus on what you’ll be able to do with it in the future. All the coolest things done by your favorite companies are possible because of good product data management.
How Netflix tackles enterprise product data management
One of the companies which is most famous in the product world for managing and leveraging a massive amount of data is Netflix. After all, data comes from users and Netflix has 167 million of them!
Netflix uses information collected from its millions of users to personalize the user experience, in various ways. Information on which shows person A enjoyed can be cross referenced with information on which shows person B enjoyed, to make recommendations. If Sally liked House of Cards and The Wire, then it’s likely that Jim, who also enjoyed House of Cards, will like The Wire too.
Netflix constantly tests the user interface right down to which cover art a user is more likely to prefer. If a user consistently clicks on content showing dramatic close-ups of characters faces, Netflix will show them more of these. If a user prefers content featuring female characters, Netflix will change the cover of The Avengers, for example, from a closeup of Thor to one of Black Widow. All of this is done using data management.
Airbnb’s approach to data product management process
The problem with using basic tools like Google Analytics, is that the booking flow at Airbnb is complex. Users can browse properties whether they’re logged in or not, making it hard to track their progress. They can browse from their mobile device while logged out, and then come home to a desktop, log in, and make the booking immediately. According to Google Analytics, all they did was log in and book the first property they saw within minutes. This gives the product team a skewed vision of the customer journey. This is why it’s important to use more comprehensive PDM tools which allow for the tracking of multiple flows.
Airbnb is famously in love with experimenting, and they built their own A/B testing framework to do so. Jan Overgoor, a former data scientist at Airbnb, wrote about how experiments at Airbnb improved the product.
As a product manager, you don’t need to be able to design these experiments yourself. But you have to be able to understand which experiments need to be run, what metrics you’re looking to measure, and what actionable steps you’ll take once the experiment is done. You’ll work closely with Data Scientists and Product Data Analysts, and communicating with them is crucial.
Apple’s PDM strategy
Apple is another company that runs on Big Data. As an industry leader in data-driven products, Apple collects, manages, and leverages a massive amount of data. One of the newer ways of gathering data is through wearable technology. With 43 million Apple Watch users, Apple and IBM created a partnership to make the most out of big data analytics in digital health information.
Sensors are collecting information on how much sleep users get, how active they are, their calorie intake, along with many other factors. This information can measure the general health of a population easily and in huge quantities. It’s a potential that has the product world buzzing with excitement!
Facebook’s approach to PDM
Another company that relies heavily on data analytics is Facebook, whose ads platform is one of the most popular forms of paid social advertising out there. Its ad platform is one of the most sophisticated for marketers, because it knows exactly what its 1.62 billion daily users like and where they go on the web.
Not only does this make the ads manager an appealing data product for marketers (making Facebook an absolute fortune in revenue), but it also personalizes the product experience for users. It’s this endless cycle of good content for users and good click rates for advertisers that has helped Facebook grow at scale.
However, we couldn’t talk about Facebook and Big Data without addressing the elephant in the room…the scandals. While data science for product development is exciting, it’s the responsibility of a product manager to ask “we can do this, but should we?” Taking the ethical implications of data usage into account will pay off in the long run, and could save you a lot of trouble in the future, especially if you’re working with data products.
The Best PDM Software
When it comes to choosing product data management software, there’s no one-size-fits-all. The right tool depends on your product complexity, team size, and existing tech stack. Below are five widely used PDM systems, each with distinct strengths, features, and ideal use cases.
1. Atlan
Atlan is a modern data catalog and governance platform designed to bring order to the chaos of scattered datasets, dashboards, and metrics. It creates a shared dictionary so teams stop debating “which number is right” and start moving faster.
For software companies scaling their data layer, it’s a way to put ownership and trust back into product analytics.
Key features:
Data dictionary and business glossary
End-to-end lineage from source to dashboard
Ownership and access policies
Certification and deprecation workflows
Best for mid-sized to large digital product teams that need a single catalog to govern data without the overhead of heavy on-prem systems.
2. dbt with Semantic Layer
dbt makes data transformations behave like product code: version-controlled, reviewed, and documented. Combined with its semantic layer, it standardizes metric definitions across teams, so product, growth, and finance all report on the same numbers. It brings discipline and transparency to all analytics, including AI data analytics.
Key features:
SQL-as-code with Git and CI/CD checks
Auto-generated documentation and lineage
Central metric definitions
Isolated dev and production environments
Best for companies running a modern data warehouse (Snowflake, BigQuery, Databricks, Redshift) that want reliable, reviewable changes and consistent metrics across their stack.
3. Confluent Schema Registry
Confluent Schema Registry ensures event-driven products don’t break when producers and consumers evolve at different speeds. It enforces data contracts on events and APIs so new features don’t bring down downstream dashboards or ML models.
Key features:
Versioned schemas for Avro, JSON, Protobuf
Compatibility checks during CI/CD
Backward and forward schema evolution
Full audit trail of changes
Best for platform or event-driven software teams where telemetry and APIs are critical, and data reliability matters as much as feature velocity.
4. Amplitude
The proddy awarded tool, Amplitude, governs event tracking so your key metrics stay consistent and useful over time. With a strong taxonomy system it helps product managers keep analytics clean while giving teams self-serve access to insights.
Key features:
Event taxonomy and property governance
Funnels, cohorts, and retention analysis
Duplicate event flagging and quality checks
Collaborative notebooks and dashboards
Best for product-led teams that want governed, self-serve analytics tied to experiments, growth loops, and feature usage patterns.
5. Siemens Teamcenter
Siemens Teamcenter is an enterprise-scale PDM system that can grow into a full PLM solution. It’s built with multi-CAD environments in mind, has strong security, and includes mature workflows that support compliance-heavy industries.
This makes it a reliable choice for organizations that need rigorous data control with a pathway to broader product lifecycle management.
Key features:
CAD integration across multiple platforms
Secure file vaulting and access control
Revision and variant control
Structured release processes
Powerful search across parts, documents, and requirements
Best suited for large manufacturers and complex hardware teams, Teamcenter provides the control needed to manage sensitive data while enabling long-term scalability into PLM.
6. PTC Windchill
PTC Windchill centralizes product data with role-based access and advanced change management. It supports both ECAD and multi-CAD workflows, making it particularly strong for electromechanical product development. Cloud options also make it a practical choice for distributed teams.
Key features:
CAD and ECAD data management
Role-based permissions and security
Engineering change and release workflows
Multi-CAD editing and collaboration
Cloud deployment for global teams
It’s best for enterprises that need deep configuration, strong compliance controls, and flexibility to manage products that blend mechanical and electronic components.
7. SOLIDWORKS PDM
SOLIDWORKS PDM is designed for teams working primarily in SOLIDWORKS, offering straightforward vaulting, version control, and fast search capabilities. Automated workflows reduce rework and make collaboration more efficient, even across distributed teams.
Key features:
Centralized repository with full revision history
File permissions and user notifications
Process automation for approvals and releases
Support for multiple office locations
This system is a great fit for small to mid-sized engineering teams standardized on SOLIDWORKS, who want reliable data control without the complexity of a full PLM system.
8. Autodesk Vault
Autodesk Vault integrates natively with Autodesk tools like Inventor, AutoCAD, and Fusion, making it a natural choice for teams already in that ecosystem. It enhances collaboration by providing lifecycle management, version control, and built-in security.
Key features:
Direct CAD integration with Autodesk products
Lifecycle states for better control
Automated file relationship management
Controlled access with auditing
Vault is best for teams who need to organize and safeguard fast-moving design work while staying close to their Autodesk toolset.
Choosing the Right Product Data Management Software
As a product manager, part of your responsibility may be choosing a PDM tool.
Some software will be more suited to giant corporations that need many people to have access to the same data sets, others will be better for a smaller startup that only needs a handful of people to work with the data. So choose based on your situation.
However, there are some basic requirements that you should look for when choosing your PDM tool. It should:
Facilitate collaboration easily between teams
Reduce development errors
Fit your budget/usage
Improve productivity
Help you find the data you need as quickly as possible
Maintain your company’s security standards
What is a Data Product Manager?
Data is an integral part of product management, as it is with all aspects of product. You may have seen the job title ‘Data Product Manager’ and thought, ‘well, aren’t all product managers…data product managers?’ It’s a common misconception, but being a data-driven product manager doesn’t necessarily make you a data product manager. Let’s look at the differences.
The data product manager role is responsible for all the same things as a product manager but are more skilled in areas like machine learning and UX/UI. While most PMs will only need a working knowledge of these technical abilities, a data product manager must know them inside and out, and be able to use them in product development.
It’s more than just being data-driven, it’s actively building ways to collect and manage data, integrate data into features and products, and leverage it to build and perfect a product.
The more PMs utilize AI, the more crucial it is that they contribute to maintaining the data infrastructure. After all, even the best AI agent is useless if it doesn’t have access to the latest data in a format that it can interpret directly. An important aspect of evaluating AI products in making sure that they leverage the right data the right way.
The benefit of having a data product manager is that it takes data management out of the hands of individual contributors and keeps your data sets and insights centralized. This closes the gaps between teams. It also takes the pressure off of individual contributors who might not be able to handle data at scale and focus on their key duties.
The added responsibilities of a data product manager include choosing and maintaining your product’s product data management software.
Product Data Management in a Nutshell
If that felt like a lot of information to take in, we can’t blame you! But after all, taking in a lot of information and understanding it is what being a data-driven product manager is all about!
Let’s go over what we talked about, and condense it down to the main points
Data is an integral part of PM, but some use it more than others.
All successful products are driven by data.
The most successful products are the ones that use massive amounts of data in new and innovative ways.
Make data more exciting by thinking about the possibilities, not the theory.
Use the right DPM software to increase productivity and give all team members access to all data.
All Data PMs are PMs, but not all PMs are Data PMs.
If you liked this article, and want to keep learning and build your product knowledge base, you can explore this AI Product Strategy piece on our blog.
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Updated: November 20, 2025
