Data product management: Best practices

The modern data stack delivers scale, but clarity demands discipline and rigor. Data products are a method of packaging polished datasets, making them easier to discover, secure, and govern. The Gartner Survey 2024 reported that 50 percent of organizations already implement data products, and 29 percent of them are currently considering it.

Despite this growing adoption, the majority of organizations struggle to make data products work in practice. The issue stems from vague ownership, patchwork processes, and disconnected teams. Spreadsheets, reports, and dashboards accumulate without a common standard or ownership.

What would happen if you could treat data as you treat product development? Think of versioned, documented, and reliable datasets that support decision-making instead of ad hoc requests.

That’s the essence of data product management. It turns one-off tasks into consistent, scalable resources that enable the business to act decisively.

In this article, we’ll outline best practices in data product management, and how dbt enables teams to apply software engineering best practices to analytics workflows.

Core best practices for data product management

Data product management requires more than tools or talent. Teams must have a defined group of practices to create dependable, trusted outputs. Here are a few of them:

Goals, metrics, and user-centered design

Base data products on quantifiable results. Teams must align all data products directly with a business goal. Setting clear SMART (Specific, Measurable, Achievable, Relevant, Time-bound) objectives ensures everyone understands how to measure progress and track success. For example:

• Reduce manual work: Lower the time required to create monthly financial reports by eight hours (10 to two) in three months.
• Increase usage: Achieve at least 70 percent of campaign managers using the new marketing dashboard actively within six weeks.

To effectively monitor progress, think of metrics that indicate user engagement and the general health of your data products, which include:

• Adoption rate: Proportion of planned users who are actively searching the data product.
• Data freshness: The gap between the availability of data in the source system and the product.
• User satisfaction: Review survey data or NPS scores to learn how users rate ease of use and trust.

Interview stakeholders, analysts, data scientists, and business teams directly to uncover any pain points. Take the time to observe how they operate with the data on a daily basis, so you can identify friction that may not be apparent during interviews.

Create lightweight, rapid prototypes, such as sample dbt models or simple dashboards, to obtain early feedback. Continue on a small-scale cycle to narrow the scope and ensure alignment with your objectives before expanding further.

Defining, documenting, and discovering data products

An effective data product ought to be:

• Discoverable
• Addressable
• Self-describing
• Interoperable
• Secure
• Properly documented

Ensure data products are discoverable by maintaining a central catalog or marketplace. The catalog can enumerate all models, sources, and exposures. Metadata tags such as domain, update cadence, and owner allow users to find what they need fast.

Interoperability and documentation

Addressability and interoperability require the provision of a stable, human-readable identification for each product, using a consistent naming convention. Outputs must be in standardized formats (such as Parquet, CSV, or materialized views) to promote frictionless integration with downstream tools and teams, eliminating the need for further reformatting.

Self-describing data products have well-defined metadata that conveys information related to the business logic, ownership, purpose, and frequency of updates. Such metadata makes everyone aware of how the data was generated and how to use it effectively.

Additionally, good documentation clearly explains the purpose of the data, the transformations applied, and the intended decision context, encouraging understanding and trust.

Security and automation

To ensure security, use role-based access controls on your data warehouse to assign roles. Implement row-level security to restrict access to specific records as needed.

dbt assists in automating many of these tasks. Its in-built documentation generator creates a browsable catalog, containing lineage graphs, model descriptions, and metadata annotations. The models can be enriched with tags and classifications, allowing data products to be discovered, audited, and trusted more easily throughout the organization.

Data quality and governance

Reliable data products rely on stable data quality and governance. Establish explicit data quality criteria, such as ensuring that there are no more than 0.1 percent of duplicate records or fewer than 0.01 percent of null values in key dimensions.

Testing and checks

Automated testing plays a significant role in ensuring you meet these standards and maintain strong governance. Standard validations cover uniqueness checks to prevent duplicate keys, not-null constraints for required fields, and relationship tests to ensure referential integrity.

Most teams can go further and add complex assertions. For example, add checks to validate that numbers fall within a valid range. These checks act as a gateway in the CI/CD pipeline, preventing critical errors from being deployed to production.

Lineage and stewardship

Lineage and traceability facilitate the easier comprehension of how data flows as it originates from raw sources and appears in downstream reports. Teams can trace errors back to their origin and understand how upstream changes impact dependent datasets and reports.

To strengthen accountability, organizations appoint data stewards for each domain. Stewards can monitor data quality, enforce compliance, and manage schema changes. Meanwhile, data owners troubleshoot failed tests, update policies as requirements change, and keep stakeholders informed of any updates.

Cross-team collaboration and roles

Coordinate across business and technical functions to make data products manageable. The key to success is a common playbook, which clarifies responsibilities, organizes communication, and fosters feedback loops.

Establish roles

The first step is to define clear roles so that everyone understands their responsibilities, roles, and contribution to the data product lifecycle.

• Product owner: Designs the roadmap and concentrates on the most important features to the business.
• Analytics engineer: Constructs and maintains data transformations, tests, and documentation to maintain data trustworthiness.
• Data governance lead: In charge of compliance, access control, and quality standards in the areas.
• Data consumer: Provides data product usage, confidence, and adoption feedback.

Build communication connections

Second, establish formal communication networks to ensure teams are in sync and remain up-to-date with the latest developments. Here are some of the ways you can create a communication channel:

• Frequent stand-ups: Daily or weekly meetings ensure that analytics, engineering, and business teams are aligned on priorities, blockers, and progress.
• Office hours and ad-hoc support: Special hours to exchange knowledge, technical support, and onboarding.
• Feedback loops: Surveys and retrospectives conducted after every sprint will enable the collection of feedback, identification of problems, and process improvements over time.

The role of dbt

dbt enables collaborative workflows using modern engineering.

• Git integration: Teams can integrate branching strategies and pull requests to review and merge changes securely.
• Multi-environment deployments: Prevent the unexpected impact of developing, testing, and promoting models in various environments.
• Slack notifications: dbt Cloud sends real-time notifications of the build status and lineage updates, so that stakeholders always know what has shipped.

Implementing these practices and tools establishes a solid foundation that fosters accountability, transparency, and teamwork, ultimately delivering data products that people can trust.

Agile iteration and prioritization

An agile mindset enables data teams to rapidly produce actionable insights and data products that can be trusted to drive smarter decisions and accelerate the return on investment (ROI). Rather than shipping all the information at once, ship the smallest data asset that fulfills a business need, such as a single fact table or a simple dimension.

Early delivery

Early delivery allows teams to elicit feedback, correct logic, and demonstrate value before scaling. Sprints and a prioritized backlog make development organized and predictable. Sprints of two to four weeks offer a well-understood cadence of planning, building, and reviewing work.

The backlog tracks all potential improvements and corrections, ranked by impact and urgency. This model helps the team stay focused on business goals and improves in phases.

Feature flags

Introduce new functionality under feature flags or small-scale beta releases to monitor usage and verify outcomes without impacting all users simultaneously. Even rapid changes must undergo automated testing and adhere to your governance criteria before being deployed in production. The method enables teams to ship with confidence, iterate more quickly, and ensure data quality.

Modular design

dbt supports this approach through its modular design and incremental models. Teams can develop, test, and review small subsets of models in isolation before merging them into the main. This makes it easier to ship frequent, controlled updates with clear impact analysis and minimal risk.

Scalability, performance, and maintenance

Scalability and long-term maintenance of data products make them reliable as demand increases. The initial strategy is to de-modularize pipelines. The small-scale reusable prototypes are also easier to test, debug, and build without affecting the other parts of the system.

Performance tracking

The secret to pipeline efficiency is performance tracking. Observing query performance and resource utilization will help identify slow transformations or bottlenecks. After that, you can further optimize by rewriting some of the logic, adjusting indexing strategies, or changing how data is materialized to fit your workloads and query patterns.

For example, transitioning a view-based model to an incremental table can provide enormous performance improvements as your data grows. Organizations must also scale beyond technical optimization as pipelines and data products expand.

Domain ownership

Moving to a domain ownership model enables every business unit to own its data products, create local expertise, and minimize the dependency on a central team.

This model helps to share the workload more evenly, enhances responsibility, and accelerates the iteration process. Periodically review and refactor or deprecate old models to maintain a clean and manageable environment in the long run.

How dbt helps

dbt allows flexible materializations such as views, tables, and increments to design at scale and allows teams to customize performance strategies to different datasets. Its modular design makes it easy to break pipelines into manageable components. Integrations with orchestration tools like Airflow or Prefect reliably coordinate complex workflows as your environment scales.

Monitoring, observability, and feedback

Data products are living systems that require constant improvement and monitoring. Monitoring usage and freshness helps keep data relevant and trusted. Checking API calls and query logs provides insight into the frequency of each product and the team accessing it.

Data latency can be avoided by performing freshness checks. They help detect outdated data before it impacts the decision-making process.

Robust monitoring

Solid monitoring practices can help maintain system reliability. Setting up alerts for test failures, sudden spikes in resource usage, or unexpected drops in data volume enables teams to catch problems early and keep systems running reliably.

Monitoring platforms such as Prometheus and Datadog offer a clear view of how pipelines and infrastructure are performing, which simplifies the process of identifying and resolving issues.

User feedback

Maintaining a strong connection with data users is crucial. Gathering regular input through embedded feedback tools or scheduled discussions ensures that their queries are heard and addressed. Over time, this steady feedback loop builds trust and encourages broader adoption, as people see their suggestions driving real improvements.

Ready to get started?

Data product management enables teams to consolidate disparate data processes into stable, custom-built resources. A clear focus on business goals, robust governance, and iterative development helps create reliable and trusted data products.

With dbt, you can forge a unified analytics development process. For example, you can:

• Set up development, staging, and production environments that are isolated from each other, allowing tests to be performed without impacting live data.
• Create a minimum viable product in dbt by modeling fundamental tables, including YAML-based tests of data quality, and snapshots to show changes over time.
• Iterate on pull requests, using CI jobs that test only the affected models, and merge when all quality checks succeed.

Take the free dbt Fundamentals course to learn how to model, test, document, and deploy in dbt. To gain a more in-depth understanding, consider more advanced training on data mesh, CI/CD optimizations, and dbt Copilot integrations.