Why your data products need governance & collaboration (2025)
29 October 2024 | 7 mins read | Data Governance
by Jessica, Tech writer
Data products are emerging as a valuable way for organizations to unlock hidden insights, drive strategic decisions, and deliver business value.
However, as data products gain traction, their success hinges on the technology behind them and the quality of the data they use.
Without strong data governance and collaboration, even the most sophisticated data products can fall short, producing unreliable results or worse, exposing businesses to unforeseen risks and reputational harm.
As the adoption of data products accelerates, it’s important that organizations recognize that well-managed, secure, and accurate data is the foundation for building powerful, trusted data products.
Solid data governance practices and collaboration with business stakeholders are the keys to their success.
The role of data governance in data product development
Data governance plays a critical role in the development of data products, ensuring the data underpinning them is accurate, secure, and high-quality.
Proper governance establishes clear policies, processes, and procedures that govern data access, management, and use.
It also assigns clear roles and responsibilities for managing the data and establishes ownership and accountability throughout the organization.
By creating a strong data governance foundation, organizations can minimize risks from data breaches and comply with regulatory mandates. By clearly defining policies to protect sensitive information, data governance ensures that the data products organizations develop comply with regulations such as GDPR and HIPAA.
It also helps to reduce the risk of fines or reputational damage stemming from the misuse of information.
Designing data & AI products that deliver business value
To truly derive value from AI, it’s not enough to just have the technology.
- Clear strategy
- Reasonable rules for managing data
- Focus on building useful data products
Moreover, data governance fosters trust in the data by fostering stronger collaboration with stakeholders across the business.
Because data products transform raw data into valuable, actionable, trustworthy insights that everyone across the organization can use to make better decisions, it’s critical that business users weigh in on their development and the data that they ingest.
With strong data governance practices in place, organizations can also ensure that data users assume data stewardship responsibilities and take accountability for the quality of their data.
Businesses that fail to embrace data governance face consequences ranging from faulty insights and misguided decisions to hefty fines and reputational harm. It’s an investment worth making, especially as organizations seek greater value from their data through data products.
Data governance best practices
When building data products, the data they use must be accurate, secure, and compliant. Implementing data governance – and embracing the best practices that support it – is a good place to start.
Here are five best practices to consider for embedding data governance in your data product build process.
Implement data quality controls
Data quality controls help to ensure that the data your data products ingest is accurate, complete, consistent, and trustworthy.
Organizations can use these controls to validate the data by verifying that it aligns with defined standards, preventing inaccurate or incomplete data from entering the data product.
Further, they help detect errors and inconsistencies and validate the data’s reliability.
Finally, data quality controls establish consistent, standardized formats, definitions, and naming conventions to ensure the organization can easily integrate and master data across disparate systems for use in the data product.
Visualize data lineage
Data lineage tracks the history of the data and how it has changed over time.
By visualizing the end-to-end journey of data across systems and silos, users can see how the data has transformed, validating its integrity and traceability throughout its lifecycle.
Data lineage also provides visibility into ownership of the data, enabling users to collaborate with data owners to spot and fix issues in the data, which helps to maintain the integrity of the data product.
Establish security protocols
Protecting sensitive information and complying with regulatory requirements are top priorities for organizations.
Security protocols not only establish the policies and procedures for securing data against unauthorized access, breaches, and misuse, but they also help to safeguard sensitive information and ensure it complies with regulatory mandates.
Further, by auditing the data regularly, organizations can more easily spot data that is inaccurate, incomplete, or out of compliance, enabling them to fix the issue before it enters the data product and impacts decision-making.
Define clear roles & responsibilities
Establishing clear data roles and responsibilities is critical to the data product-build process.
It helps to drive greater collaboration with the business, which, in turn, drives greater adoption of data products.
Organizations underscore the importance of data quality and the data users’ roles in maintaining it by assigning data stewards and owners.
Standardize metadata
Standardizing metadata using tools like a data catalog helps to create consistent descriptions and definitions for data across systems and sources, ensuring the data is easily understood by everyone who uses it.
As data enters a data product, everyone with access can easily understand the data’s origin, meaning, and use, which drives greater usability and adoption across the business.
Fostering collaboration between data teams & business stakeholders
As organizations look to gain more from their data through data products, it’s critical that they prioritize collaboration and communication between data teams and business stakeholders.
Operationalizing
CDEs
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data elements to accelerate data value.
Doing so helps to drive greater adoption of the data product because the people using it are involved in the process right from the start.
By creating an environment built on communication and collaboration, organizations can create the data-driven culture they seek. To help foster collaboration, organizations should look to:
Create cross-functional teams
Building data products requires bringing together data teams and business stakeholders.
By giving both parties a seat at the table, you can ensure that the products you develop are fit for purpose and meet the needs of the people who will use them.
Solicit feedback
When building data products, it’s important to show momentum.
Start small and deliver a minimum viable product. Then, ask for feedback from the business. Take their feedback, iterate, and release another version.
This ongoing process will not only help to improve the quality of the data product but also forge trust with the business.
Host workshops
Involving business stakeholders throughout the process is a surefire way to build trust.
These forums can be used to solicit feedback, provide updates, and train users so they are ready to hit the ground running when the data product is available for use.
When data teams and business stakeholders work together, the quality of the data – and the data product – improves.
By focusing on collaboration and communication, data teams and business stakeholders alike will reap the benefits of valuable and trustworthy data products.
The future trends of data products
As data products continue to gain traction, it’s important to acknowledge data governance’s critical role in building effective and trustworthy data products.
Without strong governance, data products risk being unreliable, leading to ill-informed decisions that erode trust.
A solid data governance framework not only safeguards data integrity but also empowers teams across the organization to collaborate efficiently and use data products to confidently make decisions.
FAQ
- How do you build a data product?
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Building a successful data product begins with a clear business need, trusted data, and user-focused design. DataGalaxy simplifies this process by centralizing data knowledge, fostering collaboration, and ensuring data clarity at every step. To create scalable, value-driven data products with confidence, explore how DataGalaxy can help at www.datagalaxy.com.
- Do I need a data catalog?
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If your teams are struggling to find data, understand its meaning, or trust its source — then yes. A data catalog helps you centralize, document, and connect data assets across your ecosystem. It’s the foundation of any data-driven organization.
👉 Want to go deeper? Check out:
https://www.datagalaxy.com/en/blog/what-is-a-data-catalog/ - How do I know if my data is “governed”?
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If your data assets are documented, owned, classified, and regularly validated — and if people across your org trust and use that data consistently — you’re well on your way.
👉 Want to go deeper? Check out:
https://www.datagalaxy.com/en/blog/choosing-the-right-data-governance-tool/ - Can you govern AI without governing your data?
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No — AI is only as good as the data it learns from. Poor data governance leads to biased models, opaque decisions, and compliance risks. Responsible AI starts with trustworthy, well-governed data.
- How does a data catalog integrate with my existing tools?
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Modern catalogs integrate with your full data ecosystem — from Snowflake to Power BI. DataGalaxy includes prebuilt connectors, APIs, and automation tools that make syncing metadata seamless and scalable.
👉 See supported integrations
About the author
With a passion for turning data complexity into clarity, Jessica Sandifer is an experienced content manager who crafts stories that resonate across technical and business audiences. At DataGalaxy, she creates content and product marketing messages that demystify data governance and make AI-readiness actionable.
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