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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 – and that 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.

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.

Best practices for data governance

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 to help you embed 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. 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.

Conclusion

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.

Are you interested in learning more about using your data as an asset to achieve higher levels of data governance and quality? Book a demo today to get started on your organization’s journey to complete data lifecycle management with DataGalaxy!

Structuring a data-driven organization

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