Data governance solutions

Data governance plays a pivotal role in the successful implementation of data mesh and data fabric architectures, which are designed to manage data across diverse environments effectively.

Both data mesh and data fabric aim to enhance data accessibility, quality, and integration, but they approach data governance differently:

• Data mesh decentralizes data governance, allowing domain-specific teams to manage their data autonomously, fostering agility and innovation.
• Data fabric centralizes data governance, ensuring data is harmoniously integrated and consistently governed across the entire organization.

With data mesh, effective data governance is critical. It empowers your domain owners to maintain their data independently while adhering to overarching organizational policies, and ensures that each domain’s data is accurate, compliant, and securely accessible. With this structure, you can:

• Gain stronger trust in and reliance on the data products generated
• Power rapid, localized decision-making
• Increase responsiveness to specific departmental needs – without creating risk around privacy and security of data

With data fabric, data governance provides the framework to ensure that data across your organization is standardized, compliant, and properly managed, regardless of where it resides. This centralized approach to data governance supports:

• A unified view of accurate and timely data
• Comprehensive risk and compliance
• Better decision-making through greater data literacy

Regardless of which architecture is right for you, robust data governance is essential for mitigating risks, ensuring compliance with regulations, and maintaining data quality.

It fosters a culture of data literacy and trust by establishing clear protocols for data usage, privacy, and security. By enhancing visibility and control over your data, governance frameworks within data mesh and data fabric architectures help you make more informed, confident, and effective data-driven decisions across your business.

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Need to harness the full potential of your data assets to keep up in a data-driven world?

Effective data and analytics governance is crucial.

But data governance presents unique challenges, particularly in empowering business users with the tools and understanding necessary to leverage data efficiently and responsibly.

That’s what makes a business-friendly, flexible data governance framework essential. It should enable your business users, in near real time, to:

• Easily understand metadata for critical data products
• Associate the policies governing their usage
• Know the procedures for gaining access

This level of transparency ensures data governance while also fostering a culture of data literacy and compliance across your organization, so you can be sure that your data insights are timely and relevant. It’s no surprise, then, that flexible data governance has become a requirement in modern data architectures like data fabric and data mesh that are deployed to increase trust and agility in data and analytics.

An intuitive user interface or data catalog is also vital for users to quickly identify and understand existing data products within your organization – including technical metadata, data quality, and lineage.

Data catalogs provide a clear, up-to-date inventory of data, which helps you prevent redundancy and accelerate the data discovery process. Then, you can move forward with better, more confident decision-making and greater operational efficiency.

Integrating these elements into a modern governance solution ensures that your data and analytics governance enhances business agility while maintaining compliance.

Empower your users with easy-to-use tools and clear, real-time data insights, so you pave the way for innovative data-driven strategies and solutions that drive business growth and competitive advantage.

Watch the webinar: Enabling Real Time Data and Analytics: Key Considerations and Best Practices

Everyone’s talking about artificial intelligence (AI). And with the rapid expansion of business opportunities created by these solutions, it’s easy to see why, whether you want to harness its power to:

• Increase internal productivity
• Attract and retain customers with more personalized experiences
• Generate new ideas for competitive advantages
• … and much more

But most organizations are struggling to get their data ready for training models, leading to AI systems that produce skewed outputs and inaccurate recommendations. Not to mention, expanding data privacy regulations bring the added risk of noncompliance fines if you don’t ensure proper data handling.

The success of your AI initiative relies on having comprehensive data governance to avoid common pitfalls and ensure it’s ready to be used in training models.

A robust data governance solution answers critical questions like:

Is this dataset appropriate for my model?

What policies apply to this data?

Does this data contain sensitive PII?

Is the data high quality?

This ensures AI models are trained on accurate, relevant data, leading to higher-quality insights and results.

But achieving robust data governance can be extremely difficult without knowing what data you have, where it’s stored, or how it’s used. And without the right data governance framework, your AI initiative can slow down to a crawl.

Adopting the right data governance framework and capabilities ensures the quick success of your AI initiative, while avoiding common challenges and embracing all of the benefits and competitive advantages that come along with it.

Get ready for AI-powered wins by managing your AI data policies and processes with the Precisely Data Integrity Suite Data Governance service.

A business-friendly approach to data governance is imperative to engage all users and accommodate diverse business use cases spanning analytics, operational improvements, and compliance requirements. To increase adoption and collaboration, business and technical data users across your organization need to have a common, agreed-upon, and documented understanding of which data is most important, what it’s called, and where it’s used.

Modern data governance solutions jumpstart this alignment with an intuitive and easily understood framework – or metamodel – that mirrors your business model to increase familiarity and facilitate adoption. It’s also important to be able to search with everyday language with semantic tagging to easily find similar and related assets.

Best-in-class data governance solutions provide dynamic diagrams that help users quickly visualize the lifecycle of your data and critical relationships with other systems and processes. Business-friendly data lineage, impact analysis, and process maps help deliver even greater context around your data assets.

Automation is the final key to increasing data literacy and delivering solutions that are truly business-friendly. Your data governance solution should be able to quickly crawl, profile, and score complex metadata. Accelerators to automate metadata ingestion should also be natively available to keep your data catalog fresh and current. Look for easily configurable workflows to document data requests and enable collaboration to build a truly data-driven culture within your company.

Learn about the top 4 priorities for building a data governance program that consistently delivers business value to your team.

Proving the value of your data governance program should be approached both as an ongoing strategic practice and embedded in your chosen data governance solution. Without metrics of how your governance initiative is impacting your business goals, your program is at risk of losing sponsorship and funding.

To accelerate new governance programs, you’ll want to fortify existing data governance programs through proven ROI. To reinforce confidence in the value of governance initiatives, you must identify, connect, and measure data assets linked with:

• Business Goals, objectives, KPI’s and metrics
• Critical business processes and operational performance
• Transformational, compliance or analytical initiatives

Look for a data governance solution that enables you to create personalized dashboards that bring visibility into the governance and quality measurements of critical data assets that matter to each user. With dashboards, users can track and document the impact of governance on their business goals and outcomes.

A value-based approach highlights that data governance is not just another ‘have to do’, but a critical strategic program with measurable outcomes.

Check out this case study that explores how Keurig Green Mountain implemented an enterprise data governance solution to realize their strategic vision and build a scalable framework for long-term success.

Data quality and data governance share a ‘symbiotic relationship’ . The common goal of many data governance programs is to increase visibility around the quality of data for better business outcomes.

Data governance needs appropriate data quality tools not only to clean raw data, but also to illustrate data errors and issues, help compile the best standards, and monitor data quality against policies for critical data elements over time. Increasingly, machine learning capabilities can optimize data quality best practices by identifying trends and patterns.

Your data governance solution should be compatible with industry-leading data quality solutions to bring visibility to data quality scores on key assets… and promote engagement with the right teams to pro-actively troubleshoot or optimize standards and best practices with a documented workflow.