Data governance & observability: 3 steps to combined value

Did you know that data governance and data observability are interdependent?

While data governance establishes the rules and standards for data management, data observability ensures those rules are followed in real-time. Together, they create a feedback loop that reinforces data trust and AI readiness.

• Data governance ensures accountability: Data observability tools monitor compliance with governance policies, such as data lineage and access controls.
• Data observability provides insights: Data observability can inform governance decisions by highlighting areas where policies need refinement or additional enforcement.

This blog post will discuss the benefits of using a Data & AI Governance Platform like DataGalaxy with a data observability platform like Bigeye to create a unified, best-of-breed data governance and observability experience, enabling organizations to deliver clean and trustworthy data to their data teams at scale.

DataGalaxy x Bigeye for combined customer value

DataGalaxy and Bigeye bring the power of a unified, best-of-breed data governance and observability experience, enabling organizations to deliver clean and trustworthy data to their data teams at scale.

Our unique solution is designed to seamlessly integrate with existing data sources to create a centralized data inventory and provide a deeper understanding of data quality.

This offers a fully configurable, out-of-the-box operating model that helps data teams efficiently catalog their data inventory, add business context to metadata, track end-to-end lineage, establish ownership, workflow management, and monitor data quality with alerts for ongoing governance and quality maintenance.

Our shared values

1. Integrated user experience: DataGalaxy's adoption and expansion are enhanced through an integrated user experience that supports data governance, cataloging, quality, discovery, validation, and quality remediation
2. Seamless observability & quality analysis: A unified approach enables seamless data observability and quality analysis down to the column level within DataGalaxy
3. Single source of truth: Provides enterprise users with a single source of truth to find, review, and trust data across their enterprise and modern data stacks

Data governance & observability implementation options

There are several implementation options for data governance and data observability:

Homegrown solutions: Using spreadsheets and internal resources

Pros:

• Cost-effective start: Homegrown solutions such as spreadsheets, Python scripts, or internal databases are often inexpensive or even free to implement initially.

• Customizable: Organizations can tailor the system to their specific needs and processes, allowing for quick adaptations in early stages.

Cons:

• Scalability challenges: As data grows and the organization expands, these solutions struggle to scale efficiently, often leading to inefficiencies and data inconsistencies.

• Fragmented ownership and documentation: Managing data definitions, quality, and lineage becomes cumbersome, leading to difficulty in tracking ownership and ensuring compliance.

Considerations:

• Resource-intensive: Building and maintaining a homegrown solution requires substantial time and effort from engineering, data, and product teams. Over time, this can shift from a cost-effective strategy to an expensive burden.

• Limited collaboration: Lack of standardized processes can hinder collaboration across departments and increase the risk of errors or misunderstandings.

Example technologies: Uber, Airbnb, and Netflix started with homegrown solutions that evolved as the organizations scaled. However, they eventually shifted to more robust third-party platforms to manage data governance and data observability at scale.

2. All-in-one platform: A single vendor solution

Pros:

• Simplified procurement: Working with a single vendor for all data governance and data observability needs reduces complexity and streamlines the purchasing and implementation process.

• Seamless integration: Components from the same vendor typically work together more easily, reducing the likelihood of integration issues between different systems or tools.

Cons:

• Solutions may be spread too thin: A single vendor may not offer best-in-class capabilities across all aspects of data governance, leading to compromises in functionality or performance.

• Inconsistent user interface: Vendors providing an all-in-one solution may not have the resources or focus to maintain a consistently user-friendly interface across all components.

• Limited negotiation power: With all tools from one vendor, there’s less room to negotiate on pricing or customize the solution for your specific needs.

Considerations:

• Legacy systems integration: The implementation of an all-in-one platform may be complicated by existing infrastructure or legacy systems, making integration more difficult than anticipated.

• Resource constraints: Depending on the scale of your organization and data governance needs, resource constraints could limit the effectiveness of an all-in-one solution. 

Example Technologies: IBM, Informatica, and Microsoft offer comprehensive data governance and data observability platforms that aim to provide a one-stop solution for organizations.

3. Best-of-breed integration: Specialized vendors

Pros:

• Comprehensive, tailored solution: By selecting specialized vendors for different components of data governance and data observability, you can ensure that each aspect of the solution is best-in-class.

• Expertise from specialized vendors: Each vendor brings focused knowledge and expertise to their specific area, providing a more in-depth and robust solution than an all-in-one platform might offer.

Cons:

• Integration complexity: Integrating multiple specialized vendors requires seamless data flow between different systems and careful management to ensure compatibility, which can lead to technical and operational challenges.

• Inconsistent user experience: With multiple vendors involved, it can be challenging to maintain a consistent user interface across tools, leading to potential frustration for end users.

Considerations:

• Vendor alignment: It’s crucial to understand how well your selected vendors can work together, as partnerships and integrations between tools play a critical role in the success of a best-of-breed strategy.

• Complex management: While each tool excels in its domain, managing multiple vendors can be resource-intensive, and integration between systems must be actively monitored to prevent issues from arising. 

Example Technologies: DataGalaxy and Bigeye are examples of specialized vendors that, when integrated, offer a powerful solution for both data governance and data observability, ensuring comprehensive coverage across the organization’s needs.

DataGalaxy & Bigeye: Combined value

The combined value of DataGalaxy and Bigeye includes:

Product highlights

Key product highlights include:

Conclusion

Building AI readiness and data trust requires a strategic approach to data governance and observability.

By implementing these strategies, organizations can ensure data reliability, improve operational efficiency, and unlock the full potential of AI.

The combined value of best-of-breed solutions like DataGalaxy and Bigeye provides a comprehensive foundation for achieving these goals.