In today's digital era, data is increasingly becoming the most valuable asset for businesses. Enterprises are collecting data from various sources, including transactions, customer engagement, and other data points. However, this growing volume of data presents unique challenges for organizations, such as how to manage, catalog, and ensure it can be trusted. This is why businesses are increasingly turning towards solutions that can provide comprehensive data governance.
Data Governance: The convergence of the Data Catalog and Data Quality
Data governance is a framework that defines the policies, processes, and standards for managing data assets across the organization. It encompasses a set of policies, procedures, standards, and technologies to ensure that data is properly managed, protected, and trusted to ensure it meets the needs of the organization and its stakeholders.
Two critical components of data governance are data quality (trust in your data) with a data catalog (understanding and access to your data). This convergence of the data catalog with data quality tools is a natural evolution; it is a result of enterprises realizing that managing a data catalog and ensuring data quality are interconnected.
A data catalog provides a centralized view of data assets, metadata, and relationships, making it easier to discover, understand, and use data across an organization. Data quality, on the other hand, ensures that the data is accurate, complete, and consistent so that it can be trusted and relied upon for decision-making.
By combining data quality and a data catalog, organizations can ensure that they have a complete and accurate understanding of their data assets, while also ensuring they are high quality and can be trusted. Both components are essential for any organization that wants to leverage the full potential of its data while minimizing risks and ensuring regulatory compliance.
How to choose the right strategy for your organization
Choosing a single platform that provides both data cataloging and data quality solutions may seem like the most straightforward option, but it's not always the best choice for businesses. The reality is that most single-platform solutions are limited in their functionality and may not meet all the unique requirements of a business. While traditional vendors like Informatica have been providing data quality solutions for years, their approach is based on a set of predefined rules that can limit flexibility in handling unique data use cases. This is especially true for businesses looking to enable effective use of their data by building a modern data stack with data warehouses like Snowflake and Databricks. Legacy tools like Informatica may not be able to keep up with the demands of a modern data stack, especially when it comes to scalability, flexibility, and agility.
On the other hand, opting for two best-of-breed catalog and quality vendors, such as Alation and Anomalo, can offer significant advantages. For instance, Alation is specifically designed as a data catalog for business users and analysts and provides a centralized view of data assets, metadata, and relationships. This platform helps organizations easily find and use data to support their analytical and operational needs. Similarly, Anomalo is a best-of-breed data quality solution that uses AI and machine learning to identify and resolve data quality issues in real time.
Consider this analogy. Imagine you need to furnish a new house with furniture. You could opt for a single vendor that provides a one-stop shop for all your furniture needs, but they may not have the best option for each piece of furniture. Alternatively, you could choose different vendors for each furniture item, such as one vendor for sofas and another for consumer electronics, to ensure you get the best options.
In a similar way, Anomalo’s native integration with Alation grants customers choice and flexibility, so they don’t sacrifice interoperability to get the best of both worlds. With Alation's data catalog, users can easily search and find relevant data assets across their organization. And with the native integration with Anomalo, users can quickly identify data quality issues associated with these assets directly in the Alation UI, enabling them to make more informed decisions about the data they use.
What our shared customers have to say
Mutual customer Keller Williams is undergoing a significant data modernization strategy and needed to catalog and monitor hundreds of critical data assets using a small team that has responsibility for both data engineering development as well as tactical data operations monitoring.
"By combining Anomalo's ML-based automatic checks and no-code validation rules with Alation's user-friendly UI, our technology teams can proactively identify data intake issues, prioritize tables, and tag sensitive data to enhance operations and predictive and prescriptive analytics,” says Cliff Miller, Enterprise Data Architect, Keller Williams. “We love the powerful nature of the ML engine behind the data quality checks, all while avoiding the burden of complex code deployments. We are particularly delighted by the seamless integration of our data quality monitoring configurations from Anomalo into the Alation data catalog and governance platform. This allows our users to remain within a unified interface, enabling them to effortlessly explore, assess, and leverage our extensive range of foundational data solutions."
Overall, the combination of Alation and Anomalo can help organizations improve their data quality, governance, and trust, while also enabling them to collaborate more effectively and make more informed decisions. With these two best-of-breed solutions, data leaders within large organizations can create a modern data stack that is agile, flexible, and scalable, which empowers more people to meet the evolving needs of the business.
To learn more, contact a member of the Alation or Anomalo team.