Best Practices for Effective Data Cataloging

We’ve heard for years that every company is a data company and that being “data-driven” is imperative to every facet of success. It’s true, with data-driven enterprises seeing higher growth rates, being many times more likely to acquire, retain, and profit from customers , and reduce costs more effectively.

Being data-driven, however, means more than just giving teams unfettered access to data and the spreadsheets to analyze it. Building a data culture is the key — every worker must be able to find the right data, understand the data, and be confident the data is managed and governed appropriately. Most enterprises do this with a data catalog, widely accepted as a fundamental element of a data culture.

Today, many catalogs are instead self-describing as data intelligence platforms, which conveys a deep use of artificial intelligence and machine learning (AI and ML) to automatically analyze the data itself and how it is used by people in the organization. Deep insights into the data and its usage are then automatically captured by the platform and communicated via metadata in articles. “Data cataloging” as a verb describes the ongoing processes of ensuring the platform is used and usable, encompassing a range of activities from curation to governance oversights and training.

Launching a data intelligence platform for your enterprise requires careful planning and consideration to ensure its effectiveness for those roles critical to making it a success: business analysts, data leaders, and data stewards.

Read on for data cataloging best practices tailored to each of these roles.

Data cataloging best practices for data leaders

Enterprises need data leaders to champion the value of data across the business so that every worker can be comfortable with it. Data governance sets the tone for how data should be gathered and used, data collaboration ensures workers can understand and trust the data they find, and insightful data catalog monitoring helps spot opportunities for increased data catalog adoption and data culture expansion.

Here are three data cataloging best practices for data leaders:

1. Establish governance policies. Data leaders must define governance policies and standards for data cataloging to ensure consistency, quality, and compliance across the organization. Guidelines for data classification, metadata management, data ownership, and access controls should be included.

Learn more in Alation’s “ultimate guide” to data governance.

2. Foster collaboration. A data intelligence platform helps data leaders promote collaboration and knowledge sharing by facilitating discussions, sharing best practices, and exchanging insights within the data catalog platform. Data leaders can encourage peers, data stewards, and data analysts to contribute their expertise and domain knowledge to enrich metadata and improve data quality.

Watch this webinar to learn how Aon, a global professional services firm, uses a data intelligence platform to foster a data culture that thrives on collaboration and sharing.

3. Monitor usage and adoption. A data intelligence platform makes it easy to track usage metrics and adoption rates to gauge the platform’s effectiveness and identify improvement areas. Data leaders can monitor user feedback, queries, and search patterns to understand user needs and preferences and make enhancements accordingly.

Learn how to develop a data catalog adoption plan for your organization.

Data cataloging best practices for data stewards

A data steward has formal accountability for data in an enterprise. While data governance refers to the policies and procedures to ensure effective data usage, data stewards are those who execute and enforce authority over data to create active data governance. A data steward’s role involves data curation, ensuring governance policies are followed, and tracking how data flows through an organization.

Here are three data cataloging best practices for data stewards:

1. Curate metadata. The data intelligence platform provides a metadata curation platform to house accurate, consistent, and comprehensive information about each data asset. Data stewards are then responsible for maintaining metadata quality, updating data classifications, and resolving discrepancies.

Read more about metadata curation and its importance in building a data culture.

2. Enforce data policies. A data intelligence platform helps data stewards enforce data policies and data governance rules to comply with regulatory requirements and organizational standards. Features such as data lineage tracking, role-based access controls, and data usage policies are available to help further bolster data security and privacy protections.

Discover how to effectively implement data policies and ensure compliance by reading the white paper “Making Data Policies Work For You.”

3. Facilitate data lineage and impact analysis. Data stewards can perform data lineage and impact analysis within the data catalog to understand how data flows through the enterprise. Stewards can further assess the potential impact of changes or updates to data assets. Data catalogs also provide tools and visualization capabilities to easily trace data lineage and dependencies.

Learn more by reading “What is Data Lineage?”

Data cataloging best practices for business analysts

Business analysts ensure the data rubber hits the business benefits road. They execute the data search & discovery and follow data governance rules to ensure data is used properly; they also need a level of data literacy to ensure impactful outcomes from the use of data. To make it happen, business analysts need an easy-to-use data intelligence platform that enables self-service data access and the related business context to work effectively.

Here are three data cataloging best practices for business analysts:

1. Demand an intuitive user interface. Work with a vendor where the interface is user-friendly and intuitive for business analysts who may need more technical expertise. Popular features include search, filters, articles, and tags to help analysts quickly discover relevant data assets.

Learn more about Alation’s next-generation user interface, which is designed to meet the evolving demands of the modern enterprise.

2. Ensure self-service access. With a data intelligence platform, business analysts can access and explore data on their own easily with self-service capabilities. Other features like data profiling, data lineage visualization, and data previews can further encourage and accelerate data exploration and analysis. An Intelligent SQL Editor that supports query reuse via forms will enable business users to leverage expertly crafted queries independently.

Read these six benefits of self-service analytics for more insights.

3. Add business context. Adding business context and descriptions for each data asset in the platform can enhance data understanding. Business analysts can benefit from information such as data definitions, business rules, and usage guidelines when interpreting and utilizing the data effectively.

Learn why some data intelligence platforms include data maps for visual context that can increase understanding of data.

Data cataloging best practices with Snowflake

With its Data Cloud solution, Snowflake enables data teams to scale their data storage in the cloud, with flexible pricing based on volume and analytics usage. Here are some best practices to streamline data management of Snowflake with a data intelligence platform:

• Enable Self-Service Data Discovery: Your data intelligence platform should include the metadata Snowflake users need to find data and the experts who know it best. Curate data with helpful context in the catalog, so newcomers understand assets more readily when they encounter them in the Data Cloud. Empower data seekers with easy-to-use semantic search and access to lineage so they can self-serve with confidence.

• Automate Policy Enforcement and Data Classification: How will you know what Snowflake data is subject to policies governing its appropriate usage? Tools like Alation’s Policy Center can automate policy enforcement across Snowflake data, streamlining data classification processes. This ensures compliance with regulations while activating metadata for informed decision-making – scaling up data governance across your data ecosystem.

• Ensure Lineage Across Tools: As data pipelines grow, so too does the need to comprehend how data transforms as it flows. Seek out a data catalog with interactive, column-level lineage features, so you can comprehend the impact of upstream changes and troubleshoot breaks in real time.

• Ease Data Migration. The curation capabilities of a data intelligence platform give data leaders critical visibility into the most used (and high-value) data in an enterprise. This is the data leaders should prioritize for migration to the Data Cloud! By creating a robust data governance foundation with a data intelligence platform, migration leaders can also comprehend which assets are subject to compliance – which impacts what you migrate when.

By incorporating these practices into your data cataloging strategy with Snowflake, you can grow trust in data, improve decision-making, and optimize the utilization of your Data Cloud.

See how Disover Finanancial Services uses Snowflake with Alation to build more robust, efficient data pipelines more quickly.

Data cataloging best practices with Databricks

When it comes to effective data cataloging with Databricks, success lies in ensuring your lakehouse data is accessible, understandable, and trustworthy. Here are some best practices to optimize your data management with a catalog and Databricks:

• Facilitate Smarter and Faster Migration: Streamline your lakehouse migration process by identifying valuable data assets within the data catalog. Understand data usage analytics so you can prioritize the most impactful data to migrate first. Use a platform like Alation to track migration progress.

• Enable Data Lakehouse Success: Empower all data consumers to confidently access and utilize data within the lakehouse. By providing comprehensive lineage, you can foster trust in data-driven decisions that propel business objectives forward.

• Integrate Unity Catalog: Gain a holistic view of your data landscape by integrating your data intelligence platform into Databricks Unity Catalog. Alation’s solution, which functions as “a catalog of catalogs”, centralizes security, management, and metadata. It also offers insights into Databricks workspaces, source data systems, and analytics tools in one convenient spot.

• Promote Collaboration and Insights: Enhance collaboration among data scientists and engineers by providing access to comprehensive data context and lineage. Enable exploration through source-to-destination lineage, facilitating deeper insights and accelerating results delivery.

By implementing these best practices, data leaders can optimize their Databricks environments while fostering a culture of collaboration, trust, and innovation in data-driven decision-making.

Data cataloging best practices with AWS

How can IT teams leverage data cataloging to optimize performance on AWS? Here are some best practices to keep in mind based on use case:

• Accelerate Cloud Data Migration: Overcoming migration challenges is paramount for timely transitions to the AWS environment. A data intelligence platform can streamline this process by identifying and prioritizing critical assets, facilitating faster migration timelines.

• Promote Data Accessibility and Governance: Who can access what data, and how are they permitted to use it? A platform like Alation can provide real-time visibility into governance policies within AWS, enabling users to confidently navigate data resources and mitigate risks effectively.

• Foster Data-Driven Culture: Enhancing productivity and efficiency for data scientists and analysts is crucial for driving organizational insights. A data intelligence platform simplifies data discovery and utilization, allowing teams to focus on analysis rather than data wrangling.

By embracing these best practices for data cataloging within AWS, organizations can harness the full potential of their cloud infrastructure and optimize data value. While AWS offers a cloud computing platform akin to Snowflake and Databricks, AWS’ solution offers less infrastructure and fewer features. For this reason, many IT teams that implement Snowflake or Databricks will also partner with AWS (or Azure or Google Cloud).

Curious to see how organizations can leverage AWS with Databricks? Airline company Virgin Australia used these two tools to accelerate data discovery while improving data literacy and governance.

Turning best practices into a more mature data culture

Building a robust data culture provides obvious business benefits. Enterprises can benchmark, build, and grow a data culture using a data culture maturity model, which gauges maturity levels across data search and discovery, data governance, data literacy, and data leadership.

• Search & discovery ensures that users can find, understand, and trust the information they need. If they can’t find the information, users should have access to the subject matter experts who can guide them and answer their questions about specific datasets or data needs. For data leaders, search & discovery metrics can provide valuable insights into who is putting data to use, which data assets are in demand, and where data culture opportunities exist.

• Data governance creates clear policies and implements them to maintain the quality and security of data across the enterprise. The data catalog offers a platform for establishing a structured framework, documenting policies, and ensuring access to those policies.

• Data literacy enhances collaboration and understanding so users can make better data-driven decisions. Users should also be able to access and analyze data without specialized knowledge, which underscores the importance of self-service data access. For data leaders, a data intelligence platform can provide metrics on the organization’s data literacy across roles, departments, and other slices to highlight training needs or maturity advancement opportunities.

• Data leadership ensures that data initiatives align with business outcomes and leaders have the insights to see added value. Change management is crucial to data leader effectiveness, and tools and platforms like a data catalog are typically required to facilitate change and improve data usage. Every organization should have visible, vocal executive sponsors to reinforce the value of the data culture in furthering business success.

Overall, fostering collaboration, promoting self-service access, ensuring data governance, and facilitating metadata management are essential best practices for data cataloging that cater to the needs of business analysts, data leaders, and data stewards within an enterprise.

By following these best practices, enterprises can maximize the value of data assets and drive data-driven decision-making across the organization.

A great place to start is with Alation’s Data Culture Maturity Assessment, which gauges data culture maturity across the key components of data search & discovery, data governance, data literacy, and data leadership.