Data Cataloging: 3 Best Practices for DvSum: Building a Data-Driven Enterprise

25 May, 2024 •

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For years, the notion that every company is a data company and that being “data-driven” is crucial to success has been emphasized. Data-driven enterprises consistently see higher growth rates, are more likely to acquire, retain, and profit from customers, and can reduce costs more effectively.

The Importance of Data Cataloging in a Data-Driven Culture

However, being data-driven entails more than merely providing teams with access to data and spreadsheets. Building a robust data culture is essential—every employee must be able to find the right data, understand it, and trust that it is managed and governed properly. This is where data cataloging comes in, serving as a cornerstone of a data culture in most enterprises.

From Data Catalogs to Data Intelligence Platforms

Many modern data catalogs have evolved into data intelligence platforms. These platforms leverage artificial intelligence (AI) and machine learning (ML) to automatically analyze data and its usage within the organization. They capture deep insights and communicate them through metadata, facilitating a comprehensive understanding of data assets.

“Data cataloging” involves continuous processes to ensure that these platforms are effectively utilized, including data curation, governance, and training.

Effective Data Cataloging: Key Roles and Best Practices

Launching an effective data intelligence platform requires strategic planning to ensure it meets the needs of key roles such as business analysts, data leaders, and data stewards. Here are data cataloging best practices tailored to these roles:

Data Cataloging Best Practices for Data Leaders

Data leaders must champion the value of data across the business. Effective data cataloging involves:

  1. Establishing Governance Policies
    • Define clear governance policies and standards for data cataloging to ensure consistency, quality, and compliance across the organization.
  2. Fostering Collaboration
    • Promote collaboration and knowledge sharing by facilitating discussions and best practices within the data catalog platform.
  3. Monitoring Usage and Adoption
    • Track usage metrics and adoption rates to assess the platform’s effectiveness and identify areas for improvement.

Data Cataloging Best Practices for Data Stewards

Data stewards hold formal accountability for data management. Their best practices include:

  1. Curating Metadata
    • Maintain accurate, consistent, and comprehensive metadata to enhance data quality and resolve discrepancies.
  2. Enforcing Data Policies
    • Use the platform to enforce data policies and governance rules, ensuring compliance with regulatory requirements.
  3. Facilitating Data Lineage and Impact Analysis
    • Perform data lineage and impact analysis to understand data flows and assess the impact of changes within the enterprise.

Data Cataloging Best Practices for Business Analysts

Business analysts need tools to make informed decisions. Their best practices include:

  1. Demanding an Intuitive User Interface
    • Ensure the platform interface is user-friendly, allowing quick discovery of relevant data assets.
  2. Ensuring Self-Service Access
    • Enable business analysts to explore data independently with self-service capabilities.
  3. Adding Business Context
    • Enhance data understanding by adding business context and descriptions for each data asset.

Data Cataloging with Popular Platforms

Snowflake

  • Self-Service Data Discovery: Include metadata and facilitate easy data search and lineage access.
  • Policy Enforcement and Data Classification: Automate policy enforcement to ensure compliance.
  • Data Migration: Prioritize high-value data for migration and ensure robust governance.

Databricks

  • Migration: Identify and prioritize impactful data for lakehouse migration.
  • Lineage: Provide comprehensive lineage to foster trust in data-driven decisions.
  • Collaboration: Enhance collaboration among data scientists with comprehensive data context.

AWS

  • Cloud Data Migration: Streamline migration by identifying critical assets.
  • Data Governance: Provide real-time visibility into governance policies.
  • Data-Driven Culture: Simplify data discovery and utilization to enhance productivity.

Building a Mature Data Culture with DvSum

Building a robust data culture involves:

  • Search & Discovery: Ensure users can find and understand data, providing access to subject matter experts as needed.
  • Data Governance: Create and enforce policies to maintain data quality and security.
  • Data Literacy: Enhance user understanding and enable self-service data access.
  • Data Leadership: Align data initiatives with business outcomes and facilitate change management.

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

Start Your Data Cataloging Journey with DvSum

Begin by assessing your data culture maturity. Utilize DvSum’s tools and expertise to gauge maturity across search & discovery, data governance, data literacy, and data leadership, and build a more data-driven enterprise today.

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