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Data Warehouse and Data Vault Adoption Trends

Modeling, Modernization, and Automation

This BARC Study research focuses on data warehouse and data vault adoption trends in modern analytics environments, including architecture types, priorities, and automation. 

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Lessons about

data modeling, modernization, and automation

Focus on fundamentals

Companies place the highest priority on data quality, ease of use, analytics performance, and data governance.

Automate with commercial tools.

Implement commercial automation tools rather than homegrown scripts because they help improve data quality and standardize and reuse tasks.

Get smarter about the data vault.

Study how best-in-class adopters selected the data vault, trained their teams on the 2.0 solution, and plan to expand its footprint.

Data Vault Takeaways

31% of adopters say their implementation fully aligns with solution standards, and 53% of adopters cite “extensibility” as their primary technical reason for adopting the data vault.

Standards.

Only one-third of adopters say their overall implementation fully aligns with solution standards. A lack of training contributes to this gap, with only 65% of data vault adopters saying they have been trained on the data vault 2.0 solution

Business drivers.

Respondents cite accelerated data delivery, team skills/preference, and advice of consultant as their primary business reasons for adopting the data vault.

Technical drivers.

Extensibility, scalability (data volume and velocity), flexible architecture, simpler data management, unified data model, and data quality are the primary technical reasons for adopting the data vault.

Drawbacks.

Half of data vault adopters cite skills and training requirements as a primary drawback, followed by implementation complexity and query performance. Other responses include design complexity and multiple versions of data.

Key Takeaways

0%

of companies have multiple architectural types

0%

of companies plan to improve data quality

0%

of responders say they automate most or all processes for data integration

Best-in-class companies use commercial automation tools that help them standardize, streamline, and repeat data management tasks. Follow their lead and evaluate tools to integrate data, optimize platforms, and improve data quality.

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