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Data Observability Driven Development

Data quality management needs a paradigm shift
for data teams to scale up and trust what they deliver.

Principles of DODD

The Data Observability Driven Development method follows 3 key principles to provide even more precise information about both the data and the applications using the data.

The contextualization, synchronization, and continuous validation not only make it easier to understand data and its uses, troubleshoot issues, and avoid data catastrophes, but it also enables accountability, facilitates documentation, and increases reliability.

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Contextual
observability

Data observability should not only provide information about the data itself but also about the context of its use to provide a complete understanding of the data usage.

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Synchronized
observability

Data observability should be performed at the exact moment of data usage to avoid any misleading lag between monitoring and use.

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Continuous
validation

Data observability should be included in the whole development lifecycle, including production, in order to guarantee constant monitoring.

How does it work?

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Log
information

The data-related information is logged from within the application.

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Set up
rules

The data control rules are defined to accurately monitor usage along the data value chain.

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"Observe"
data systems

The rules are continuously validating data before and in production.

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Maintain
and improve

Data teams review and refactor the code of the applications upon notifications.

What are the benefits?

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Maintain and improve

Teams developing applications not only understand how the data is supposed to be used but also how its quality must be controlled. This ownership by the development team leads to better coding, faster debugging, and more creativity.

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Data usage documentation

Contextual observability provides insight about the data, its transformations, the relationship with applications producing the incoming data, or using its output.

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Easier maintenance

Teams developing applications not only understand how the data is supposed to be used but also how its quality must be controlled. This ownership by the development team leads to better coding, faster debugging, and more creativity.

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Higher reliability

Continuous validation significantly improves the reliability of applications since conditions about data tested during the development phases are ensured to be held true even in production.

Discover our platform

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Our data observability platform tracks and measures in real-time your data usage performance across systems, projects, and applications.

It enriches your data management ecosystem by sharing schemas, lineages, and quality information with data glossaries, catalogs, and incident management systems.

Our use cases

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Less documentation.
Stronger governance
& productivity.

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Protect sensitive and private data

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Create a protective shell around your data applications

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Accelerate your time to market

Trust what you deliver.