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.
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.
Data observability should be performed at the exact moment of data usage to avoid any misleading lag between monitoring and use.
Data observability should be included in the whole development lifecycle, including production, in order to guarantee constant monitoring.
How does it work?
The data-related information is logged from within the application.
The data control rules are defined to accurately monitor usage along the data value chain.
The rules are continuously validating data before and in production.
Data teams review and refactor the code of the applications upon notifications.
What are the benefits?
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.
Data usage documentation
Contextual observability provides insights about the data, its transformations, the relationship with applications producing the incoming data, or using its output.
Embedding data observability within the applications facilitates the maintenance and the debugging of code since no additional system is required. All the material is self-contained while being simultaneously connected to the other applications.
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
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
Protect sensitive and private data
Create a protective shell around your data applications
Accelerate your time to market
Trust what you deliver.
Do you have any questions regarding
data quality management or data observability?