AI on Pi Day Highlights

We are excited to announce a new column in our Forecaster article where we’ll be showcasing our sessions! You’ll find them listed in chronological order, and as a bonus, we’ll be providing some complimentary gifts. To find them, just check the presentation links. Easy peasy!

As you are AIDA User Group Member, you know that our Bitol,  Linux Foundation AI & Data Sandbox project defines an open standard for data contracts called Open Data Contract Standard (ODCS). You will find the next column very interesting.

Conceptual model of Data Quality of Service as Code by Jarkko Moilanen

Government Data Expert with a Scholar’s Eye

Jarkkos’s academic background positions him as a scholar and researcher delving into the data and API economies. Currently, his PhD research tackles emerging data economy standards, with a focus on fostering interoperability between them.

Standardization Silos: A Challenge for Data Quality

The data landscape celebrates standardized data contract copying and data product metadata models. However, these standards often operate in isolation, hindering the creation of valuable tools and data chains. To bridge this gap, interoperability between these standards becomes paramount. This is the cornerstone for defining data quality of service (Data QoS) as code – a reusable component that seamlessly integrates with various standards.

Learning from the API Economy’s Playbook

The API economy’s early days saw a proliferation of standards like API Blueprint and RAML. Ultimately, Swagger (now OpenAPI) emerged as the dominant force, with an estimated 84-90% of REST APIs defined using it today. We’re witnessing a similar trend in the data economy, with distinct standards for data products and data contracts taking shape. Prominent data contract standards include the Open Data Contract Standard, or ODCS (Linux Foundation’s Bitol project), and InnoQ’s offering.

The challenge lies in promoting interoperability between these nascent standards. Isolated standards hinder efficiency, limit tooling options, and impede development. Jarkkos’s research aims to provide models and opportunities for interoperable components, paving the way for a more unified data ecosystem.

YAML: A Common Thread for Readability and Automation

Many emerging data contract standards embrace YAML for its readability and smooth integration with automation processes. These standards prioritize compactness and extensibility, but extensions can introduce interoperability roadblocks.

Inspiration from OpenSLO and Perrin’s Data QoS

The OpenSLO standard, which applies “everything as code” to service level objectives (SLOs), served as a key inspiration. Combining this with Jean-Georges Perrin’s comprehensive Data QoS concept, encompassing 19 data and service quality indicators, I formulated a model that defines Data QoS as code. This reusable component can be effortlessly incorporated into data product or data contract specifications.

Imagine an availability indicator that leverages the OpenSLO schema, outlining business objectives and “as code” specifications for monitoring. Similarly, the conformity indicator could utilize SodaCL to define data quality testing rules.

Conclusion: A Reusable Future for Interoperable Data

By fostering reusable components that enhance interoperability between data products and data contracts, we can significantly elevate efficiency and tooling within the data economy. This adaptable approach caters to diverse use cases, making standardization a more practical and achievable goal. By building bridges between data standards, we can unlock the full potential of a truly interconnected data ecosystem.

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Bitol is a Linux Foundation AI & Data Sandbox project. As of now, it defines an open standard for data contracts called Open Data Contract Standard.

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