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Why Build a Data Management Strategy on Standards?

Standards introduce a common language for APIs and protocols, as well as for the data being exchanged and finally persisted. This way, the standard defines the interface for interoperability and data exchange. Standards foster quality and avoid vendor lock-in, allowing you to choose from various offers and providers.

In addition, standards enhance collaboration and alliance formation, especially around the aforementioned non-competitive vectors.

👉 On page 8 of the ASAM:GUIDE you can find further arguments for “Standards – A Business Consideration”. 

What Is the Right Data Management Standard for Me?

There is no single answer to this question because different kinds of data require different data management solutions. For managing test data, the ASAM:GUIDE references ASAM ODS as reliable persistent storage and retrieval for testing data. The kernel of the ASAM ODS data management standard is a data model that adds semantics for correct data interpretation. The data model is highly adaptable yet well-defined, allowing clients to query and correctly interpret data. Data access via HTTP is independent of the IT architecture, making it adaptable and versatile for different application scenarios.

An ASAM ODS server implements the data model and integrates data from various test beds and measurement devices, providing uniform data access regardless of origin.

👉 On page 35 of the ASAM:GUIDE you can read why “ASAM ODS” provides a comprehensive, adaptable, and scalable framework for managing test data.

Who is Using Data Management Standards Today?

Looking on page 11 off of the ASAM:GUIDE you can see that the “who is who” of the automotive industry is a member of the ASAM organization, and more and more non-automotive companies are joining. For existing data management applications, you can filter the ASAM Product Directory by the ASAM ODS standard to receive a long list of ASAM ODS products and services. You can also find ASAM ODS-related ASAM Application Stories on the ASAM web site.

👉 On page 66 of the ASAM:GUIDE you can read how Siemens Digital Industries Software partnered with Peak Solution to help BMW Group transition from a proprietary data management system for NVH test data to a more performant, open, and modern solution: Simcenter Testlab based on the ASAM ODS standard: “Ready for openness managing NVH test data”.

Does a Data Management Standard Integrate with Data Analysis and Machine Learning?

The most important functionality of a data management solution is to unlock the potential of the data contained – your data!

Keeping up with the latest innovations and technology in data management, Python is the “lingua franca” for data analytics and ML tools. The existing ASAM ODS HTTP API does not seem very practical from a Python perspective, so Peak Solution introduced ASAM ODSBox, a lean Python wrapper on top of the ASAM ODS HTTP API, suited for Python analytics and ML libraries by providing Pandas DataFrames.

Additionally, the user-friendly JAQueL query language and notebook-based examples lower the entry barrier for using ASAM ODS.

👉 On page 64 of the ASAM:GUIDE you can find an application story of “ASAM ODS data analytics with Python, Notebooks, and AI-Agents”.

Conclusion

Adopting data management standards such as ASAM ODS can significantly enhance your business strategy by ensuring interoperability, fostering collaboration, and avoiding vendor lock-in. By leveraging standards, companies can focus on their core competencies while ensuring their data management practices are robust, scalable, and future-proof. The ASAM ODS standard, in particular, offers a comprehensive framework for managing test data, integrating seamlessly with modern data analysis and machine learning tools. Embracing these standards positions your company for long-term success in an increasingly data-driven world.

👉 Download the ASAM Application Stories from ASAM:GUIDE to see some examples.