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Data Management based on Standards as a Business Strategy
In today’s competitive landscape, companies must work on differentiators to gain market share. Equally important is the ability to identify non-competitive vectors to free up limited resources and to focus on the core business.
Data management is one possible non-competitive vector, as it is the often data itself that defines the competitive advantage, and not the way it is stored and managed. However, it is crucial to choose the right data management strategy that provides long-term data storage and access capabilities and aligns with your company’s data governance policies.
One possible answer is provided by the newly released ASAM:GUIDE from ASAM e.V..
ASAM ODS enabling AI/ML-solutions
ASAM International Conference, December 4-5, 2024, Munich, Germany
In most situation standardization is seen as a blocker when it comes to innovation and integration with latest technology trends. However, in this article we explain that the ASAM ODS standard – suited for long term and future-proof data management – already provides the needed building blocks for preparing and storing test data in a way that it is suitable for machine learning and how the data can be accessed with standard analytics and machine learning tools as well as getting support by AI agents.
Peak BigODS Exporter: Bridge Your ASAM ODS Data to Big Data Lakes
Data-driven decision-making is becoming increasingly important, such that the automotive and aerospace industries face growing needs for scalable test data analysis. Much of this data is stored using the ASAM ODS standard, which excels in managing test data across systems and formats. However, traditional ASAM ODS storage isn’t well-suited for advanced analytics in big data environments. To address this need, ASAM ODS has defined a new “Associate Standard” designed to connect ASAM ODS databases with big data ecosystems—an innovation that Peak Solution has already implemented in the form of the Peak BigODS Exporter as part of Peak ODS Adapter for Apache Spark.
Peak ODS Adapter for Apache Spark: Unlocking profound Insights from ASAM ODS Test Data
In automotive and aerospace industries, data-driven insights are crucial for maintaining a competitive edge. ASAM ODS has long been the standard for the persistent storage of test data, providing manufacturers and suppliers with a robust foundation for managing diverse measurement systems and data formats. However, to fully harness the potential of their existing ASAM ODS test data, companies need advanced tools to explore, analyze, and share insights efficiently. This is where Peak ODS Adapter for Apache Spark comes into play — a powerful extension designed by Peak Solution to gain deeper insights from test data.
The role of metadata for effective Test Data Management
Unlock the Full Potential of Your Test Data with Metadata Management: In today's fast-paced world of automotive and aerospace testing, valuable test data often remains underutilized due to decentralized storage and inconsistent metadata. Discover how a well-structured metadata management strategy can transform your test data into a powerful resource for decision-making and future innovations. Learn why metadata must be captured throughout the entire test process and how this foundation enables advanced technologies like artificial intelligence to revolutionize test operations.
Peak ODS Server: Enhanced Data Management for Simcenter™ Testlab™
In the ever evolving world of automotive and aerospace testing, efficient test data management is crucial. Simcenter™ Testlab™ provides a fast and easy comparison of measurement, analysis and simulation results of a wide range of test objects. This is based on powerful functions for data organization, visualization and reporting. With the new version of Simcenter™ Testlab™, users can now upgrade their data backend using an ASAM ODS database. In such a configuration, the data can be published in a customizable data model along with contextual information. This allows users to more effectively organize, analyze and easily share the data with colleagues, other teams and departments, or even with suppliers and customers. All stakeholders can search for the data based on uniform engineering properties and context information in an ODS-compliant way.
ODS Test Data Analytics with Jupyter Notebooks
A Jupyter Notebook is an interactive way to "prototype and explain your code, explore and visualize your data, and share your ideas with others". This makes a Jupyter Notebook - with a Python kernel - the ideal tool to programtically explore the data contained in the Peak ODS Server.
To get you started working with ASAM ODS data in a Notebook we provide you with our ODS Library for easier using ODS in Python together with Example Notebooks.
Have fun exploring your data…
