"Data is the new oil" (Clive Humby)
Just like in fossil times, oil found in geological formations must be recovered through drilling, fracking, or alternative methods. For most use cases, the extracted oil needs to be processed and refined into various products, such as gasoline or heating oil, before it can be efficiently used.
Data is the new oil—comparable, yet fundamentally different.
Unlike oil, we generate vast amounts of new data every day. As of now, an estimated 149 zettabytes exist—though exact sources vary. In other words, over 90% of the world’s data has been generated in just the past two years. The data volume doubles approximately every four years. This contrasts with oil, where production rates remain relatively constant, while reserves tend to increase each year.
"Data is valuable but if unrefined it cannot be really used" (Michael Palmer)
Data without context is inherently worthless. Only through contextualization—by defining what a dataset represents—does raw data transform into meaningful information you can use to support your business.
Context turns data into insights
Simply providing a data value pair like [48.6560, 9.2204] leaves its meaning unclear. Only by adding context—such as specifying that the pair represents geographic coordinates in decimal degrees—does it become an informative statement, like 📍 Latitude: 48.6560° N, 📍 Longitude: 9.2205° E: The geocoordinates of my hometown.
SPDM Tech focuses on Simulation Automation and Data
For the purpose of numerical simulations, it is essential to convert vast quantities of raw data into meaningful and actionable insights - preferably well described, tool-supported and automated to satisfy credibility requirements - enabling deeper analysis and informed decision-making.
"Most Simulation runs hold no value" (Daniel Krätschmer)
As Simulation is still an expert domain in Engineering and just badly democratized it suffers comparable limitation as digitalization does. The pure distance in competence and understanding between the decisive Engineering Business represented by Line Managers, Program and Project Managers and the Simulation Expert (or in analogy: IT and Software Development Experts) is often high and there is a noticeable risk that simulation activites are mainly driven and initiated by Experts not understood by the Business. That might be one of the reasons why a lot of existing virtualization and simulation potential remains unleashed: It is just not connected to the Business where decisions are regularly met.
SPDM Tech helps to contextualize simulation artefacts to foster profound decision-making
Since simulation runs constitute only a fraction of the complete engineering process in e.g. automotive engineering, establishing a seamless connection between product master data and simulation objects is essential to ensure consistency, accuracy, and integration across the entire value chain: It is a necessary prerequisite to realize various use cases ranging from required CO2E calculations to virtual referencing and release of reliability-driven product testing. Collecting information about the device to be tested sounds simple - but in heterogeneous engineering reality it is often difficult to connect Devices under Test (DuT)-instances with transparent product master data.
SPDM Tech specializes in establishing data architectures and ontologies for comprehensive engineering processes, with a focus on simulation data as our core element. By semantically describing both attributes and interrelations of data objects, we create knowledge graphs that enhance data integration and accessibility without the need to incorporate entire model information.
Knowledge graphs play a pivotal role in enhancing tracability, lineage and relationships of data points by semantically linking data from various sources. Additionally they foster AI-driven Engineering Applications providing the contextual backbone necessary for advanced data integration and analysis.
Insights require reliable data sources
Despite its importance, documentation is often the first casualty in real-world engineering — just like metadata, which is frequently missing or incorrect. These errors are directly transferred into knowledge graphs, where they are amplified through data connections, ultimately leading to incorrect results in downstream applications. Simply put: the quality of your connected data sources is mission-critical for success in any data-driven business. So take care!
