Mixing data governance and standardized processes is the best antidote to a big-data headache.
by Kenneth Wong
Those who've witnessed the evolution of product lifecycle management (PLM) might see history repeating in the burgeoning simulation lifecycle management (SLM) market. Like PLM, SLM began as an attempt to clean up the data warehouses. In SLM's case, it's to sort and archive the mounds of data generated from repeated simulation so engineers can, when necessary, retrieve, refer to and consult past exercises for guidance.
Like PLM, SLM quickly ballooned into process management. The multidisciplinary approach--involving multiple experts investigating the design's fitness using multiple software packages from competing vendors--is adding complexity to the process. The repetitive nature of simulation--subjecting the same design to slightly different load variables to find the best option--increases the volume of data to sort afterward.
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