Unlike structured data, moving or migrating, unstructured data often has a negative effect on its value. Unstructured data, when moved—and by definition, renamed—makes finding data again very difficult. Stated differently, because of the way today’s dominant traditional storage systems are architected, hardware upgrades often orphan data from core analytics processes and the geoscientists who need it to inform interpretations and decision-making.
At multi-petabyte ingest rates, seismic analytical tools—many of which were designed decades ago—are quickly showing their age. The industry is at the doorstep of a shift to more flexible, scalable constructs designed to specifically solve the problems of data access, data longevity, data management, and storage. While traditional storage technologies often perform effectively over the near term, traditional seismic architectures and monolithic data constructs leave a good deal of unstructured data value untapped. Generally, these systems were not designed to span multiple-technology refreshes or facilitate data access over longer periods of time.
Multi-national Offshore drilling companies, oil and gas companies, and others involved in seismic pursuits are starting to realize that the inability to re-harvest this data over decades is not just a technical issue, but also a business problem that can affect future competitiveness. Companies that treat this data as a business-critical asset are becoming aware of the shortcomings within the current systems of architectural constructs, upon which organizations have relied for the past 30 years. Finding and accessing data readily are two must-have features that now threaten traditional storage approaches that for decades have focused on getting data into their system. Until now, getting the data out again has been a tertiary or, at best, a secondary consideration, but not for much longer. See more here how companies manage to find right solutions.
Hyperfiler is a data management system that allows companies to create a petabyte-scale “dataplane” that logically combines disparate datasets.
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