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Open Environments provides its members:
- simplified access to Open Sourced and Public Datasets
- authoritative reference data with codes and descriptions
- analytic services with models against these data resources
We welcome all:
- Subscribers with their subjects of interest.
- Addition or modification requests for our curators.
- Alerts to data quality issues or concerns
If you have interest in speaking to a curator, feel free to contact us by email to
support@openenvironments.com
More of the back story
Until the early years of this century, companies pursued analytics with a familiar design: a large computer drove a large disk array, both of proprietary design.
Scaling up and out were both constrained by the innovation of the vendors involved.
But the digital era has given us a confluence of:
- virtualized (cloud) computing,
- commoditized hardware,
- adoption of open sourced software,
- many (many) more interactions to collect
These have unleashed both the data storage (Big Data) and subsequently the compute abilities needed (GPU, Spark, etc) to put computational mathematics into practice.
Initially, the work was called Data Science generally, then Machine Learning with the promise of the more advanced field of Artificial Intelligence.
That is, if you have:
- sizable data resources
- the technology needed
- and some scarce analytical hands
Without operations at scale, some are being left behind in this revolution and risk becoming irrelevant altogether.
Small businesses, non-profits, academics and government agencies often serve focally fewer constituents without many properties captured on each.
They may have limited abilities for technology and staff spending needed to use data they do maintain.
Open Environments was founded to meet these disadvantages with services, data and models.
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