Data are needed at much finer scales and when used appropriately, more data translates to a lower risk in the decision process.
Whether using our sensors or just our data, we deliver information localized and relevant to your specific cause.

In an effort to reduce or eliminate our vulnerability to climate change, either our risk of exposure must decrease, or our adaptive capacity must increase. Reducing our risk to exposure is most often done by exploiting knowledge (data) about that change in order to strategically develop or position our vulnerable assets where they are least likely to be affected by the change.

Existing environmental data are expensive to collect, difficult to assemble and translate into actionable information, and are inadequate to allow clear understanding in order to act to reduce climate vulnerability.

Implementing effective measures to reduce vulnerability depends not only on a comprehensive understanding of the vulnerability but also a comprehensive understanding of the variability and change of the environment at the scale of relevance. Both of these dependencies define a system’s climate risk, and both are wholly reliant on adequate environmental data, which existing observation networks do not provide.

A substantial amount of accessible data are scattered across the myriad of organizations that manage the networks. A client would need to assemble these disparate data into a comprehensive dataset and apply an appropriate suite of quality control and analysis algorithms before they could extract information that can become useful in assessing relevant environmental changes and one’s exposure to them. Though climate vulnerability affects an entire interconnected system, most climatic vulnerabilities are driven by specific geographical, temporal, or asset impacts.  Thus, reliance on such coarse and patchy data can lead to suboptimal decisions.