Enterprise data clouds are especially valuable to organizations as they can analyze an agency’s data regardless of the IT storing that information.
With our work in natural resource and surface water, we utilize our asset management system in many ways. The ability to spatially conceptualize data helps our team make more effective policy decisions and communicate environmental and resource issues with better clarity.
Big data, as it’s called, has taken over government and forced agencies to piece together policies and practices that will allow them to manage all of their incoming information securely.
What would be possible if your organization could predict future occurrences? A new generation of advanced analytics—high-level diagnostic, predictive, and prescriptive—can now provide that opportunity.
To really move the needle in a way that sustainably transforms the organizational culture to a data-driven one, agencies need to progress beyond these initial ad-hoc use cases. They can do this by strategically harnessing the creativity and operational know-how of departmental staff to identify analytics opportunities enterprise-wide.
The role of a data analyst is evolving in the era of emerging artificial intelligence (AI). So much so, we might need to relabel this role as Analyst 2.0. Here’s a look at what Analyst 2.0 means for the next-gen data guru helping organizations use data for better decision-making.
As video cameras become even more ubiquitous, video analytics will be one of the front faces of AI.
Agencies are seeking innovative ways to boost internal efficiencies, while also improving how they manage and safeguard data.
Agencies are looking for better approaches to data storage and backup. They are are shedding those complex, legacy multi-tiered solutions for simplified data management that still has physical support but is built for virtualized environments, and native cloud capabilities.
Adaptive Networks are automated and programmable networks that can configure, monitor and maintain themselves, as well as adapt to changing requirements.