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Description

Regrid experts gather and harmonize data from various sources, including government archives and proprietary information, to create a comprehensive dataset. This integration provides you with the most precise and current information available, which is crucial for effective decision-making. Reliable parcel data serves as an essential tool for site selectors, acting as the x-ray vision for geographic application developers, and guiding organizations navigating the expansive ocean of data-driven insights. Think of Regrid as your roadmap leading to the valuable treasure trove of parcel and spatial data solutions. Specializing in making property boundary information accessible and straightforward, Regrid offers detailed insights into land use, ownership, and associated property data. With a staggering dataset of over 156 million parcel boundaries and records, Regrid's coverage encompasses more than 99.9% of the US population, presenting a cohesive and standardized tapestry of property details for use in your maps, applications, and spatial analyses. This extensive resource empowers users to make informed decisions based on reliable and comprehensive data.

Description

Selector’s software-as-a-service leverages machine learning and natural language processing to deliver self-service analytics that facilitate immediate access to actionable insights, significantly decreasing mean time to resolution (MTTR) by as much as 90%. This innovative Selector Analytics platform harnesses artificial intelligence and machine learning to perform three critical functions, equipping network, cloud, and application operators with valuable insights. It gathers a wide array of data—including configurations, alerts, metrics, events, and logs—from diverse and disparate data sources. For instance, Selector Analytics can extract data from router logs, device performance metrics, or configurations of devices within the network. Upon gathering this information, the system normalizes, filters, clusters, and correlates the data using predefined workflows to generate actionable insights. Subsequently, Selector Analytics employs machine learning-driven data analytics to evaluate metrics and events, enabling automated detection of anomalies. In doing so, it ensures that operators can swiftly identify and address issues, enhancing overall operational efficiency. This comprehensive approach not only streamlines data processing but also empowers organizations to make informed decisions based on real-time analytics.