The advent of spatial technology has automated spatial problem solving. As a result, we are seeing a rebirth in the age of discovery.
Posts Tagged: data analytics
Agency leaders and program management professionals must seek practical, secure, and effective methods to extract the valuable information captured in their data repositories — from legacy systems to real-time data streams.
Data analytics is a hot topic in government and one that continues to gain traction. Agencies are increasingly relying on analytics to help fight diseases, combat fraud, improve citizen services and more. To better understand how the Centers for Disease Control and Prevention is using analytics, we heard from Ana Penman-Aguilar, Associate Director for ScienceRead… Read more »
Customer experience should be at the heart of digital service design. This requires knowing your customers — determining what their needs are and understanding how they encounter and experience federal digital services.
Incorporating new technologies, such as software-defined networking (SDN) and network function virtualization (NFV), can provide improved network functions, management and operations. There are a number of challenges to achieving digital transformation, but intelligent automation can better enable agencies to adopt SDN and NFV into their infrastructure.
Each year, government agencies lose billions of dollars due to fraud, waste and abuse. Government agencies tasked with fraud prevention are increasingly turning to geographic information system (GIS) platforms — utilizing maps, geo-enrichment and sophisticated data analytics — to tackle fraud, accurately identify patterns and problem areas and improve organizational efficiency.
Platforms like Amazon Web Services (AWS) are transforming the way government thinks about storage, integration, cybersecurity and analytics, and are helping agencies overcome common data challenges.
Learn how government agencies are tackling threats of fraud and abuse using Data Analytics.
What do maps have to say about your community?
Are you making your data meaningful and accessible?