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 »
Amid increasing natural disasters and cybersecurity threats, state and local governments need business continuity plans to minimize disruptions to employees, citizens and IT resources.
There is quite a bit of writing these days about how data is the catalyst to government innovation. Over the last several years, the conversation has turned towards how the innovation needed to face the 21st-century challenges requires us to get these data conversations right.
With so much information from multiple sources, agencies need a way to connect the dots, and that’s where data analytics comes in.
The NYC Housing Authority maintains 2,500 buildings and generates 2.6 million work orders annually, all with the assistance of data analysis.
In the uphill struggle for more data-driven governance, the insider’s best resource might just be experience, expertise and methods that have been developed outside of government.
Four key lessons based on conversations with chief data officers across varying levels of government.
For being such a small unit, the Forensic Audits and Investigative Service (FAIS) has a mammoth task: to review the integrity of programs with multi-billion dollar budgets.
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.