Next-Gen Crime Fighting Software Links All Your Intelligence Data in One Place
Law enforcement and intelligence agencies deal with large volumes of disparate data on a daily basis. Analyzing all this data across multiple data silos and structures, each with their own levels of security permissions, is a big challenge.
Federal Government Data Maturity Model: Drive Your Agency’s 2019 Data Strategy and Roadmap
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Form follows function – and so should your organization’s data!
[eBook] Get Insights from Government Data, Before It Perishes and Goes Stale
So much data, so little time. Disparate sources such as sensors, machines, geo-location devices, social feeds, server and security system logs, and more, are generating terabytes of data at unfathomable speeds. Getting any kind of real-time insight and, we dare you to dream, acting on that data as it flows in, is not an easy feat for resource-constrained government agencies.
Big Data Month: New eBook Sheds Light on How Government is Overcoming Persistent Big Data Challenges
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There are many opportunities in the public sector for data science and data analytics, yet, almost as many challenges. When we kicked off Big Data Month at DLT, we asked our Chief Data Scientist, Sherry Bennett, for her insights. What became clear is that the obstacles to big data success are universal to both the public and private sector: “…everybody…is grappling with the same thing.
Big Data Month: How Government Can Move Beyond Being Data Rich, But Information Poor
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Data is everywhere in government but turning that data into actionable information and insights remains a persistent problem. “We are data rich and information poor,” said Shelley Metzenbaum, a former associate director for performance and personnel management at the Office of Management and Budget (OMB) at a recent IBM Center for The Business of Government session on “Envision Government in 2040”.
Big Data Month: DLT Chief Data Scientist Shares Insights
At DLT, July is Big Data Month, where we will be highlighting all things big data in the public sector. To kick off Big Data Month, we sat down with DLT Chief Data Scientist Sherry Bennett to get her insights into what is going on the world of public sector data and analytics:
INTERVIEWER: So Sherry, what's your story and what led you to DLT?
Even When the Lights Go Out in Government, Data Never Sleeps
Government shutdowns are a costly business. The 2013 shutdown cost $24 billion in lost economic output while the 1996 shutdown resulted in $2.1 billion in government costs. We are yet to learn the impact, if any, of the three-day 2018 shutdown.
But, what we do know is that shutdowns are not universal. For many critical government employees, the lights never go out. Here’s just a shortlist:
Veterans Affairs (VA) remained operational.
“Hadoop” You Deliver Data-Driven Actions in Support of Good Government?
Public sector leaders are facing an uphill battle when it comes to managing data. Their needs are growing while their budgets are often shrinking. How can federal agencies do more with less?
Leveraging enterprise open source solutions is part of the answer. They can help agencies close this gap and meet today’s needs—whether that’s analyzing warfighter data or building Smart Cities—while also planning for tomorrow’s challenges.
Bringing Big Data to the Fight Against Government Benefits Fraud, Waste, and Abuse
The U.S. Government spends trillions of dollars on benefits programs like Social Security, Medicare and Medicaid each year. Unfortunately, billions of those dollars are improperly paid, reducing the benefits to those who most rely upon them. In 2016, the White House estimated these losses at $144B.
Improper payments, fraud, and abuse takes many forms, consider some of these examples of Medicaid fraud and abuse:
Too Many Data Silos? How Your Agency Can Improve Data Insights and Mission Outcomes
With a constant influx of data, one of the biggest challenges facing government agencies is determining “What is the right data?” or “What data does my agency need for mission success?”
Then, once the data is discovered, how do you make that data actionable? How do you integrate and visualize it for better insights?