Prioritizing Data in Government Technology Modernization
In government, digital modernization often overlooks enterprise data management, which results in fragmented ecosystems that limit modernization’s full potential.
In government, digital modernization often overlooks enterprise data management, which results in fragmented ecosystems that limit modernization’s full potential.
Both leaders and followers shape workforce culture, including what is and isn’t acceptable to discuss. By examining your leadership style through the concept of an Overton Window, you can “read the room” and identify growth opportunities.
Agencies can improve their cyber defenses by integrating artificial intelligence (AI) into their cybersecurity strategies. Here are specific benefits of an AI-powered approach.
Agencies can optimize the performance of their IT systems and applications by taking a comprehensive approach to collecting and analyzing data. Artificial intelligence, and a unified data platform, can help agencies maximize those observability efforts.
Mismanaged data can lead to poor decision-making, loss of trust, increased risk and other fallout, and artificial intelligence has made data use more complicated. Fast, secure, energy-efficient data storage, however, helps agencies manage what they have.
The adoption of agile tools and techniques is not just a technical upgrade, but a cultural transformation aligning with their strategic goals.
USA.gov has launched a program to recruit the public for research programs. Here’s why that’s so important.
Agencies that rely exclusively on manual software testing are missing the opportunities that automation provides, such as finding and resolving pain points quickly and efficiently, giving employees a more satisfying work experience, strengthening service delivery, and building constituent trust.
In the journey toward building more equitable and inclusive AI systems, diversity is not just a buzzword — it’s a fundamental principle that must be embraced at every stage of development and implementation.
Dive into the heart of MLOps, where data science meets DevOps, ensuring seamless integration of machine learning models into production. Discover the essential practices and tools for automating, monitoring, and managing the entire ML lifecycle.