Tag: data lakes

To mitigate synthetic fraud, government agencies should consider complementing their rules-based improper payment detection systems with machine learning (ML) techniques. By using ML on a large number of disparate but related data sources, including social media, agencies can formulate a more comprehensive risk score for each individual or transaction to help investigators identify improper payments efficiently. In this blog post, we provide a foundational reference architecture for an ML-powered improper payment detection solution using AWS ML services.

The IMAGINE: Nonprofit conference in Washington, DC brought together leaders, technologists, and mission-driven innovators in the nonprofit sector to learn, connect, and inspire. Over the course of the conference, attendees explored the ways technology helps nonprofits make a positive impact around the world. At the keynote, Dave Levy, vice president of US government, nonprofits, and healthcare at Amazon Web Services (AWS), and Allyson Fryhoff, managing director of nonprofits at AWS, highlighted how nonprofits are on the front lines of addressing some of society’s greatest needs, and how harnessing the power of data can be a force multiplier for change.

In this blog post series on getting started building a data lake for healthcare with AWS, I focus on improving the security posture of our example build by incorporating the relatively new Attribute Based Access Control (ABAC) feature of Amazon Cognito. This helps to both streamline and improve the granularity of access control for various user profiles connected to our data lake scenario.

Using data lakes, nonprofits can use data to influence strategy and inform decisions that produce value and impact. In this post, learn how to build a data lake, ingest data from a PostgreSQL server, give permissions to users to consume the data using AWS Lake Formation, and access and analyze the data using Amazon Athena.

By taking the lead in digital healthcare transformation, Wellforce is estimated to save as much as 20 percent annually (approximately $3 million USD) through the modernization of the healthcare IT ecosystem using the cloud. This innovative approach serves as one of the first examples that healthcare systems across the nation, and world, can replicate.

Now more than ever, governments, education institutions, healthcare organizations, and nonprofits globally look to modernize IT to operate more effectively and meet their missions. Cloud technologies are being used to power machine learning and data analytics for quicker insights to advance medicine and science, manage data at scale, and build a resilient workforce and foster a culture of innovation. To help and inspire organizations to use the cloud to accelerate modernization and digital transformation, AWS will hold the AWS Public Sector Summit Online on October 20, 2020.

According to the National Center for Education Statistics, only 60 percent of college students receive a degree within six years. Universities—like Portland State University (PSU) and Oklahoma State University (OSU-OKC)—are using data lakes for analytics and machine learning to improve academic achievement by helping students reach their educational goals faster. Read on for how institutions use Amazon S3 for data lakes.

Organizations across the globe are using advanced analytics and data science to predict and make decisions. They are finding ways to use their vast and diverse data stores to predict the best place to put their next retail store, what products to recommend to customers, how many employees they need for peak hours of operation, and how long a piece of machinery has until it needs maintenance. Public sector organizations in government, education, nonprofit, and healthcare are looking to use data to advance their missions too. Learn how.

Oklahoma State University in Oklahoma City (OSU-OKC), a two-year, technical-focused college, has historically faced challenges with consistent reporting, database management, and analytics. Technology generalists hired to do these tasks required extensive training to successfully extract data from traditional student information systems, manipulate data for state and federal compliance reporting, and generate limited campus reporting for operational or academic program review and analysis.

Virginia Tech wanted to build a modern data warehouse to complete new requests and quickly answer difficult questions in order to make more informed decisions. To do this, we turned to AWS. We were looking for a way to build forecasting models faster so we could quickly react to changing conditions.