Tag: analytics

HolonIQ is a global education intelligence platform that has partnered with Amazon Web Services (AWS) to transform how governments, institutions, and companies access and utilize critical information. By harnessing artificial intelligence (AI) and natural language processing (NLP) technologies, HolonIQ is empowering decision makers in the education, climate tech, and health sectors with instant access to comprehensive, trustworthy data and research. Read this post to learn more.

In this blog post, learn key AWS concepts and services that can help agencies modernize their cloud and data architecture. First, learn two fundamental concepts that agencies need to examine regardless of their technical approach. Then, discover the AWS services that enable agencies to apply these concepts to meet mission needs.

What if paper forms could be processed at the same speed as digital forms? What if their contents could be automatically entered in the same database as the digital forms? Medicaid agencies could analyze data in near real time and drive actionable insights on a single dashboard. By using artificial intelligence (AI) and machine learning (ML) services from AWS, Medicaid agencies can create this streamlined solution. In this walkthrough, learn how to extract, analyze, and interpret relevant information from paper-based Medicaid claims forms.

Public sector organizations across the globe want to harness the power of data to make better decisions by putting data at the center of every decision-making process. Data-driven decisions lead to more effective responses to unexpected events. For government organizations, a more detailed understanding of citizen ambitions and requirements creates a better citizen experience.

Data spaces can help break down these silos between industries and sectors. Data spaces facilitate a decentralized and secure data exchange across organizations and industries that come together to pool, access, process, and share data. They enable simple, secure, open standards-based, and interoperable data-sharing, while allowing for organizations to maintain full control over their data. Learn how data spaces support simple and secure data-sharing between organizations and sectors, current data space initiatives in Europe and beyond, and how to leverage a data space with AWS.

AMILI is a healthcare technology (HealthTech) company based in Singapore that seeks to advance precision medicine and personalized health and nutrition by harnessing the potential of the microbiome. AMILI uses artificial intelligence (AI) and machine learning (ML) on AWS to comprehensively quantify and characterize gut microbiomes. AMILI aims to build and curate the world’s largest multi-ethnic Asia microbiome database.

In this blog post, learn a high-level architecture, built on AWS, that uses a graph database to analyze unstructured and structured educational data that can, for example, help inform a recommendation to a student for the appropriate courses to take in their next semester based on multiple personalized data factors.

Texas Region 4 ESC (TX-Region 4) is a regional education service center that offers a range of services that help K12 education organizations improve student performance, enable faculty success, and implement state initiatives. When TX-Region 4 wanted to migrate its business intelligence (BI) solution to the cloud, they turned to AWS and Amazon QuickSight to save time and produce more insights to better their educational offerings.

Cybersecurity analytics is a systematic methodology designed to collect, ingest, process, aggregate, and analyze security events. This methodology empowers organizations to proactively perform security investigations, powered by advanced analytics and machine learning (ML), which help mitigate cyber issues more effectively and efficiently at scale. Learn about the core components of a cybersecurity analytics framework and how organizations can use AWS to design a cybersecurity analytics platform with analytics and ML services.

It takes a medical village to discover and diagnose rare diseases. The National Institutes of Health’s Undiagnosed Diseases Network (UDN) is made up of a coordinating center, 12 clinical sites, a model organism screening center, a metabolomics core, a sequencing core, and a biorepository. For many years prior to the UDN, the experts at these sites were limited by antiquated data-sharing procedures. The UDN leadership realized that if they wanted to scale up and serve as many patients as possible, they needed to transform how they process, store, and share medical data—which led the UDN to the AWS Cloud.