Category: Technical How-to

Managing donors, members, and constituents is essential to the success of most nonprofits. Customer relationship management (CRM) systems, like the no-cost, nonprofit-focused CiviCRM, are an important part of this process. In this post, learn how to deploy CiviCRM using AWS, and explore an architecture for deploying CiviCRM in a way that is highly available and resilient to service disruptions or events.

In the last few years, many state motor vehicle departments agencies quickly transformed their processes and adopted new procedures to accommodate changes caused by the COVID-19 pandemic, like social distancing, contactless interactions, decreased staffing, and other constraints. Now, agencies can build upon these changes by modernizing their systems with intelligent automation—transitioning from reactive to proactive engagements with their citizens. Learn how to use AWS to connect and retrieve data either from an enterprise on-premises database or other third-party integration that allows for both a modernized outreach or an inbound customer experience.

Attacks on digital citizen services can cause citizens to lose trust in their governments. Services such as real estate land title searches, emergency response, and more need to be operational in times of need. As an IT leader for digital citizen services, your mission to automate incident management runbooks is necessary. Learn how the automation of incident response starts with what you already have: your existing incident response runbook.

This blog post introduces Amazon Web Services (AWS) Trusted Internet Connections (TIC) 3.0 overlay artifacts. TIC is a federal cybersecurity initiative intended to enhance network and data security across the Federal Government.

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.

An Aadhaar number is a 12-digit unique identification number issued by the Unique Identification Authority of India (UIDAI) to every individual in India. Considering the sensitivity of the Aadhaar number and the potential implication of having one’s Aadhaar number compromised, UIDAI mandated the need for all Aadhaar and Aadhaar-related data to be encrypted and stored separately in a secure, access-controlled data repository known as an Aadhaar Data Vault. This blog post explains how government and private entities that collect, process, and store Aadhaar data for various use cases can use AWS CloudHSM from AWS to create an Aadhaar data storage solution that can meet guidelines provided by UIDAI.

Diabetes is a major chronic disease that often results in hospital readmissions due to multiple factors. An estimated $25 billion is spent on preventable hospital readmissions that result from medical errors and complications, poor discharge procedures, and lack of integrated follow-up care. If hospitals can predict diabetic patient readmission, medical practitioners can provide additional and personalized care to their patients to pre-empt this possible readmission, thus possibly saving cost, time, and human life. In this blog post, learn how to use machine learning (ML) from AWS to create a solution that can predict hospital readmission – in this case, of diabetic patients – based on multiple data inputs.

Citizens want to report issues to their local governments in a fast and simple manner and not have to worry about identifying the right government agency or phone number—for instance, if a fire hydrant is broken, or a road sign has fallen over. In this blog post, learn how to set up a solution with AWS deep learning services that creates a fluid experience for reporting and addressing these issues.

Many public sector customers in government, healthcare, and life sciences have data lakes that contain addresses (e.g., 123 Main Street). These customers frequently ask how they can quickly visualize these addresses on a geographic map to get a more intuitive understanding of how these addresses are distributed. In this post, learn how to use Amazon Athena and Amazon Location Service to perform ad hoc geocoding on an example dataset and visualize these geocoded addresses on an Amazon QuickSight map.

Healthcare providers and healthcare systems want to modernize their healthcare data exchanges so they can better analyze and gain more insight from their clinical data. In this walkthrough, learn how to use AWS to migrate legacy healthcare messaging data into Amazon HealthLake, which can use artificial intelligence (AI) and machine learning (ML) to discover meaningful and actionable healthcare information embedded in unstructured text.