RESOURCES

Common misconceptions pertaining to AI/ML adoption, including investments, skills, and getting business buy-in.

Common AI and Machine Learning Applications in Retail, Manufacturing & Automotive, and Financial Services & Insurance Sectors

A new translation layer for Amazon Aurora PostgreSQL

Building a Simple Machine Learning model for Fraud Detection with Amazon SageMaker

A guide on how Redshift compares with Legacy Databases and Aroa's methodology for Migrating to Redshift

This Data Quality Methodology Handbook explores Aroa’s best practices and outlines a step-by-step methodology for instilling data quality within targeted data sets, with an aim of accelerating value across your organization.

The usage of third-party IT Managed Service Providers and the criteria you should look for when selecting a vendor.

Read about how Aroa accelerated the delivery of AstraZeneca's modern data warehouse, offering improved insights and data accuracy to empowering the business to make data-driven decisions.

Organizational responsiveness is critical, especially amongst the rapidly evolving business landscape. Organizations must ensure that their Data Estate is prepared to support their business strategy.

Our AI Methodology is designed to ensure you garner value from your data quickly, enhancing your processes and extracting useful insights.

A deep dive into the best practices and critical steps to achieving data quality excellence, especially as it pertains to customer data and improving the customer experience.

The Ideologies of Digitalization and Digital Transformation