Artificial Intelligence (AI) has revolutionized how various industries operate in recent years. But with growing demands, there’s a more nuanced need for enterprise-scale machine learning solutions and better data management systems. The 2021 Data Impact Awards aim to honor organizations who have shown exemplary work in this area. The category “Data for Enterprise AI” awards […]
Artificial Intelligence (AI) has revolutionized how various industries operate in recent years. But with growing demands, there’s a more nuanced need for enterprise-scale machine learning solutions and better data management systems. The 2021 Data Impact Awards aim to honor organizations who have shown exemplary work in this area.
The category “Data for Enterprise AI” awards companies from around the world that have built and deployed use cases for enterprise-scale machine learning and have industrialized AI to automate, secure, and optimize data-driven decision making and/or applications.
In 2021, the finalists under this category include the following organizations.
…and congratulations to the winner: Internal Revenue Service
Internal Revenue Service (IRS) is a bureau of the U.S. Department of Treasury that needs to quickly analyze petabytes of data across hundreds of servers. If this information is analyzed efficiently, fraud can be prevented in real-time.
So to improve the speed of data analysis, the IRS worked with the combined technology integrating Cloudera Data Platform (CDP) and NVIDIA’s RAPIDS Accelerator for Apache Spark 3.0. They created a system to spread data across several servers with GPU-based processing so large datasets could be managed more effectively across the board.
This process cut down fraudulent activity and increased savings, creating a win-win solution for the government and the taxpayers whose resources are now better protected. It is also the winning solution in this category.
During the economic upheaval brought about by the COVID pandemic, the government of Australia was working with the Commonwealth Bank of Australia (CBA) to access real-time data about everyday financial transactions to understand the economic and social impacts of the crisis. This information would help shape timely policies to best serve the people in need.
However, the CBA is a huge institution with 15 million customers and 700M daily transactions — managing the growing influx of data was challenging.
To simplify and partly automate things, the organization worked with Cloudera to collect and send 250+ insight reports straight to the government’s offices. The AI process also helped them easily create tailored responses to 730M in-app messages and offer personalized plans to users across the country.
LG Uplus, a South Korean telecommunications service provider, had just launched the world’s first 5G service in April 2019 but was struggling to commercialize it. Then, the company used Cloudera’s Data Platform as a foundation to build its own Network Real-time Analytics Platform (NRAP) and created the proper infrastructure to collect and analyze large-scale big data in real-time.
Through this process, large amounts of data were distributed across 300 Cloudera nodes, and the NRAP infrastructure functioned as a data lake. This improved the quality of service LG Uplus provides, helped increase their user base, and significantly reduced the time taken to process customer complaint calls.
The Roads and Transport Authority (RTA) operating in Dubai wanted to apply big data capabilities to transportation and enhance travel efficiency. For this, the RTA transformed its data ingestion and management processes.
As a result, the RTA was able to use sentiment analysis to better understand over 3M customers’ feedback posts and tweets to pinpoint their needs, identify issues in the service, and discover the root cause of common complaints. In fact, they were able to perform historical data analysis dating back to 2010 to understand problems encountered by users and find ways to solve them.
Join us in congratulating the Internal Revenue Service (IRS) for winning the award in this category for improving its data analysis speed by 8 times and significantly lowering infrastructure costs.