Data from the Edge-to-AI

Data for enterprise AI

United Overseas Bank

United Overseas Bank uses the Cloudera suite of tools and services in support of their artificial intelligence and data science roadmap to drive the adoption of AI initiatives. Using Cloudera Data Science Workbench as the standard operating platform for data scientists to collaborate, the delivery of AI and data science models is made faster and more efficient. The adoption of AI and data science is quickly growing across the organization where there is a good variety of AI and Data Science use cases across 12 business units and functions. The projects range from using natural language processing to report on market trends, to personalizing features and services to engage customers more meaningfully. Notable implementations include a suite of advanced machine learning capabilities that help analysts conduct AML surveillance more efficiently by greatly reducing the amount of false positives in generated alerts, as well as a market sentiment solution that uses natural language processing to analyze multiple years of investment analyst reports, enabling executives to better understand analyst sentiment and predict potential questions and opportunities as they prepare for briefings.

Data anywhere – TIE

First National Bank

First National Bank (FNB) – one of South Africa’s “big five” banks – built a Data Reservoir to break down silos across the 40+ business unit within their Enterprise. In partnership with IBM and Cloudera, their data and analytics platform transformation initiative is enabling the business to better understand clients, ultimately providing a better banking experience. FNB is expanding its leadership position through its scaled and trusted data-rich platform. By using technology efficiently and increasingly leveraging the big data toolset, FNB helps customers navigate their financial journey with new solutions via the contextual financial services platform. This has allowed the bank to drive cross and upsell opportunities that continue to deliver product adoption across segments. FNB has seen substantial savings over three years by offloading from their legacy data warehouse and they continue to offload into their Cloudera-powered Data Reservoir.

PwC

PwC built their Global Data Platform to help meet client needs in their operational geographies and help enable their teams with global insight for uncovering hidden risks and identifying areas for growth opportunities. The platform ecosystem empowers teams to connect with data in new and innovative ways to serve clients more efficiently and securely. The platform was particularly challenging to build given PwC’s organizational structure, a global network of firms in 158 countries with different data sovereignty requirements, and professionals leveraging the platform across a network of firms. Through use of the platform, PwC is able to achieve faster scalability, lower analytics costs, built-in disaster recovery, and improved access control and data protection. Cloud computing deployment in the platform provides flexible infrastructure that can be scaled up and down according to the business needs at any time, simplifying the ability to manage workloads. The platform has resulted in major operational efficiencies across many of PwC functions and teams.

Data Security and Governance

Bank Rakyat Indonesia

By replacing its legacy systems with Cloudera Enterprise, BRI’s Big Data and Analytics teams are now able to derive new insights and obtain an in-depth understanding of their customers, allowing BRI to make strategic data-driven decisions for better efficiency, productivity and growth. But none of that would be possible without addressing the mounting data security concerns from both regulators and consumers. Striving to enhance the security of its operation and services, the team at BRI developed a real-time fraud detection system, BRIFORCE, using Cloudera Data Science Workbench and Apache Kafka, which allows operators to process and highlight anomalies found in event streams from customer touchpoints such as ATMs and internet banking portals. Where it previously took fraud teams anywhere between two weeks and two months to detect and flag fraud, BRIFORCE has made this process instantaneous. Working with Cloudera has enabled BRI to leverage artificial intelligence and machine learning in new ways, powering solutions that enhance data security and governance while delivering a delightful customer experience.

Data Champions

United States Census Bureau

The mission of the U.S. Census Bureau is to be the nation’s leading provider of quality data about its people and economy. With that in mind, the Census Bureau is using open-source technology and high-performance hybrid cloud infrastructure to transform how the agency processes demographic and economic data using an Enterprise Data Lake (EDL). The EDL will enable the Census Bureau to aggregate and manage data from various sources and improve datasets and data products for all types of users. Communities rely on Census Bureau statistics for everything from planning for new businesses, schools, roads, hospitals, job training centers and seniors’ health care needs. All of the information collected through the Census Bureau is confidential and protected by federal law. The EDL will manage complete data lineage, security, and governance of the data lifecycle within the Census Bureau while supporting new capabilities for processing and analyzing the data to produce quality data products.

Ascension (nominated by StreamSets)

Antimicrobial resistance is one of the most pressing global public health issues today – significantly complicating the treatment of infectious diseases, driving healthcare costs up, and resulting in increased morbidity and mortality. With Cloudera and StreamSets, Ascension’s Clinical Research Institute (ACRI) launched a full-stack big data solution for tracking Antimicrobial Use and Resistance and automating the reporting of those results to the CDC. On the Cloudera platform, Ascension can analyze a variety of health records and can gather a detailed understanding of the distribution patterns of infectious disease. Facilities can interpret antimicrobial usage data monthly facility-wide and by location, which allows for a more detailed analysis of areas of opportunity. By sharing this data and defining standards and protocols, Ascension has given its facilities the ability to benchmark their usage and adopt best practices related to antimicrobial stewardship and infection prevention. The use of the Antimicrobial Use & Resistance (AUR) module has been an effective tool to steer teams to areas of focus to improve care. Over the last year, Ascension has seen improvement in mortality for patients cared for with sepsis and other infectious diagnoses by more than 11% – underscoring the importance of combining the use of data, incorporating the best practices into the electronic health records, and clinician engagement to achieve best outcomes.

Industry Information

Axiata

In 2017, Axiata established Axiata Analytics, a cross-company center of excellence which delivers transformational projects through the use of innovations in AI, ML, and Data Science. In two years’ time, Axiata has achieved large-scale business transformation with use cases spanning from customer churn reduction, optimizing marketing spend, telecom network optimization, raising revenue, all-the-while improving productivity and achieving massive cost savings. With the ability to extract valuable insights from telecom data – such as customers’ mobile usage and network traffic – and third-party data on customer preferences, offline marketing data, and behavior patterns, Axiata can provide stakeholders with data-driven actionable intelligence. As one example, Axiata developed an omni-channel multilingual customer service chatbot in local languages such as Sinhalese and Khmer, which handles 50% of queries without human intervention. Furthermore, by automating network complaint reports, Axiata reduced incident handling time. To further combat network coverage issues, an advanced analytical model helps to optimize coverage, energy, and microwave capacity towards sites with high revenue and higher customer impact.