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Wayfair, a Korber HighJump WMS customer just evaluated Manhattan WMS

Cantor Fitzgerald, a Kyriba Treasury customer evaluated GTreasury

Swedbank, a Temenos T24 customer evaluated Oracle Flexcube

Westpac NZ, an Infosys Finacle customer evaluated nCino Bank OS

Citigroup, a VestmarkONE customer evaluated BlackRock Aladdin Wealth

Michelin, an e2open customer evaluated Oracle Transportation Management

Moog, an UKG AutoTime customer evaluated Workday Time and Attendance

List of Cloudera Enterprise Data Hub Customers

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Logo Customer Industry Empl. Revenue Country Vendor Application Category When SI Insight
Bank Rakyat Indonesia Banking and Financial Services 60000 $8.0B Indonesia Cloudera Cloudera Enterprise Data Hub ML and Data Science Platforms 2020 n/a
In 2020, Bank Rakyat Indonesia deployed Cloudera Enterprise Data Hub as the foundation for its ML and Data Science Platforms initiative, establishing an agile predictive and augmented intelligence layer for retail credit and fraud use cases. The deployment focused on operationalizing machine learning workflows and ingesting high velocity transactional signals to support scoring and real time detection. The implementation delivered a predictive credit scoring application that analyzes customer transaction data to predict the probability of customers defaulting on payments the following month. Cloudera Enterprise Data Hub hosted the model training and scoring pipelines, and the scoring outputs produce alerts to loan officers about at risk customers, prompting them to take actions intended to reduce the likelihood of net profit loss. BRI also developed a real time fraud detection service named BRIForce, powered by Cloudera and Kafka for streaming, with HBase used as the model backend data store. Data scientists built a behavioral scoring model that leverages savings, loan transactions, deposits, payroll and other financial data, and the platform automates processing of inputs from ATMs, electronic data capture devices and internet banking channels to surface anomalies quickly. Operational coverage centers on retail lending and fraud monitoring functions, with model scoring embedded into frontline loan officer workflows and fraud operations. Governance emphasis was placed on addressing regulatory and consumer data security concerns, and data science teams manage model updates and the operational scoring cadence. As implemented, the solution enables identification of anomalies in real time and issues predictive default probabilities and operational alerts to loan officers, supporting efforts to reduce the likelihood of net profit loss.
CredEx Professional Services 70 $10M United States Cloudera Cloudera Enterprise Data Hub ML and Data Science Platforms 2016 n/a
In 2016, CredEx implemented Cloudera Enterprise Data Hub as its ML and Data Science Platforms solution. The deployment established a modern data in motion environment designed to consolidate data capture, storage, processing and analysis into a single platform to support advanced analytics and machine learning workstreams. Cloudera Enterprise Data Hub was configured to deliver core capabilities typical of ML and Data Science Platforms, including scalable data ingestion, distributed storage, batch and stream processing, model development and deployment, and enterprise grade security and access controls. The single product with many applications approach served to centralize data pipelines and analytics tooling, enabling repeatable model training, validation and production scoring workflows. Governance emphasis centered on securing data in motion and at rest, and on consolidating analytics operations to create standardized processes for model lifecycle management. Company leadership expressed confidence in receiving timely, convenient and secure service and indicated readiness to embrace future technologies such as biometrics and artificial intelligence, while stating a belief that Cloudera can provide a powerful experience and professional service for the long term.
Docomo Digital Banking and Financial Services 900 $100M United Kingdom Cloudera Cloudera Enterprise Data Hub ML and Data Science Platforms 2016 n/a
In 2016, DOCOMO Digital deployed Cloudera Enterprise Data Hub to underpin customer journey analytics and real-time machine learning across its European-based payment operation. The deployment established Cloudera Enterprise Data Hub as the company's central ML and Data Science Platforms capability for ingesting and processing large-scale streaming and batch data. Implementation emphasized streaming data processing, scalable big data storage, and machine learning model development workflows, enabling A/B testing instrumentation and fraud detection model training. Cloudera Navigator and Cloudera Support were applied to provide lineage, metadata management, and operational support for model governance and data security. The platform was configured to handle data growth of about 40 terabytes a year and to run distributed analytics and machine learning algorithms on consolidated event and transaction datasets. The data hub supported digital payment services, digital content, and mobile marketing functions, helping mobile marketers segment users and refine campaign targeting. Operational scope included processing transaction streams from more than 200 mobile telecom operators and over 300 global and local payment methods across 35 countries, and analytics workflows that fed real-time inference into conversion and fraud detection pipelines. The implementation integrated streaming ingestion, feature engineering, model scoring, and A/B experimentation workflows within a single platform environment. Security controls were validated against the Payment Card Industry Data Security Standard PCI DSS, and security, lineage, and vendor support were cited as key selection criteria. As a result, DOCOMO Digital reported detection of about 95 percent of fraud attempts and a 20 percent increase in conversion rate in a machine learning driven A/B test, outcomes surfaced from the consolidated Cloudera Enterprise Data Hub environment. The Cloudera Enterprise Data Hub now functions as the primary ML and Data Science Platforms asset for operationalizing analytics across customer journey analysis and fraud prevention.
Insurance 1423 $150M United Kingdom Cloudera Cloudera Enterprise Data Hub ML and Data Science Platforms 2015 n/a
Professional Services 10 $1M Spain Cloudera Cloudera Enterprise Data Hub ML and Data Science Platforms 2016 n/a
Banking and Financial Services 28000 $1.8B India Cloudera Cloudera Enterprise Data Hub ML and Data Science Platforms 2016 n/a
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FAQ - APPS RUN THE WORLD Cloudera Enterprise Data Hub Coverage

Cloudera Enterprise Data Hub is a ML and Data Science Platforms solution from Cloudera.

Companies worldwide use Cloudera Enterprise Data Hub, from small firms to large enterprises across 21+ industries.

Organizations such as Bank Rakyat Indonesia, Yes Bank, Markerstudy, Docomo Digital and CredEx are recorded users of Cloudera Enterprise Data Hub for ML and Data Science Platforms.

Companies using Cloudera Enterprise Data Hub are most concentrated in Banking and Financial Services, Insurance and Professional Services, with adoption spanning over 21 industries.

Companies using Cloudera Enterprise Data Hub are most concentrated in Indonesia, India and United Kingdom, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of Cloudera Enterprise Data Hub across Americas, EMEA, and APAC.

Companies using Cloudera Enterprise Data Hub range from small businesses with 0-100 employees - 33.33%, to mid-sized firms with 101-1,000 employees - 16.67%, large organizations with 1,001-10,000 employees - 16.67%, and global enterprises with 10,000+ employees - 33.33%.

Customers of Cloudera Enterprise Data Hub include firms across all revenue levels — from $0-100M, to $101M-$1B, $1B-$10B, and $10B+ global corporations.

Contact APPS RUN THE WORLD to access the full verified Cloudera Enterprise Data Hub customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of ML and Data Science Platforms.