List of Cloudera Enterprise Data Hub Customers
Cloudera
395 Page Mill Road,
Palo Alto, 94306, CA,
United States
Palo Alto, 94306, CA,
United States
1 165-036-0488
Professional Services
2728
$783M
Since 2010, our global team of researchers has been studying Cloudera Enterprise Data Hub customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Cloudera Enterprise Data Hub for ML and Data Science Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Cloudera Enterprise Data Hub for ML and Data Science Platforms include: Bank Rakyat Indonesia, a Indonesia based Banking and Financial Services organisation with 60000 employees and revenues of $8.00 billion, Yes Bank, a India based Banking and Financial Services organisation with 28000 employees and revenues of $1.76 billion, Markerstudy, a United Kingdom based Insurance organisation with 1423 employees and revenues of $150.0 million, Docomo Digital, a United Kingdom based Banking and Financial Services organisation with 900 employees and revenues of $100.0 million, CredEx, a United States based Professional Services organisation with 70 employees and revenues of $10.0 million and many others.
Contact us if you need a completed and verified list of companies using Cloudera Enterprise Data Hub, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the Machine Learning software purchases.
The Cloudera Enterprise Data Hub customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of Machine Learning software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
Apply Filters For Customers
| 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.
|
 |
Markerstudy | Insurance | 1423 | $150M | United Kingdom | Cloudera | Cloudera Enterprise Data Hub | ML and Data Science Platforms | 2015 | n/a |
In 2015 Markerstudy implemented Cloudera Enterprise Data Hub to centralize analytics across its UK insurance operations. The Cloudera Enterprise Data Hub deployment served as the companys ML and Data Science Platforms foundation for real-time pricing, fraud detection, and customer retention analytics.
The implementation configured the Cloudera Enterprise Data Hub to ingest and store large, diverse customer and business activity data sets, and to expose business intelligence and analytic workflows. Functional capabilities included centralized data governance, real-time scoring at point-of-quote, and analytics pipelines used for insurer hosted rating and modeling. The deployment was integrated with existing analytic platforms from SAS and RDT to support rating, fraud analytics, and actuarial modeling.
Data ingestion and processing were scoped to support high-volume quote traffic and underwriting inputs, enabling the enterprise data hub to consolidate customer, quote, and claims data. Cloudera Enterprise Data Hub became the single analytical repository across underwriting, pricing, fraud prevention, and customer lifecycle systems, supporting near-real-time decisioning for quote responses.
Governance and operational changes focused on centralizing data governance and operationalizing analytics into point-of-quote workflows, allowing product and pricing teams to adjust offers based on observed customer behavior. The EDH architecture supported pipeline orchestration and governed data sets used by business intelligence, actuarial, and fraud teams, shifting some decisioning logic into real-time analytic execution.
Outcomes reported by Markerstudy from the Cloudera Enterprise Data Hub implementation included an approximately £5 million reduction in claim costs through improved fraud detection and prevention at point-of-quote, a 120% increase in policy count over an 18 month period, and a 50% reduction in customer cancellation rates with increased retention at renewal. The program also received the Data Mastery and Analytics Award at the Celent Model Insurer Awards 2015.
|
 |
Shoppermotion | Professional Services | 10 | $1M | Spain | Cloudera | Cloudera Enterprise Data Hub | ML and Data Science Platforms | 2016 | n/a |
In 2016, Shoppermotion deployed Cloudera Enterprise Data Hub to apply big data processing and machine learning analytics and to build an IoT solution that measures consumer engagement in stores. The implementation is categorized under ML and Data Science Platforms and focuses on ingesting high volume sensor telemetry to generate actionable retail insights.
Cloudera Enterprise Data Hub was configured for end-to-end data workflows, combining scalable data ingestion, distributed storage and batch processing, model training, and near real time scoring and serving. The deployment emphasized machine learning pipelines and analytics modules native to the ML and Data Science Platforms class, enabling iterative model development and operationalization of predictive models for foot traffic and engagement patterns.
Operational coverage targeted in-store telemetry, with pipelines that consolidate IoT sensor feeds and sessionize engagement events for downstream analysis. The implementation centralized analytics workflows so that merchandising and store operations could consume consistent engagement metrics and visualizations across retailer locations.
Governance practices established model lifecycle controls and data provenance within the Cloudera Enterprise Data Hub environment to support repeatable scoring and updates. As a result of the implementation, retailers used the insights to improve product placement, boost sales, and create more dynamic in-store experiences.
|
 |
|
Banking and Financial Services | 28000 | $1.8B | India | Cloudera | Cloudera Enterprise Data Hub | ML and Data Science Platforms | 2016 | n/a |
|
Showing 1 to 6 of 6 entries
Buyer Intent: Companies Evaluating Cloudera Enterprise Data Hub
ARTW Buyer Intent uncovers actionable customer signals, identifying software buyers actively evaluating Cloudera Enterprise Data Hub. Gain ongoing access to real-time prospects and uncover hidden opportunities.
Discover Software Buyers actively Evaluating Enterprise Applications
| Logo | Company | Industry | Employees | Revenue | Country | Evaluated | ||
|---|---|---|---|---|---|---|---|---|
| No data found | ||||||||
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.