List of dotData OPs Customers
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Since 2010, our global team of researchers has been studying dotData OPs 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 dotData OPs for MLOps 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 dotData OPs for MLOps Platforms include: Otsuka, a Japan based Professional Services organisation with 9421 employees and revenues of $6.28 billion, Exeter Finance, a United States based Banking and Financial Services organisation with 1400 employees and revenues of $550.0 million, sticky, a United States based Professional Services organisation with 90 employees and revenues of $9.0 million and many others.
Contact us if you need a completed and verified list of companies using dotData OPs, 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 software purchases.
The dotData OPs 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 software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Exeter Finance | Banking and Financial Services | 1400 | $550M | United States | dotData | dotData OPs | MLOps Platforms | 2023 | n/a |
In 2023 Exeter Finance deployed dotData OPs within its MLOps Platforms footprint to accelerate feature discovery and improve predictive models for delinquency default risk and pricing across its auto finance operations. The engagement targeted rapid iteration of credit risk and pricing models, and the vendor case study reports rapid model improvements with a reported 90 day ROI.
dotData OPs was applied to automate feature generation and discovery workflows, with implementation focused on automated feature engineering, feature store style management, and model training orchestration consistent with MLOps Platforms capabilities. Module usage for dotData OPs is inferred from the vendor case study which references a Feature Factory pattern and Azure ML integration, and that inference is stated for clarity rather than as a direct product configuration extract.
Integrations emphasized pipeline handoff into cloud model training environments and operational model deployment, reflecting an integration with Azure ML as described in the source material. Operational coverage spanned data science, credit risk, and pricing teams in auto finance, and governance changes centered on standardizing feature generation, accelerating iteration cycles, and establishing repeatable model development and deployment workflows, consistent with MLOps Platforms implementation practices.
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Otsuka | Professional Services | 9421 | $6.3B | Japan | dotData | dotData OPs | MLOps Platforms | 2019 | n/a |
In 2019 Otsuka implemented dotData OPs to power an AI Sales Lead Guide and to support sales forecasting. dotData OPs was deployed as an MLOps Platforms solution to analyze decades of sales and support data and produce highly targeted proposals for the sales organization.
The implementation leveraged dotData Feature Factory and enterprise feature discovery to surface curated feature sets and the case study documents use of model outcomes for decisioning. Configuration emphasized operationalization of feature pipelines and the use of dotData OPs modules for production model outcome tracking, enabling repeatable feature generation and model promotion workflows.
Operational scope focused on sales and support functions, ingesting historical sales and support datasets to inform forecasting and to drive proposal recommendation workflows. The deployment embedded dotData OPs into sales forecasting processes and proposal generation pipelines to support AI-driven lead guidance.
Governance activities centered on formalizing feature discovery practices, instituting model outcome review cycles, and establishing operationalization workflows within sales operations. Outcomes reported include approximately 74,300 AI-driven proposals in the first half of 2021, demonstrating the application of dotData OPs to sales lead guidance and forecasting.
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sticky | Professional Services | 90 | $9M | United States | dotData | dotData OPs | MLOps Platforms | 2021 | n/a |
In 2021 sticky implemented dotData OPs to build predictive models that optimize payment retry timing and recover declined transactions across its subscription and ecommerce platform. sticky used dotData OPs, an MLOps Platforms solution, to operationalize model generation and decisioning for payment retry orchestration and declined transaction recovery within its payment lifecycle.
The implementation leveraged dotData Feature Factory and dotData Enterprise capabilities for model and feature discovery, while dotData OPs provided the operational layer for deploying models, orchestrating scoring pipelines, and automating retraining and monitoring workflows typical of MLOps Platforms. Configuration focused on feature engineering automation, model promotion gates, and production inference endpoints to feed retry decision logic.
Operational scope encompassed payments operations and subscription revenue teams, integrating model outputs into transaction streams and payment processing workflows to drive retry timing decisions. Governance and rollout included staged deployment and model governance processes to manage decision rules and monitoring, and sticky projected roughly $8M per month in recovered revenue in the first year based on the implemented predictive model workflows.
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Buyer Intent: Companies Evaluating dotData OPs
Discover Software Buyers actively Evaluating Enterprise Applications
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