Apps Purchases: 10+ Million Software Purchases
Founded in 2010, APPS RUN THE WORLD is a leading technology intelligence and market-research company devoted to the application space. Leveraging a rigorous data-centric research methodology, we ask the simple B2B sales intelligence question: Who’s buying enterprise applications from whom and why?
Our global team of 50 researchers has been studying the digital transformation initiatives being undertaken by 2 million + companies including technographic segmentation of 10 million ERP, EPM, CRM, HCM, Procurement, SCM, Treasury software purchases, 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.
Apps Run The World Buyer Insight and Technographics Customer Database has over 100 data fields that detail company usage of emerging technologies such as AI, Machine Learning, IoT, Blockchain, Autonomous Database, and different on-prem and cloud apps by function, customer size (employees, revenues), industry, country, implementation status, year deal won, partner involvement, Line of Business Key Stakeholders and key decision-makers contact details, including the systems being used by Fortune 1000 and Global 2000 companies.
Apply Filters For 10+ Million Software Purchases
- Professional Services
| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | VAR/SI | Insight | Insight Source |
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Xceedance | Professional Services | 3500 | $500M | United States | H2O.ai | H2O Driverless AI | ML and Data Science Platforms | 2017 | n/a | In 2017 Xceedance implemented H2O Driverless AI to accelerate its insurance data science services. The deployment positioned H2O Driverless AI within Xceedance’s client delivery and data science operations, aligning the application with the company’s role as a global provider of insurance data science services and professional services for insurers. Xceedance configured H2O Driverless AI to exploit automated machine learning capabilities typical of ML and Data Science Platforms, including automated feature engineering, model selection and hyperparameter tuning, and built in model interpretability and explainability functions. The implementation emphasized repeatable model pipelines and model export for production scoring, enabling data scientists to operationalize experiments and standardize model artifacts for client engagements. Operational ownership rested with Xceedance’s centralized data science and client delivery teams, who used Driverless AI to embed advanced analytics into customer facing solutions at scale. Rajesh Iyer, SVP and Head of Data Science at Xceedance, said Driverless AI is a game changer and that the company is now able to put the power of an entire expert data science team in front of customers at scale, and that Xceedance has enjoyed a strong working experience with H2O.ai. This implementation reflects a focus on accelerating model development lifecycles and improving the consistency of analytics delivered to insurer clients within the ML and Data Science Platforms category. | |
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Lufthansa Industry Solutions | Professional Services | 1279 | $195M | Germany | Altair Engineering | Altair AI Cloud (formerly RapidMiner Cloud) | ML and Data Science Platforms | 2016 | n/a | In 2016, Lufthansa Industry Solutions deployed Altair AI Cloud (formerly RapidMiner Cloud) as an ML and Data Science Platform to deliver predictive analytics services to its customers. The adoption of Altair AI Cloud positioned the company to operationalize data science workflows against available airplane and service data for service analytics and maintenance forecasting. The implementation used Altair AI Cloud capabilities for real time analytics of time series data, automated feature extraction pipelines, supervised machine learning for regression and classification, and frequent item set mining to identify operational patterns. These functional modules were applied to aircraft telemetry and service records to develop models targeting maintenance timing and service optimization. Operational coverage focused on maintenance operations and service teams, with models embedded into analytics service offerings provided to external customers. Governance emphasized model development and deployment workflows, data pipeline orchestration from airplane and service data sources, and the productionization of predictive models that were able to predict when maintenance should be performed. | |
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Professional Services | 600 | $102M | Netherlands | Domino Data Lab | Domino Data Science Platform | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 160 | $20M | United States | DataRobot | DataRobot Cloud | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 3936 | $1.3B | United States | DataRobot | DataRobot Cloud | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 23300 | $7.5B | Ireland | H2O.ai | H2O Open Source ML | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 300 | $90M | United States | Databricks | Databricks Unified Analytics Platform | ML and Data Science Platforms | 2017 | n/a |
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Professional Services | 15000 | $3.5B | United States | Databricks | Databricks Unified Analytics Platform | ML and Data Science Platforms | 2017 | n/a |
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Professional Services | 4500 | $2.0B | United States | Databricks | Databricks Unified Analytics Platform | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 450 | $50M | Italy | Cloudera | Cloudera Enterprise Platform | Data Warehouse | 2017 | n/a |
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