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Catawiki Technographics
Discover the latest software purchases and digital transformation initiatives being undertaken by Catawiki and its business and technology executives. Each quarter our research team identifies on-prem and cloud applications that are being used by the 600 Catawiki employees from the public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources.
During our research, we have identified that Catawiki has purchased the following applications: Workday Financial Management for ERP Financial in 2016, Domino Data Science Platform for ML and Data Science Platforms in 2016, Google Looker for Analytics and BI in 2016 and the related IT decision-makers and key stakeholders.
Our database provides customer insight and contextual information on which enterprise applications and software systems Catawiki is running and its propensity to invest more and deepen its relationship with Workday , Domino Data Lab , Google or identify new suppliers as part of their overall Digital and IT transformation projects to stay competitive, fend off threats from disruptive forces, or comply with internal mandates to improve overall enterprise efficiency.
We have been analyzing Catawiki revenues, which have grown to $102.0 million in 2024, plus its IT budget and roadmap, cloud software purchases, aggregating massive amounts of data points that form the basis of our forecast assumptions for Catawiki intention to invest in emerging technologies such as AI, Machine Learning, IoT, Blockchain, Autonomous Database or in cloud-based ERP, HCM, CRM, EPM, Procurement or Treasury applications.
ERP Financial Management
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
| Workday | Legacy | Workday Financial Management | ERP Financial | ERP Financial Management | n/a | 2016 | 2017 |
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Expense Management | ERP Financial Management |
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2016 | 2017 |
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AI Development
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
| Domino Data Lab | Legacy | Domino Data Science Platform | ML and Data Science Platforms | AI Development | n/a | 2016 | 2017 |
In 2016 Catawiki implemented Domino Data Science Platform as part of its ML and Data Science Platforms strategy, arming business users with insights that help them make smarter, faster decisions. The deployment explicitly embedded machine learning to optimize the company recommendation engine, product pricing predictions, marketing promotions, lead scoring and cancellation predictions.
The Domino Data Science Platform provided a centralized environment for model development, experimentation, reproducible runs and model deployment to production, enabling data scientists to iterate and package models for operational use. Functional implementation focused on operationalizing predictive models that feed recommendation logic, dynamic pricing predictions, promotional targeting and customer cancellation forecasting.
Operational scope covered cross functional business users and analytics teams in merchandising and recommendations, pricing, marketing and customer operations, delivering model driven insights into decision workflows. Governance and workflow changes emphasized reproducible experiments, model lifecycle tracking and closer collaboration between data science and business stakeholders to accelerate model handoff and operationalization.
Catawiki used Domino Data Science Platform to arm business users with actionable insights and to embed machine learning across core commerce and customer retention functions, aligning ML and Data Science Platforms capabilities with product, marketing and operations priorities.
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Analytics and BI
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
| Legacy | Google Looker | Analytics and BI | Analytics and BI | n/a | 2016 | 2016 |
In 2016 Catawiki implemented Google Looker to centralize Analytics and BI capability and provide a single semantic layer for cross‑department reporting and experiment analysis. The deployment served as the core reporting platform for the Data Science Insights team, enabling dashboards, ad hoc analysis, and strategic recommendations to all departments.
Google Looker was configured as a semantic modeling and dashboarding platform, with LookML-driven explores and curated dashboards used for recurring reporting and self-service analytics. The implementation included scheduled deliveries and interactive dashboards to support A/B experiment analysis and product performance monitoring, aligning with typical Analytics and BI functional workflows.
The implementation integrated directly with Google BigQuery as the primary data warehouse, with data pipelines coordinated by Airflow and Dataform for model build and transformation. Operational tooling included Python for data engineering, Git and GitHub for version control of LookML and transformation code, and Google Analytics for web event and traffic instrumentation feeding the BI layer.
Governance used Git-backed model development and code review workflows to control semantic definitions and metric consistency, while the Data Science Insights team managed model releases and dashboard lifecycle. This architecture centralized metric definitions in Google Looker, supported cross-functional access to curated insights, and underpinned experiment design and analysis across Catawiki.
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Data Warehouse | Analytics and BI |
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2016 | 2016 |
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Collaboration
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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Collaboration | Collaboration |
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2011 | 2011 |
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Content Management
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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Web Content Management | Content Management |
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2019 | 2019 |
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CRM
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
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Customer Experience | CRM |
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2015 | 2015 |
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Customer Experience | CRM |
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2020 | 2020 |
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Marketing Analytics | CRM |
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2017 | 2017 |
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Marketing Automation | CRM |
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2016 | 2016 |
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IT Asset Management
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
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Hardware Asset Management (HAM) | IT Asset Management |
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2021 | 2021 |
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PaaS
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
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Transactional Email | PaaS |
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2015 | 2015 |
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IaaS
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
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Application Hosting and Computing Services | IaaS |
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2021 | 2021 |
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Content Delivery Network | IaaS |
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2016 | 2016 |
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CyberSecurity
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
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Identity and Access Management (IAM) | CyberSecurity |
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2021 | 2021 |
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