TOREADOR · Project

Affordable Big Data Analytics Service for Companies Without In-House Data Teams

digitalTestedTRL 6

Imagine you know data analysis could transform your business, but hiring a data science team costs a fortune and you don't even know where to start. TOREADOR built a platform that packages big data analytics as a ready-made service — like renting a fully equipped kitchen instead of building one from scratch. It uses reusable blueprints to automate the messy parts: collecting data, cleaning it, keeping it private, and running the analysis. The goal is to make powerful data analytics as easy to order as cloud storage, so even small companies can afford it.

By the numbers

consortium partners involved in platform development

countries represented in development and testing

industry partners validating the platform

SMEs in the consortium testing affordability for smaller companies

69%

industry partner ratio in the consortium

total deliverables produced across all work packages

demo deliverables including platform releases and toolkit versions

The business problem

What needed solving

Most European companies — especially SMEs — know that analyzing their data could give them a competitive edge, but they lack the in-house expertise and budget to set up big data analytics. Hiring data scientists is expensive, building infrastructure is complex, and navigating privacy regulations adds another layer of difficulty. The result is that valuable business data sits unused while only large corporations with deep pockets can afford to extract insights from it.

The solution

What was built

The project delivered a complete Big Data Analytics-as-a-Service platform in two major releases (v1 and v2), including a Real Time Analytics engine, a Batch Data Analytics engine, visualization and interactive service tools, and dedicated toolkits for data preparation, anonymization, and security (also in two versions each). In total, 57 deliverables were produced, with 17 being demonstrable platform components.

Audience

Who needs this

Mid-size retailers and e-commerce companies with customer data but no data science teamRegional banks and insurers needing privacy-compliant analytics on sensitive dataManufacturing companies collecting sensor data without specialists to analyze itIT service providers looking to offer packaged analytics to their own clientsPublic sector organizations required to analyze large datasets under strict compliance rules

Business applications

Who can put this to work

Retail and E-commerce

mid-size

Target: Mid-size online retailers with customer data but no data science team

If you are a mid-size retailer sitting on years of customer purchase data but lacking the budget for a dedicated analytics team — this project developed a Big Data Analytics-as-a-Service platform with 17 demo components that lets you set up automated data analysis pipelines without deep technical expertise. The platform handles data preparation, anonymization, and privacy compliance out of the box, so you can extract buying patterns and demand forecasts while staying compliant with data protection rules.

Financial Services

enterprise

Target: Regional banks and insurance companies managing sensitive customer records

If you are a financial institution dealing with growing volumes of transaction data and strict privacy regulations — this project built toolkits for Big Data preparation, anonymization, and security alongside machine-readable Service Level Agreements. The platform was tested with 13 consortium partners across 4 countries, covering both real-time and batch analytics, so you can run fraud detection or risk scoring while automated compliance checks handle the legal side.

Manufacturing and Supply Chain

mid-size

Target: Factory operators collecting sensor and production data

If you are a manufacturer generating sensor data from production lines but struggling to turn it into actionable insights — this project delivered both a Real Time Analytics engine and a Batch Data Analytics engine as part of a complete service toolkit. With 9 industry partners validating the approach, the platform automates data pipeline setup so your operations team can monitor quality and predict equipment issues without hiring specialized data engineers.

Frequently asked

Quick answers

What would it cost to adopt this platform compared to building an in-house analytics team?

The project was specifically designed to drive down the cost of big data analytics for organizations that cannot afford expensive data consultancy. While specific pricing was not published, the model-based as-a-service approach is designed to enable price competition among service providers, making analytics affordable for SMEs. Based on available project data, the platform automates setup and management steps that normally require costly specialist labor.

Can this handle enterprise-scale data volumes in production?

The project delivered both a Real Time Analytics engine and a Batch Data Analytics engine, with two full platform releases (v1 and v2). The consortium included 9 industry partners who contributed to validation. Based on available project data, the architecture supports distributed data acquisition and storage with parallel deployment of analytics.

What is the IP situation — can we license or use this technology?

TOREADOR was funded as a Research and Innovation Action (RIA) under Horizon 2020. The project emphasized open, suitable-for-standardization models. Licensing terms would need to be discussed with the coordinator CONSORZIO INTERUNIVERSITARIO NAZIONALE PER L'INFORMATICA in Italy. Specific IP arrangements should be confirmed directly with the consortium.

How does this handle data privacy and GDPR compliance?

The project built dedicated toolkits for Big Data preparation, anonymization, and security — released in two versions (v1 and v2). It also developed machine-readable Service Level Agreements covering privacy, timing, and accuracy needs, plus automated auditing and assessment of legal compliance for data analytics processes.

How long would it take to integrate this with our existing data infrastructure?

The platform was designed as a service layer that sits on top of existing data sources. Based on available project data, the model-driven approach automates pipeline setup from data acquisition through to results presentation. Two platform versions were delivered during the 3-year project, suggesting iterative refinement of the integration process.

Is there ongoing support or has the project ended?

The project officially ended in December 2018. The final toolkit implementation and Platform v2 were the last major deliverables. For ongoing support or access to the technology, contact would need to be made through the coordinator or individual consortium partners who may have commercialized components.

What data formats and sources does the platform support?

Based on available project data, the platform was designed to handle Big Data opacity and diversity through automated data preparation. The architecture covers distributed data acquisition and storage, suggesting support for multiple data source types. Specific format details would need to be confirmed with the consortium.

Consortium

Who built it

The TOREADOR consortium is strongly industry-oriented, with 9 out of 13 partners (69%) coming from the private sector — well above average for EU research projects. The partnership spans 4 countries (Germany, Spain, Italy, UK), with the coordinator being an Italian interuniversity computing consortium. Three SMEs participated, which aligns with the project's mission to make analytics affordable for smaller organizations. The presence of 3 universities and 1 research organization provided the scientific backbone, while the heavy industry involvement suggests the platform was shaped by real commercial needs rather than purely academic interests. For a business considering this technology, the strong industry validation is a positive signal that the tools were built with practical deployment in mind.

CONSORZIO INTERUNIVERSITARIO NAZIONALE PER L'INFORMATICACoordinator · IT
Lightsource Renewable Energy Holdings Limitedparticipant · UK
SAP SEparticipant · DE
ENGIWEB SECURITY SRLthirdparty · IT
ATOS SPAIN SAparticipant · ES
CITY ST GEORGES UNIVERSITY OF LONDONparticipant · UK
DISTRETTO TECNOLOGICO AEROSPAZIALE S.C. A R.L.participant · IT
GE AVIO SRLthirdparty · IT
BIRD & BIRD LLPparticipant · UK
ENGINEERING - INGEGNERIA INFORMATICA SPAparticipant · IT
JOT INTERNET MEDIA ESPANA SLparticipant · ES
UNIVERSITA DEL SALENTOthirdparty · IT

How to reach the team

The coordinator is CONSORZIO INTERUNIVERSITARIO NAZIONALE PER L'INFORMATICA based in Italy. SciTransfer can help establish the right introduction to the team leads who built the platform components.

Order a report on this consortium See CONSORZIO INTERUNIVERSITARIO NAZIONALE PER L'INFORMATICA profile →

Next steps

Talk to the team behind this work.

Want to find out if TOREADOR's analytics-as-a-service platform fits your data challenges? SciTransfer can arrange a direct introduction to the technical team and help you evaluate adoption options. Contact us for a tailored briefing.

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