Tomás Pariente Lobo

Associate Head of AI, Data & Robotics Unit, Atos

AI, Data & Robotics Unit at Atos Research & Innovation, Madrid Area, Spain. Tomás Pariente Lobo has more than 30 years of experience in IT. His technical expertise is mainly in Artificial Intelligence, Big Data, Linked Data and knowledge management. Since June 2006 Tomas works as a project manager and technical coordinator for EU-funded projects leading a group of researchers dealing with all aspects related to the data value chain, with special focus on data architectures, data analysis and technologies such as Natural Language Processing and semantics..

Tomás is speaking at

Focus Track 3 - Market uptake: Bringing AI and Data Sciences to Practice
November 3, 2020
3:30 pm - 5:30 pm



The Data-driven Innovation (DDI) Workshop is based on the (DDI) Framework addressing the challenges of identifying and exploring data-driven innovation in an efficient manner. It guides entrepreneurs in scoping promising data-driven business opportunities by reflecting the dynamics of supply and demand by investigating the co-evolution and interactions between the scope of the offering (supply) and the context of the market (demand) in systematic manner.

In this workshop, business champions, entrepreneurs and interested techies will gain practical experiences of how to use the DDI Framework and Canvas for the continuous analysis of all influencing factors of data-driven business opportunities. The participants of the workshops will get to know a set of methods and tools that will guide them in redefining their own data-driven business opportunity.

We will take the opportunity of this EBDVF workshop to officially launch the DDI canvas site. We encourage you to contact us to assist you in using DDI, to share with us your return of experience to help us to improve the methodology or provide domain specific know how.

The DDI framework was developed and tested in the context of the Horizon 2020 BDVe project[1] and is backed by empirical data and scientific research encompassing a quantitative and representative study of more than 90 data-driven business opportunities. The results of the research study guided the fine-tuning and updating the DDI framework as well as helped to identify success patterns of successful data-driven innovation. Currrently the DDI framework is used to run workshops with PPP projects, data-driven start-ups, SMEs and with corporates. It consists of a

  • The DDI Canvas guides you in exploring all relevant dimensions on the supply and demand side of a data-driven innovation in systematic manner.
  • The DDI Navigator will support you in exploring each dimension in more detail by carefully selected deep dives.
  • Specific DDI Tools will help you to work through each of the eight DDI dimensions, producing a conclusive set of results that will guide a company-specific setup of new, data-based products and services.
The DDI Framework is based on a conceptual model in form of an ontology with a set of categories and concepts describing all relevant aspects of data-driven business opportunities. Its categories are divided into supply side and demand side aspects. On the supply side the focus is on the development of new offerings. For a clearly defined value proposition, this includes the identification of and access to required data sources, as well as the analysis of underlying technologies. On the demand side the focus is on the dynamics of the addressed markets and associated ecosystems. The analysis includes the development of a revenue strategy, a way forward of how to harness network effects as well as an understanding of the type of business. As data-driven innovations are never done in isolation, the identification and analysis of potential development partners as well as partners in the ecosystem help to align / balance the supply and demand aspects in a way that its competitive nature will stand out. 

Focus Track 3 - Market uptake: Bringing AI and Data Sciences to Practice
November 4, 2020
10:00 am - 1:30 pm



For many years companies have been running technical benchmarks to compare the performance of different technologies and systems. This benchmarking process provides very valuable information but in many cases, it is not correlated with the impact of the solutions at business level. This was precisely the starting point of the DataBench project, funded by the EC under the Big Data Value PPP. After three years of intensive work DataBench has created an evaluation framework that is already openly available for the community. The session will introduce the different elements of the framework, including the toolbox, the way it can be used and some concrete examples run with companies and projects that work heavily with data.

DataBench has addressed one of the major barriers to the adoption of Big Data technologies, which has to do with the ability to measure the potential benefits of using Big data technologies in different scenarios. This is not exactly about deciding between one database and another one, but about understanding what the impact of such decision with have on a business process beyond the fulfillment of a technical KPI. This session will allow participants to understand the outcomes produced by DataBench and how to use them in their context through presentations, discussion panels and demos where they will also be entitled to contribute. Attendees will get knowledge on:
  • The current landscape of Big Data and AI benchmarks
  • The DataBench framework, which includes a complete set of metrics for the assessment of Big Data technologies
  • The DataBench toolbox, a web-based tool that provides a unique environment to search, select and deploy big data benchmarking tools, giving the possibility to generate unified technical metrics and derive business KPIs
  • A comprehensive set of use cases that we have run with companies in different industrial domains and projects of the Big Data PPP to illustrate the way you can get value out of using DataBench
  • Pipelines and blueprints
The DataBench toolbox follows a platform model and provides a single place where benchmarking communities and users of such benchmarks can meet. It goes beyond any work done so far by translating major technical KPIs into business KPIs, allowing us to establish relationships between technical and business decisions. As such, the session targets a multiplicity of stakeholders: benchmark providers, companies interested in benchmarking big data, projects and members of the big data and AI communities, decision-makers of companies that are thinking about their data-driven transformation and policy makers looking for evidence-based decision-support tools.

On our side, we will bring a comprehensive set of speakers falling precisely under all those categories.

The workshop will be structured around two parts (each of them can be attended independently; like this we want to facilitate that those participants that are not available for the entire workshop, select the most interesting part for them). Attending the complete workshop is in any case recommended to take maximum advantage of the contents.

Detailed agenda

10:00-10:05 Intro. Objectives of the session (Nuria de Lama (Atos)

10:05-10:15 DataBench General Overview (Richard Stevens (IDC, DataBench coordinator)

PART I. Big Data Benchmarking landscape and Big Data Pipelines

10:15-11:15 Session 1. The current landscape of Big Data benchmarks

  • 10:15-10:25: DataBench Framework for Benchmarks, Arne J. Berre, SINTEF
  • 10:25-10:40: Benchmarking platforms and AI, Axel Ngonga, BDVA TF6 Benchmark Lead, University of Paderborn    
  • 10:40-10:55: BenchCouncil Big Data and AI Benchmarks, Wanling Gao, Chinese Academy of Sciences
  • 10:55-11:10: MLPerf AI and ABench, Rekha Singhal, Senior Scientist and Head of the Computing Systems-Software Research area at TCS
  • 11:10-11:15: Conclusion on Big Data and AI Benchmarks, Todor Ivanov, LeadConsult
11:15-12:15 Session 2. A Project perspective on Big Data and AI architectural pipelines and benchmarks
  • 11:15-11:20: Introduction to Architectural pipelines, Arne J. Berre, SINTEF
  • 11:20-11:30: I- BiDaaS - Leonidas Kallipolitis, AEGIS
  • 11:30-11:40: TBFY - Brian Elvesæter, SINTEF
  • 11:40-11:50: Track&KNow - Athanasios Koumparos, Vodafone Innovus 
  • 11:50-12:00: DataBio - Caj Södergård, VTT
  • 12:00-12:10: DeepHealth - Jon Ander Gómez Adrián, Universitat Politecnica de Valencia
  • 12:10-12:15: Conclusion on Pipelines and related benchmarks, Arne J. Berre, SINTEF

12:15-12:30 Short coffee break to relax and maybe grab a coffee

PART II. Big Data Business Framework and benchmarking tooling support

  • 12:30-13:00 Session 3. The DataBench Business framework: a compelling offering to measure the impact of Big Data Technologies
  • 12.30-12.40: The DataBench business framework, by Gabriella Cattaneo, Erica Spinoni and Chiara Francalanci
  • 12: 40-12.50: The Whirlpool use case, Pierluigi Petrali (Whirlpool)
  • 12.50-13.00:  The Fill use case, Harald Sehrschön (FILL)
13:00-13:30 Session 4. A practical journey on how to use the DataBench Toolbox 
  • 13:00-13:15 Demo of the DataBench Toolbox (Tomás Pariente, Atos)
  • 13:15-13:25 AI Observatory (Marko Grobelnik, JSI)
  • 13:25-13:30 Fostering adoption of DataBench results. Needs from the point of view of Digital Innovation Hubs (Daniel Sáez, ITI, EUHubs4Data coordinator)

13:30 Concluding Remarks and closing of the session

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