Daniel Saez Domingo

Strategic Intelligence & Technology Transfer Director, ITI

Telecommunication Engineering from Polytechnic University of Valencia (2001) and Master in Business Administration and management by EOI Business School. His professional experience covers areas in the field of competitive intelligence and strategy in ICT (Information and Communication Technologies), project and area management, innovation policies, Business development and Valorisation. He is the Technology Transfer Director and the Strategic and Competitive Intelligence Director in ITI, where is in charge of empowering ITI clients, partners and alliances' ecosystem to leverage and evolve the R&D&I activity in ITI and the transference of value to the market. Daniel is member of the Board of Directors of Big Data Value Association and has been actively involved in the creation of the BDV PPP, AI-PPP and 5G PPP, coordinating ITI’s contributions to them. He also represents ITI in other European platforms and groups, like ARTEMIS, IDSA, NESSI, AIOTI or Networld2020. Daniel has actively contributed to the S3 and Industry 4.0 Agenda and is the manager of TECH4CV project, the Alliance of Competence Centres in Technology Enablers in the Valencia Region. He is the coordinator of the Interplatforms group of Big Data and AI at Spanish level and is the coordinator of the Data Cycle Hub, the reference one-stop shop DIH in the Valencia region to foster data driven and artificial intelligence based innovation.

Daniel is speaking at

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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