Practical on-site workshop

  • Face-to-face session at Eurecat Cerdanyola headquarters
  • Free

From data to decision making: culture and governance for industrial companies

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Why not miss it?

In an increasingly competitive, digitized and regulated industrial environment, the ability to transform data into decisions has become a key differentiating factor. However, many organizations still find it difficult to derive real value from their data, not because of a lack of technology, but because of challenges related to organizational culture and the absence of a clear governance model.

This session addresses precisely this critical point: how to move from having data to using it effectively to improve decision making, optimize operations and generate new business opportunities.

With a practical and industrial business-oriented approach, the session combines conceptual framework, real cases and applied exercises to help organizations identify their main blockages and define the first steps towards a solid and viable data management model.

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General objectives

At the end of the session, participants will be able to:

  • Understand the strategic role of data as a business asset and not only as a technological resource.
  • Clearly differentiate between data culture, data governance and use of application cases (including AI).
  • Identify the main barriers that prevent industrial organizations from being truly data-driven.
  • Know the key elements of a practical and adaptable data governance model.
  • Analyze real cases from the industrial sector and extract applicable lessons learned.
  • Perform a self-diagnosis of your organization in terms of culture and data governance.
  • Define a first concrete and actionable roadmap to advance data management within your company.

Program

Context and strategic framework

Objective: to position data as a business asset, not as an exclusively IT issue.

  • Why now (AI, regulation, industrial competitiveness)?
  • What it really means to be “data-driven” (and what it doesn’t)
  • Relationship between:
    • Data culture
    • Data governance
    • Use cases (AI, optimization, new services)

2. Data culture: the missing factor

Objective: to show that the problem is organizational, not technological.

  • What is data culture in an industrial company?
  • Typical barriers:
    • Data in silos
    • Excel dependency
    • Decisions based on intuition
  • Key roles:
    • Data owner
    • Data steward
    • Business vs IT

3. Data governance: bringing order

Objective: to provide an actionable model.

  • What is data governance?
  • Key elements:
    • Data quality
    • Data Catalog
    • Security and access
    • Policies and processes
  • Lightweight vs. corporate models

4. Industrial case studies

Objective: to translate culture + governance into tangible business results.

  • Real problem
  • What went wrong (culture / governance)
  • What was done (concrete actions)
  • Result (impact)

Case 1: Data quality in production (OEE / plant)
Case 2: Predictive that does not scale (maintenance)
Case 3: Commercial and after-sales service (new revenues)

5. Workshop / guided reflection

Objective: to identify the organization’s real problem (not the technological one).

Work in small groups or individually if the group is small.

  • Part 1: Self-diagnosis
  • Part 2: Identification of the bottleneck
  • Part 3: Sharing

6. Generation of an individual task list

Objective: that participants leave with a first actionable (not theoretical) plan.

  • Step 1: Starting point
  • Step 2: Business objective
  • Step 3: Priority use case
  • Step 4: 3 key actions
  • Step 5: Minimum viable governance
  • Step 6: First steps

Speaker

laia eurecat 2

Laia Garriga Mas - EURECAT

Industrial Engineer (ESTEIB-UPC) by profession. She is a promoter of new business projects and the development of collaborative projects. She has developed her professional career, always linked to value-added technologies and business development in different sectors: railway and logistics, industrial, health and insurance. In Eurecat she has been for more than 5 years responsible for railway business development and logistics, and is currently Technology Transfer Manager in the field of Applied Artificial Intelligence. She also currently holds the position of vice president of innovation of the IN-Move by Railgrup cluster. It has more than 20 years of experience in identifying funding opportunities at European, national and regional levels for the development of cooperative projects, as well as in the generation of consortiums with public and private entities throughout Europe.

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© 2025 amec | Gran Via de les Corts Catalanes 684, Pral. 08010 Barcelona

Privacy Policy

Legal Notice

Cookie Policy

Internal information system

T. (+34) 934 150 422

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