Summary of "Crash Course on Data Excellence - Part I. By Roberto Maranca"

Summary of "Crash Course on Data Excellence - Part I" by Roberto Maranca

This seminar, presented by Roberto Maranca, President of Data Excellence at Schneider Electric, provides a comprehensive introduction to the concept of Data Excellence in organizational contexts, focusing on the cultural, governance, and value-driven aspects of managing data effectively. The talk is structured as the first of a three-part series covering:

Data Culture
Data Change
Data Value

Main Ideas and Concepts

1. Introduction to Data Excellence

Data is more than just technical work (algorithms, pipelines); it encompasses organizational culture, processes, and value creation.
Data should be viewed as a dynamic, flowing asset (like water or fusion energy), not just static "new oil."
Different parts of a company (tribes) use different terminology and have different data needs, which complicates data management.
Data quality issues often arise during organizational changes and projects, where data considerations are neglected until late in the process, causing failures and complexity.
There is a paradox in data quality: users want perfect data but rarely define what "good enough" means.

2. Digital Transformation and Data Challenges

Many digital transformation efforts fail because they neglect real-world data quality and integration challenges.
Data scientists spend 80% of their time cleaning/curating data and only 20% analyzing it.
Introducing data excellence helps overcome these challenges by embedding data quality and governance into company DNA.

3. Difference Between Data Governance and Data Excellence

Data Governance: Focuses on processes and controls to manage data securely, compliantly, and efficiently.
Data Excellence: A broader, sustainable cultural and organizational state where data practices are consistent, embedded, and value-driven.

4. Three Pillars of Data Excellence

Culture: Shared values, language, and behaviors around data.
Change: Managing data-related transformations and innovations.
Value: Ensuring data initiatives deliver measurable benefits and sustainability, including environmental and ethical considerations (e.g., ESG, CSR).

5. Organizational Roles and Structures

Data roles are complex and multifaceted (governance, science, privacy, risk management).
The Chief Data Officer (CDO) role is evolving with varying titles and responsibilities.
Reporting lines (CFO, CMO, etc.) influence data priorities.
Organizational models for Data Governance:
- Centralized
- Decentralized
- Federated (hybrid, often most effective)
Tailoring governance and data management approaches to company structure and culture is essential.

6. Data Quality and Standardization

Standardization of data definitions and terms is critical to avoid confusion and ensure consistent use.
Resilience in data means maintaining data integrity despite changes or disruptions.
Compliance with regulations (e.g., GDPR) is mandatory and complex, especially for multinational companies.
Lobbying regulators is important to shape practical, balanced data laws.

7. Data Domain Ownership and Models

Introducing Data Domain Owners who have intellectual ownership and responsibility for specific data areas (e.g., customer, sales).
Creating a Data Domain Model that organizes company data into logical, manageable groups with clear definitions and owners.
This approach reduces data silos and conflicting versions of truth.

8. Communication, Engagement, and Culture Building

Effective communication is crucial to spread data culture ("viral effect").
Emotional intelligence (EQ) and storytelling are more effective than dry presentations.
Gamification can motivate compliance and engagement by fostering friendly competition.
Building trust in data products is essential, especially given ethical concerns and public skepticism.

9. Maturity Models and Continuous Improvement

Data excellence is a marathon, not a sprint.
Use maturity models to assess current state and set progressive targets.
Avoid binary thinking (yes/no compliance); instead, focus on gradual improvement.
Prioritize data initiatives based on business objectives and maturity assessments.

10. Sustainability and Ethical Considerations

Data centers consume significant energy and water, raising environmental concerns.
Ethical data use is critical, especially in AI and sensitive domains.
Transparency and trustworthiness of data-driven products are paramount.

Methodology / Instructions Presented

Data Excellence Framework: Focus on embedding culture, managing change, and delivering value sustainably.
Organizational Setup:
- Identify and empower Data Domain Owners.
- Develop a Data Domain Model with clear ownership and definitions.
- Choose appropriate governance structure (centralized, decentralized, federated).
Governance Process:
- Implement governance with security, authorization, and compliance controls.
- Continuously monitor and adapt to regulatory requirements.
Communication Strategy:
- Know your audience and tailor messages.
- Use metaphors and storytelling to create mental models.
- Employ gamification to motivate