Metadata with a French Accent
I never thought I would be writing a post about French verbs. Metadata, sure. That goes with the territory. But French verbs?
Since moving to France in 2022, I’ve been learning the language. I’m not fluent yet, but I can get by - especially in high-stress situations. I have French lessons and supplement them with Duolingo. I’m not a linguist, but I’m fascinated by the way language works. I love the way it can be used to express complex ideas and emotions. The foundation of learning any language is through verbs.
Yesterday, I was reading a document about metadata tagging approaches and ontologies. It was boring. And used “ontology” when they should have used “taxonomy”. Ontology is about “being”, whereas Taxonomy is about classification. Then it got me thinking about the French verb “être” (to be).
Let’s Get Philosophical
The French like to think of themselves as a philosophical nation. They have a long history of philosophers and thinkers who have shaped the way we think about the world. The French verb “être” is at the heart of this philosophical tradition. It is a verb that expresses existence, identity, and being. It is a verb that is used to describe the essence of something. It is the linguistic equivalent of “ontology”.
So, if one verb can be used to make sense of metadata, why not use other verbs to enrich our understanding of it?
Here’s a list of French verbs, their meanings, and background information on their philosophical implications:
| Verb | Meaning | Philosophical Domain | Conceptual Function | Comments / Associations |
|---|---|---|---|---|
| Être | To be | Ontology | Existence, identity | What something is |
| Avoir | To have | Phenomenology | Possession, state, relation | Experience of having/feeling (e.g., avoir peur) |
| Faire | To do / make | Praxeology / Ethics | Agency, creation, will | Human as actor or creator |
| Savoir | To know (facts) | Epistemology | Knowledge (declarative) | Knowing that X is true |
| Connaître | To know (someone/thing) | Phenomenological Epistemology | Relational knowledge, familiarity | Knowing with or through experience |
| Pouvoir | To be able to | Modal Ontology / Power | Capability, potential, freedom | What is possible or permitted |
| Venir | To come | Temporality / Event Ontology | Arrival, becoming, unfolding | The emergence of the new; future entering the present |
| Devoir | To have to / must | Normative Ethics | Obligation, moral or social necessity | Duty, constraint, responsibility |
| Aller | To go | Teleology / Existentialism | Intention, direction, movement toward purpose | Future orientation, project of the self |
| Vivre | To live | Existential Phenomenology | Lived experience, being-in-the-world | Core to human condition and temporality |
| Croire | To believe | Epistemology / Theology | Belief, trust, faith | Often without certainty; a precondition of knowing |
| Comprendre | To understand | Hermeneutics | Interpretation, grasping meaning | Understanding as situated, not just factual |
| Tenir | To hold | Embodied Cognition / Ethics | Maintaining, responsibility, grasp | ”Tenir parole” (keep one’s word); to carry or endure |
| Voir | To see | Phenomenology / Perception | Perception, awareness | What appears to consciousness |
Language lesson over! sigh of relief
How does this relate to metadata and data governance? Sit back with the French breakfast of your choice (a stereotypical one is a coffee and a cigarette) and let me explain.
Être (To Be) - Defining Existence
Time to put a beret on and get philosophical. The verb “être” is the foundation of existence. It is the verb that defines what something is. In the context of metadata, “être” is about defining the essence of your data. What is it? What does it represent? What is its identity?
When you create metadata, you are essentially creating a definition of your data. You are saying, “this is what this data is”. This is the ontology of your data. It is the foundation upon which everything else is built.
Questions that you’ll be asking when defining the existence of your data include:
- “What is a Customer?”
- “What is a Product?”
- Are these definitions consistent, shareable, and understood?
Clarity of being is essential for effective metadata and the start of good governance.
Avoir (To Have) - Defining Possession
Once you know what data is, you need to know what it has. The verb “avoir” is about possession. It is the verb that defines the state of your data. What does it have? What are its attributes? What are its relationships?
This is metadata. It is the information that describes your data. It is the context in which your data exists. It is the relationships that your data has with other data. It is also the framework that allows for effective data governance.
Questions that you’ll be asking when defining the possession of your data include:
- What attributes belongs to this data class?
- What relationships does this data class have with other data classes?
- What are the owners of this data class?
Without avoir, your data lacks dimension. It is flat and lifeless. With avoir, your data comes alive. It has depth, context, and meaning.
I cannot stop the inner Frenchman from coming out. I am sorry.
Faire (To Do) - Defining Agency
Governance isn’t just about defining what data is and what it has. It is also about defining what data can do. The verb “faire” is about agency. It is the verb that defines the action of your data. What can it do? What are its capabilities? What are its limitations?
Faire reminds us that data is not just a passive entity. It is an active participant in the world. It has the ability to do things. It can be used to create, to inform, to persuade, and to change.
Questions that you’ll be asking when defining the agency of your data include:
- What can be done with this data?
- What use cases does this data support?
- What transformations does it undergo?
Tracking the agency of your data is essential for effective governance. It allows you to understand how your data is being used and what impact it is having on your organisation.
Savoir (To Know) - Defining Knowledge
Governance is also about defining what data knows. The verb “savoir” is about knowledge. In the realms of governance, it is about knowledge derived from data.
Questions that you’ll be asking when defining the knowledge of your data include:
- What does this data reveal?
- What decisions can be made based on this data?
- How trustworthy is its knowledge?
Data governance is about ensuring that your data is trustworthy. It is about ensuring that your data is reliable and accurate. It is about ensuring that your data can be used to make informed decisions. It’s not just about safeguarding the raw inputs, but the truths they produce.
Pouvoir (To Be Able To) - Defining Capability
Instead of applying the verb “pouvoir” to the data itself, we can apply it to the people who use the data. The verb “pouvoir” is about capability. It is the verb that defines what people can do with data.
Questions that you’ll be asking when defining the capability of your data include:
- What can people do with this data?
- What skills are required to use this data?
- Who can use this data?
- Who can change this data?
This is about creating a culture of data literacy. It is about ensuring that people have the skills and knowledge to use data effectively. It is also about ensuring the right access controls and ethical limits are in place to protect your data and your organisation.
Devoir (To Have To) - Defining Obligation
With great power comes great obligations. The verb “devoir” is about obligation. It is the verb that defines what regulations and laws apply to your data.
Questions that you’ll be asking when defining the obligation of your data include:
- Is this data subject to GDPR?
- What retention policies apply to this data?
- Who is accountable for compliance?
- What commitments does it uphold? e.g., SLAs
Policies and regulations are essential for effective data governance. They are often seen as a burden, but they are expressions of devoir.
Aller & Venir (To Go & To Come) - Defining Direction
Data is not static. It is constantly moving. The verbs “aller” and “venir” are about direction. They are the verbs that define where your data is going and where it has come from. Understanding this movement is crucial for effective data governance, as it helps in tracking the lifecycle and lineage of data and its relevance over time.
Venir (to come) and aller (to go) help us understand the flow of data. They are about the direction of data. They are about the journey of data.
Questions that you’ll be asking when defining the direction of your data include:
- Where does this data come from?
- Where is this data going?
This is lineage, flow, and dependency tracking in the circulatory system of trust.
Connaître (To Know) - Defining Familiarity
The verb “connaître” is about familiarity. It is the verb that defines the relationship between people and data. It is about understanding the context in which data exists. It is about knowing the data in the context of its use.
Questions that you’ll be asking when defining the familiarity of your data include:
- How is this data used in sales?
- How do customers interact with this attribute?
This is typically the role that data stewards, domain SMEs play. They are the ones who know the data. They are the ones who understand the context in which it exists. They are the ones who can help you make sense of it. They can help shape the semantic context of your data.
Croire (To Believe) - Defining Belief
We rarely govern perfect data. We govern imperfect data. The verb “croire” is about belief. It is the verb that defines the trustworthiness of your data. It is about understanding the limitations of your data. It is about knowing what you can and cannot believe. It is about understanding the assumptions that are made about your data.
Questions that you’ll be asking when defining the belief of your data include:
- What assumptions are made about this data?
- What biases are present in this data?
- What is the quality of this data?
- How uncertain is this data?
Strong governance isn’t just about truth, it’s the degrees of confidence in how much you can believe the data.
Conclusion
You’ve made it to the end! Congratulations!
We have explored only a small selection of French verbs and their implications for metadata. There are many more verbs that could be explored, each with its own unique meaning and implications. The key takeaway is that language is a powerful tool for understanding the world around us. By applying French (other languages do exist) verbs to the structure of your data catalog, you can build a model that is not only precise, but expressive, contextual, and full of Gallic personality.