A data partnership is an agreement to share, exchange, or jointly exploit data for mutual benefit. Done well, it creates capabilities neither party could build alone. Done poorly, it creates liability, regulatory exposure, and competitive risk.
Data sharing: Both parties share data with each other. Example: a bank and a telecom share transaction and behavioral data to jointly build a credit scoring model for thin-file customers. Each gains access to data they don't have; the combined model outperforms what either could build alone.
Data pooling: Multiple organizations contribute data to a shared pool and access the combined dataset. Example: insurance consortia where members contribute claims data to build better fraud detection models. Individual contributors benefit from the collective signal.
Data licensing: One party licenses its data to another for specific defined uses. Example: a mapping company licenses location data to a mobility startup.
Joint ventures: Partners create a new entity to jointly exploit data assets. Example: two retailers creating a shared data company to sell aggregated purchase insights to brands.
Data-for-access exchanges: Data is exchanged for access to capabilities, platforms, or distribution. Example: a startup sharing behavioral data with a cloud provider in exchange for infrastructure credits.
Data Partnerships and Data Sharing Agreements
Vérification des acquis
1. What fundamentally distinguishes 'data pooling' from a simple bilateral 'data sharing' arrangement?
2. A startup gives a cloud provider access to its behavioral data in return for infrastructure credits. Which type of data partnership is this?
3. Why does the due diligence framework recommend making data quality standards a contractual condition for the partner?
4. Select ALL questions that belong to the 'competitive assessment' dimension of the due diligence framework.
Sélectionnez toutes les réponses correctes.
5. Select ALL statements that correctly reflect the reasoning behind data partnerships in the lesson.
Sélectionnez toutes les réponses correctes.
Before entering a data partnership:
Legal and regulatory review: Does sharing this data require consent? Does GDPR, CCPA, or sector-specific regulation (HIPAAHIPAAHealth Insurance Portability and Accountability Act, loi américaine imposant la protection des données de santé (PHI). Violations : amendes jusqu'à 1,9M$ par catégorie de violation., BCBS 239BCBS 239Principe du Basel Committee on Banking Supervision imposant aux grandes banques une traçabilité stricte des données de risque, ayant catalysé la création de nombreux postes de CDO dans le secteur bancaire.) restrict use? Can we de-identify sufficiently? Does the data license allow sharing?
Competitive assessment: Will sharing this data reduce competitive advantagecompetitive advantageA lasting edge over competitors: a resource, capability or position they cannot easily replicate, letting a firm earn above-average returns over time.Voir la définition complète →? Could the partner use our data to compete against us? What information asymmetry does the partnership create?
Technical compatibility: Can we exchange data reliably? In what format? At what frequency? Who bears the integration cost?
Data quality reciprocity: If the partner's data qualitydata qualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.Voir la définition complète → is poor, it degrades the joint capability. Require data qualitydata qualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.Voir la définition complète → standards as a contractual condition.
Exit provisions: What happens to the data if the partnership ends? Who retains what rights? Clear exit provisions prevent costly disputes.
For partnerships where data privacy constraints limit direct sharing, privacy-preserving technologies enable collaboration without exposing raw data:
Federated learning: Models are trained locally on each partner's data. Only model updates (gradients), not raw data, are shared. The resulting model benefits from all partners' data without any party seeing another's raw data. Used by Apple for on-device ML, Google for keyboard prediction.
Differential privacy: Mathematical noise is added to query results or data exports, making individual records impossible to identify while preserving aggregate statistical patterns. Apple and Google apply differential privacy to usage data.
Secure multi-party computation (SMPC): Multiple parties jointly compute on their combined data without any party seeing the others' data. Highly theoretical for a decade, now increasingly practical for specific use cases.
Synthetic data: Statistically representative fake data generated from real data. Can be shared freely without privacy risk. Useful for development and testing, less reliable for production model training.
These technologies are moving from research to practice. CDOs who understand them can enable partnerships that would otherwise be blocked by privacy constraints.
The key negotiation points in a data partnership:
Data rights scope: What can each party do with the data? Train internal models only? Share insights with third parties? Create derivative products? Be specific, vague language creates disputes.
Exclusivity: Is this partnership exclusive in any dimension (geography, use case, time period)? Exclusivity has real value, price it accordingly.
Revenue sharing: If the partnership generates commercial value, how is it split? Pre-agree on a formula, not a future negotiation.
Liability: If shared data causes harm (a privacy breach, a regulatory violation), who bears liability? Negotiate caps and indemnification provisions upfront.
1. Qu'est-ce que le "federated learning" permet dans le contexte des partenariats data ?
A) Centraliser toutes les données chez un partenaire de confiance
B) Entraîner des modèles de manière distribuée sur les données de chaque partenaire sans jamais partager les données brutes, seuls les gradients du modèle sont échangés
C) Vendre les données de façon anonymisée à plusieurs partenaires simultanément
D) CrCrThe percentage of visitors or prospects who complete a desired action (purchase, sign-up, contact form), calculated as conversions divided by total opportunities.Voir la définition complète →éererThe ratio of interactions (likes, comments, shares) to reach for a given piece of content, used to gauge how well audiences respond relative to how many people saw it.Voir la définition complète → une base de données commune accessible à tous les partenaires
Réponse: B
2. Quel point de négociation est souvent négligé mais peut crcrThe percentage of visitors or prospects who complete a desired action (purchase, sign-up, contact form), calculated as conversions divided by total opportunities.Voir la définition complète →éererThe ratio of interactions (likes, comments, shares) to reach for a given piece of content, used to gauge how well audiences respond relative to how many people saw it.Voir la définition complète → des disputes coûteuses dans un partenariat data ?
A) Le prix de la donnée
B) Le format de transfert des données
C) Les droits sur les données, que peut faire chaque partie (modèles internes uniquement ? produits dérivés ? partage tiers ?), et les clauses de sortie
D) La fréquence de mise à jour
Réponse: C
3. Quelle technologie permet de partager des statistiques agrégées tout en rendant l'identification d'individus mathématiquement impossible ?
A) Federated learning
B) Secure multi-party computation
C) Differential privacy
D) Données synthétiques
Réponse: C
Ces actions sont compilées dans le plan d'action du rôle.