BARC x Actian Research: Why Organizations are Investing in Data Products

barc why organizations invest in data products

#IA Data Contracts gestion des données Data Products

Résumé

Organizations adopt data products primarily to ensure trustworthy data for AI (60%) and decision-making (59), not just to improve access.
AI initiatives are increasing the need for reliable, consistent, and well-governed data inputs.
Data products and data contracts provide structure through ownership, quality standards, and stable data interfaces.
Regulatory pressure is accelerating adoption, especially in industries requiring strong governance and traceability.

Conclusions de l'étude mondiale BARC x Actian de 2026, menée auprès de plus de 300 responsables des données en entreprise

Dans tous les secteurs, les entreprises investissent massivement dans l'IA. Pourtant, de nombreuses initiatives peinent encore à dépasser le stade de l'expérimentation et à générer une valeur ajoutée durable pour l'entreprise.

Pour mieux comprendre ce qui distingue les initiatives d'IA qui se développent de celles qui stagnent, Actian s'est associé à BARC, un cabinet d'analyse mondial de premier plan spécialisé dans les données et l'analyse, pour mener une étude mondiale auprès des responsables des données d'entreprise.

S'appuyant sur les témoignages de plus de 300 personnes interrogées issues de divers secteurs et régions, cette étude examine la manière dont les organisations adoptent et mettent en œuvre data products data contracts, ainsi que l'influence de ces approches sur la réussite des initiatives en matière d'IA.

One of the most revealing insights from the research is why organizations are adopting data products in the first place.

Rapport mondial de recherche BARC × Actian (2026)

Trustworthy Data is the Primary Driver of Data Product Adoption

The research shows that organizations are not adopting data products primarily to increase data access. Instead, the strongest motivation is trust.

When respondents were asked about the main drivers for implementing data products, two factors clearly dominated:

Trustworthy inputs for AI systems (60%).
Trustworthy inputs for decision-making (59%).

These two drivers ranked significantly higher than traditional objectives such as improving data accessibility or enabling broader data democratization.

This finding reflects a shift in how organizations think about data management. Earlier waves of data modernization focused heavily on enabling data access across the organization. While accessibility remains important, enterprises increasingly recognize that reliable outcomes require reliable data foundations.

implementing data products chart

What are the main drivers for implementing data products? – © BARC 2026

AI is Raising the Stakes for Data Reliability

The growing importance of trustworthy data is closely linked to the rapid rise of AI initiatives.

AI systems are highly sensitive to the quality and consistency of their inputs. Inconsistent datasets, unclear ownership, or undocumented changes to data structures can quickly undermine model performance and erode trust in AI-driven decisions.

As organizations expand their AI initiatives, they increasingly require data environments that provide:

Clear ownership and accountability.
Defined data quality standards.
Stable interfaces between data producers and consumers.

In many organizations, data contracts formalize these expectations between producers and consumers, helping ensure that changes to data structures or quality standards do not disrupt downstream AI and analytics systems.

Data products help provide this structure by organizing data assets as managed products with defined governance, quality expectations, and reusable interfaces.

These characteristics make data products particularly well-suited to support AI systems operating at enterprise scale.

Regulation is Accelerating Data Product Adoption

Another important factor influencing adoption is regulatory pressure.

Highly regulated industries—including financial services, healthcare, and energy—are among the earliest adopters of data product approaches. These sectors have long been required to maintain strong data governance, traceability, and compliance controls.

As regulatory requirements around data usage, privacy, and AI transparency continue to expand, many organizations are formalizing their data management practices. Data products provide a practical way to meet these expectations by introducing clearer ownership, standardized data definitions, and stronger governance mechanisms.

In this context, regulatory requirements often act as a catalyst for the adoption of structured data management approaches, such as data products.

adoption of data products by industry

Télécharger le rapport de recherche complet

Cet article présente plusieurs conclusions clés issues de l'étude mondiale menée par BARC et Actian.

Le rapport complet, Data Products Data Contracts 2026 : les fondements du succès de l'IA, examine comment les organisations de tous les secteurs et de toutes les régions adoptent et mettent en œuvre data products data contracts, et comment ces pratiques influencent la maturité de l'IA, gouvernance et les résultats concrets.

Téléchargez le rapport complet pour découvrir les conclusions de cette étude et savoir comment les grandes entreprises déploient l'IA à grande échelle grâce à data products.

Télécharger le rapport complet

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