Academic Journal
The Data Product Canvas: Designing Data Products for Sustained Value From Enterprise Data.
| Τίτλος: | The Data Product Canvas: Designing Data Products for Sustained Value From Enterprise Data. |
|---|---|
| Συγγραφείς: | Hasan, M. Redwan, Finkel, Bastian, Legner, Christine |
| Πηγή: | Information Systems Journal; Jan2026, Vol. 36 Issue 1, p144-158, 15p |
| Θεματικοί όροι: | Cross-functional teams, Data science, Design techniques, Statistical decision making, Software frameworks, Artificial intelligence, Electronic data processing, Value engineering |
| Εταιρία/Οντότητα: | SAP SE |
| Περίληψη: | Organisations are increasingly striving to become more data‐driven by embedding data into decisions, interactions and processes and by leveraging advanced AI technologies to unlock innovative use‐cases. However, many remain unprepared to meet the rising demands for data, analytics and AI. A data product mindset—combining, packaging and delivering data as a product—has emerged as a promising approach to meet the needs of an expanding user base. Despite their popularity, data products are often seen as a purely technical concept, with suitable methodologies and tools for designing them still underdeveloped. This paper introduces the data product canvas, a visual and versatile tool that helps cross‐functional teams—comprising business, data, analytics and IT experts—collaboratively design new data products and assess existing ones. The canvas ensures that critical themes are addressed: desirability from the customer perspective, feasibility from the technical perspective and viability from the economic perspective. The practical application at SAP illustrates how the data product canvas supports its data democratisation initiative, showcases real‐world examples and offers practical insights to guide future adopters: (a) tailoring designs to different data product types, (b) periodically refining data products to increase their value and (c) systematically assessing requests to build a cohesive data product portfolio. [ABSTRACT FROM AUTHOR] |
| Copyright of Information Systems Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: The Data Product Canvas: Designing Data Products for Sustained Value From Enterprise Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hasan%2C+M%2E+Redwan%22">Hasan, M. Redwan</searchLink><br /><searchLink fieldCode="AR" term="%22Finkel%2C+Bastian%22">Finkel, Bastian</searchLink><br /><searchLink fieldCode="AR" term="%22Legner%2C+Christine%22">Legner, Christine</searchLink> – Name: TitleSource Label: Source Group: Src Data: Information Systems Journal; Jan2026, Vol. 36 Issue 1, p144-158, 15p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Cross-functional+teams%22">Cross-functional teams</searchLink><br /><searchLink fieldCode="DE" term="%22Data+science%22">Data science</searchLink><br /><searchLink fieldCode="DE" term="%22Design+techniques%22">Design techniques</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+decision+making%22">Statistical decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Software+frameworks%22">Software frameworks</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Value+engineering%22">Value engineering</searchLink> – Name: SubjectCompany Label: Company/Entity Group: Su Data: <searchLink fieldCode="CO" term="%22SAP+SE%22">SAP SE</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Organisations are increasingly striving to become more data‐driven by embedding data into decisions, interactions and processes and by leveraging advanced AI technologies to unlock innovative use‐cases. However, many remain unprepared to meet the rising demands for data, analytics and AI. A data product mindset—combining, packaging and delivering data as a product—has emerged as a promising approach to meet the needs of an expanding user base. Despite their popularity, data products are often seen as a purely technical concept, with suitable methodologies and tools for designing them still underdeveloped. This paper introduces the data product canvas, a visual and versatile tool that helps cross‐functional teams—comprising business, data, analytics and IT experts—collaboratively design new data products and assess existing ones. The canvas ensures that critical themes are addressed: desirability from the customer perspective, feasibility from the technical perspective and viability from the economic perspective. The practical application at SAP illustrates how the data product canvas supports its data democratisation initiative, showcases real‐world examples and offers practical insights to guide future adopters: (a) tailoring designs to different data product types, (b) periodically refining data products to increase their value and (c) systematically assessing requests to build a cohesive data product portfolio. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Information Systems Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=189935458 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/isj.12603 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 144 Subjects: – SubjectFull: SAP SE Type: general – SubjectFull: Cross-functional teams Type: general – SubjectFull: Data science Type: general – SubjectFull: Design techniques Type: general – SubjectFull: Statistical decision making Type: general – SubjectFull: Software frameworks Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Value engineering Type: general Titles: – TitleFull: The Data Product Canvas: Designing Data Products for Sustained Value From Enterprise Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hasan, M. Redwan – PersonEntity: Name: NameFull: Finkel, Bastian – PersonEntity: Name: NameFull: Legner, Christine IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13501917 Numbering: – Type: volume Value: 36 – Type: issue Value: 1 Titles: – TitleFull: Information Systems Journal Type: main |
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