Data is Not a Byproduct-It's the Product: Applying Data-as-a-Product Thinking in Media & Entertainment
Metadata
- Publisher
- SMPTE — Pasadena, CA
- Doc Type
- Conference Paper
- Content Type
- Original Research
- Volume
- 00, pp. 1–19
- Abstract
- The Media & Entertainment industry is generating more data than ever before, yet much of his data is underutilized. Decades of centralized architectures, fragmented and siloed ownership, and tool-driven initiatives have produced data lakes and dashboards, but not trusted, scalable insights the industry needs. At the same time, the rise of artificial intelligence has made the availability of high quality, accessible data more critical than ever. This paper argues for a shift to a Data as a Product (DaaP), supported by the architectural framework of a Data Mesh, as a path to unlocking value, and securing the necessary trust. By treating data with the same discipline as software products, complete with ownership, lifecycle management, service-level agreements (SLAs), and feedback loops, organizations can establish trust, reusability, and accountability. The paper outlines the three planes of a Data Mesh (infrastructure utility, mesh experience, and data product experience), defines what constitutes a true data product within this model, and highlights rights management as a representative use case where productization resolves longstanding pain points. Finally, the paper examines barriers to adoption and offers a roadmap to success. Benefits include faster insights, reduced costs, new monetization opportunities, AI readiness, and productivity gains. For Media & Entertainment companies facing rising complexity and competition, Data as a Product is no longer optional. It is a business imperative.
- Publication Date
- 2025-10-13
- ISBN
- Electronic:
978-1-61482-966-9 - Author(s)
- Dan Gesshel
- Keyword(s)
- Data as a Product, DAAP, Data Mesh, Media and Entertainment, Content Supply Chain, Data Architecture, Data Governance, Data Pipelines, Artificial Intelligence, Machine Learning
- Copyright
- © 2025 SMPTE
Bibliographic Reference(s)
- Z. Dehghani, *Data Mesh: Delivering Data-Driven Value at Scale*. Sebastopol, CA:O'Reilly Media, 2021. EXTERNAL
- G. G. Parker, M. W. Van Alstyne, and S. P. Choudary, *Platform Revolution: How Networked Markets Are Transforming the Economy and How to Make Them Work for You*. New York:W. W. Norton & Company, 2016. EXTERNAL
- M. Schrage, F. Westerman, and M. Bonnet, “Why 95% of generative AI pilots are failing,” *Fortune*, Aug. 18, 2025. [Online]. Available: https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/ EXTERNAL
- Fivetran, “Fivetran report finds nearly half of enterprise AI projects fail due to poor data readiness,” press release, Aug. 2023. [Online]. Available: https://www.fivetran.com/press/fivetran-report-finds-nearly-half-of-enterprise-ai-projects-fail-due-to-poor-data-readiness EXTERNAL
- BI Technology, “Transforming data engineering with Talend Data Preparation: A self-service approach,” BI Technology, 2024. [Online]. Available: https://www.bitechnology.com/transforming-data-engineering-with-talend-data-preparation-a-self-service-approach/ EXTERNAL
- Gartner, “Data and analytics: Essential guides,” Gartner Research, 2024. [Online]. Available: https://www.gartner.com/en/information-technology/insights/data-and-analytics-essential-guides EXTERNAL
- McKinsey & Company, “Intelligence at scale: Data monetization in the age of GenAI,” McKinsey Insights, 2024. [Online]. Available: https://www.mckinsey.com/capabilities/business-building/our-insights/intelligence-at-scale-data-monetization-in-the-age-of-gen-ai EXTERNAL
- PwC, *Global Entertainment & Media Outlook 2025–2029: Insights and perspectives*, Jul. 24, 2025. [Online]. Available: https://www.pwc.com/gx/en/issues/business-model-reinvention/outlook/insights-and-perspectives.html EXTERNAL
- SMPTE, *ST 2067: Interoperable Master Format (IMF)*, Society of Motion Picture and Television Engineers, White Plains, NY, 2016. [Online]. Available: https://en.wikipedia.org/wiki/SMPTE_ST_2067 EXTERNAL
- SMPTE, *ST 2125: Mapping Material Exchange Format (MXF) to Archive eXchange Format (AXF)*, Society of Motion Picture and Television Engineers, White Plains, NY, 2018. EXTERNAL
- SMPTE, *ST 2102: Core Metadata Set*, Society of Motion Picture and Television Engineers, White Plains, NY, 2017. [Online]. Available: https://www.normsplash.com/Samples/SMPTE/179887188/SMPTE-ST-2102–2017-en.pdf EXTERNAL
Source Data (JSON)
Full registry record with provenance metadata. Open directly: /api/doc/978-1-61482-966-9-25.json
Reference Tree
Explore all references and references to this document, as a navigable tree.
Open Reference TreeReference this Doc
Plain text (ISO 690 compliant)
Preview:
Dan Gesshel; Data is Not a Byproduct-It's the Product: Applying Data-as-a-Product Thinking in Media & Entertainment, MTS 2025, Article 25 (pp. 1 to 19); SMPTE, 2025, ISBN: 978-1-61482-966-9
Snippet:
Dan Gesshel; Data is Not a Byproduct-It's the Product: Applying Data-as-a-Product Thinking in Media & Entertainment, MTS 2025, Article 25 (pp. 1 to 19); SMPTE, 2025, ISBN: 978-1-61482-966-9
HTML (ISO 690 compliant)
Preview:
Dan Gesshel; Data is Not a Byproduct-It's the Product: Applying Data-as-a-Product Thinking in Media & Entertainment, MTS 2025, Article 25 (pp. 1 to 19); SMPTE, 2025, ISBN: 978-1-61482-966-9
Snippet:
<span class="citation">Dan Gesshel; <cite>Data is Not a Byproduct-It's the Product: Applying Data-as-a-Product Thinking in Media & Entertainment</cite>, MTS 2025, Article 25 (pp. 1 to 19); SMPTE, 2025, ISBN: 978-1-61482-966-9</span>
SMPTE's HTML Pub
Preview:
Dan Gesshel; Data is Not a Byproduct-It's the Product: Applying Data-as-a-Product Thinking in Media & Entertainment, MTS 2025, Article 25 (pp. 1 to 19); SMPTE, 2025, ISBN: 978-1-61482-966-9
Snippet:
<li> Dan Gesshel; <cite id="bib-978-1-61482-966-9-25">Data is Not a Byproduct-It's the Product: Applying Data-as-a-Product Thinking in Media & Entertainment</cite>, MTS 2025, Article 25 (pp. 1 to 19); SMPTE, 2025, ISBN: 978-1-61482-966-9 </li>