[Replay] Maximizing data value: How data products and marketplaces accelerate data consumption for business users

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Anne-Claire Bellec
04 February 2025
Data product marketplaces empower businesses and data leaders with the ability to industrialize data sharing and consumption to create data value. We look at the key foundations you need to put in place to ensure your data product marketplace successfully generates value, increases usage and drives ROI.
Anne-Claire Bellec
22 April 2025
Data contracts are key to building trust in data in distributed environments, and are at the heart of data products. We look at how to build and enforce data contracts through a data product marketplace to unlock greater value from data.
Pauline Plancke
15 April 2025
It can be hard to understand exactly what a data product is, given the many ways that the term is defined and applied. To provide clarity this article provides a business-focused definition of a data product, centered on how it makes data accessible and usable by the wider organization, while creating long-term business value.
Anne-Claire Bellec
11 March 2025
Learn how a data product marketplace underpins more effective data governance by providing a centralized and secure environment that delivers seamless, compliant, and structured access to data.
Anne-Claire Bellec
04 March 2025
As Chief Data Officer (CDO), you lead and shape your organization's data strategy. However, given the increasing complexity of data flows, evolving regulations and the growing pressure to generate value from data, your tech stack needs to be comprehensive and robust.
Anne-Claire Bellec
18 February 2025
The role of the data leader has never been more vital, but they face growing pressure to increase their business value and support AI initiatives. Based on a new Gartner guide, we outline the vision to follow to maximize success for their organization.
Anne-Claire Bellec
04 February 2025
Data product marketplaces empower businesses and data leaders with the ability to industrialize data sharing and consumption to create data value. We look at the key foundations you need to put in place to ensure your data product marketplace successfully generates value, increases usage and drives ROI.
Anne-Claire Bellec
21 January 2025
To meet their need to increase data consumption and maximize value, businesses are increasingly implementing centralized, self-service data product marketplaces. We look at what they are, and how they deliver value for employees, the business, data teams and data leaders.
Pauline Plancke
07 January 2025
In an increasingly data-driven world, understanding the differences between data, metadata, data assets, and data products is essential to maximizing their potential. This is because these interrelated yet distinct concepts each play a key role in driving digital transformation by facilitating data sharing and consumption at scale.
Anne-Claire Bellec
03 December 2024
Growing data volumes, increasing complexity and pressure on budgets - just some of the trends that CDOs need to understand and act on. Based on Gartner research, we analyze CDO challenges and trends and explain how they can deliver greater business value from their initiatives.
Anne-Claire Bellec
05 November 2024
To give customers choice when it comes to AI, the Opendatasoft data portal solution now includes Mistral AI's generative AI, alongside its existing deployment of OpenAI's model. As we explain in this blog, this multi-model approach delivers significant advantages for clients, their users, our R&D teams and future innovation.
Anne-Claire Bellec
01 October 2024
Today’s enormous growth in data volumes brings a new challenge for businesses – how can they harness and use this data at scale? Organizations are therefore looking for solutions that can transform their data assets by making them available and useful, accelerating and improving performance to benefit the entire business.
Anne-Claire Bellec
16 July 2024
Data products and data assets both aim to make data usable and valuable. What are the differences between the two and how do you incorporate them into your data strategy?
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