What Are Collaborative Data Ecosystems?

Enterprises across industries are realizing that collaboration on data, machine learning and data science is the key to solving the biggest challenges of our time. This leads to a massive rise of collaborative data ecosystems around the globe. Read more if you want to know what collaborative data ecosystems are
Published 30 August 2022

Enterprises across industries are quickly realizing that collaboration on data, machine learning and data science is the key to solving the biggest challenges of our time. Whether it is about improving supply chain transparency, developing precision medicine and highly individualized treatments, or sharing and analyzing environmental, social or governance data – there is no question that data collaboration offers huge potential. Companies, academia, research institutes and others are now coming together to create shared value in Collaborative Data Ecosystems.

A collaborative data ecosystem is an alignment of business goals, data and technology, among two or more participants, to collectively create value that is greater than each can create individually.

Breaking Down Collaborative Data Ecosystems

To properly define a collaborative data ecosystem, let us break it down into its components:

  • Business Ecosystems

  • Data Ecosystems

  • Collaborative Data Ecosystems

Business Ecosystems: A Commercial Arrangement to Create Value

On business ecosystems, Greg Sarafin, EY Global Alliance, and Ecosystem Leader has a great definition:

"An ecosystem business model is a commercial arrangement among two or more companies to collectively create value propositions that are greater than each can create individually."

Greg Sarafin, EY Global Alliance & Ecosystem Leader

"Every business ecosystem has participants, and at least one member acts as the orchestrator of the participants. All members in a business ecosystem, whether orchestrators or participants have their brands present in the value propositions."

Crucially, business ecosystems exist to collectively create more value than members can individually, given time, capital, capabilities, market access, and other real-world constraints.

Data Ecosystems: From Departmental to Organizational Data Silos

Building on the previous definition, a data ecosystem is a partnership between multiple organizations to share and manage data, with the goal of creating value and innovation. A fundamental challenge in these data ecosystems are data silos. Instinctively, we associate it with data storage locations within a company, for example, where data is locked in different departments or business units, such as in Finance, Marketing, Sales, or Customer Supply Chain. Data sits in silos in entirely different data formats, and whose information exchange is therefore limited. The same concept applies if we zoom out to an ecosystem level. The complexity quickly becomes clear.

Spotlight: Healthcare Data Ecosystem

In the category of "health data" alone, there are countless data sources, data formats, and various technologies, locked in silos under different data sovereignty and ownership.

Organizations, institutes, and academia in the healthcare value chain sit on copious amounts of valuable data. But data sharing and collaboration are hugely complex due to issues of data ownership, quality, security, and privacy. Collaborative data ecosystems aim to overcome these complexities.

In every industry, we can find valuable data sets that complement each other and are, therefore, "complementary data". If one could now combine this previously isolated data, one would obtain a complete picture and could - as in the business ecosystem definition – create value that far exceeds the capabilities of a single organization.

Collaborative Data Ecosystems: Creating Value from Data With AI

ollecting data and connectivity alone is far from enough. You need to be able to co-create and innovate, analyze, and draw insights from data to derive any value from it. This requires a new approach to collaboration that goes beyond the scope that someone would expect from internal projects. And it requires new capabilities that foster innovation and value creation across company boundaries.

While the path to becoming a data-driven enterprise is clear, and its adoption can be accelerated through the implementation of end-to-end data and MLOps platforms, the required capabilities of collaborative data ecosystems aren’t yet fully understood. As soon as different participants in an ecosystem collaborate with each other, different worlds collide. Let us have a look at an example:

Organization A has already made strong investments in AI and aligned its entire core business with it – not only in terms of technology but also regarding internal processes and expertise.

Organization B is at an earlier stage and is working to effectively capture, clean, and validate data. It is also possible this organization does not look into building its own AI capabilities as it is not relevant to the core business.

There are a plethora of capability gaps that are exacerbated as more participants join the ecosystem. Closing these gaps is instrumental in determining the ecosystem’s success or failure and to bridge them, organizations need to build ecosystem capabilities either in-house or by sourcing them from partners from within or outside.

Ecosystem Capabilities Why it matters How it can be approached

Strategy

Managing an ecosystem is very different from managing an integrated company - defining the strategy and governance of a collaborative data ecosystem is a major success factor.

Orchestrators must establish and align all partners on a shared value proposition, set the rules, coordinate the activities of the other participants, aggregate their data and expertise, and deliver a range of products or services to the end customer.

Data

Data is the foundation of the ecosystem. To enable true data interoperability across organizations, there must be alignment on how the data is captured, contextualized, and made available in the ecosystem.

All participants within the ecosystem must be aligned on a Common Data Model, and privacy, as well as security, must be ensured during the entire data lifecycle.

Innovation

Data Science is highly experimental in its nature and requires an open-minded culture of co-creation and experimentation.

Unlike most traditional product or service businesses, collaborative data ecosystems should start with a focus on establishing their value proposition by innovation before putting too much emphasis on monetization.

Trust and Governance

The value and effectiveness of a collaborative data ecosystem grows over time. For this to happen, the ecosystem must be designed for sustainability and address all potential risk factors, which means that data and AI assets need to be protected and data compliance must be maintained with all applicable regulations.

Modular application of Privacy-enhancing Technologies, additional security and access policies that ensure compliance, ethics, traceability, and reproducibility.

Operating Model

Achieving success with data science and ML needs constant delivery, iteration, and collaboration on data and models across data scientists, data engineering and the business.

Scaling these efforts needs a collaborative data science lifecycle methodology, interoperability across data and analytics tech stacks and ultimately the capability to operationalize data applications.

Extension of known methodologies: From DevOps and MLOps to “Federated MLOps”.

Highly integrated platforms that support multiple data science tools, packages, and languages, to derive insights from data with advanced analytics, AI and ML.

Value in Collaborative Data Ecosystems is Created on Multiple Levels

It is worth taking a closer look at value creation in data ecosystems, as it emerges at different levels:

What It Takes For Collaborative Data Ecosystems to be Successful

Winning collaborative data ecosystems address certain attributes that differentiate them from others. A key question is how much impact the ecosystem can have on the rest of the industry, and how innovative it is. From a technical perspective, it needs to be highly interoperable and scalable, to foster productivity and innovation. Finally, it needs to be sustainable, and to proactively address privacy, security, sovereignty and to protect the IP of all parties.

Never in the history of data science and AI have so many technologies converged that could enable the most innovative among us to push the boundaries of what is possible today: Moving from the internal to the external, to establish truly secure and collaborative data ecosystems on a global scale.

Apheris is an integrated, secure and governable federated data platform that allows organizations to build and deploy data applications across organizational boundaries.

If you are interested in exploring how Apheris enables your collaborative data ecosystem, contact us. We would love to chat!

Data & analytics
Collaboration
Platform & Technology
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Machine learning & AI
Federated learning & analytics
Data science
Collaborative data ecosystems
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