The difference between data sharing and a data ecosystem

What is the difference between data sharing and a data ecosystem and why does it matter

As businesses progress along their digital transformation journey, they need to see a steady increase in the value their data generates. Data leaders that are already sharing data internally and externally are well set for the next step in making data work for them and their collaboration partners: data ecosystems.

What is a data ecosystem?

A data ecosystem is a partnership between multiple enterprises to share and collaborate with data in a way that creates new value for all stakeholders. These expansive data-sharing models typically cross over different industries, such as tracking retail advertising or sharing pharma R&D data.

According to the Capgemini Research Institute, the financial benefits of “data sharing ecosystems can reach 9% of [the] annual revenue of an organization in total in the next five years (equivalent to over $940 million for a typical organization with annual revenues of $10 billion).”

And the advantages for businesses don’t end there. Data ecosystems can also improve customer satisfaction (by 15%), productivity (by 14%), and reduce costs (by 11%), their research indicates.

It’s no wonder that up to 48% of organizations are accelerating their plans to launch new data ecosystems, with telecoms taking the lead in kickstarting data-sharing at this scale.

Yet, the full potential of this innovative way of sharing data remains largely untapped.

The benefits of data ecosystems

Financial benefits would naturally be a key driver for any business that wants to gain more value from their data. And these gains include a reduction in costs, creating new revenue streams, and ramping up their financial performance.

Along with the financial upside, data ecosystems help participants solve customer pain points better. With a diverse data set from internal and external ecosystem partners, businesses can gain a new understanding of their customers and their behavior. And come up with innovative solutions that would otherwise not have been discovered.

An example of this relationship is banks and retailers working together to create a bigger picture of who their customers are, how they spend, and their preferences. In healthcare, this may be partnerships between hospitals and other enterprises like pharma, and hospital suppliers to provide next-level patient care.

What does success look like

A successful data ecosystem should balance two priorities, McKinsey shares:

  • Creating economies of scale – by pulling in stakeholders through lower barriers to entry. And conversely, establish high exit barriers by creating clear dependencies and stakeholder benefits
  • Nurturing a collaborative network – where both data consumers and creators with similar interests team up to achieve common goals while fostering a data-sharing culture

Five types of data ecosystems

Data ecosystems span various industries along with different types suited to specific use cases.  Let’s take a look at the five main ones we came across in our research:

1. Specialized industry – these are ecosystems with a specific industry focus, such as aviation. Here you’ll find multiple stakeholders that share data to amplify the insights within a particular specialized industry. Per Capgemini, Airbus’ Skywise program is a good example, where the ecosystem includes over “9,000 connected aircrafts from more than 100 airlines from around the world,” saving one carrier $13 million in fuel costs!
2. Open – sharing data-for-good among organizations that forward a positive societal agenda. This model enables the greatest freedom and flexibility with data sharing, helping stakeholders uncover powerful, global insights.
3. Cloud with Big Data – companies with their Big Data in a cloud platform can easily share select data with other cloud partners. Using this model, stakeholders can fast-track the time to uncover fresh insights that help them differentiate.
4. Reciprocal sharing with APIs – sharing insights and data with other partners. To reduce security risks and allow granular data sharing, this is managed by an Application Programming Interface or API.
5. Data mesh – with data warehouses no longer meeting the need for real-time queries, data leaders are turning to a ‘data mesh’ architecture that decentralizes data. The benefit is that data ownership is distributed to domain-specific teams, which helps productize data.

How to scale up your data sharing

Scaling up your data sharing from internal to external and then to the next level – data ecosystems – requires a good understanding of potential challenges, and how to get started. McKinsey explains: “Typically, the hardest pieces to figure out are finding the best business model to generate revenues for the orchestrator and ensuring participation.”

Challenges in creating a successful data ecosystem

The barriers to stepping up your data sharing would likely fall into these categories:

  • Governance, risk, and compliance – this could include cybersecurity risks or the security of sensitive data
  • Trust – the trust between the businesses sharing data and consumers sharing their personal data
  • Financial – insufficient supply of the right skills and a lack of clarity about the potential return on investment (ROI) for participating partners
  • Technological – not having the right data sharing platform for the necessary access control and poor data quality

The strategy to get started

Enterprises that follow a clear roadmap will stand a better chance of establishing a sustainable data ecosystem. Data leaders would do well to also consider components such as “data availability and digitization, API readiness to enable integration, data privacy and compliance,” McKinsey adds.

 

Data ecosystem models
Image courtesy of Capgemini Research Institute

 

Here are four steps to help you get your data ecosystem off the ground and ready for future collaboration:

1. Create a data ecosystem strategy, which includes the purpose and goals, likely use cases, governance and privacy issues, and who’ll be in charge of what.
2. Decide on the best data sharing model, such as which partners will form part of your ecosystem, what data you’ll share, and how best to collaborate.
3. Run a few small pilot projects to establish what works or needs rethinking.
4. Scale up the effective use cases while you closely monitor their success.

 

As a business, you need your data to work for you and maximize the value it generates. Modern data sharing practices, like data ecosystems, are a vital enabler. But Forbes says it best: “The more open, collaborative and, yes, complex the data ecosystem, the more organizations will be able to use data to create a sustained advantage for all.”

 

Explore how Astrato’s cloud-native analytics can help all users extract and share insights internally and externally, instantly.

Start a free Astrato trial today

Enable everyone to make live data decisions

Experience all the possibilities of live data insights with Astrato, free for up to five users.

Sign up now

Book a demo

Leave your details and we’ll be in touch shortly to discuss how you can use Astrato to live query data analytics anytime in the cloud.