Designing Data Ecosystems

Following yesterday’s discussion on data in a web2.0 world vs web3.0, today’s blogpost looks at how Dataswyft designs data ecosystems, in the second of four blogposts written by Dataswyft CEO Irene Ng in conjunction with her presentations at the Singapore Fintech Festival and OpenX Congress in London.

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In web2.0 where data is siloed, it tends to focus on data aggregation and prediction for better recommendations with better data products, or to ascertain risks of an individual’s behaviour. In other words, data is the virtualization of human traits and attributes in a web2.0 world. 

In web3.0 with decentralized self-sovereign data, multiple traits and attributes can now be grouped together across companies, giving visibility to micro-behaviours. These micro-behaviours can be linked up to form journeys that are enabled by data within a data wallet.  At Dataswyft, we are often called to design these journeys, together with the types of data that form them.

Of course, getting multiple data sources to decentralize their data requires them to come together to form a data ecosystem. 

But why a data ecosystem? 

• Because it reduces customer churn, improves engagement, and cuts our clients’ customer acquisition costs. 
• Data ecosystems also create collective stickiness,  much like how Star Alliance competes with One World in the airline sector. 
• Most of all, every use of their customer data in the ecosystem earns revenue for ecosystem owners, potentially increasing their income 

How we scale data ecosystems

In designing data ecosystems, we use a market to scale that ecosystem by designing for a 'triple coincidence of wants':

• Individuals want better offers and more contextual and personalized recommendations.
• Partners want to reach audiences with selected attributes.
• The app/website and ecosystem owner wants greater loyalty, better engagement, lower acquisition costs and most of all, to generate revenues from the data they hold of their customers.

We adhere to certain market design principles that will ensure the ecosystem is able to grow, reinforce itself and self regulate. When all this happens, revenues for the ecosystem owner should start flowing in and outcomes are achieved with minimal intervention. For example, it should take mere minutes for a completely new partner to sign up to use verified data when an individual shows up at their business; they should not need to deal with cumbersome tech and legal integration. This is how revenues of a data ecosystem can scale exponentially.

Data ecosystems usually attract the interest of private equity funds. They often partner us by bringing low-performing investees who hold customer data, so that we can help them achieve a larger multiplier of returns with a data ecosystem. 

Next: The art of the possible with data ecosystems

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Irene is in Singapore from Nov 2 to 4 to present at the Singapore Fintech Festival’s session on Distributed Data Ecosystems in a Web3 World, and also at the Asian Development Bank's session on their sandbox. She'll be in London on Nov 7 to present at the OpenX Congress.  Do get in touch to catch her for a chat about how Dataswyft can help your business earn revenues from decentralizing your customer data.