Data Strategy

The data ecosystem: how data sharing unlocks innovation

Data ecosystems help us innovate superior value propositions and tackle the big issues of our times. Here we explore the value they can deliver to you and your customers.

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A data ecosystem fuels new business models, fresh ways of working, and solutions to problems it's hard to see without open and transparent dataflow. Here’s what you need to know…

What is a data ecosystem? 

A data ecosystem describes a group of organisations working together to share data, for reasons of mutual commercial interest or social good.  

These ecosystems differ from typical business-to-business models when it comes to data use, because the emphasis here is on open collaboration, rather than selling datasets for the sole benefit of one entity.  

Because of that, they require you to adopt an ethos of decentralisation – where walls are knocked down to allow for cross-company data exchanges

When they work effectively, data ecosystems allow organisations to: 

  • Face new challenges. Issues that need many hands, like the COVID-19 pandemic, for example, are easier to solve when all the necessary data sources are open and readily available. 
  • Solve previously unsolvable problems. When you bring disparate, siloed datasets together, you unlock insights that can help uncover solutions that were previously impossible to spot. 
  • Make better-informed decisions. Datasets need to be complete, accurate, and up-to-date if decision-makers want to be able to act on insight. An open data ecosystem helps eliminate glaring knowledge gaps.  

In other words? Just as ecosystems in nature enable multiple species to thrive in partnership, a data ecosystem lets businesses grow and evolve in an environment shaped by shared information. 

What are the business benefits of a data ecosystem? 

Data ecosystems enable problem-solving in the same way you’d solve a jigsaw puzzle where the pieces are distributed among a group. 

Without sharing all the parts of that puzzle – and your output – each participant would end up with just a fraction of the full image. They might not even know there’s a bigger picture, let alone be able to see it. 

Simply put, these ecosystems allow us to unearth trends and patterns you wouldn’t be able to see without complementary datasets.  

In a lot of cases, they marry commercial and governmental datasets to overcome the barriers to solving emerging problems. And that’s a growing trend. In 2021, Statistica found that 81% of telecoms, 73% of banking, and 60% of consumer goods businesses were planning to launch new data-led innovation ecosystem initiatives.  

'Over half of the world’s biggest companies are now actively engaged in data ecosystem models'.  

In fact, BCG Henderson Institute research shows that over half of the world’s biggest companies are now actively engaged in data ecosystem models. And while compliance with evolving regulation is one reason for that growth, another is the real-world applications that this kind of cross-company data exchange can enable. 

In practice, that could be healthcare providers sharing data with councils in order to identify vulnerable citizens. Or it could be insurance firms and highway infrastructure groups sharing data in order to identify accident zones

In April 2023, for example, the UK government announced that a new digital map of the entire underground power cable, gas pipe, and water mains network is being launched to help plan for issues and mitigate repair times. That’s a textbook example of a data ecosystem, wherein information sources from multiple groups – commercial and public – are actively pooled together.  

At Zühlke, a recent example of our work in this space is the Electric Vehicle (EV) Infrastructure Investor App – a proof of concept that pooled data from transport, energy, and geographical sources to highlight where future EV charging stations are needed most urgently. 

The app used those sources to create a map of high-density EV traffic, which can be cross-examined against things like weather, time of year, the direction of traffic, and even ferry timetables. You can read more about the project here

Meanwhile, the data ecosystem we developed with AO Foundation is connecting disparate datasets – from clinics, hospitals, and medical research bodies – without compromising patient privacy. This is driving a breakthrough in AI empowered healthcare.

Note: our article on the innovation ecosystem model has even more examples of emerging ecosystem partnerships.

Ultimately, data ecosystems underpin innovation. But this doesn’t just need to be philanthropic, ‘social good’ innovation – there are limitless commercial opportunities in adopting a data ecosystem model, too.  

To do that, businesses need to adapt their thinking and practices to enable more mutual – rather than competitive – commercial benefit… 

What needs to change to enable data ecosystems? 

The growth of the internet has helped create a host of thriving industries focused on selling data. So it’s natural that any initiative encouraging these businesses to share data openly might be met with skepticism.  

Data ecosystems, then, require a bit of a mindset shift. More accurately, two mindset shifts need to happen to make this work. The first one is that there's a culture out there of ‘this is my data, I don't give it to anybody’. 

And the second is that organisations that have a lot of data are inclined to sell it to somebody else. But that’s not an ecosystem, it’s a marketplace. 

'Real value doesn't come from being sold a single stream of information. But from the gold nuggets you find inside multiple crossmatched datasets'.

Solving those issues requires businesses to put egos aside and refrain from any instinct to become the ‘leader’ in what should be a democratic space.  

Real value comes not from being sold a single stream of information, but from the gold nuggets that you find inside multiple crossmatched datasets. So you only need one break away, and suddenly the ecosystem doesn't work. 

5 ways to get ready for data ecosystems

Evolving the way your business thinks about and uses data can take time, but there are a few best-practice tips that can help codify your vision and reach your desired outcomes: 

  1. Communicate clearly:
    Define and communicate the rules of engagement between organisations in the ecosystem. Your aim is for transparency, and to encourage data collaboration within the context of any competition or anti-trust regulations. 
     
  2. Run in-house data due diligence: 
    Data needs to flow, and be available where and when it’s needed – not stored and static. Timeliness is crucial, and having data available in real-time means that it can influence decisions and actions, rather than just report on them long after the fact. That data should be available in its raw, unstructured form so that it doesn’t become partial or misleading.
     
  3. Opt-out, not in: 
    Participants should be strongly encouraged to work on a ‘presumed open’ basis for data sharing. By doing so, data is shared by default and restricted only on an exception-by-exception basis – rather than vice versa. This is essential for accelerating collaboration.
     
  4. Think like a team player: 
    Look for opportunities to benefit your fellow organisations by allowing (non-sensitive) data to flow back to them. Allow participants to see both the whole picture and the ‘working out’ so you can build a shared understanding and benefit from the peer review of observations and decisions.
     
  5. Formalise things: 
    Create shared tools and techniques for working with data across the ecosystem – with curated datasets, feeds, APIs, methods, and algorithms. Then define the governance for who is trusted – and the scope of that trust.
     

The risks of ignoring data ecosystems

Building an ecosystem approach to data means future-proofing your business around new ways to collaborate and grow. But ignoring this tectonic shift could see you fall victim to a number of key risks: 

Practicing outdated business models 

As data becomes more open, the existing model of building and selling large data sets could soon seem like an archaic practice in the face of increasingly open and accessible data – on an ever more granular scale. A big evolution will happen when people realise that instead of buying big data, they actually need to buy different sets of very small data – and that business models associated with buying very small amounts of data aren’t really appropriate.’ 

Reporting on the past 

Too often, data is something that primarily reports on the past. When you need to drive change or manage risk across a complex ecosystem, data needs to inform and direct action – so it needs to be up to date. That’s more easily done when the data you’re accessing is available at all times, rather than bought or sold as chunks that represent a set period of time. 

Missing the bigger picture 

If you only have access to your own data, you’ve often only got part of the solution. Worse still, you’re unlikely to even know what it is you’re missing. Data sharing, then, helps all involved see the bigger picture – and provides a clear, common basis for planning, delivering, and measuring change. 

How Zühlke can help 

Data sharing between diverse players is essential for solving the biggest issues of our time and creating pioneering solutions. But complex data silos, poor data quality, and regulatory red tape can make this an impossible task.

Speak to us today about how our ISO-accredited strategists, scientists, and engineers can help you create new value at scale with the right data strategy and AI solutions

Contact person for United Kingdom

Dan Klein

Global Chief of Data & AI

Dan is the Global Chief of AI & Data and has extensive experience working across a diverse range of sectors, including government, transport, telecoms, and manufacturing. As a skilled engineer and strategic advisor, Dan effectively connects the needs of leadership with the technical expertise of teams to successfully drive data transformation initiatives for organisations. He brings a unique combination of strategic thinking and deep knowledge of data and engineering to his consulting work. 

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