Sharing information is simply not a natural maneuver for businesses. The basic tendency is to try and hold on to whatever we think gives us an edge. And for companies in a monopolistic situation, that makes sense. But most of us are not monopolies, and collaborating on data actually opens up bigger opportunities than if we keep our cards close to our chests.
Krzysztof Malicki, CEO, Trusted Twin
Is the Cloud the destination for business? Or a temporary stop on the way to something new, bigger and even more exciting?
Increasingly, it looks as if everything that brought cloud computing to where it is today – a near $600 billion-per-annum behemoth according to Gartner, Inc. – has been a preamble, a mere introduction. Sure, organizations had really good reasons for migrating to the cloud. By digitalizing all that data and making it available in the Cloud, they have achieved some great things, mostly in data economics - taming the cost of storage, computing, back-up and resilience.
But data is not the same thing as information. While the Cloud provides an economically advantageous home for data, it does not magically turn it into information. That comes from consolidating and blending data, sharing it and then collaborating on it to add value and gain insight.
From sharing to collaboration
A 2021 Forrester report noted that data sharing was increasing rapidly, with 35% of data and analytics decision-makers sharing or exchanging data with suppliers and partners. And Gartner has noted that organizations that share data externally generate three times more measurable economic benefit than those who do not. In fact, Gartner goes further and recommends a “must share data unless” mindset for business leaders – a far cry from the conventional wisdom of “‘never share!”
However, sharing data is not the same thing as collaborating on data. Sharing simply means making data accessible to somebody else. Collaborating requires the ability to work together on the data with tools you either have in common or which are interoperable without losing the data ownership. Collaborating on data requires a shift in data architecture, from bilateral data flows to shared objects existing between the collaborating parties as a foundation for a common product, service or process.
In fact data collaboration is even more than that. It’s a move beyond the use of analytical data – looking back over the history of what has been happening – to the incorporation of operational, real-time data as well, which can create the most relevant picture available in real time.
Technically, all this is a lot more complex than data sharing. It means enabling IT systems owned by different organizations to operate on living objects that aggregate data provided independently by these entities.It is more than just a connection for data "passing,” it is about pooling data in shared infrastructure with tools to access and collaborate in real-time.
Getting to this point of data collaboration involves infrastructure challenges to overcome data silos and to develop the range of integrations necessary for specific ecosystems. It requires advanced data ownership and data governance controls to meet regulatory requirements. Security is another major challenge.
And then there is the fundamental question of trust between the data partners. Entirely legitimate concerns about competition and reputation mean that data providers need reassurance that their data will not be misused once accessed by others. Sharing information is simply not a natural maneuver for businesses. The basic tendency is to try and hold on to whatever we think gives us an edge. And for companies in a monopolistic situation, that makes sense. But most of us are not monopolies, and collaborating on data actually opens up bigger opportunities than if we keep our cards close to our chests.
Data collaboration is the step beyond the Cloud that companies are starting to explore in earnest now. It’s where data becomes the basis of collaboration with the objective of creating new information and new opportunities for value.
Collaboration turns data into value
Paradoxically, in a world that seems to be drowning in data, the drive towards data collaboration often rests on a sense of data poverty. AI is fueling a growing business hunger for access to new sources of data. Rapid adoption of machine learning means that AI maturity is rising and so is the demand for data to fuel new models. Better data can translate into improved customer and competitive insights, product innovation, customer experience, and optimized business operations. Organizations can build new digital products and services based on data and collaboration.
No surprises, then, that 86% of executives agree that data collaboration is "desperately needed" in their industries. That’s according to Accenture, which notes that “companies are coming together to unlock value across ecosystems and leveraging different consortia and ownership constructs to co-create solutions for entire value chains.”
As a real-world example of what this might look like, we can look at insurance. Detailed customer profiling is crucial for the insurance industry, as it determines the margin generated for a given customer segment. With data collaboration, smaller insurance providers could pool customer profiles without losing ownership or security of any data. Each provider could run its own AI/ML models on a much larger dataset, thereby gaining an edge against much larger competitors.
Likewise with large data owners (e.g., telecom operators), offering access to customer profiles through shared data collaboration platforms would open up a world of new business opportunities, especially in the ad tech industry. Seamless data collaboration will lower barriers and accelerate the development of new digital products.
The possibilities are endless, including financial services, manufacturing, agriculture, energy, and health – basically any organization where data plays an important role and partner organizations are also invested in data ops. In every case, this new business model is going to be based not only on new technologies but also on an updated understanding of how to turn operational data into value.
The next phase of “coopetition”
Collaborating with competitors, or “coopetition” among “frenemies”, was fashionable in the ‘80s and ‘90s and can even be traced back to the early years of the 20th century. It leans on game theory to understand that it is sometimes better for competitors to work together. In a 2021 coopetition redux article in the Harvard Business Review, Adam Brandenburger and Barry Nalebuff note that “Now the practice is common in a wide range of industries, having been adopted by rivals such as Apple and Samsung, DHL and UPS, Ford and GM, and Google and Yahoo.”
What comes next is data collaboration as the basis for a new phase of coopetition. It has not happened to date because of technical, privacy, regulatory and cultural barriers to commercial exploitation, but a new era is upon us thanks to developments in data technology. That makes data collaboration an emerging trend to investigate for those early adopters who are ready to create new coopetition models and reap the greatest rewards.
About the author:
Krzysztof Malicki is the CEO and co-founder of data collaboration startup Trusted Twin. After his start in business managing the internet startup portfolio of Poland’s largest IT company, Krzysztof founded his first startup in 2004, which he brought to an IPO on the Warsaw Stock Exchange in 2011. He then worked as a consultant and advisor on projects for the World Bank, the European Commission, and the Polish National Center for Research and Development. In 2020 he and a team founded Trusted Twin to enable businesses to build digital products and services based on data collaboration. Krzysztof holds two Master’s degrees from the Gdańsk University of Technology and was the youngest laureate of the Polish national olympiad in informatics.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.