We exist in a digital economy in which the capacity to generate value is based on the discovery of meaningful trends and insights from relevant data. The technologies which allow us to conduct this kind of analysis – such as data mining or machine learning – generally require extremely large and varied datasets in order to be effective. In practice, this means that most small- and medium-sized organisations do not on their own possess sufficient data to be able to access the benefits offered by such tools. This tier of the digital economy therefore remains largely the preserve of big tech companies, large corporations and state entities.
If smaller organisations are to benefit from the kind of strategic insights which can be provided by large-scale data analysis, they are typically obliged to pool their data together with other organisations in the same position. These kinds of data sharing initiatives can produce transformative effects for those involved, but there are also significant barriers which can prevent organisations from taking part. Data collaboration often requires that institutions make substantial changes to their habits, assumptions and working practices; they tend to require a commitment of time and resources outside of a company’s regular expenses; data sharing can also run contrary to ethical or regulatory red lines. These restrictions mean that for many organisations, the prospect of a collaborative data project is simply a non-starter.
After many years both researching these problems and experiencing them first hand, Etic Lab has developed an approach to data collaboration which enables groups of people or organisations to access the benefits of cutting-edge data technologies by mitigating the barriers which have hitherto stood in their way. This involves the establishment of what we call a “Data Federation” – a consortium of actors who agree to commit their resources towards addressing a common problem or goal whilst retaining ownership and control of the data assets they choose to contribute to the project. The possibility of such a collaboration is based on the use of a range of decentralised, privacy-preserving data technologies such as Distributed Data Mining and Federated Learning, which allow data analytics and machine learning to be performed on a range of separate datasets without requiring that the data be collected in a single central location. This allows partners to commit their data resources to a collaborative project without exposing their private or proprietary data to each other or a third party – Etic Lab included.
Etic Lab offers a bespoke consultancy and design service based on the specific needs of our partners. We will work with you to frame the questions you would like to answer, identify available data sources, highlight potential barriers, and produce a custom technical intervention which will allow you to achieve your goals without requiring you to violate ethical or legal boundaries or transform your working practices overnight. What you will receive at the end of the process is far more than just a set of insights or a specific tool – you and your collaborators will also have developed a set of shared understandings, professional relationships and practical protocols which can be extended to accommodate new activities and partners. Data Federations offer a way for groups of actors to share strategic insights and build cooperative power, providing the opportunity for them to change their relationship with the social and economic systems in which they operate. If this sounds like the future you want for your organisation, get in touch today to discuss setting up your own Data Federation, or take a look through our website to find out more.
Looking for where to start? Here’s a snippet from one of our articles relating to our work with Data Federations:
Introduction to Federated Learning
A Data Federation is a new model for digital collaboration, in which organisations commit their data to a common project without being obliged to share it with their partners. This possibility depends on the use of data technologies which allow us to perform analytics or predictive modelling on distributed datasets whilst preserving their privacy and integrity. At Etic Lab, we have access to a range of these technologies, and each has its own set of potential uses. In this blog, we’re going to take a closer look at one of these tools in particular: Federated Learning.
What Is Federated Learning?
Federated Learning (FL) is a new Machine Learning (ML) technique which allows for the development of predictive models from a range of separate datasets, without requiring that this data is shared…
Interested in establishing your own Data Federation?