How to stimulate private companies to share data by safeguarding their competitive advantage

One of the major objectives of BlueBRIDGE was to improve the efficiency and support the growth of aquafarms by providing them with information on benchmarking, currently unavailable for the sector. This is a completely new perspective in a domain where stakeholders operate individually, without the ability to comprehend the details of their business performance w.r.t. their competition.

To achieve this, BlueBRIDGE has built tools to collect data from the companies and to transform it into valuable benchmarking information. One of the major issues in this process, was to convince aquafarms to share their own production/operation data in order to create a substantial volume of high quality data to be transformed into benchmarking information. A factor that needs to be taken into account is that those production/operation data is sensitive information and is one of the assets of the company. However, for the approach set by BlueBRIDGE, the more, in quantity and accuracy, data collected, the better for the coverage, accuracy and privacy of the analysis performed by the tools of the project. In essence SMEs are invited to share their data in exchange of better quality of analysis of their performance, comprehending of the competition trends and more advanced tools for their executive skill building.

The BlueBRIDGE best practice


To overcome the confidentiality issue and encourage companies to share data, BlueBRIDGE has defined the following set of policies and tools:

  1. data anonymization to prevent exposure of installation-specific production and performance information to competitors and
  2. Removal of location information in favour of location characteristics.
  3. Indirect exposure of outputs through simulation models, rather than direct access to production/operation data.
  4. Delivery of comparative results instead of absolute performance estimation accuracy.
  5. Establishment of thresholds: Unless a threshold of data availability is reached, particular services are not available for a given set of system parameters (e.g. location characteristics), in order to preserve anonymity;
  6. Return data with improved quality.


The adoption of this practice allows private stakeholders to gain information on benchmarking without the fear of revealing their competitive advantage when sharing confidential data. This clearly brings a wide range of benefits to the different stakeholders:

  • Aquafarming production SMEs get additional data to understand their own performance, leading to better informed decisions that will boost efficiency and financial results.
  • Aquafarming consultants get the means to perform better analyses for their target clients.
  • Regulators and policy makers take more informed decisions on policies and rules to be applied to the sectors.
  • Strategic investors have better chances of comprehending the competitive advantages of one way/location of operating a business vs another one.


Why this is considered a best practice  

Best Practice Analysis


Anonymization techniques are the primary means for protecting sensitive data in the literature and in all cloud applications where such sharing of information applies. Furthermore, the adoption of such techniques allowed BlueBRIDGDE to overcome the barriers preventing SMEs from using the platform. So far 10 SMEs are providing data to feed the BlueBRIDGE benchmarking tools.


Innovation of the practice resides in the new data sharing business model, as well as on the tools that emerged previously and were not implementable due to the lack of data. Engaged SMEs also enter a new mentality w.r.t to cloud infrastructures and information sharing.

Success Factors

The success of the practice depends on the following factors.

  • Flexible SMEs that are open to exchange and share data needs to be involved in the process.
  • The infrastructure needs to ensure data confidentiality.
  • A significant amount of data sets need to be continuously fed into the infrastructure.
  • The companies need to be provided with added value information.


The practice requires that the infrastructure provider applies measures to evaluate the performance of the infrastructure w.r.t. effectiveness of the confidentiality measures taken. This is not a once-off action (i.e. at the time of definition of the techniques and processes around data confidentiality preservation) but needs to be a continuous activity enumerating new threats and measures as technology and comprehension of data involves.

Replicability and/or up-scaling

The practice applies to all domains where primary data would be of value to analysts and where performance benchmarking would be valid. Agriculture production, production and supply lines, sales and services lines, fisheries etc. could be domains to replicate the strategy.


Upscaling increases the value of the data gathered and reduces the barriers for adoption.


Lessons Learnt

Operational information sharing can enhance informed decision making at all levels of a sector (operational, strategic, regulatory).


Stimulating data provisioning in return for advanced or higher quality services seems to be a valid assumption however no rule-of-thumb can be applied regarding achievement and sustainability, as the success of the endeavour would heavily depend on the landscape and maturity of tools in the domain, the mentality of the stakeholders and the innovation of ideas and services delivered in return.


Addressing confidentiality on data that could potentially reveal competitive advantages is most certainly a key element and it is a domain-specific task that cannot be applied without good knowledge of the business domain, its reservations and the data processes engaged. Data anonymization is the key to unlocking confidential data sharing among aquafarming stakeholders and a good communication of confidentiality measures can assuage data owners of their concern in this respect.