The year in review: project highlights across our data ecosystem

Five data collaboration project highlights we remember from 2019

Throughout 2019 at Data Republic, we’ve seen organizations leverage our Senate software in many creative and collaborative ways.

From enterprises to startups, organizations of all different industries and geographies have used Data Republic’s secure platform as a neutral zone for data sharing.

As Data Republic’s Brad Moult, Head of Customer Success, says,

We are endlessly amazed by the innovative approaches and value our clients can deliver to their customers when collaborating with each other.

For some 2020 inspiration, here are five data collaboration projects hosted on Senate that stood out this year.


Joint customer marketing: Loyalty A* and Pension Services B*

Context: Pension funds are constantly searching for new ways to reach potential members.

Goal: Two organisations, Loyalty A and Pension Services B, wanted to bring data together to drive a member acquisition programme for Pension Services B.

Problem: Neither party was comfortable with their customers’ data leaving their organisation and, as such, were unable to move forward with the project.

Solution: By using Data Republic’s Senate platform, Loyalty A and Pension Services B were able to create common customer insights and new marketing initiatives without surrendering any data to one another. Data Republic’s legal framework and ‘private and secure by design’ architecture enabled Pension Services B to proceed with a type of affinity marketing project they’d never done before. 

The ability to de-identify and match customers at an individual level led to:

  • Improved customer experience thanks to protection from irrelevant emails for current Financial Services B customers
  • Increased understanding of the characteristics in Loyalty A’s data that were associated with being a high value pension member
  • Deployment of machine learning techniques to project a suitable target audience definition into Loyalty A’s customer base

Governed data accelerator programs: AI/ML Consultancy* and Fast Food Chain* 

Context: Fast Food Chain were experiencing internal concerns that they weren’t doing as well as traditional fast food outlets.

Goal: In order to effectively analyze their market share against competitors, Fast Food Chain needed to access their own transactional data as well as the transactional data of other similar restaurants. 

Problem: Fast Food Chain is a franchise, which means the trademark, IP, business model and brand is used by multiple different franchisees – and there was no existing requirement for these franchisees to report back transaction data to HQ on how each store is performing. As a result, Fast Food Chain had little first party data and the broader group struggled to accurately report on their market analytics. 

Solution: Data Republic’s Senate capability gave AI/ML Consultancy access to de-identified bank transactional data in an isolated analytical Workspace. This external data set, along with the expert support of our team, helped AI/ML Consultancy solve the missing Fast Food Chain data issue. The versatility and granularity of the transactional data enabled analysis of not only the demographics of their customers but the frequency, amount, time and geography of purchase.

Outcomes: AI/ML Consultancy was able to build an informative segmentation of the behavior of elements of the Fast Food Chain customer base, which could be used to inform better promotional strategy versus competitors, and to better target the most lucrative customer segments through media. 


Governed data accelerator programs: ABC Bank* and AI platform*

Goal: ABC Bank were interested in evaluating the capabilities of an advanced enterprise AI Platform, to transform their model for evaluating consumer account management.  

Solution: Using the ABC Bank consumer credit application data for input, AI Platform was installed in Data Republic’s secure analytical workspace and accessed only by ABC Bank analysts. 

Outcome: AI Platform was able to provide more sophisticated techniques to evaluate retail risk, including a vast range of linear models that could be applied to the data. The bank were able to bring real data up to a high potential software solution without having to surrender data to the software organization. 


Innovation Sandbox: IX Insurance* 

Goal: IX Insurance’s innovation division approached Data Republic in 2018. They needed to solve for the agile engagement and evaluation of artificial intelligence (AI) or machine learning (ML) capability providers in a governed way, ensuring their data is always in their control.

Problem: IX Insurance felt constrained by data privacy and security concerns, preventing them from taking innovative steps forward. 

Solution: Using Data Republic’s secure Workspaces, IX Insurance quickly engaged and evaluated capability providers in challenges, to assess their valuable AI and ML solutions. For example, training a machine learning algorithm to determine which of the insured are most likely to have a repeat episode within 30 days.

Outcomes: IX Insurance were able to securely evaluate new technologies and tools using real data with six different AI and ML startups – with three more ahead in the pipeline. 


Audience data profiling: Airline X and Telco Y

Goal: Airline X and Telco Y needed to land on a final list of guidelines for deidentified data matching project. With these rules, they could move more decisively through use cases and find value quickly. 

Solution: These two parties would iterate the matching process several times. They began to experiment with matching and sizing exercises as part of a proof of concept (POC), in order to indicate the compatibility of their respective datasets.


  • Both companies discovered a substantial overlap between their datasets.
  • They determined how to filter out the matches they don’t want (e.g. a high number of bookings coming through under a travel agent’s email address).
  • They found alignment on specific match field characteristics, answering questions about their data similarities. 


Want to learn more about how to future-proof your innovation potential by defining scalable ecosystem strategies for data assets? Watch our webinar ‘How to define an enterprise data ecosystem strategy’ on demand.

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