Data partnerships and data ecosystems: 1+1=3

Posted on: December 16, 2018 by

Data partnerships? Yes, they do exist, and we see them happening more and more in today’s market – and with great potential as well. Data partnerships are projects or partnerships focused on the collaboration of two or more entities, sharing data for smart data analytics purposes. The difference between a data partnership and a ‘regular’ data project is the cooperation between several entities – consisting of different organizations, governments and corporations. For some issues, one company might have data that is too narrow to find an answer to fix these, whereas its data combined with its partners’ data could solve the case. In these situations, the combined data is sufficient to answer both the problems of one company as well as its partner company. With the GDPR affecting the use of data (and preventing to share data without a necessary goal) data partnerships are shifting toward ad hoc data ecosystems.

By enriching your data, both sides of the medal win

Let's take the example of smart parking, where traffic management is used by combining network data with real-time prediction of crowds, and opportunities to find a parking spot. Here, data of commercial parties such as Q-park can be combined with sensors and cameras to enrich analyses. Municipalities can strengthen this model with information on traffic flow at set times.

In other contexts, comparable ecosystems are possible. For example, at the Olympic Games where the IOC, Atos as Technology partner, telco’s, broadcasting companies and local governments closely collaborate. This does not only provide the municipality with relevant data, the data ecosystem is also creating value for the commercial party as its data is cleaned, tested and analyzed, resulting in beneficial outcomes for the organization. By investing together, more use cases and better results are reached for all parties involved in the data ecosystem.

The benefit of data ecosystems is that data can be collected more precisely, combining forces from multiple sources. Using iterations provides the opportunity to quickly accelerate the data models’ potential. We have seen this work for the Big Data Innovation Hub, where established companies, start-ups and education collaborate closely.

In data ecosystems, the service integrator functions as a mediator

As I see data ecosystems evolving more and more in the market, an IT service integrator is beneficial in satisfying all parties’ demands. With the service integrator functioning as an orchestrator, all values from privacy to Return on Investment for involved parties get attention. It functions as a mediator, so to say, between all parties.

Of course, after having started a data ecosystem, choosing the environment, letting data scientists crunch the data, involve a data architect and start the agile roll-out with mandating an agile approach. For tips on how to do this, and how to successfully set up your data governance, i invite you to read our previous articles in this series : Assisted decisions: yes or no? ; Why Data Governance is necessary in transforming companies; Optimizing sales through data and analytics ;

This is the last article in a series of publications on your organizations’ Data Analytics strategy. The series, called ‘Moving beyond point solutions and pilots in Data Analytics’ addresses several challenges. These are challenges that organizations most experience when they want to upgrade their Data Analytics from point solutions to strategic, data-driven and coherent governed activities.

This series has been written by my team at Atos Consulting: Carline Nauta, Erik Schroten, Tom Konings, Gerli Meijerink and Sjoerd Rieske.


Share this blog article