In the eyes of organizations, if data sharing has become a business imperative, generalizing its application is far from an easy task. Because, while we instill its value in children from an early age, we need to realize that sharing a toy is not the same as sharing its data with an entire organization.
This is a further headache for organizations and data professionals, who already have to juggle the value and return on investment of the project on the one hand, and the ability of a team and organization to ensure adequate and necessary governance from the ‘other. Data sharing projects struggle to see the light even as the recent pandemic has highlighted how large-scale data sharing can help innovation.
Shared intelligence at the service of all
Technology and data, as well as Web 2.0 and the social Web, of which Internet users have become real stakeholders, are at the center of new political concerns.
For example, the EU intends to place digital technology at the heart of its economic development and make it a vector of innovation; to achieve this, it is implementing new initiatives and proposals. Therefore, the General Data Protection Regulation (GDPR) marked a real turning point in the digital sector, which now wants to be more transparent and accountable, and the Data Governance Act has made it possible to provide better supervision and better organization of data sharing. , in favor of innovation and collective intelligence at the service of society as a whole.
The common interest is therefore becoming a new challenge for the government’s open data initiatives and the Covid applications prepared in the various countries constitute an excellent use case: the data on the evolution of the pandemic are made available to the citizens who are themselves encouraged to declare themselves in the eventuality of a positive test to alert people who would be in contact. This open and free access to data promotes innovation. Transportation or urban planning sectors are not excluded because there are road applications such as Waze, which use published data on road works or possible road accidents to provide their users with a real-time location status of road conditions.
Access to data from the largest number
However, the operation of open data is compromised by problems common to each data project, despite initial demonstrations of success and an awareness of its strategic value. Technology and its implementation, trust and data governance, and organizational culture are barriers that organizations struggle to overcome.
A data sharing strategy cannot do without APIs and paradoxically this is what prevents the strategies from being implemented. The public and private sectors don’t use the same type of API. Public sector organizations mainly use and publish open APIs that are available without restrictions as part of Open Data projects, while private sector organizations partly use private APIs within the organization. These are also called “partners” and have very specific access rules. API development is a long process, taking three to five days and requiring resources such as hiring developers. That said, new technologies and practices are simplifying this process. For example, there are tools that provide standard API libraries, such as those that allow for example to share data between SAP and Salesforce. This type of tool greatly facilitates the task of data analysts, large API consumers, as well as automatically managing access to data, and in just one hour, they can publish an API and have access to the information they need. There is therefore an increase in the efficiency of processes as well as a guarantee of access control thanks to this type of self-service consumption.
In addition to ensuring this access, organizations need to worry about data quality. These must be trusted and governed if they are to be shared and monetized. However, this presents a challenge for many business managers. Indeed, if they do not guarantee their health, from creation to their use, and do not regularly check their quality, how can we hope for a sustainable life cycle of this data? They require rigorous control, in the form of regulated access and integration into a governance cycle, made possible through the use of data quality and data cataloging technologies, a tool that ensures the security of shared data. Some sectors have been pioneers in this sector and have not waited for the implementation of European regulations to optimize the quality of their data. The retail and distribution sectors, for example, have implemented centralized data sharing systems such as data clean rooms, which have access management and governance rules. These are similar to data lakes, in the cloud or on-premises, where data is encrypted and anonymized and made accessible from a closed, dedicated space. Powerful data sharing models such as Lydia have also been developed by Fintech companies which, in partnership with Bitpanda, allow their users to invest in cryptocurrency with just a few clicks.
Despite all the ambitions and tools available, the biggest obstacle on the road to open data is data culture, which remains ubiquitous because it is difficult for organizations to break with the concept of “ownership”. Furthermore, data sharing projects do not concern all lines of business. Currently, professions in marketing, commerce or finance are the most interested and it is up to them to educate IT about the benefits of data sharing and the need to provide a secure framework and technology levers. It is still necessary to be able to understand each other and unblock silos. If the Chief Data Officer is to lead this new data change, will he become a Chief Data Sharing Officer? Or does it have to come directly from the management committee?
Another significant benefit of data sharing is that it will help promote the development of “data literacy” within an organization. Technologies are often easier to use, and people unfamiliar with data will find business use cases much more concrete. This is therefore a great way to engage employees, federate them, and train them a little more about the data.
For data sharing to be successful, it must be based on the transparency of data shared with customers and consumers. Otherwise, the projects will not see the light.
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