On creating a data sharing culture

Last week I facilitated a discussion on “Creating a data sharing culture” at the Chief Analytics Officer Forum, in Johannesburg.

What is data sharing?

Data sharing refers to the practice of exchanging or transferring data between individuals, organizations, or systems. It involves making data accessible to others, either partially or entirely, for various purposes such as collaboration, analysis, research, or service provision.

Data sharing is increasingly important as companies seek to achieve enterprise goals such as advanced analytics (the focus on last week’s event) and master data management.

Forms of data sharing

Data sharing can take different forms depending on the context and the intended recipients. It can be as simple as sending a file or document containing data via email or file-sharing platforms. In more complex scenarios, data sharing may involve the integration of databases, the establishment of data-sharing agreements or protocols, or the use of application programming interfaces (APIs) to enable automated data exchange between systems.

Data sharing can occur within a single organization or across multiple organizations, such as government agencies, research institutions, or businesses. It can involve sharing data with specific individuals or groups or making data publicly available for anyone to access and use.

Motivations for data sharing

The motivations for data sharing can vary. Some common reasons include:

Collaboration: Data sharing facilitates cooperation and coordination between different individuals or teams working on a common project or goal. By sharing data, stakeholders can contribute their expertise, combine resources, and achieve more comprehensive insights or outcomes.
Knowledge advancement: Sharing data with the broader research or scientific community allows for the validation of findings, replication of experiments, and the generation of new knowledge. It promotes transparency, accountability, and the collective growth of knowledge in various disciplines.
Innovation: Making data available to developers, entrepreneurs, or innovators encourages the creation of new applications, products, or services. It can spur innovation by leveraging the creativity and capabilities of external individuals or organizations.
Policy-making and decision-making: Data sharing supports evidence-based policy-making and informed decision-making. By sharing relevant data, policymakers and decision-makers can access accurate, up-to-date information to develop effective strategies, evaluate interventions, and address societal challenges.

What is a data-sharing culture?

A data-sharing culture refers to a set of values, practices, and norms within an organization or community that encourages and supports the sharing of data. It encompasses the attitudes and behaviours of individuals towards data sharing, as well as the organizational policies and infrastructure that facilitate and promote data-sharing activities.

In a data-sharing culture, individuals and organizations recognize the value and benefits of sharing data and actively engage in collaborative data exchange. They understand that data sharing can lead to improved decision-making, increased efficiency, innovation, and collective knowledge advancement.

Characteristics of a data-sharing culture

Key characteristics of a data-sharing culture may include:

Trust and Collaboration: A data-sharing culture fosters an environment of trust and collaboration among individuals and organizations. There is a willingness to share data openly and a belief that collective efforts lead to better outcomes.
Openness and Transparency: The culture promotes openness and transparency in data-sharing processes. There is clear communication about data availability, access rights, and potential limitations or restrictions.
Data Literacy: Individuals are equipped with the necessary knowledge and skills to effectively work with data. They understand data formats, quality, and how to interpret and analyze data appropriately.
Infrastructure and Tools: Adequate infrastructure and tools are in place to support data-sharing activities. This includes secure data storage, data management systems, data-sharing platforms, and collaboration tools.
Recognition and Incentives: Organizations acknowledge and reward individuals or teams that actively participate in data-sharing initiatives. Incentives may include recognition, career advancement opportunities, or funding for data-sharing projects.
Data Governance and Privacy: A data-sharing culture places importance on responsible data governance and privacy. There are policies, procedures, and guidelines in place to ensure compliance with legal and ethical requirements, as well as data protection measures to safeguard sensitive or personal information.
Training and Education: Continuous training and education programs are offered to promote data literacy, data management best practices, and awareness of the benefits and risks associated with data sharing.

A strong data-sharing culture can lead to increased collaboration, more efficient decision-making processes, and improved outcomes across various domains, including research, business, healthcare, and public policy. It encourages the free flow of data, stimulates innovation, and maximizes the value of data assets within an organization or community.

Challenges of Driving a Data Sharing Culture

While data sharing offers numerous benefits, it also raises important considerations, such as privacy, security, and ethical implications. Proper data governance frameworks, consent mechanisms, anonymization techniques, and data protection practices are essential to ensure the responsible and ethical sharing of data while safeguarding individuals’ rights and maintaining data integrity.

Are you a data producer or a data consumer?

The simple question, “Are you a data producer or a data consumer?” reveals the start divide between those that want (or need) access to more data and those that are then impacted by this need. Our analytics audience largely categorises themselves as consumers.

Therein lies the basic challenge of driving a data-sharing culture.

Frequently, data sharing is driven purely from the perspective of the consumer.

An extreme perspective may be illustrated as “I am the Chief Analytics Officer. I own all data. We need it by next week.” This kind of announcement may be followed by the expectation that the technical teams will meet to define the technical details.

Yet, this approach will often meet resistance.

Data producers (operations staff and their IT support) have a range of reasons for rejecting this kind of request.

They may cite privacy or legal concerns, cite technical constraints, or simply delay on the basis of other priorities.

Or they may, with the best of intentions, deliver the data they thought was required, only to find that they have misinterpreted or misunderstood the requirement. This can cause significant frustration as it creates rework and wastes time for both the consumer and the producer.

Political Challenges

Whilst technical constraints may hinder data sharing the real challenges are political.

Producers need to prioritise the needs (data feeds) of consumers against other IT priorities. They also need to ensure that their legal obligations with respect to the data will be respected by the consuming party.

The Role of Data Governance in facilitating data-sharing

Data governance ensures that both consumers and producers are engaged in decision-making processes around data sharing
Data governance ensures that data usage policies are defined and agreed upon. Consumers are bound to producers’ contracts and, in turn, understand how shared data may be reused and shared with downstream consumers. These policies may be based on internal contracts or ethical approaches or may interpret legislation such as the Protection of Personal Information Act.
The output of a data-sharing agreement should move beyond the technical specification of required fields to include definitions of terms, required data quality standards, etc. This documentation process should also be governed to build a shared knowledge base that facilitates reuse and impact analysis. Over time, this shared repository will cut IT costs and time to delivery. For example, we would be able to quickly see what data is available, where it can be sourced from, who to talk to, and what restrictions are placed on its use
Formalising data sharing can also help us to understand what data is available from external providers – such as credit bureaus – and may save companies millions in duplicated or unnecessary data spending.
Most importantly, data governance ensures that the needs and priorities of all stakeholders – both consumers and producers – are catered for when sharing data. This is how we reduce resistance and shift the culture to adopt data sharing

Conclusion

Shared data is critical to achieving business goals such as advanced analytics or customer experience management.

We need to stop treating this as a technical problem if we want to achieve these goals. Otherwise, we may as well resort to begging.

FAQs

What is a data-sharing agreement?

A data-sharing agreement is a legally binding document that outlines the terms and conditions under which data will be shared between parties. It specifies the purpose of data sharing, the types of data to be shared, data ownership, access rights, data security measures, confidentiality provisions, and any limitations or restrictions on data usage.

Why is a data-sharing agreement necessary?

A data-sharing agreement is necessary to establish clear guidelines and expectations regarding data-sharing activities. It helps protect the interests of both data providers and recipients by ensuring that data is shared in a responsible, secure, and compliant manner. The agreement helps prevent unauthorized use, misuse, or disclosure of data and defines the rights and responsibilities of all parties involved.

What should be included in a data-sharing agreement?

A data-sharing agreement should include key elements such as the purpose of data sharing, data ownership and intellectual property rights, permitted data uses, data security and protection measures, data access and sharing mechanisms, data retention and disposal policies, dispute resolution procedures, and any legal or regulatory requirements that must be adhered to.

How can data-sharing agreements address privacy concerns?

Data-sharing agreements can address privacy concerns by including provisions for data anonymization or de-identification techniques to protect personal information. They can also outline data access controls, encryption methods, data breach notification procedures, and confidentiality clauses to ensure that shared data is handled in a manner that safeguards privacy rights.

Can a data-sharing agreement be modified or terminated?

Yes, a data-sharing agreement can be modified or terminated under certain circumstances. It should typically include provisions specifying how modifications or terminations can be requested and agreed upon by all parties. Changes may occur due to evolving data-sharing needs, legal or regulatory requirements, or changes in the relationship between the parties involved. Termination clauses define the conditions under which the agreement can be terminated, such as breach