Factors that define open data impact

The final impact that can be obtained through an open data initiative will ultimately depend on multiple interrelated factors that will be present (or absent) in these initiatives. That is why the GovLab of New York University has analyzed these factors thanks to the study of the several use cases collected by their project about the open data impact throughout the world, even ellaborating a periodic table of the enabling elements of the impact.

These elements  have been finally classified into five main categories, reviewing the different sections below.

Definition of the problem and the associated data demand

Obtaining a better anticipated knowledge of the problems we wish to solve and the data demand needed to be solved  is a logical first step to obtain the desired impact. The elements that go into action in this category are:

• In-depth analysis of future users and optimization regarding their needs from the beginning of the project.

• Definition of causes and context, clearly distinguishing among the causes of the origin of the problems we intend to address and the simple symptoms caused by these same problems.

• Refinement through the decomposition of the problem in each one of the factors that define it.

• Definition of the benefits and objectives expected to be carry out the subsequent measurement of their degree of achievement.

• Audit of the data necessary to carry out the proposed value proposition and inventory of the data actually existing in this regard.

Capacity and civil and institutional culture

The lack of knowledge or of the minimum technological and management capacities could give rise to a barrier difficult to overcome when obtaining the expected impact. The elements that are part of this category include:

• The minimum elements of hardware and software that constitute the data infrastructures necessary to to provide access and enable their use.
• Human capital, public services and elements of civil society that constitute the essential public infrastructure to guarantee the availability of data in a healthy ecosystem.
• The level of digital literacy and the degree of internet penetration necessary to take advantage of the available data.
• Cultural or institutional barriers as regards openness that could act as a brake on the publication or expansion of open data.
• The existence of the necessary technical knowledge and skills to take advantage of the data.
• The feedback channels enabled when collecting the experiences of the users and final beneficiaries of the data.
• Availability of the necessary resources to guarantee the sustainability and availability in the long term of the data already shared.

Data gobernance 

The diversity among the different governance models regarding the publication standards and policies is another clearly differentiating variable when talking about impact. The elements that are part of this category include:

• Development of performance metrics that inform the decisions to be taken in the opening projects through the different iterations.
• Control of risks that could affect the privacy of the data or sensitive information to prevent unwanted disclosure. 
• Open data by default as a guiding principle of the existing strategy and policies to guarantee political commitment at the highest level.
• Free access to information and other policies that work as necessary pillars on which to build open data projects.
• Measures to ensure a minimum quality of the published data so they are sufficiently precise and updated to be able to take advantage of them.
• Authentic ability to respond to the changing reactions and needs of data users.

Collaboration with other ecosysten agents 

Collaborations with all types of organizations and individuals that are part of the data ecosystem play a fundamental role to face a successful open data process. The elements that are part of this category include:

• Establish close connections with data managers, both public and private, is a good strategy to address the gaps in the data with their help.
• Domain experts that provide the specific knowledge required when working in specific and well-defined sectors.
• Collaborations with other individuals and related organizations regarding the opening philosophy.

Risk management

An open data will always be exposed to a certain level of risks that must be identified and adequately addressed. The elements that are part of this category include:

• Privacy problems for which it will be necessary to guarantee the data anonymization against the different techniques of individual identification.
• Non-intrusive data security techniques to protect sensitive information against unwanted exposure but without compromising the opening up of other data.
• Problems in decision making due to being based on incorrect or incomplete information.
• Deepening the power asymmetry in the face of the inability to access data by some marginalized groups for the benefit of a privileged minority.
• Use of open data as a simple image clearing instead of pursuing a true transformative change.

Although there are obviously other contextual variables that will affect our chances of success in each specific case, working on the elements previously seen will undoubtedly have a positive effect on the final impact of our open data initiatives.