Over 20 years in the industry

Manifesto for Data Practices

The Manifesto for Data Practices emerged from discussion surrounding the following question: What is the most effective, ethical, and modern approach to data teamwork?

Link Digital has singed in support of the values and practices outlined below as we believe these values and principles, taken together, describe the most effective, ethical, and modern approach to data teamwork.

Learn more at datapractices.org

Data Practice Values


Inclusion: Maximise diversity, connectivity, and accessibility among data projects, collaborators, and outputs.

Experimentation: Emphasise continuously iterative testing and data analysis.


Accountability: Behave ethically and transparently, fix mistakes quickly, and hold ourselves and others accountable.

Impact: Prioritise projects with well-defined goals, and design them to achieve measurable, substantive outcomes.


Data Practice Principles

As a team, we aim to…

  1. Use data to improve life for our users, customers, organisations, and communities.
  2. Create reproducible and extensible work.
  3. Build teams with diverse ideas, backgrounds, and strengths.
  4. Prioritise the continuous collection and availability of discussions and metadata.
  5. Clearly identify the questions and objectives that drive each project and use to guide both planning and refinement.
  6. Be open to changing our methods and conclusions in response to new knowledge.
  7. Recognize and mitigate bias in ourselves and in the data we use.
  8. Present our work in ways that empower others to make better-informed decisions.
  9. Consider carefully the ethical implications of choices we make when using data, and the impacts of our work on individuals and society.
  10. Respect and invite fair criticism while promoting the identification and open discussion of errors, risks, and unintended consequences of our work.
  11. Protect the privacy and security of individuals represented in our data.
  12. Help others to understand the most useful and appropriate applications of data to solve real-world problems.
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