Indigenous Data Sovereignty

He whenua hou, Te Ao Raraunga Te Ao Raraunga, He whenua hou

In Maori that phrase means, “Data is a new world, a world of opportunity.”

The lack of reliable and consistent data results in a paucity of evidence-based Indigenous policy-making.For Indigenous peoples worldwide, the lack of good data about their communities and their limited control over the collection and use of the data have serious consequences. The lack of reliable and consistent data results in a paucity of evidence-based Indigenous policy-making. This GIJN/NAJA guide explores what investigative opportunities exist for journalists regarding the bundle of issues known as “Indigenous data sovereignty.” Although this topic may sound philosophical and ethereal, the real-world ramifications are significant, affecting the creation of policies and the dispersal of funds. Background
Indigenous data sovereignty (IDS) issues are multifaceted.

Data Journalism on Indigenous Communities

The absence or poor quality of data on Indigenous communities presents both challenges and opportunities for data journalism. Because it is widely recognized that official data on Indigenous communities is faulty or sparse, reporters may need to look for alternative sources, or even create it themselves. Although data journalism commonly refers to the use of existing data, it also can mean filling a data void. Creating data is more work, but the results can be impressive, unique, and highly impactful. This GIJN/NAJA guide will:

Look at some of the issues concerning the available data on Indigenous people
Discuss alternative sources of data
Provide information on learning about data journalism
Review data journalism tools
Suggest some of the official places to look for data

Problems with National Data
Complaints about the data on Indigenous peoples are similar around the world.

Data Biographies: Getting to Know Your Data

Too often, inexperienced data users accept the data they receive at face value. Data scientist Heather Krause cautions that data should be treated similar to a human source. Just as you’d do a background check on a human source before publishing what they told you, you need to understand your data.