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 In Current Events, Data Analysis Concepts Simplified, Experts

As the world becomes more and more data driven, discrimination is being eliminated and global equality is on the rise, right?

Wrong.

While logic dictates that relying more heavily on statistics and data for decision-making, policy creation, and strategy development should result in equity for all, data deserts are developing in some parts of the world and driving a kind of hidden inequity.

What is a Data Desert?

Data deserts develop where some parts of the population lack access to digital services. Because they don’t access the services and systems used to generate the data we rely on for decision-making, they are not producing the same kinds (or quantities) of data as the rest of the community.

In short, these people are not being counted.

In addition, many organizations opt to use data at aggregate levels — sometimes for basic convenience, in other cases, because finer community-level data is unavailable. Regardless of the reason, aggregate data use is a form of stereotyping that occurs when the people responsible for analysis don’t truly understand the nuances in local patterns.

Policies, decisions, and strategies are being based on generalizations of whole groups, rather than real data about individuals.

Do either of these situations sound like discrimination? Of course they do — because they are.

And before you discount data deserts as a third-world issue that doesn’t apply to your efforts in the Western world, take a look at this Pew Research Center article on how the digital gap between urban and rural Americans persists. The rise of data deserts — and as a result, digital discrimination — is a global issue.

Finding the Oasis

There is a straightforward solution to the problems posed by data deserts:

Data must be collected and analyzed at the most granular level possible.

Simple, right?

Unfortunately, individual and community-level data collection can be a time-consuming, costly prospect that many organizations are resistant to investing in. More unfortunately still, there is no easy trick or shortcut to addressing this issue — we must simply accept that there is inherent value in allocating extra resources to collect data on those who would otherwise be lost to data deserts.

There is value in spending resources to eliminate data deserts.

I’ve written before about the importance of telling stories in the right direction to avoid the trap that ecological fallacy can pose:

An ecological fallacy is the interpretation of statistical data where inferences about individuals are made from data about a group to which those individuals belong.

I’ve also talked about how critical it is to collect data at the appropriate level for the story you’re trying to tell. But perhaps the best way I can convey the threat presented by data deserts is using an example.

Recognizing Digital Discrimination

The Next Billion initiative at the William Davidson Institute at the University of Michigan provides a compelling example from Madura Microfinance, an organization aiming to improve the lives of India’s poor through financial inclusion:

Lakshmi and Bharani are both women. They live in the same region of India, both in rural villages. They adhere to the same customs and cultural norms and have similar skills, abilities, and personalities. Data-driven program development would probably tell us that they need the same types of assistance, right?

Two similar women in nearby villages face very different problems.

But while Lakshmi comes from Sulya, a village with an advanced industrial economy and high female literacy and employment rates, Bharani — mere miles away — lives in Heggadadevankote, a village reliant on agriculture, where only two-thirds of women are employed and even fewer can read. Services that would provide Lakshmi with a leg up would be woefully inadequate for Bharani, while programming aimed at assisting Bharani might offer limited value to Lakshmi.

We might have been tempted to generalize about these two women, assuming they are the same because:

  • They are both females
  • They both live in rural India
  • They are both poor
  • They share a culture
  • They have similar capabilities

But collecting more granular, village-specific data has enabled us to see they are facing different problems and require different solutions.

Need Help Avoiding the Trap of Data Deserts?

At Datassist, our focus is helping nonprofits, journalists, and policymakers access and analyze the data they need to make informed decisions and tell a complete, accurate story.

If you’d like help with data collection, analysis, or visualization, or simply have a data-related question, we’d love to hear from you. Get in touch with us now.

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