It still amazes how it has taken us this long to realize the importance of data or more specifically, the extensive role that data plays in our daily lives. Data does not pertain to one sector; data is fluid. It can come in different sizes and forms. It is a shape-shifter. It is dependent on who is collecting the data or who is paying for the data being collected. Through data, we can find out what our problems are, what their solutions could be, and how we can make better decisions. Without it, we are lost; with it, it’s a hit or miss. 

In this article, I aim to shed light on the pitfalls of data within the development context and what steps data collectors can take to rectify them. 

Despite its elevated status, data is not without its pitfalls. First, if data does not meet certain criteria (i.e. reliability, validity, integrity) then at the very least it will not be beneficial nor useful. Data needs to uphold a certain standard to lead to a desirable outcome(s).  Second, the data is quite cliquey. It mostly likes to mingle with the ones that it’s familiar with. It could even be said that it shies away from unfamiliar territory. In other words, we could say it values surface-level alliances more than deeper one-on-one relationships. Even though data has developed vastly over the past few years and continues to progress, there are groups of people still being left behind.

Because of their shunned status, vulnerable groups are still not being given the due consideration they so rightfully deserve in the data sphere. Data is not always representative of the groups it collects data from. Instead, it sometimes seems like it applies a one-size-fits-all approach, even when the project necessitates otherwise.  How data is collected is key. ‘Traditional data collection methods (i.e. surveys, interviews, and focus groups), have shown limitations in their ability to collect quality data about female subjects and other marginalized groups, such as persons with disabilities. They have also been found to incorporate stereotypes and other sociocultural factors that induce biases such barriers raise valid concerns about construct and external validity and lead to incomplete or inaccurate information that obstructs both the formulation of evidence-based policies and the determination of the root causes of discrimination.’ 

Here comes a valid concern: how can we ensure development, if a) we have incomplete information about the people we aim to help; b) if we’re being biased against our target groups; or c) if we don’t have the necessary tools in place to help facilitate data collection for marginalized communities. If we want to create meaningful and inclusive data that will promote evidence-based policies, we need to move away from fixating on one identity as it can lead us to overlook other groups who may be marginalized or at risk of exclusion in a prospective project’s environment.

For data to be inclusive, stakeholders involved in the data collection process can take a couple of steps to ensure that no one is getting left behind including:

  1. Planning ahead of time;
  2. Disaggregating data according to the target group;
  3. Tailoring tools according to the group (i.e. accessibility audits, screening tools, i.e.); 
  4. Choosing a participatory approach for data collection. One way of doing this is to invite representatives of the target groups to participate in the design of the data collection process.  

Overall, in this day and age, data is the key to everything. It is necessary to have quality data to produce sound evidence-based policy. However, it is not enough to have quality data, it also has to be inclusive, actively making sure that biases and discrimination do not hinder the sanctity of data. Moreover, we need to ensure that this data will be used the way it has been intended to be used. In the right hands, the right data will help create the right policies to help foster a better environment for all. 

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