We live in an uncertain world. Facing an ongoing global pandemic, worsening climate change, persistent threats to human rights, and the more mundane uncertainties of our day-to-day lives, we try to use data to make sense of it all. Relying on data to guide decisions can feel safe. 

But you might not be able to trust your data-driven decisions if data is missing from your data analysis process. If you can identify and address gaps in your data analysis process, you can reduce the bias introduced by missing data in your data-driven decisions, regaining your confidence and certainty while ensuring you limit possible harm. 

This is post 1 of 8 in a series about how missing data can negatively affect a data analysis process. 

In this post, I’ll cover:

• What is missing data?
• Why missing data matters
• What’s missing from all data-driven decisions
• The stages of a data analysis process

I hope this series inspires you and prepares you to take action to address bias introduced by missing data in your own data analysis processes. At the very least, I hope you gain a new perspective when evaluating your data-driven decisions, the success of data analysis processes, and how you frame a data analysis process from the start.

What is missing data?

Data can go missing in many ways. If you’re not collecting data, or don’t have access to some kinds of data, or if you can’t use existing data for a specific data analysis process—that data is missing from your analysis process. 

Other data might not be accessible to you for other reasons. Throughout this series, I’ll use the term “missing data” to refer to both data that does not exist, data that you do not have access to, and data that is obscured by your analysis process—effectively missing, even if not literally gone.

Why missing data matters

Missing data matters because it can easily introduce bias into the results of a data analysis process. Biased data analysis is often framed in the context of machine learning models, training datasets, or inscrutable and biased algorithms leading to biased decisions.

But you can draw biased and inaccurate conclusions from any data analysis process, regardless of whether machine learning or artificial intelligence is involved. As Meg Miller makes clear in her essay Finding the Blank Spots in Data for Eye on Design, “Artists and designers are working to address a major problem for marginalized communities in the data economy: “If the data does not exist, you do not exist.””. And that’s just one part of why missing data matters.

You can identify the possible biases in your decisions if you can identify the gaps in your data and data analysis process. And if you can recognize those possible biases, you can do something to mitigate them. But first we need to acknowledge what’s missing from every data-driven decision.

What’s missing from all data-driven decisions?

It feels safe to make a data-driven decision. You’ve performed a data analysis process and have a list of results matched up with objectives that you want to achieve. It’s easy to equate data with neutral facts. But we can’t actually use data for every decision, and we can’t rely only on data for a decision-making process. Data can’t capture the entirety of an experience—it’s inherently incomplete.

Data only represents what can be quantified. Howard Zinn writes about the incompleteness of data in representing the horrors of slavery in A People’s History of the United States:

“Economists or cliometricians (statistical historians) have tried to assess slavery by estimating how much money was spent on slaves for food and medical care. But can this describe the reality of slavery as it as to a human being who lived in side it? Are the conditions of slavery as important as the existence of slavery?” “But can statistics record what it meant for families to be torn apart, when a master, for profit, sold a husband or a wife, a son or a daughter?”  (pg 172, emphasis original).

Statistical historians and others can attempt to quantify the effects of slavery based on the records available to them, but parts of that story can never be quantified. The parts that can’t be quantified must be told, and must be considered when creating a historical record and of course, in deciding whose story gets told and how.

What data is available, and from whom, represents an implicit value and power structure in society as well. If data has been collected about something, and made available to others, then that information must be important—whether to an organization, a society, a government, or a world—and the keepers of the data had the privilege and the power to maintain it and make it available after it was collected. 

This power structure, this value structure, and the limitations of data alone when making decisions are crucial to consider in this era of seemingly-objective data-driven decisions. Because data alone isn’t enough to capture the reality of a situation, it isn’t enough to drive the decisions you make in our uncertain world. And that’s only the beginning of how missing data can affect decisions.

Data can go missing at any stage of the data analysis process

It’s easy to consider missing data as solely a data collection problem—if the dataset existed, or new data was collected, no data would be missing and so we can make better data-driven decisions. In fact, avoiding missing data when you’re collecting it is just one way to reduce bias in your data-driven decisions—it’s far from the only way.

Data can go missing at any stage of the data analysis process and bias your resulting decisions. Each post in this series addresses a different stage of the process.

1. 🗣 Make a decision based on the results of the data analysis. Decide with the data: How missing data biases data-driven decisions.

2. 📋 Communicate the results of the data analysis. Communicate the data: How missing data biases data-driven decisions.

3. 📊 Visualize the data to represent the answers to your questions. Visualize the data: How missing data biases data-driven decisions.

4. 🔎 Analyze the data to answer your questions. Analyze the data: How missing data biases data-driven decisions.

5. 🗂 Manage the data that you’ve collected to make it easier to analyze. Manage the data: How missing data biases data-driven decisions.

6. 🗄 Collect the data you need to answer the questions you’ve defined. Collect the data: How missing data biases data-driven decisions.

7. 🙋🏻‍♀️ Define the question that you want to ask the data. Define the question: How missing data biases data-driven decisions.

In each post, I’ll discuss real world examples of how data can go missing, and what you can do about it!