Chapter 1: The Evolution of Data Education

As a data educator who bridges analytical and creative thought, I have spent over two decades in the technology sector and nearly 15 years guiding both novices and experienced individuals in computing concepts. My teaching has consistently focused on essential topics such as algorithm design, computational thinking, programming language similarities, effective coding practices, and fundamental data structures and algorithms. Given my passion for data, my sessions often delve into data modeling, entity-relationship diagrams, SQL, and the principles of data stewardship and integrity.

At the outset, my students posed many insightful inquiries concerning the mechanics of coding and database management—questions that were relatively straightforward to answer. Numerous textbooks, static resources, online tutorials, and YouTube videos effectively illustrated these concepts.

However, as my students grew more adept in their coding and data management skills, they began to ask more profound questions that were not easily found online. They started contemplating how their coding and data management activities influenced society. No longer were they content with merely producing lines of code and data models; they became increasingly aware of the widespread implications of their work on countless individuals. Their analytical reasoning began to intersect with their creative instincts, presenting a pivotal moment in their journey.

The tech community often faces a dilemma: should one adhere to the analytical path or embrace the creative one? There exists, however, a third option—remaining at the intersection of both.

I identify as an analytical thinker with a creative flair. In my role as a data enthusiast, I continually strive to reconcile the structured nature of computing with the dynamic landscape of the data industry. This intersection of analytical and creative thinking challenges the practicality of computing and data management theories, while also scrutinizing the risk mitigation strategies employed in data operations within organizations. The objective is to learn how to adapt our perspectives on algorithm-based outputs and the handling of data operations in the context of ongoing changes.

This means that the outputs generated by coded algorithms can sometimes become irrelevant as contexts evolve since the code was originally written. It may also mean recognizing that a previously deemed ethical data collection practice no longer aligns with an organization’s standards for responsible data use. For instance, data collection methods may inadvertently cause harm to particular communities.

The journey through this intersection of analytical and creative thought is not without challenges. With enough experience in coding or data management, one may uncover tech-related harms that arise from the scaling of algorithms and the mishandling of data, particularly affecting communities that have endured such issues for generations. The realization that there is a constantly moving target is daunting, especially as our understanding of context evolves with our knowledge.

This becomes even more pressing when we consider how we interact with technology. For example, when utilizing an online banking app, the developers gather data on user interactions. They analyze these insights to enhance user experiences, resulting in what we know as software updates. This cycle of downloading, using, and modifying is ongoing, indicating that we are integral to the evolving landscape of technology. Essentially, the iterative process of tech development serves as a model for incorporating ethical and responsible practices into data operations.

However, the tech community faces a significant challenge: measuring ethical and responsible practices in data operations. When designing and implementing a tech tool, its advantages are often assessed based on user patterns and frequency. The community has benefitted from the ability to rely on quantitative metrics as proxies for impact. In contrast, responsible data practices and the mitigation of algorithmic harms necessitate a focus on qualitative metrics. The quality of impact should be informed by consensus from diverse communities. If a community experiences harm, their response may be to disengage from that technology, leaving the tech community with little understanding of their absence—unless they have built genuine relationships over time, beyond a few Zoom calls, to devise effective harm reduction strategies.

For years, I felt isolated at this intersection of analytical and creative thought. Yet, an increasing number of tech professionals are now joining the mission to address algorithmic harms and promote equity in data operations. With sufficient financial support, we could achieve sustainable breakthroughs.