I discovered data visualization in undergrad while studying cases like the Three Mile Island nuclear accident<\/a>, where poor information design contributed to near or actual harm. It was one of the first moments in engineering where my ears perked up, especially around how data visualization bridges the analytical, creative, and human.<\/p>\n\n\n\n My early roles in quality improvement in hospitals only deepened that passion. I was fortunate to work alongside clinicians, designers, and researchers who introduced me to co-design methods, the importance of evaluation, and reframed users as collaborators.<\/p>\n\n\n\n Eventually, I landed my first role on an analytics team to support with BI design and development. However, it was during a time when my mom was battling appendix cancer, and I was living at home to support with caregiving. And my passion for this work quickly collided with the realities of how analytics gets done.<\/p>\n\n\n\n When my mom was admitted to palliative care a year later, it happened to line up closely with a due date for a \u201chigh-stakes\u201d report I was responsible for developing in Tableau, which I was learning how to use on my own. Because of the project\u2019s size and weight, and the responsibility I felt to deliver, I would work a full day, bring my laptop to hospice care, and continue working near her bedside.<\/p>\n\n\n\n I could have asked for an extension or support. However, analytics routinely feels like a pressure cooker, especially on \u201chigh-stakes\u201d projects. Plus, my qualifications were openly being questioned by others, I was identified as one of the “single points of failure”, and was also cautioned about the potential for blame if anything went wrong. Stepping away didn\u2019t truly feel like an option – it was easy to feel cornered. On top of that, I was in my twenties, with undiagnosed neurodiversity, zero concept of needs and boundaries, and overwhelmed, confused, and exhausted.<\/p>\n\n\n\n At my mom\u2019s funeral, a colleague asked when I might return to work, and relayed that people were getting anxious about report delivery. <\/p>\n\n\n\n Her funeral was on a Friday. I went back to work on Monday. I finished developing and testing the report\u2014and from what I remember, everyone received it when expected. <\/p>\n\n\n\n I\u2019m not sure if it felt like \u201ca win\u201d for me. It made me question, how are analytics workers perceived? And, what did I just do? <\/p>\n\n\n\n The elements of that experience were not isolated to any individuals, teams, or organization, but recurring threads I\u2019ve encountered and witnessed time and time again as my career in analytics has progressed. <\/p>\n\n\n\n Fast-forward many years later to a more recent contract, again as a BI designer and developer, where layers of challenging, but common, systemic pressures rattled my nervous system. I eventually had a major Autistic shutdown<\/a> (an involuntary neurological response to sensory overload), and needed to leave.<\/p>\n\n\n\n I\u2019ve listed some of the challenges below – do any of these resonate, neurodiverse or not?<\/p>\n\n\n\n Structural<\/strong><\/p>\n\n\n\n Cultural\/interpersonal<\/strong><\/p>\n\n\n\n Environmental<\/strong><\/p>\n\n\n\n For this experience, I was able to be fully remote. From research and my own previous jobs, I know several factors that can be challenging with in-office environments for Autistic workers. These can include adherence to a 9 \u2013 5 schedule, open concept office spaces with bright lighting and noise, and pressure to attend social functions. <\/p>\n\n\n\n When layers like these start to compound, my nervous system gets flooded with input and demands, and can\u2019t catch up. I get stuck in survival mode, and eventually break or shut down. Autistic burnout<\/a> can look very different from our typical understanding of burnout, and recovery can require weeks to months (or even years) of deliberate care. Just to note, other Autistic people may have different experiences, supportive conditions, and responses – these are just my own. aces with bright lighting and excessive noise, constant interruptions, and pressure to attend social functions.<\/p>\n\n\n At this point, I\u2019m afraid of returning to analytics as it currently exists. It can feel inaccessible to neurodivergence, and unforgiving to responsibilities outside of work. But am I the only one who feels this way?\u00a0<\/p>\n\n\n\n From what I\u2019m seeing across industry research, I don\u2019t think I\u2019m the only one finding this field challenging and unsustainable. Here are some highlights:<\/p>\n\n\n\n Data teams are already overcapacity, despite ever-growing demands<\/strong><\/p>\n\n\n\n In a 2023 survey<\/a> of more than 900 data team practitioners and leaders across the United States and the United Kingdom, 84% said their workload exceeded their capacity, and 90% reported that it had increased from the year prior.<\/p>\n\n\n\n The vast majority of data engineering teams feel burnt out <\/strong> \u201cWhen a deliverable is met, data engineers are considered heroes. However, \u201cheroism\u201d is a trap. Heroes give up work-life balance. Yesterday\u2019s heroes are quickly forgotten when there is a new deliverable to meet.\u201d<\/em><\/p>\n\n\n\n \u2014 2021 Data Engineering Survey: Burned-out Data Engineers Call for DataOps<\/a><\/p>\n<\/blockquote>\n\n\n\n Analytics products aren\u2019t sufficiently supporting our end users<\/strong><\/p>\n\n\n\n In a 2025 survey<\/a> of more than 200 product leaders, data teams, and executives, 40% said their data doesn\u2019t support decision-making sufficiently, 51% can\u2019t meaningfully interact with the data provided, and 29% export data to spreadsheets daily. <\/p>\n\n\n\n Findings I\u2019m not surprised to see, considering how we\u2019re expected to work. From a design perspective, it can be a struggle to carve time and space to sufficiently understand the data and users before I\u2019m asked to quickly turnaround a prototype. Plus, post-launch follow-up and evaluations don\u2019t seem to gain traction before we\u2019re onto the next priority.<\/p>\n\n\n\n We\u2019re hoping AI will save us<\/strong><\/p>\n\n\n\n In the same survey as above, 75% believe AI-powered analytics might finally help uncover value buried in data. But in a new study by MIT and Snowflake<\/a>, 77% of data engineering teams are finding their workloads even heavier, despite AI integration. <\/p>\n\n\n\n While AI has the potential to streamline tasks and improve product quality, a cracked foundation could limit its impact, and cause further complexity and burnout. <\/p>\n\n\n In analytics, we often point to diversity as evidence that we\u2019re on the right path. When concerns are raised about how pressures, workloads, and expectations may weigh differently across identities, they can be dismissed with the reassurance that our workplaces are \u201calready pretty diverse.\u201d<\/p>\n\n\n\n That might be partially true in terms of representation. A recent study by Statistics Canada<\/a> showed that 60% of data scientists (one of many roles within analytics) are immigrants, with the majority of first languages being neither English nor French. About one-third of data scientists identify as women+ (defined by the study to include \u201cwomen and some non-binary people\u201d). <\/p>\n\n\n\n It is important to recognize that diversity does not always equal inclusion. In other pieces published by Nightengale, Catherine D\u2019Ignazio and Lauren F. Klein, authors of Data Feminism<\/em><\/a>, speak to how racism and sexism are imbued in the end to end data lifecycle, reinforced by structures of power, and ultimately surfacing in our products. An online poll by Christian Osborne<\/a> showed that 90% of respondents said that they\u2019ve experienced microaggressions at work, which can cause emotional and psychological harm, decrease job satisfaction, and increase turnover. <\/p>\n\n\n\n We can also be sensitive to trends across all workplaces. In 2024, the Diversity Institute<\/a>, Future Skills Centre<\/a>, and Environics Institute for Survey Research<\/a> published a Canada-wide study<\/a> on gender, diversity, and discrimination at work. The survey reinforces that workplace discrimination is more likely to be experienced by racialized and Indigenous peoples, women, persons with disabilities, 2SLGBTQ+ individuals, and young adults. It is crucial to recognize that intersectionality amplifies these effects, with racialized and Indigenous people more likely to face multiple forms of discrimination, especially related to gender, age, and disability. And, those who reported experiencing discrimination also reported poorer mental health. <\/p>\n\n\n\n Even with diversity, we still need to ensure that our analytics workplaces make everyone feel safe, healthy, empowered, and valued. Diversity, equity, and inclusion (DEI) programming remains urgent and necessary, and should not be deprioritized or defunded. In the systemic pressures previously discussed, I wonder how these are felt across different identities. For example – what are the experiences of a woman in a leadership role, a recent immigrant who is supporting family both at home and overseas, or a new grad with one or more disabilities – are they really all the same?<\/p>\n\n\n\n The tendency for analytics workplaces to be top-down, reactive, chaotic, transactional, and overburdening clearly isn\u2019t working\u2014not for our people, and not for our products. We\u2019ve got more than enough burned out workers and leaders, and more than enough underused products to prove it. And I\u2019m only seeing signs that analytics (and tech more broadly) might be becoming even more unsustainable\u2014from 996 culture<\/a>, mandatory RTO policies<\/a>, pressure to upskill for AI<\/a>, low data readiness for AI<\/a>, to the defunding of DEI<\/a>.<\/p>\n\n\n\n I think systemic change (or a reset button) is required to humanize our approach to analytics work. The shift has to include not only analytics teams, but also the ecosystems that rely on us. <\/p>\n\n\n\n For example, earlier this year, the Canadian Occupational Health and Safety Magazine suggested that workplaces adopt a trauma informed care<\/a> (TIC) approach to work. This approach places safety, trust, and empowerment at the center, and recognizes that many of us have experienced trauma\u2014trauma that workplaces can trigger, perpetuate, or even create. Normalized approaches to analytics work can actually be quite harmful, like unpredictability, constant urgency, ambiguity, and the erosion of autonomy. <\/p>\n\n\n\n The article references the six pillars of TIC laid out by the Substance Abuse and Mental Health Services Administration<\/a> (SAMHSA), and cites research that shows its positive impacts to employee well-being, satisfaction, retention, operational functionality and effectiveness, and cost efficiency. <\/p>\n\n\n I have listed the six pillars from SAMHSA below, along with my attempt at (extremely) high-level and brief descriptions tailored to those of us working in analytics. I am still on my own learning journey. <\/p>\n\n\n\n Integrating TIC is a deep, long-term commitment that isn\u2019t about checking boxes, a quick workshop, or adding a few supportive practices. It requires honest and sustained cultural and structural assessments, learning, planning, and shifts<\/a>, and a more balanced distribution of power<\/a>. But with a new reframing, maybe we can begin to view:<\/p>\n\n\n\n This isn\u2019t meant to be a silver bullet, and I know there are many other challenges in analytics that involve data, tools, processes, and more. It may also seem overly idealistic in our current systems. But I feel like tech is at a precipice, especially in the rush toward AI creation and adoption. We\u2019re already seeing increased exploitation of labour<\/a> and the environment<\/a> in the AI space, without consideration of short or long term consequences. If we don\u2019t care to stop and make our systems more sustainable, ethical, equitable, and accessible now\u2014what does this mean for our (very near) future? <\/p>\n\n\n\n I\u2019m curious about what a different approach to analytics work might bring:<\/p>\n\n\n\nDeadlines versus trauma<\/h2>\n\n\n\n
Breaking points<\/strong><\/h2>\n\n\n\n
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Ripple effects: Tired teams, leaders, products, and users<\/strong><\/h2>\n\n\n\n
Another survey<\/a> of over 600 data engineers and managers found that nearly all of them (97%) reported feeling burnt out, primarily due to time spent fixing errors, maintaining data pipelines, and constantly playing catch-up with stakeholder requests. Nearly 90% reported frequent work-life disruptions. 70% said they were likely to leave their current company within a year, and almost 80% were considering leaving the field altogether.<\/p>\n\n\n![\"\"](\"https:\/\/i0.wp.com\/nightingaledvs.com\/wp-content\/uploads\/2025\/11\/Fig-3.-Experiences-of-data-engineers-1024x512.png?resize=720%2C360&ssl=1\")
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Diverse does not equal inclusive<\/strong><\/h2>\n\n\n\n
What if we worked differently, and prioritized people first?<\/strong><\/h3>\n\n\n\n
![\"\"](\"https:\/\/i0.wp.com\/dvsnightingale.wpenginepowered.com\/wp-content\/uploads\/2025\/11\/Fig-5.-TIC-pillars-1024x410.png?resize=720%2C288&ssl=1\")
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