Analytics Products Will Never Be Truly Human-Centered Until the Workplaces Behind Them Are

I’ve been really excited to see a shift in analytics and business intelligence around more integration of human-centred design, ethics, and accessibility. I learn something new almost every day. However, I feel something is still missing from these conversations: whether these are being considered beyond the interface, and in our workplaces too. 

From what I’ve experienced and witnessed working in analytics, I don’t think I see the same strides in how analytics work gets done. For example, how many of us have kept producing while our lives were going through upheaval? How many have wondered if we can stay in our jobs, or even careers, because the way we’re expected to work is unsustainable to our well-being and personal lives? What might happen if we approach our work in a way that decenters speed, volume, and heroics, and recenters all humans involved?

My early days

I discovered data visualization in undergrad while studying cases like the Three Mile Island nuclear accident, 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.

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.

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.

Deadlines versus trauma

When my mom was admitted to palliative care a year later, it happened to line up closely with a due date for a “high-stakes” report I was responsible for developing in Tableau, which I was learning how to use on my own. Because of the project’s 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.

I could have asked for an extension or support. However, analytics routinely feels like a pressure cooker, especially on “high-stakes” 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’t 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.

At my mom’s funeral, a colleague asked when I might return to work, and relayed that people were getting anxious about report delivery. 

Her funeral was on a Friday. I went back to work on Monday. I finished developing and testing the report—and from what I remember, everyone received it when expected. 

I’m not sure if it felt like “a win” for me. It made me question, how are analytics workers perceived? And, what did I just do? 

Breaking points

The elements of that experience were not isolated to any individuals, teams, or organization, but recurring threads I’ve encountered and witnessed time and time again as my career in analytics has progressed. 

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 (an involuntary neurological response to sensory overload), and needed to leave.

I’ve listed some of the challenges below – do any of these resonate, neurodiverse or not?

Structural

• Unclear or missing roles, scoping, processes, and standards
• Unrealistic expectations around task complexity and timelines
• Unpredictability requiring frequent context switching and quick adaptation to change

Cultural/interpersonal

• Persistent state of urgency, with hustle and “just get it done” culture
• Lack of autonomy and space, with ongoing progress checks and pressure points
• Repeatedly having to overexplain, raise concerns, and justify boundaries 
• Interdepartmental conflict and tension
• Feeling held responsible for the success of the project

Environmental

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 – 5 schedule, open concept office spaces with bright lighting and noise, and pressure to attend social functions. 

When layers like these start to compound, my nervous system gets flooded with input and demands, and can’t catch up. I get stuck in survival mode, and eventually break or shut down. Autistic burnout 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.

At this point, I’m 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? 

Ripple effects: Tired teams, leaders, products, and users

From what I’m seeing across industry research, I don’t think I’m the only one finding this field challenging and unsustainable. Here are some highlights:

Data teams are already overcapacity, despite ever-growing demands

In a 2023 survey 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.

The vast majority of data engineering teams feel burnt out 
Another survey 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.

“When a deliverable is met, data engineers are considered heroes. However, “heroism” is a trap. Heroes give up work-life balance. Yesterday’s heroes are quickly forgotten when there is a new deliverable to meet.”

— 2021 Data Engineering Survey: Burned-out Data Engineers Call for DataOps

Analytics products aren’t sufficiently supporting our end users

In a 2025 survey of more than 200 product leaders, data teams, and executives, 40% said their data doesn’t support decision-making sufficiently, 51% can’t meaningfully interact with the data provided, and 29% export data to spreadsheets daily. 

Findings I’m not surprised to see, considering how we’re 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’m asked to quickly turnaround a prototype. Plus, post-launch follow-up and evaluations don’t seem to gain traction before we’re onto the next priority.

We’re hoping AI will save us

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, 77% of data engineering teams are finding their workloads even heavier, despite AI integration. 

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. 

Diverse does not equal inclusive

In analytics, we often point to diversity as evidence that we’re 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 “already pretty diverse.”

That might be partially true in terms of representation. A recent study by Statistics Canada 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 “women and some non-binary people”). 

It is important to recognize that diversity does not always equal inclusion. In other pieces published by Nightengale, Catherine D’Ignazio and Lauren F. Klein, authors of Data Feminism, 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 showed that 90% of respondents said that they’ve experienced microaggressions at work, which can cause emotional and psychological harm, decrease job satisfaction, and increase turnover. 

We can also be sensitive to trends across all workplaces. In 2024, the Diversity Institute, Future Skills Centre, and Environics Institute for Survey Research published a Canada-wide study 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. 

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?

What if we worked differently, and prioritized people first?

The tendency for analytics workplaces to be top-down, reactive, chaotic, transactional, and overburdening clearly isn’t working—not for our people, and not for our products. We’ve got more than enough burned out workers and leaders, and more than enough underused products to prove it. And I’m only seeing signs that analytics (and tech more broadly) might be becoming even more unsustainable—from 996 culture, mandatory RTO policies, pressure to upskill for AI, low data readiness for AI, to the defunding of DEI.

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. 

For example, earlier this year, the Canadian Occupational Health and Safety Magazine suggested that workplaces adopt a trauma informed care (TIC) approach to work. This approach places safety, trust, and empowerment at the center, and recognizes that many of us have experienced trauma—trauma 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. 

The article references the six pillars of TIC laid out by the Substance Abuse and Mental Health Services Administration (SAMHSA), and cites research that shows its positive impacts to employee well-being, satisfaction, retention, operational functionality and effectiveness, and cost efficiency. 

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. 

1. Safety: Prioritize physical and psychological safety in all elements of the workplace. In analytics, this can mean that people are able to seek clarity, name concerns, and admit uncertainty without fear of punishment or loss of credibility. It can also mean that we respect limits on things like working hours, cognitive load, personal space, and sensory needs.
2. Trustworthiness and Transparency: Build trust through consistent transparency around decisions, timelines, priorities, and changes. Clarity and predictability can reduce uncertainty, prevent reactivity, and stabilize teams.
3. Peer Support: Reduce isolation and barriers to connection to foster peer support within and across teams. This can allow for greater understanding across disciplines and parts of the organization, smoother workflows, supportive relationships, shared problem-solving, and better knowledge transfer.
4. Collaboration and Mutuality: Involve workers in decisions about policies, procedures, tools, standards, and more. Also, when business units and analytics teams better understand each other’s capacities, workflows, complexities, timelines, needs, etc., collaboration might be more smooth, respectful, and productive. 
5. Empowerment, Voice, and Choice: Choice and control are essential for trauma-impacted people. In analytics, empowerment could mean giving workers more agency in defining things like their own scope, workflows, documentation, timelines, training needs, and work arrangements.
6. Cultural, Historical, and Gender Sensitivity: Address systemic inequities and promote diversity, equity, and inclusion. Design systems from the start to acknowledge, understand, and respect differences. Do not rely on people to constantly identify, overexplain, or advocate for their needs.

Integrating TIC is a deep, long-term commitment that isn’t 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, and a more balanced distribution of power. But with a new reframing, maybe we can begin to view:

• Workers as human, collaborators, creators, and both autonomous and interdependent 
• Leaders as human, coordinators, facilitators, coaches, guides, and anchors
• Work as collective, learning, growth-oriented, and sustainable 
• Technology as supportive, enhancing, synchronizing, and shared 

This isn’t 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’re already seeing increased exploitation of labour and the environment in the AI space, without consideration of short or long term consequences. If we don’t care to stop and make our systems more sustainable, ethical, equitable, and accessible now—what does this mean for our (very near) future? 

I’m curious about what a different approach to analytics work might bring:

• Will we have the space to maintain our health, relationships, and lives outside of work?
• Will relationships within and between teams become more stable, empathetic, and productive—especially between analytics and business units?
• Will we have more space in between deliverables to recover, reflect, and refine our systems?
• Will our products become clearer, more cohesive, more aligned, actually used, and have impact?
• Will we feel safe and supported to show up at work in our own unique ways?