By Saeed Mirshekari
May 1, 2025
The Importance of Communication in Data Science
Data Science is more than just code, models, and metrics. At its heart, it's about solving real problems through people. And without communication, people break down into silos, and so does everything else.
Introduction
In the era of machine learning, big data, and artificial intelligence, it’s easy to think that technical prowess is the only currency that matters in Data Science. And while technical competence is crucial, an underrated and often under-discussed skill—communication—is arguably even more critical for long-term success.
Without communication, data scientists operate in silos. When leadership fails to foster dialogue and instead focuses on running a weekly performance act rather than building a connective tissue within the team, the entire data function begins to rot from the inside. This blog delves deep into how a lack of communication undermines people, promotes dysfunction, and sabotages the true potential of data teams.
Communication Is Not Optional
At its core, communication in Data Science is about alignment—understanding what problem is being solved, for whom, and why. Whether you're an entry-level data analyst or a senior ML engineer, you need to:
- Communicate your assumptions
- Understand stakeholder goals
- Translate technical outcomes into actionable insights
- Align with engineers, product managers, and business leads
When this communication is missing, the results can be catastrophic: irrelevant models, misunderstood requirements, missed deadlines, and complete misalignment between business and data teams.
The Silo Effect: A Symptom of Broken Communication
Silos occur when departments or individuals fail to share information, collaborate, or see the bigger picture. In Data Science, this might look like:
- Data engineers building pipelines without knowing the downstream use
- Data scientists working on models no one will implement
- Analysts generating dashboards no one understands
- Business teams making decisions that ignore available insights
Silos are rarely intentional. They are usually the byproduct of poor communication systems, lack of trust, and absentee leadership.
But here's the twist: in many organizations, these silos are perpetuated—not accidental—due to the way leadership performs their roles.
The Leadership Illusion: Performing Instead of Leading
In many tech teams, leadership inadvertently (or sometimes deliberately) focuses on optics rather than substance. Weekly standups become theatre. Progress updates are show-and-tell sessions meant to impress, not connect. Town halls are one-way monologues, not interactive dialogues.
The consequences are subtle but destructive:
- People feel unseen and unheard.
- Ideas remain unspoken or shot down early.
- Trust erodes.
- Team members stop talking to each other unless they have to.
Instead of building a culture of collaboration, leaders end up building a stage. Everyone plays their part—until burnout, resignation, or quiet quitting takes over.
Real-World Consequences: When Communication Fails
Let’s walk through a fictional but realistic scenario:
Scene 1: The Quiet Modeler
Sarah, a brilliant data scientist, builds a churn prediction model with 95% AUC. But she never asked marketing how they define "churn." Turns out, they care about users who unsubscribe after the free trial, not during.
Her model is technically sound—and completely useless.
Scene 2: The Disconnected Data Engineer
James sets up an ETL pipeline that pushes customer interaction data into a Snowflake table. But he doesn’t know that the analytics team needs this data in a flattened format. They spend weeks transforming the data manually, duplicating effort.
Why did this happen? No cross-functional meeting. No communication.
Scene 3: The Overconfident Manager
Linda, the analytics manager, updates leadership every week with a colorful dashboard. "We’re making great progress," she says. But internally, her team is demoralized, confused, and isolated. There’s no regular 1:1 communication. No shared goals. Only optics.
Eventually, two senior analysts leave. The remaining team stops raising concerns. Projects stall. Linda wonders why.
Communication as an Antidote
The solution to these problems is simple in theory, but hard in practice: cultivate meaningful communication.
Here’s how:
1. Intentional Leadership Conversations
Leadership must engage in active listening and open dialogue. That means not just asking for updates, but understanding roadblocks, motivations, and emotions. Leaders should:
- Have regular 1:1s with team members
- Foster psychological safety in meetings
- Make decisions collaboratively when possible
2. Cross-Functional Alignment Rituals
Hold non-performative rituals like:
- Project kickoff meetings with all stakeholders
- Shared documentation for business definitions
- Joint sprint planning sessions with product, engineering, and data
The point isn’t to add more meetings—it’s to replace empty meetings with real communication.
3. Transparent Decision-Making
Make it clear why a project was prioritized, paused, or canceled. Share metrics that matter, not just feel-good numbers.
When communication is clear, teams may not always agree—but they will always understand.
Psychological Impact of Communication Breakdown
Poor communication is not just inefficient—it’s emotionally draining.
When people feel unheard:
- Morale plummets
- Creativity dies
- Confidence shrinks
- Innovation stalls
In contrast, when communication flows:
- Team members feel valued
- Ideas are challenged respectfully
- Risks are flagged early
- Success is shared
Communication isn’t soft. It’s a strategic imperative.
Communication ≠ Presentation
Many confuse communication with presentation. The weekly "update" slide deck, the polished demo, the newsletter that no one reads.
That’s not communication. That’s broadcasting.
True communication is two-way, messy, and dynamic. It involves:
- Asking the right questions
- Giving and receiving feedback
- Resolving ambiguity
- Clarifying intent
Good data scientists are not just builders—they are translators, negotiators, and mediators.
Cultivating a Culture of Conversation
How do you go from silence to connection?
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Model Vulnerability
Leadership must admit what they don’t know. That opens the door for others to share.
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Celebrate Questions, Not Just Answers
Reward curiosity. Applaud people for asking “why?” and “what if?”—not just for delivering.
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Create Feedback Loops
Build in mechanisms for continuous feedback, both upward and lateral.
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Document and Share
Communication doesn’t always mean meetings. It can be a shared Notion page, a Slack thread, or a recorded walkthrough. The point is: make it accessible.
Let’s Stop Running a Show
Here’s the core issue: too many teams operate like they’re running a show.
Every week is a sprint to prepare a deck, polish a chart, or demo a feature for leadership. The reward system is based on what looks good, not what is good.
And in the rush to perform, we stop asking: “Are we actually talking to each other?”
Great data teams don’t need to perform every week. They need to connect. They need space to:
- Think
- Disagree
- Align
- Refocus
Only then can they unlock their true potential.
Conclusion: Communication Is the Infrastructure
In Data Science, communication is not a cherry on top. It’s the infrastructure that everything else is built on. Without it, even the smartest teams become ineffective. Even the best models become useless.
If you're a leader, ask yourself:
- Do my people talk to me openly?
- Do they talk to each other?
- Are we aligned on what we’re solving and why?
If the answer is no, start there.
In a world obsessed with dashboards and deliverables, don’t forget the one thing that makes all the difference:
Talk to your people. Listen to your people. Make it safe to do both.