Your First 90 Days as a Data Analyst: Survival Guide (From Someone Who’s Been There) – Nic

The Reality Check

What you expected: - Building cool ML models - Creating beautiful dashboards - Impressing everyone with insights

The reality: - 60% cleaning messy data - 30% meetings and explaining basic concepts - 10% actual analysis

And that’s okay! Every data analyst goes through this.

Week 1-2: Onboarding

Your mission: Learn the business, not just the tools.

Do this: - [ ] Schedule 1:1s with key stakeholders - [ ] Ask for access to all data sources - [ ] Review past reports and dashboards - [ ] Document everything in a personal wiki - [ ] Ask “stupid” questions (no question is stupid)

Don’t do this: - ❌ Try to impress with complex analysis immediately - ❌ Criticize existing work - ❌ Say “this is easy” about anything

Key questions to ask: 1. “What are our top 3 business metrics?” 2. “What data sources do we use?” 3. “Who are the main stakeholders?” 4. “What are the biggest data challenges?” 5. “What does success look like in this role?”

Week 3-4: Quick Wins

Find one small problem and solve it well.

Good first projects: - Automate a manual report - Fix a broken dashboard - Clean up a messy dataset - Document an undocumented process

Bad first projects: - Rebuild entire data warehouse - Implement ML from scratch - Question executive strategy

Month 2-3: Build Relationships

Success in data = 50% technical + 50% relationships

Build alliances with: - Business stakeholders (understand their pain) - Engineers (they control data access) - Other analysts (learn from them) - Your manager (set clear expectations)

How to build trust: 1. Deliver on time, every time 2. Communicate proactively 3. Admit when you don’t know 4. Show business impact, not just technical prowess 5. Make others look good

Common Mistakes (Avoid These!)

Mistake 1: Perfectionism

Bad: Spending 2 weeks on perfect analysis
Good: Ship 80% solution in 3 days, iterate

Mistake 2: Jargon Overload

Bad: “The multivariate regression shows heteroscedasticity”
Good: “Sales are more unpredictable in certain regions”

Mistake 3: Analysis Paralysis

Bad: “I need more data before deciding”
Good: “Based on what we have, here’s my recommendation”

Mistake 4: Not Asking for Help

Bad: Struggling alone for days
Good: Ask after 30 minutes of trying

Mistake 5: Over-promising

Bad: “I can have this done by tomorrow”
Good: “I’ll need 3 days, but can give you preliminary findings tomorrow”

Essential Skills for First 90 Days

Technical (you probably know these): - SQL - Excel - Python/R - Tableau/Power BI

Soft skills (you probably underestimate these): - Translating business questions into data questions - Presenting to non-technical audiences - Managing stakeholder expectations - Prioritizing requests - Saying “no” diplomatically

How to Handle Common Situations

“Can you pull this data real quick?”

Don’t: Drop everything and do it
Do: “I can get this to you by [realistic time]. Is that okay? I’m currently working on [priority task].”

“Why don’t we have this data?”

Don’t: Blame IT/engineering
Do: “Great question! Let me look into what it would take to collect this. In the meantime, here’s similar data we do have…”

“This dashboard is wrong!”

Don’t: Get defensive
Do: “Thanks for catching this! Can you show me what you’re seeing? Let me investigate and get back to you by [time].”

“Make the numbers look better”

Don’t: Manipulate data
Do: “I can show different views of the data, but the underlying numbers are what they are. Here are some positive angles we can highlight…”

Your 30-60-90 Day Goals

Day 30: - [ ] Understand business and key metrics - [ ] Complete 2-3 small projects - [ ] Built relationships with key stakeholders - [ ] Documented common processes

Day 60: - [ ] Delivered one impactful project - [ ] Proactive analysis (not just reactive) - [ ] Identified process improvements - [ ] Comfortable with all data sources

Day 90: - [ ] Seen as reliable team member - [ ] Driving 1-2 strategic initiatives - [ ] Mentoring newer team members - [ ] Planning next career step

What Good Performance Looks Like

Your manager cares about: 1. Reliability: Do you deliver on time? 2. Quality: Is your work accurate? 3. Communication: Do stakeholders understand you? 4. Initiative: Do you find problems to solve? 5. Growth: Are you learning and improving?

NOT: - How many models you built - How complex your code is - How many tools you know

Red Flags to Watch For

Company red flags: - No clear data strategy - Analysts treated as report monkeys - Data quality is terrible and nobody cares - No investment in tools/training - Stakeholders ignore all analysis

If you see these, start planning exit in 12-18 months

Resources for Success

Read these books: 1. “The First 90 Days” - Michael Watkins 2. “Storytelling with Data” - Cole Nussbaumer Knaflic 3. “How to Win Friends and Influence People” - Dale Carnegie

Follow these communities: - r/datascience - r/businessintelligence - DataTalks.Club Slack

Find a mentor: - Internal: Senior analyst on your team - External: LinkedIn connections, online communities

Take Care of Yourself

Avoid burnout: - Set boundaries (no 10pm Slack messages) - Take lunch breaks - Use your PTO - Exercise and sleep - Have life outside work

Remember: This is a marathon, not a sprint.

Final Advice

From someone 8 years in:

You’ll feel like an impostor. Everyone does. It goes away after ~6 months.
Business context > technical skills. Understanding why matters more than how.
Your value is insights, not code. Nobody cares about your beautiful code if it doesn’t drive decisions.
Build relationships early. They’ll save you later.
Document everything. Your future self will thank you.
Be patient with yourself. You’re learning a job AND a business.

You’ve got this!

Welcome to the data analytics family. 🎉

Tags: #Career #FirstJob #DataAnalyst #Advice #NewGrad #CareerDevelopment

--- title: "Your First 90 Days as a Data Analyst: Survival Guide (From Someone Who's Been There)" subtitle: "What They Don't Tell You About Your First Data Analytics Job" author: "Nichodemus Amollo" date: "2025-10-06" categories: [Career, First Job, Advice] --- ## The Reality Check **What you expected:** - Building cool ML models - Creating beautiful dashboards - Impressing everyone with insights **The reality:** - 60% cleaning messy data - 30% meetings and explaining basic concepts - 10% actual analysis **And that's okay!** Every data analyst goes through this. --- ## Week 1-2: Onboarding **Your mission:** Learn the business, not just the tools. **Do this:** - [ ] Schedule 1:1s with key stakeholders - [ ] Ask for access to all data sources - [ ] Review past reports and dashboards - [ ] Document everything in a personal wiki - [ ] Ask "stupid" questions (no question is stupid) **Don't do this:** - ❌ Try to impress with complex analysis immediately - ❌ Criticize existing work - ❌ Say "this is easy" about anything **Key questions to ask:** 1. "What are our top 3 business metrics?" 2. "What data sources do we use?" 3. "Who are the main stakeholders?" 4. "What are the biggest data challenges?" 5. "What does success look like in this role?" --- ## Week 3-4: Quick Wins **Find one small problem and solve it well.** **Good first projects:** - Automate a manual report - Fix a broken dashboard - Clean up a messy dataset - Document an undocumented process **Bad first projects:** - Rebuild entire data warehouse - Implement ML from scratch - Question executive strategy --- ## Month 2-3: Build Relationships **Success in data = 50% technical + 50% relationships** **Build alliances with:** - Business stakeholders (understand their pain) - Engineers (they control data access) - Other analysts (learn from them) - Your manager (set clear expectations) **How to build trust:** 1. Deliver on time, every time 2. Communicate proactively 3. Admit when you don't know 4. Show business impact, not just technical prowess 5. Make others look good --- ## Common Mistakes (Avoid These!) ### **Mistake 1: Perfectionism** **Bad:** Spending 2 weeks on perfect analysis **Good:** Ship 80% solution in 3 days, iterate ### **Mistake 2: Jargon Overload** **Bad:** "The multivariate regression shows heteroscedasticity" **Good:** "Sales are more unpredictable in certain regions" ### **Mistake 3: Analysis Paralysis** **Bad:** "I need more data before deciding" **Good:** "Based on what we have, here's my recommendation" ### **Mistake 4: Not Asking for Help** **Bad:** Struggling alone for days **Good:** Ask after 30 minutes of trying ### **Mistake 5: Over-promising** **Bad:** "I can have this done by tomorrow" **Good:** "I'll need 3 days, but can give you preliminary findings tomorrow" --- ## Essential Skills for First 90 Days **Technical (you probably know these):** - SQL - Excel - Python/R - Tableau/Power BI **Soft skills (you probably underestimate these):** - Translating business questions into data questions - Presenting to non-technical audiences - Managing stakeholder expectations - Prioritizing requests - Saying "no" diplomatically --- ## How to Handle Common Situations ### **"Can you pull this data real quick?"** **Don't:** Drop everything and do it **Do:** "I can get this to you by [realistic time]. Is that okay? I'm currently working on [priority task]." ### **"Why don't we have this data?"** **Don't:** Blame IT/engineering **Do:** "Great question! Let me look into what it would take to collect this. In the meantime, here's similar data we do have..." ### **"This dashboard is wrong!"** **Don't:** Get defensive **Do:** "Thanks for catching this! Can you show me what you're seeing? Let me investigate and get back to you by [time]." ### **"Make the numbers look better"** **Don't:** Manipulate data **Do:** "I can show different views of the data, but the underlying numbers are what they are. Here are some positive angles we can highlight..." --- ## Your 30-60-90 Day Goals **Day 30:** - [ ] Understand business and key metrics - [ ] Complete 2-3 small projects - [ ] Built relationships with key stakeholders - [ ] Documented common processes **Day 60:** - [ ] Delivered one impactful project - [ ] Proactive analysis (not just reactive) - [ ] Identified process improvements - [ ] Comfortable with all data sources **Day 90:** - [ ] Seen as reliable team member - [ ] Driving 1-2 strategic initiatives - [ ] Mentoring newer team members - [ ] Planning next career step --- ## What Good Performance Looks Like **Your manager cares about:** 1. **Reliability:** Do you deliver on time? 2. **Quality:** Is your work accurate? 3. **Communication:** Do stakeholders understand you? 4. **Initiative:** Do you find problems to solve? 5. **Growth:** Are you learning and improving? **NOT:** - How many models you built - How complex your code is - How many tools you know --- ## Red Flags to Watch For **Company red flags:** - No clear data strategy - Analysts treated as report monkeys - Data quality is terrible and nobody cares - No investment in tools/training - Stakeholders ignore all analysis **If you see these, start planning exit in 12-18 months** --- ## Resources for Success **Read these books:** 1. "The First 90 Days" - Michael Watkins 2. "Storytelling with Data" - Cole Nussbaumer Knaflic 3. "How to Win Friends and Influence People" - Dale Carnegie **Follow these communities:** - r/datascience - r/businessintelligence - DataTalks.Club Slack **Find a mentor:** - Internal: Senior analyst on your team - External: LinkedIn connections, online communities --- ## Take Care of Yourself **Avoid burnout:** - Set boundaries (no 10pm Slack messages) - Take lunch breaks - Use your PTO - Exercise and sleep - Have life outside work **Remember:** This is a marathon, not a sprint. --- ## Final Advice **From someone 8 years in:** 1. **You'll feel like an impostor.** Everyone does. It goes away after ~6 months. 2. **Business context > technical skills.** Understanding why matters more than how. 3. **Your value is insights, not code.** Nobody cares about your beautiful code if it doesn't drive decisions. 4. **Build relationships early.** They'll save you later. 5. **Document everything.** Your future self will thank you. 6. **Be patient with yourself.** You're learning a job AND a business. **You've got this!** Welcome to the data analytics family. 🎉 --- **Related Posts:** - [Ace Your Data Analyst Interview](../12-data-analyst-interview-guide/) - [Land a Remote Data Analyst Job](../13-remote-data-analyst-jobs/) - [LinkedIn for Data Analysts](../18-linkedin-for-data-analysts/) **Tags:** #Career #FirstJob #DataAnalyst #Advice #NewGrad #CareerDevelopment