This article is especially for you:
- the marketer, who wants to learn how to analyze data so you can make the best decisions regarding marketing problems.
- the professional in any field, who is eager to explore data analysis to support your career.
- a junior data analyst looking to understand the workflow

Below are activities performed by Data Analyst in day-to-day job
- Problem
Identification
- Work with the relevant people to understand the business objectives and define what they are trying to achieve with the analysis.
- Take complex business questions and break them down into specific, measurable problems.
Collection
- Find the right data sources (like databases, APIs, spreadsheets).
- Get the raw data using tools like SQL, Python, or data pipelines.
Do you want to know data pipeline basic concept: read Data Pipelines in Data Analytics: A Beginner’s Guide
Cleaning & Preparation
- Handle
missing values, duplicates, and outliers. - Transform
data into a usable format (e.g., normalization, aggregation).
You can read Outliers in Data Analytics to understand what is outliers, why outliers matter, how to detect outliers, handle outliers, and when to keep outliers.
Data Analysis (EDA)
- Use
statistical methods and visualization to uncover patterns, trends, and
anomalies. - Generate
hypotheses to test during deeper analysis.
Analysis & Modeling
- Apply
statistical techniques (e.g., regression, clustering) or machine learning
models. - Validate
results for accuracy and reliability.
Generation
- Interpret
findings to answer the original business question. - Prioritize
actionable insights based on impact and feasibility.
Visualization & Reporting
- Create
dashboards, charts, or reports (using tools like Tableau, Power BI, or
Python libraries). - Highlight
key metrics and trends for stakeholders.
Communication
- Present
insights in non-technical language, aligning with business goals. - Address
questions and refine recommendations based on feedback.
Recommendations
- Propose
data-driven solutions (e.g., process optimizations, strategy changes). - Collaborate
with teams to implement changes.
& Iteration
- Track
the impact of implemented actions. - Refine
analyses as new data or requirements emerge.
Example in Marketing
Problem Identification:
- Marketing
team notices a 20% drop in website conversion rates.
Data Collection:
- Pull
data from Google Analytics, CRM (e.g., Salesforce), and ad platforms
(e.g., Google Ads).
Data Cleaning:
- Remove
bot traffic and incomplete user session records.
Bot traffic can skew analysis results, so it must be removed from the data.
EDA & Analysis:
- Conduct
funnel analysis to identify drop-off points. - Segment
users by device (mobile vs. desktop) and traffic source.
Insights:
- Mobile
users have a 40% higher bounce rate due to slow
page load times. - Paid
ads drive traffic but fail to convert due to irrelevant landing pages.
Visualization:
- Build
a Tableau dashboard showing conversion rates by device, traffic source,
and page performance metrics.
Communication & Action:
- Recommend
optimizing mobile site speed and redesigning ad landing pages. - Marketing
team A/B tests new mobile pages and retargets high-intent users.
Monitoring:
- Track
conversion rates weekly; mobile conversions improve by 15% post-optimization.
Documentation:
- Share
a final report detailing analysis steps, insights, and ROI of changes.
Collaboration:
- Work
with web developers and marketing teams to ensure alignment on fixes.
This end-to-end process turns raw data into measurable
business impact, ensuring analysts drive decisions rather than just reporting
numbers.
After reading the daily activities of a data analyst, are you interested and ready to become a data analyst?
If you're interested, I'd love to hear from you! Please share your thoughts and reasons for wanting to become a data analyst at the comment section. I'm here to support you on your journey and help you achieve your career goals.