Practices & Processes Used by Data Analyst in Day-to-Day Jobs

Are you a marketer looking to make data-driven decisions? Are you a professional seeking to enhance your career through data analytics? Or a junior data analyst wanting to understand data analysis workflow from start to finish? If so, this article is for you.

Here, we’ll explore the core activities in data analysis, discuss popular frameworks, and walk through a marketing-focused example. By the end, you’ll have a clear roadmap to apply data analysis in your projects.

1. Introduction: Why Data Analysis Matters

Data analysis has become the backbone of decision-making across industries.

For marketers, data analysis can reveal insights about:

Campaign performance
Audience behavior
Overall strategy effectiveness

Professionals in any field can leverage data analytics to optimize processes and drive better outcomes. And for junior data analysts, understanding the “big picture” workflow sets the foundation for a successful career.

In today’s fast-paced business environment, organizations rely on data to:

Identify issues and opportunities in marketing, sales, operations, and more.
Forecast trends to stay ahead of the competition.
Optimize resources by understanding which strategies yield the best ROI.

2. Data Analyst in day-to-day job

Below is a step-by-step breakdown of the daily tasks a data analyst undertakes. These steps will help you structure your data analysis projects, whether you’re tackling a marketing question or another business challenge.

2.1 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.

2.2 Data Collection

Find the right data sources (like databases, APIs, spreadsheets).
Get the raw data using tools like SQL, Python, or data pipelines.
(If you want to know the data pipeline basic concept, read Data Pipelines in Data Analytics: A Beginner’s Guide)

2.3 Data Cleaning & Preparation

Data cleaning activities: 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.)

2.4 Exploratory Data Analysis (EDA)

Use statistical methods and visualization to uncover patterns, trends, and anomalies.
Generate hypotheses to test during deeper analysis.

2.5 Data Analysis & Modeling

Apply statistical techniques (e.g., regression, clustering) or machine learning models.
Validate results for accuracy and reliability.

2.6 Insight Generation

Interpret findings to answer the original business question.
Prioritize actionable insights based on impact and feasibility.

2.7 Data Visualization & Reporting

Create dashboards, charts, or reports (using tools like Tableau, Power BI, or Python libraries).
Highlight key metrics and trends for stakeholders.

2.8 Stakeholder Communication

Present insights in non-technical language, aligning with business goals.
Address questions and refine recommendations based on feedback.

2.9 Actionable Recommendations

Propose data-driven solutions (e.g., process optimizations, and strategy changes).
Collaborate with teams to implement changes.

2.10 Monitoring & Iteration

Track the impact of implemented actions.
Refine analyses as new data or requirements emerge.

3. Additional Information: Popular Data Analytics Frameworks

Several well-known frameworks illustrate how the data analysis process is cyclical and iterative. Here are three examples:

3.1 EMC's data analysis process

EMC Corporation's data analytics process is cyclical with six steps:

Discovery
Pre-processing data
Model planning
Model building
Communicate results
Operationalize

EMC Corporation is now Dell EMC. This model, created by David Dietrich, reflects the cyclical nature of typical business projects. The phases aren’t static milestones; each step connects and leads to the next and eventually repeats. (Additional info: Dell EMC is an American multinational corporation headquartered in Hopkinton, Massachusetts, and Round Rock, Texas, and is a subsidiary of Dell Technologies).

3.2 SAS's iterative process

An iterative data analysis process was created by a company called SAS, a leading data analytics solutions provider. It can be used to produce repeatable, reliable, and predictive results:

Ask
Prepare
Explore
Model
Implement
Act
Evaluate

The SAS model emphasizes the cyclical nature of their model by visualizing it as an infinity symbol. Its process has seven steps, many of which mirror the other models, like ask, prepare, model, and act. But this process is also a little different; it includes a step after the act phase designed to help analysts evaluate their solutions and potentially return to the asking phase again.

3.3 Project-based data analytics process

A project-based data analytics process has five simple steps:

Identifying the problem
Designing data requirements
Pre-processing data
Performing data analysis
Visualizing data

Vignesh Prajapati developed this data analytics project process. It doesn’t include the sixth phase or the act phase, but it still covers many of the same steps. It begins with identifying the problem, preparing and processing data before analysis, and ending with data visualization.

Source: Google Data Analytics Certification in Coursera Platform

4. Real-World Example: Marketing Website Conversion

Below are data analysis process examples in the Marketing Department:

Start with a problem website conversion rates drop, and you as an analyst are asked to know why it drops and suggest what actions to be performed to solve the problem (increase the conversion rates).

4.1 Problem Identification:
The marketing team notices a 20% drop in website conversion rates.

4.2 Data Collection:
Pull data from Google Analytics, CRM (e.g., Salesforce), and ad platforms (e.g., Google Ads).

4.3 Data Cleaning:

Remove bot traffic and incomplete user session records.
Bot traffic can skew analysis results, so it must be removed from the data.

4.4 EDA & Analysis:

Conduct funnel analysis to identify drop-off points.
Segment users by device (mobile vs. desktop) and traffic source.

4.5 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.

4.6 Visualization:
Build a Tableau dashboard showing conversion rates by device, traffic source, and page performance metrics.

4.7 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.

4.8 Monitoring:
Track conversion rates weekly; mobile conversions improve by 15% post-optimization.

4.9 Documentation:
Share a final report detailing analysis steps, insights, and ROI of changes.

4.10 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 about a data analyst's daily activities, are you interested in and ready to become one?

If you're interested, I'd love to hear from you! Please share your thoughts and reasons for wanting to become a data analyst in the comment section. I'm here to support you on your journey and help you achieve your career goals.

5. Conclusion

Why This Matters for Your Career?

Marketers: Make budget decisions confidently (e.g., “Which channels drive ROI?”).
Professionals: Enhance problem-solving skills across roles.
Junior Analysts: Master workflows to deliver stakeholder-ready insights.

P.S. For more hands-on practice, explore online courses (such as the Google Data Analytics Certification) or check out community forums where analysts share tips, case studies, and the latest industry trends.