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

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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
I compiled a list of the practices and processes to make it easier for you to understand the step-by-step activities in analyzing data.
Practices & Processes Used by Data Analyst in Day-to-Day Jobs

Below are activities performed by Data Analyst in day-to-day job 

  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.
  • Data
    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


  • Data
    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.


  • Exploratory
    Data Analysis (EDA)

    • Use
      statistical methods and visualization to uncover patterns, trends, and
      anomalies.

    • Generate
      hypotheses to test during deeper analysis.

  • Data
    Analysis & Modeling

    • Apply
      statistical techniques (e.g., regression, clustering) or machine learning
      models.

    • Validate
      results for accuracy and reliability.

  • Insight
    Generation

    • Interpret
      findings to answer the original business question.

    • Prioritize
      actionable insights based on impact and feasibility.

  • Data
    Visualization & Reporting

    • Create
      dashboards, charts, or reports (using tools like Tableau, Power BI, or
      Python libraries).

    • Highlight
      key metrics and trends for stakeholders.

  • Stakeholder
    Communication

    • Present
      insights in non-technical language, aligning with business goals.

    • Address
      questions and refine recommendations based on feedback.

  • Actionable
    Recommendations

    • Propose
      data-driven solutions (e.g., process optimizations, strategy changes).

    • Collaborate
      with teams to implement changes.

  • Monitoring
    & Iteration

    • Track
      the impact of implemented actions.

    • Refine
      analyses as new data or requirements emerge.

    Example in Marketing

    Below are data analysis process example in Marketing Department:
    Start with a problem website conversion rates drop, and you as an analyst is asked to know why it drops and suggest what actions to be performed to solve the problem (increase the conversion rates).

    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.

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