data analysis person

Define the Goal / Question

Start by identifying the problem you want to solve or the insight you need.

Example: “What are the top 5 products driving sales this year?”

2. Collect the Data

Gather raw data from different sources (databases, spreadsheets, APIs, surveys, sensors, etc.).

Example: Sales data from Excel, customer info from a CRM, website traffic from Google Analytics.

3. Clean the Data

Raw data often contains errors, duplicates, or missing values. Cleaning ensures accuracy.

Steps include:

Removing duplicates

Handling missing values (filling, removing, or estimating)

Correcting inconsistencies (e.g., “USA” vs “U.S.”)

4. Explore the Data (EDA – Exploratory Data Analysis)

Use statistics and visuals to understand the data.

Examples:

Summary statistics (mean, median, standard deviation)

Visualizations (charts, histograms, box plots)

Detecting outliers or trends

5. Transform & Model the Data

Depending on the goal:

Aggregate (e.g., sum of sales per month)

Create new features (e.g., profit = revenue – cost)

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

6. Interpret the Results

Translate the findings into meaningful insights.

Example: “80% of revenue comes from 20% of customers” (Pareto Principle).

7. Communicate the Insights

Present results clearly with dashboards, reports, or presentations.

Use charts, graphs, or storytelling to make it easy to understand.

Tools: Power BI, Tableau, Excel, Python, R, etc.

8. Make Decisions & Take Action

The ultimate goal of data analysis is action.

Example: “Focus marketing budget on top 5 products to increase sales efficiency.”

In short:

Data analysis = Ask → Collect → Clean → Explore → Model → Interpret → Present → Act