The Data Analysis Process
Nowadays, many people encounter many problems when analyzing data, and they analyze it incorrectly. In this article, I will talk about my experience in how data analysis should be done.
1-) Defining the question
· What Business Problem am I trying to solve ?
Data Analysts Need To Understand
- The Business
- The Business Goals
Data Analysts Instead Of
Why are we losing…
Try Asking
- Which factors are negatively impacting the customer experience ?
- How can we boost customer retention while minimizing costs ?
- Defining the objectives
Tools :
· Databox
· Dasheroo
Free Opensource :
· Grafana
2-) Collecting the Data
· Quantitative (Numeric) Data.
· Qualitative(Descriptive) Data.
Three Categories :
- First Party Data : data you have collected directly from customers.
For example : transactional tracking data.
- Second Party Data : first-party data of other organizations.
For example : website,app,social media act.
- Third Party Data : data collected and aggregated from numerous sources by a third-party organization.
For example : big data ,gather
Data Management Platform(DMP)
· Sofware that allows you to identify and aggregate data from numerous sources.
Tools :
- SaS
- Xplenty
Opensource Platform :
- Pimcore
3-) Cleaning the Data :
· good data 70 -90 % cleaned
Tools :
- python
- pandas
4-) Analyzing the data :
· univariate or bivariate analysis
· time series analysis
· regression analysis
Four Following the Categories Descriptive Analysis :
· descriptive analysis : identifies what has already happened.
· diagnostic analysis : focuses on understanding why something has happened.
· predictive analysis : allows you identify future trends based on historical data.
· prescriptive analysis : allows you to make recommendations for the future.
5-) Sharing your results :
· You should make presentation with use ;
Dashboard,reports,interactive visualizations
· Present in important for the projects,data analysis
No coding skills visualization tools
· Tableau
· Infogram
· Power BI
With coding skills visualization tools
Python Library include ;
· Seaborn
· Plotly
If you have any questions regarding this article or any thing in particular on data analytics. Send me message on twitter or LinkedIn.
Tolga Boroğlu