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

· Freeboard

· Dashbuilder

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

- Salesforce

- Xplenty

Opensource Platform :

- Pimcore

3-) Cleaning the Data :

· good data 70 -90 % cleaned

Tools :

- openRefine

- python

- pandas

- data ladder

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

· Google charts

· Tableau

· Datawrapper

· Infogram

· Power BI

With coding skills visualization tools

Python Library include ;

· Seaborn

· Plotly

· Matplotlib

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