This is my small project to explain how we can use SQL for data extraction and to improve our business errors with the help of data analysis. It has a good impact on the business and helps it to grow. I use mostly Postgres for data extraction. built-in tools help to improve visualization.

1. B Y S U M I T J A D H A V .
US IN G S Q L
DATA ANALYSIS

2. SKILLS
Data Analysis
Data
engineering
S Q L
Data
visualization
SUMIT JADHAV
Database management,
queries, and optimization
skills
Extracting insights from
complex datasets for
informed decisions
Transforming raw data
into valuable, actionable
information.
Conveying insights through
compelling, informative
graphical representations

3. DATASET
SUPERSTORE DATA
In today's rapidly evolving business landscape, a Superstore Giant recognizes the critical
importance of data analysis in gaining a competitive edge. They seek your expertise to navigate the
intricate maze of market dynamics. Your role involves not only deciphering which products resonate
most with customers but also identifying lucrative regions, optimizing product categories, and
honing in on the most profitable customer segments.
Through meticulous data analysis, you'll unearth valuable insights, transforming raw data into
actionable information. Your findings will drive strategic decisions, enabling the Superstore Giant to
fine-tune their approach, minimize risks, and maximize profitability. Your mastery of data analysis will
be the compass guiding this retail giant toward sustainable success in a dynamic market.

4. TOP 10 CITY BY PROFIT

5. TOP 10 CITY BY PROFIT

6. TOTAL SALES BY CATEGORY

7. TOTAL SALES BY CATEGORY

8. TOTAL PROFIT BY SUB CATEGORY

9. TOTAL PROFIT BY CATEGORY

10. TOTAL SALES BY SUB CATEGORY

11. TOTAL SALES BY SUB CATEGORY

12. TOTAL PROFIT BY SUB CATEGORY

13. TOTAL PROFIT BY SUB CATEGORY

14. TOTAL PROFIT BY MONTH

15. TOTAL PROFIT BY MONTH

16. TOTAL PROFIT BY SEGMENT

17. TOTAL PROFIT BY SEGMENT

18. TOTAL DISCOUNT BY SEGMENT

19. TOTAL DISCOUNT BY SEGMENT

20. TOTAL SALES BY REGION

21. TOTAL SALES BY REGION

22. TOTAL QUANTITY SOLD BY SUB CATEGORY

23. TOTAL QUANTITY SOLD BY SUB CATEGORY

24. INSIGHTS OF SUPERSTORE DATA
 With the help of this data, we can summarize that the sales of office supplies are more as
compared to technology and furniture.
 But the profit of technology and furniture is greater than office supplies.
Hence we can reduce the sale of office supplies and focus on sales of technology and
furniture more.
 Newark and Detroit have fewer sales but more profit as compared to other top 10 cities
like San Diego and Jacksonville so we should more focus on this city.
 If we look by sub-category the sales of Biners are less as compared to other products in
the list but profit is almost double in the category, so we can increase the sales of Binders.

25. INSIGHTS OF SUPERSTORE DATA
 With the help of this data ratio of discounts offered and profit gain in the consumer and
corporate sectors has a bit of margin, so we can reduce some discounts to maximize profit.
 West region has less profit and high sales so there need to focus on that region to improve
 November month has more sales but less profit as compared to January has fewer sales
but more profit. Hence to improve profit we can reduce sales from November and increase
sales in January.

26. THANK YOU
S U M I T J A D H A V