Hi guys,
I have always wanted to write technical reports and share my knowledge in data analytics, this is my first time and if I can, you can too!
Today, I am going to be uncovering initial insights from a dataset at first glance. The data we are using contains 3 years of vehicle sales from 2003 - 2005.
WOOAHHH! Long time ago, right? 😂
Well, that's the fun thing about data analytics, you get to go back in time. I LOVE IT and I am sure you can feel the thrill too.
LET'S GOOOO!🤸♀️🚀
Data link below
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Sale data link
Here comes the big question💡, without doing any deep analysis, what are the obvious patterns, trends, or anomalies in the data?
I know , I know, data can seem overwhelming 😑, but don't worry, you've got this. Let's go!😋
Firstly, the headers are very important because they give us a description of the data we are looking at, with each variable having unique values that are relevant to understanding the full scope of the data. In this case, we can observe that some of the data headers are ambiguous which causes confusion on what the values are really describing. Example of such headers is MSRP.
Secondly, Eagle-eyed observers would have seen the inconsistency of date, postal code and PHONE format, this can be troublesome when trying to perform deep analysis. Also, there are missing values for columns like postal code, state.
Thirdly, we can perform simple calculations to find the year with the highest sales, trendy products or highest selling products, we can also find the most frequent/regular customer to award loyalty bonuses and the Country with the highest market potential or where most sales occur in order to streamline the company's target market. All these can be done using simple formulas.
Finally, the following trends were uncovered by performing light analysis on the dataset, we can see a sudden upward surge in sales in 2004 followed by decrease in 2005 (all-time low). Also, we can observe that Classic cars are bought more often than other cars.
If we perform deeper analysis, start questioning our findings and finding out the WHYs, finding cause and trends that can help business decisions. It's so much like being a detective, a business Sherlock Holmes unlocking mysteries, and growing the revenue of this company, haha!
😁
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