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What and Where are the World's Oldest Businesses-PostgreSQL

What and Where are the World's Oldest Businesses-PostgreSQL

About this project

Did you know there's a company in Japan that's been operating since 578 AD? Understanding the characteristics of these long-lasting businesses offers valuable insights for modern enterprises struggling to survive in today's ever-changing market. Through data analysis, we can unlock the secrets of their remarkable longevity. This project delves into a dataset from BusinessFinancing.co.uk, leveraging SQL queries to explore the founding years, industry categories, and geographical distribution of the world's oldest continuously operating businesses.

Key Findings

  • Kongō Gumi, a Japanese construction company founded in 578 AD, as the world's oldest continuously operating business.
  • While the construction industry boasts the world's oldest company, the data also reveals a surprising level of industry diversity.Banking & Finance, Hospitality, and Manufacturing & Production are all well-represented, suggesting that resilience can be found across a range of sectors.
  • The data indicates a geographical trend. The oldest businesses tend to be concentrated in Asia and Europe

The oldest business in the world

  • Explored the range of founding years globally.
  • Noted significant variation, with the oldest business dating back to 578.

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How many businesses were founded before 1000?

Discovered the count of businesses founded before the year 1000.

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Which businesses were founded before 1000?

Having a count is all very well, but I'd like more detail. Which businesses have been around for more than a millennium?

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undefinedExploring the categories

Now we know that the oldest, continuously operating company in the world is called Kongō Gumi. But was does that company do? The category codes in the businesses table aren't very helpful: the descriptions of the categories are stored in the categories table.

This is a common problem: for data storage, it's better to keep different types of data in different tables, but for analysis, you want all the data in one place. To solve this, you'll have to join the two tables together.

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Counting the categories

With that extra detail about the oldest businesses, we can see that Kongō Gumi is a construction company. In that list of six businesses, we also see a café, a winery, and a bar. The two companies recorded as "Manufacturing and Production" are both mints. That is, they produce currency.

I'm curious as to what other industries constitute the oldest companies around the world, and which industries are most common.

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Oldest business by continent

It looks like “Banking & Finance” is the most popular category. Maybe that’s where you should aim if you want to start a thousand-year business.

One thing we haven’t looked at yet is where in the world these really old businesses are. To answer these questions, we’ll need to join the businesses table to the countries table. Let's start by asking how old the oldest business is on each continent.

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Joining everything for further analysis

Interesting. There’s a jump in time from the older businesses in Asia and Europe to the 16th Century the oldest businesses in North and South America, then to the 18th and 19th Century the oldest businesses in Africa and Oceania.

As mentioned earlier, when analyzing data, it’s often really helpful to have all the tables you want access to join together into a single set of results that can be analyzed further. Here, that means we need to join all three tables.

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Counting categories by continent

Having businesses joined to categories and countries together means we can ask questions about both these things together. For example, which are the most common categories for the oldest businesses on each continent?

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Filtering counts by continent and category

Combining continent and business category led to a lot of results. It's difficult to see what is important. To trim this down to a manageable size, let's restrict the results to only continent/category pairs with a high count.

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Conclusion:

At the end of this project, we aim to have a comprehensive understanding of the world's oldest businesses. Through the application of data manipulation and analysis techniques, we hope to shed light on the factors that have contributed to their remarkable longevity. By sharing our insights, we not only celebrate the endurance of these businesses but also gain valuable lessons for modern-day enterprises striving to withstand the challenges of the business landscape

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