Episode #6 of the Tiny Graphics Advent Calendar, published by Marius Popescu for De Amicis.
Good to Great is a masterpiece for understanding how companies grow and stagnate or take their flight. It took a detective’s work to document all the company data needed to create this project. And a team. And time. A lot of time.
Creating, organizing, and making a product work takes a lot of time too.
In order to build your timeline behave like a detective. You don’t know this company, these people, and you somehow got access to all the network and all the files.
But how to make sure you will be able to find historical data in the future
In 2010-2011 I was on a big project with a big customer and I realized I spent too much time writing emails instead of developing and testing the project. That was taking a toll on my energy, so I started taking notes in an Excel sheet the number of emails I sent and received, and to how many people. I used GmailMeter (at the time).
Now, almost 10 years later, I’m using its successor, EmailMeter. And I can show you how the email stats look like after all this time. What information can we find in these statistics?
This helps me see when a month is too busy on the email side and how a year compares to another on this front.
Now that you want to act like a detective, where would you look? What data you would search for? Here are some ideas.
Where can you find data to create one, two or ten timelines for your company graphics
- deployment folders
- release emails
- source code history (dates, labels, tags, branches)
- conference participation notes (may also be milestones for your product)
- archive folders for reference data
- formats or standards you were using, to correlate with their timeline
- internal wiki, blogs, slack or forums
- company registration information, like creation and office addresses and numbers of employees
- CRM for customer numbers, types, countries etc
- databases – registries, messaging, quantify data on important topics
I also note down my electricity and water consumptions and costs and make charts to understand the data (call me a data visualization lover if you like)
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