The hardest part of building your first dataset usually isn't the data -- it's deciding on structure before you start filling in rows. Skip that step and you end up with a spreadsheet that only makes sense to the person who built it.

Start with the schema: list every field you need, give each one a name, a type (string, number, date, URL), and a one-line description. Decide which fields are required and which are optional. This takes twenty minutes and saves hours of cleanup later.

Next, fill in a small batch of rows -- ten to twenty -- and check them against the schema. This is where you catch the problems: a field that needs to be split in two, a category list that's missing an obvious option, a date format that's inconsistent.

Once the structure holds up, scale up the row count. Export to CSV, JSON, and XLSX so the dataset works wherever someone needs to open it, and write a short description of what the dataset covers and where the data came from. That description is what turns a spreadsheet into a dataset.