Areas where people will pay for data:
Investing, Trading
Making money, Saving money
Real Estate
You want most of these characteristics in the underlying data:
Data is free but difficult to get
Massive amounts of data
Data is dispersed over several locations, from different sources
Data is valuable for making investment/business decisions
Basically, you present a finished data product, removing the hassle of collecting, cleaning, and presenting the data.
Real World Examples
1. Real Estate Dashboard
I’m going to call this guy “Bob”. Here is his business.
He collects, cleans, sorts and aggregates data on every property sold in Australia every week. I’ve personally had access to this product on a job contract.
You might think, “Don’t we already have websites like that”. Yes, but they don’t have ALL the data, and they don’t have clever aggregations and reporting.
Bob has worked for several large real estate companies over 15 years; through this he knew exactly what was missing and how to present it.
I don’t know his exact process (he wouldn’t tell me). But, it would go something like this.
Scrape data from public real estate websites
Join and Clean the data
Load to Power BI, with extensive sorting, filtering capabilities
Yes, his product is distributed via the Power BI Service. Where all the data can be filtered by real estate agent, including other unique metrics which public real estate websites don’t provide.
He has no website. When he gets a new client (though word of mouth), he just sets up login access to the Power BI dashboard.
2. Cryptocurrency Datasets
I was looking for historical data for a crypto model I’m building for myself. I found this: https://www.cryptoarchive.com.au/
Below you can see he charges $981 for all the historical tick data for the BTC/USDT trading pair. (He sells data for all the coins and pairs). 35 Gig, with 5 billion rows.
The data he is selling is available from Binance free of charge. So, why would anyone pay $981?
Because Binance make it difficult, having stored it in monthly downloadable files, with each month having approx 1 gig of data.
People will happily pay to get this data in a simple download.
They don’t want to spend time downloading every file, unzipping it and somehow merging it all successfully without errors.
Also, a non technical person wouldn’t know how to deal with 100 million rows in a file, as they can’t open it in Excel.
(I have no connection to this website at all)
3. Stock Market Datasets
Here is an example of charging for stock market data
Most, if not all of this data you could find for free.
There is a lot of competition in stock market data, I’m just giving this as another example.
Things to think about:
In your current industry, what would an ideal dataset look like?
What data have management asked for in the past, that you didn’t have?
Take publicly available data, make it easy to sort, filter, consume.
Combining datasets from different fields, in unique ways, can add value.
People will pay for data if you make it easy for them.
How could you use generative AI, to add value to datasets?
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Cheers Shano
