The more connected a dataset is to other data elements, the more valuable it is. And the easier it is to link your data, the more valuable it becomes. The reason for this is simple: data is only as useful as the questions it can help answer. Joining, linking, and graphing datasets together allows one to ask more and different kinds of questions.

This is the #1 thing that most people who work at data companies do not understand. Most people think that they need to hoard the data. But the data increases in value if it can be combined with other interesting datasets. So you should do everything you can to help your customers combine your data with other data. One way to make data easy to combine is to purposely think about linking it — essentially creating a foreign key for other datasets.

the more join keys, the more valuable to dataset

If you are an investor and you are trying to value a dataset, the easiest thing you can do is first recognize how many join keys there are in this dataset that can allow end-users to bring in additional data.

One of the big ways that data becomes useful is when it is tied to other data.

The reason for this is simple: data is only as useful as the questions it can help answer. Joining, linking, and graphing datasets together allows one to ask more and different kinds of questions.

simple axiom when dealing with data: the easier it is to join the data, the more the data will be used.

so if you want people to use your data, lace it with join keys.

The easier it is to join data, the more data will be transacted, moved, and used.

the easiest way to increase data’s value is by linking it together.

the two most important factors that make data valuable:
1. temporal (as real time as your industry is)
2. has a dollar sign next to it (pricing data)
the best place to be as a data business is to be the *benchmark* for your industry – when the customer’s product can’t exist without you

determining data quality takes a lot of work ... and that really slows down the data sales process.

one area most data companies can do a better job in is transparency. most data companies don't even publicly publish their schema, their fill rates, or have an open docs site. that is going to be table-stakes for having a data company in the future.

From running a data company and investing in dozens of data companies, here are some learnings about data businesses that I wish I knew 6 years ago:

Data businesses are MUCH harder than I thought
* There are often only a small number of buyers
* Data businesses can be SUPER competitive
* Can take a lot of dev infrastructure which can be costly — it is a fixed cost but costly nevertheless
* If you are a horizontal data business (selling to many industries), you never are great for any one industry
* Just because you get to $10M ARR, doesn’t meant mean you have REAL product-market-fit

Being too religious is only good when you are right
If you are right, you get to go to heaven. But if you are wrong, you need to jettison your old beliefs.

To supply data is not enough. The data have to be integrated with strategy, they have to test a company’s assumptions, and they must challenge a company’s current outlook.

I would always push women towards more of learning the datasets behind anything that they’re trying to build, get information on it and, if you don’t have it, let’s talk with partnership organisations to see what information they may have. And if they still don’t have it, this is where you come in to build a solution that incorporates some of that.

Data is ultimately a winner-takes-most market.

As a data company starts to dominate its niche, it can lower its price and gain more market share and use those resources to invest more in the data … thereby gaining more market share (and the cycle continues).

Working at a data company is like being an archivist at the Library of Congress.
You know your job is important but you also know it is a supporting role that helps other people shine.
you don’t write the Constitution, but you help preserve it.