As someone running a data startup, this is the first guide that really made me nod my head, and say yes, yes, yes! All the other SaaS articles felt like they weren’t talking to me or understood the problems that come with trying to sell a data product. For instance, the idea of building a MVP in 3 months with a data product is laughable.
As the CTO of a DaaS startup in stealth mode working on our MVP, expect to take a year or more. We also offer SaaS that centralizes workflow around using the data, enterprise customers expect a level of polish and reliability that is not expected from the next facebook/airbnb clone, desk procedures, train the trainer programs and other ISO processes can't be revamped every single release. Keep fighting the good fight.
As someone in a fortune 50 company working in a data internal startup this guide has been one of the first that really gets the unique aspects of data.
Not a DaaS startup, but I've managed millions of dollars worth of data acquisition and sales before.
Having seen both the selling and acquisition side of the table, the only concrete advice I can give you is to pay handsomely for experienced sales staff. There is absolutely no rhyme or reason to data pricing, and no matter what you do you will invariably price out specific market segments while underpricing for other market segments. And as the article mentioned, there are a lot of potential licensing and usage covenants involved in data sales, which are all possible factors in pricing. Without a good sales team to do client and market discovery, it's really hard to understand what to settle on for norms as far as both pricing and licensing go.
On the flip side of that: if you're ever purchasing data at any amount of volume, ignore listed pricing. It's absolutely fungible, and well worth the tedious dance of traditional sales. In addition to better financial terms, you can usually get adjustments to the licensing and usage covenants to better accommodate your use case. Doubly so if your use case is nonstandard for that vendor - plenty of data-oriented services are tailored for specific industries and uses, and their pricing sheets and tiers are designed around the use cases and marginal value of the data within that industry. When sourcing data, I could get pretty incredibly deals from these companies, as their sales teams basically used the transaction as a form of market discovery and saw the relationship as a way to feel out a particular market segment they hadn't been going after previously.
This is one of those companies that will pay you to bug your app so they can spy on your users. They also emailed me at least 8 times without any sort of opt-in.
Yeah these companies always have some sort of dodgy trick, like unroll.me that was advertised as an email unsubscriber. Reading the docs I would guess they use their gps polygons and wifi addresses to help apps spy on users, and in return they can resell the user data.
Also keep in mind that unroll.me actually was just a convenient tool for unsubscribing from marketing emails. At first.
Then the founders stumbled into it's potential worth when the access required for that purpose was secondarily leveraged for marketing intelligence purposes, and was bought by (what's currently) Slice Technologies.
Even when companies aren't using dodgy tricks, you've got to keep an eye out for ways they could if they felt like it.
Absolutely, but the author is not wrong in general about DAAS, and that there are many, many DAAS opportunities that have nothing to do with violating individual people's privacy.
I’d temper the “many opportunities” with, “about as many as there are use cases for data that people understand”.
After 6 months of failing to hire in Lisbon a company I know still won’t spend 3k to work out what the local market expects from the sort of job they advertise through a representative sample survey ... they fully understand why it’d help, it’ just esoteric to them . Unfortunately this is pretty normal .
There are more good uses for data than there are good users.
I would suggest a way to look at DAAS is through the lens of what has long been called "Decision Support", and apply it to what might now be called "microdecisions."
So- think about the personal microdecisions you make on a daily basis, especially the small ones- shopping/cooking/eating; cleaning; dressing; commuting; etc- and appreciate how many of those are not data-driven, are rather just based on assumption, habit, history, etc. Appreciate how many things one wonders about, idly, throughout the day, in regard to those microdecisions.
With some reflection it's easy to see that having various kinds of data would lead to your making different micro decisions. Some of those microdecisions can substantively impact things that are important- health, wealth, happiness- so will become business opportunities. Apply that to microdecisions made in the course of work...
This is my space broadly speaking and personally I think the treatment in the article is unnecessarily idealised and elaborate.
People will buy data when it has a clear use which means most people don’t sell just data, they sell “leads” or “clicks” or “followers”: something actionable.
I think this is unlikely to change because having an internal data team producing leads typically doesn’t make sense as a core focus.
There are a relatively small number of companies which do sell data and a slightly bigger number of companies that actually buy data. In the UK it’s easy to quantify because they all end up buying some of exactly the same thing.
You are right though. People don't buy data, just like they don't buy a SaaS product. They buy something if it solves a direct need, saves them money, or helps them generate more revenue. And like all things, it does need to have a clear, understandable story and value proposition.
https://trends.getdata.io
The first challenge I see with data as a service is that not all features you need for your model will be made available by the vendor.
The second is their frequency of update might not be at the cadence that you need.