5 ms·

How to Build a Data Startup

78 points by rjurney 16y ago

gsteph22 16y ago
The world is data. This is a killer article.
- kno 16y ago
  How to present and sell the data is key.
- dstorrs 16y ago
  Personally, I found it a bit fluffy. It pretty much boils down to: "Data startups need three bodies (hustler, designer, prodineer). Talk to customers early. Here are the levels of knowledge: 1) data, 2) charts, 3) reports, 4) actionable analytics; higher numbered levels are more valuable."
  - rjurney 16y ago
    Some other posts at www.datasyndrome.com are less fluffy on the same topic. The 3 founders one was written to suggest a 3rd founder to a startup. Data hackers tend to underestimate the importance of hustling and design.
    - sparky 16y ago
      Surely they have numbers to back them up on that.
      - rjurney 16y ago
        I don't think data on this is available, just experience.
tricky 16y ago
My wife was part of a data startup. They'd take freely available epidemiology data, massage it into a spreadsheet, and sell it to pharma. They do really well, but only because they know their data and they know that their customers know that they know what they know. you know? exactly.
- zackattack 16y ago
  would you kindly go into greater detail?
  - tricky 16y ago
    you develop a drug that will work on a couple of different diseases (or, more likely, different cancers) You can only afford to run a clinical trial on one disease. How do you choose? You hire a company who has a good handle on data (A lot of which is gathered by the CDC and other government orgs who make it freely available.) The data people mine that data and come back to say, "x% more people die from disease 1 than 2, but insurance will reimburse more for disease 2, so go with 2." - totally making that up, but it is something you could feasibly derive from the data. Mostly it was along the lines of "teens get acne, american teens would rather use a pill than a cream so develop a pill for them, cream for the rest of the world."
    - wlievens 16y ago
      So it's market research as a product rather than a service, so to speak?
psynix 16y ago
Much easier to read on the original site: http://radar.oreilly.com/2010/10/strata-week-building-data-star.html http://radar.oreilly.com/2010/10/strata-week-building-data-s...
il 16y ago
I'm wondering how they determined that a data startup needs exactly those three founders. I think a data startup needs two founders- a hacker to collect and analyze the data and a business guy to provide actionable recommendations and sell it. But then again, I'm currently a single founder working on a data startup and wearing all of the hats, so what do I know. Shameless Plug: Anyone want to analyze huge datasets and create recommendations with me? Email me!
- rjurney 16y ago
  Mostly I determined it through failure.
- anthonyb 16y ago
  It's much clearer in the original article - this one is really just a rehash/mix and match. http://datasyndrome.com/post/1375987697/analytic-product-teams http://datasyndrome.com/post/1375987697/analytic-product-tea...
gyardley 16y ago
An ok article, but if you're seriously considering building a data startup, the #1 most-important thing above all is to know exactly how to scale beforehand. Bad architecture is expensive, and trying to switch architectures midstream is a nightmare.
- il 16y ago
  I'm going to get flamed for this, but I think, even for a data startup, worrying about scaling before you have users or traction is premature, especially with how cheap hardware is becoming. Case in point: I'm currently hacking together an inefficient, unoptimized prototype analyzing pretty large datasets on probably the worst architecture for this kind of thing known to man, and the whole thing still runs pretty well on a single $50 VPS.
  - gyardley 16y ago
    Do you have full control over the amount of data your system is taking in? The startup I founded had analytics code in a ton of iPhone applications and was handling the load just fine right up until the day it suddenly wasn't. By that point we had customers who relied on us, and we had to deal with it very quickly. Not fun. And there's certainly more to scaling than just cheap architecture. We thought EC2 would handle the overflow until we unexpectedly became completely I/O bound. Firing up a few more instances can't fix that. If you're just running some scraper and can control what you're taking in, that's a completely different story.
    - il 16y ago
      You're absolutely right, I hadn't considered analytics as an example. Some data startups I've seen as well as my own project take in existing data sets and simply generate reports from it for customers. Makes it a lot easier to scale.
  - SkyMarshal 16y ago
    I think there's a happy middle ground between premature optimization and naive development. While the former should not be allowed to impede one's progress toward a MVP, real customer feedback, and the potential need to adapt or pivot, neither should one ignore early optimization decisions where they are inexpensive and may only minimally impede (if at all) that progress. Being able to recognize the difference is a talent that comes with experience.
benzheren 16y ago
Visualization is a key part of the data, highly recommend all Edward Tufte books on information visualization.
anthonyb 16y ago
This article is really just a rehash of a tiny part of O'Reilly's roundup: http://radar.oreilly.com/2010/10/strata-week-building-data-star.html http://radar.oreilly.com/2010/10/strata-week-building-data-s... You're much better off reading the originals, which I think have already been posted on HN anyway: http://datasyndrome.com/post/1375987697/analytic-product-teams http://datasyndrome.com/post/1375987697/analytic-product-tea... and http://petewarden.typepad.com/searchbrowser/2010/10/how-to-turn-data-into-money.html http://petewarden.typepad.com/searchbrowser/2010/10/how-to-t...
physcab 16y ago
I think there are multiple ways to start a startup based off of data. 1) Take other people's data and learn how to represent it in a way that will let them understand it better. See FlowingData, Stamen, and NY Times graphics http://www.nytimes.com/interactive/2009/03/10/us/20090310-immigration-explorer.html http://www.nytimes.com/interactive/2009/03/10/us/20090310-im...) 2) Take other people's data, glean information from it, and offer a new service based off that information. See FlightCaster and TweetFeel 3) Develop tools that will allow people to play with their own data and do their own analysis in-house. See Datameer, Google Prediction API, and Palantir. Whatever the route, you'll probably need someone who is comfortable dealing with databases, a graphic artist to make the information pretty, and someone with the algorithmic knowledge to capture new insights from massive amounts of data.

78 points by rjurney 16y ago

gsteph22 16y ago

The world is data. This is a killer article.

kno 16y ago
How to present and sell the data is key.
dstorrs 16y ago
Personally, I found it a bit fluffy. It pretty much boils down to: "Data startups need three bodies (hustler, designer, prodineer). Talk to customers early. Here are the levels of knowledge: 1) data, 2) charts, 3) reports, 4) actionable analytics; higher numbered levels are more valuable."
- rjurney 16y ago
  Some other posts at www.datasyndrome.com are less fluffy on the same topic. The 3 founders one was written to suggest a 3rd founder to a startup. Data hackers tend to underestimate the importance of hustling and design.
  - sparky 16y ago
    Surely they have numbers to back them up on that.
    - rjurney 16y ago
      I don't think data on this is available, just experience.

tricky 16y ago

My wife was part of a data startup. They'd take freely available epidemiology data, massage it into a spreadsheet, and sell it to pharma. They do really well, but only because they know their data and they know that their customers know that they know what they know. you know? exactly.

zackattack 16y ago
would you kindly go into greater detail?
- tricky 16y ago
  you develop a drug that will work on a couple of different diseases (or, more likely, different cancers) You can only afford to run a clinical trial on one disease. How do you choose? You hire a company who has a good handle on data (A lot of which is gathered by the CDC and other government orgs who make it freely available.) The data people mine that data and come back to say, "x% more people die from disease 1 than 2, but insurance will reimburse more for disease 2, so go with 2." - totally making that up, but it is something you could feasibly derive from the data. Mostly it was along the lines of "teens get acne, american teens would rather use a pill than a cream so develop a pill for them, cream for the rest of the world."
  - wlievens 16y ago
    So it's market research as a product rather than a service, so to speak?

psynix 16y ago

Much easier to read on the original site: http://radar.oreilly.com/2010/10/strata-week-building-data-star.html http://radar.oreilly.com/2010/10/strata-week-building-data-s...

il 16y ago

I'm wondering how they determined that a data startup needs exactly those three founders. I think a data startup needs two founders- a hacker to collect and analyze the data and a business guy to provide actionable recommendations and sell it. But then again, I'm currently a single founder working on a data startup and wearing all of the hats, so what do I know. Shameless Plug: Anyone want to analyze huge datasets and create recommendations with me? Email me!

rjurney 16y ago
Mostly I determined it through failure.
anthonyb 16y ago
It's much clearer in the original article - this one is really just a rehash/mix and match. http://datasyndrome.com/post/1375987697/analytic-product-teams http://datasyndrome.com/post/1375987697/analytic-product-tea...

gyardley 16y ago

An ok article, but if you're seriously considering building a data startup, the #1 most-important thing above all is to know exactly how to scale beforehand. Bad architecture is expensive, and trying to switch architectures midstream is a nightmare.

il 16y ago
I'm going to get flamed for this, but I think, even for a data startup, worrying about scaling before you have users or traction is premature, especially with how cheap hardware is becoming. Case in point: I'm currently hacking together an inefficient, unoptimized prototype analyzing pretty large datasets on probably the worst architecture for this kind of thing known to man, and the whole thing still runs pretty well on a single $50 VPS.
- gyardley 16y ago
  Do you have full control over the amount of data your system is taking in? The startup I founded had analytics code in a ton of iPhone applications and was handling the load just fine right up until the day it suddenly wasn't. By that point we had customers who relied on us, and we had to deal with it very quickly. Not fun. And there's certainly more to scaling than just cheap architecture. We thought EC2 would handle the overflow until we unexpectedly became completely I/O bound. Firing up a few more instances can't fix that. If you're just running some scraper and can control what you're taking in, that's a completely different story.
  - il 16y ago
    You're absolutely right, I hadn't considered analytics as an example. Some data startups I've seen as well as my own project take in existing data sets and simply generate reports from it for customers. Makes it a lot easier to scale.
- SkyMarshal 16y ago
  I think there's a happy middle ground between premature optimization and naive development. While the former should not be allowed to impede one's progress toward a MVP, real customer feedback, and the potential need to adapt or pivot, neither should one ignore early optimization decisions where they are inexpensive and may only minimally impede (if at all) that progress. Being able to recognize the difference is a talent that comes with experience.

benzheren 16y ago

Visualization is a key part of the data, highly recommend all Edward Tufte books on information visualization.

anthonyb 16y ago

This article is really just a rehash of a tiny part of O'Reilly's roundup: http://radar.oreilly.com/2010/10/strata-week-building-data-star.html http://radar.oreilly.com/2010/10/strata-week-building-data-s... You're much better off reading the originals, which I think have already been posted on HN anyway: http://datasyndrome.com/post/1375987697/analytic-product-teams http://datasyndrome.com/post/1375987697/analytic-product-tea... and http://petewarden.typepad.com/searchbrowser/2010/10/how-to-turn-data-into-money.html http://petewarden.typepad.com/searchbrowser/2010/10/how-to-t...

physcab 16y ago

I think there are multiple ways to start a startup based off of data. 1) Take other people's data and learn how to represent it in a way that will let them understand it better. See FlowingData, Stamen, and NY Times graphics http://www.nytimes.com/interactive/2009/03/10/us/20090310-immigration-explorer.html http://www.nytimes.com/interactive/2009/03/10/us/20090310-im...) 2) Take other people's data, glean information from it, and offer a new service based off that information. See FlightCaster and TweetFeel 3) Develop tools that will allow people to play with their own data and do their own analysis in-house. See Datameer, Google Prediction API, and Palantir. Whatever the route, you'll probably need someone who is comfortable dealing with databases, a graphic artist to make the information pretty, and someone with the algorithmic knowledge to capture new insights from massive amounts of data.