I was recently reading a good Cameron Purdy post where he talks about his eight theses regarding why startups or students can pull stuff off that large enterprise IT shops can’t.

My summary/trenchant restatement of his points:

  1. Changing existing systems is harder than making a custom-built new one (version 2 is harder)
  2. IT veterans overcomplicate new systems
  3. The complexity of a system increases exponentially the work needed to change it (versions 3 and 4 are way way harder)
  4. Students/startups do fail a lot, you just don’t see those
  5. Risk management steps add friction
  6. Organizational overhead (paperwork/meetings) adds friction
  7. Only overconservative goons work in enterprise IT anyway
  8. The larger the org, the more conflict

Though I suspect #1 and #3 are the same, #2 and #5 are the same, and #6 and #8 are the same, really.

I’ve been thinking about this lately with my change from our enterprise IT Web site to a new greenfield cloud-hosted SaaS product in our R&D organization.  It’s definitely a huge breath of fresh air to be able to move fast.  My observations:


The problem of systems complexity (theses #1 and #3) is a very real one.  I used to describe our Web site as having reached “system gridlock.”  There were hundreds of apps running dozens to a server with poorly documented dependencies on all kinds of stuff.  You would go in and find something that looked “wrong” – an Apache config, script, load balancer rule, whatever – but if you touched it some house of cards somewhere would come tumbling down.  Since every app developer was allowed to design their own app in its own tightly coupled way, we had to implement draconian change control and release processes in an attempt to stem the tide of people lining up to crash the Web site.

We have a new system design philosophy for our new gig which I refer to as “sharing is the devil.”  All components are separated and loosely coupled.  Using cloud computing for hardware and open source for software makes it easy and affordable to have a box that does “only one thing.”  In traditional compute environments there’s pressure to “use up all that CPU before you add more”, which results in a penny wise, pound foolish strategy of consolidation.  More and more apps and functions get crunched closer together and when you go back to pull them out you discover that all kinds of new connections and dependencies have formed unbidden.


Overcomplicating systems (#2 and #5) can be somewhat overcome by using agile principles.  We’ve been delving heavily into doing not just our apps but also our infrastructure according to an agile methodology.  It surfaces your requirements – frankly, systems people often get away with implementing whatever they want, without having a spec let alone one open to review.  Also, it makes you prioritize.  “Whatever you can get done in this two week iteration, that’s what you’ll have done, and it should be working.”  It forces focus on what is required to get things to work and delays more complex niceties till later as there’s time.


Both small and large organizations can suffer from #6 and #8.  That’s mostly a mindset issue.  I like to tell the story about how we were working on a high level joint IT/business vision for our Web site.  We identified a number of “pillars” of the strategy we were developing – performance, availability, TCO, etc.  I had identified agility as one, but one of the application directors just wasn’t buying into it.  “Agility, that’s weird, how do we measure that, we should just forget about it.”  I finally had to take all the things we had to the business head of the Web and say “of these, which would you say is the single most important one?”  “Agility, of course,” he said, as I knew he would.  I made it a point to train my staff that “getting it done” was the most important thing, more important than risk mitigation or crossing all the t’s and dotting all the i’s.  That can be difficult if the larger organization doesn’t reward risk and achievement over conservatism, but you can work on it.