Run your enterprise like a startup

-

I've worked in a variety of companies and I notice an interesting phenomena -- It seems that the capabilities of individual programmers in companies are inversely purportional to the size on the company. Tech startups with 3 folks always seem to have superstars, even though it's a huge drain on their budget, but IT shops with 1000 people seem to always have 10 people who seem to be doing everything.

The irony in this situation is that a startup has the least amount of money to spend on programmers, but requires hiring only the best and needs to spend a disproportionate amount on payroll. On the other hand, a company flush with cash that could easily hire only the best and brightest, inevitably hires "everybody else". This means that particularly large shops end up with a handful of superstars (just by sheer luck of the draw) who do the majority of the work (and then burnout and leave) and a bunch of "also ran" folks who are really just padding their resume and being a drain on your cash flow.

A visionary IT leader at a large company would break software delivery down into a cluster of startup-like groups with very large degrees of autonomy. Forget about the mythical efficiencies of "enterprise architecture initiatives" and simply hold teams' feet to the fire to deliver real solutions with aggressive timelines. Use the incubator model to foster competition within the organization, after all, two insanely great teams working furiously on the same solution seems inefficient on the surface, but at the end of the day/week/month, you're more likely to then have TWO possible solutions to choose from. If you have one mediocre to crappy team of 50 slogging along and delivering nothing, you may be saving payroll money in the short term, but you will bleed to death waiting for solutions that will possibly never appear.

Comments

Post a Comment

Popular posts from this blog

Please use ANSI-92 SQL Join Syntax

-
Image
Way back in 1992, a standard was published for SQL. In it, the syntax for specifying joins in SQL Database was finally standardized. This was a good thing because (for folks who've been doing this a while), trying to decode the vagaries of how various Databases handled outer joins could drive you bonkers yielding different results depending on the parse order of things in the from and where clauses. Unfortunately, some DBMS vendors took a while to adopt this standard (Oracle didn't support it until 2001), but at this point if you're using a major RDBMS and NOT using it, you're probably mired in a tradition or habit that bears changing. As an example of what I'm describing, here are some examples of what I mean. Old School (no standard syntax + implementation and evaluation of predicates varied by vendor [and query]): select * from customer, order where customer.customer = order.customerid ANSI way (agreed to standard, predicate evaluation has some rules
Read more

the myth of asynchronous JDBC

-
Image
I keep seeing people (especially in the scala/typesafe world) posting about async jdbc libraries. STOP IT! Under the current APIs, async JDBC belongs in a realm with Unicorns, Tiger Squirrels, and 8' spiders. While you might be able to move the blocking operations and queue requests and keep your "main" worker threads from blocking, jdbc is synchronous. At some point, somewhere, there's going to be a thread blocking waiting for a response. It's frustrating to see so many folks hyping this and muddying the waters. Unless you write your own client for a dbms and have a dbms that can multiplex calls over a single connection (or using some other strategy to enable this capability) db access is going to block. It's not impossible to make the calls completely async, but nobody's built it yet. Yes, I know ajdbc is taking a stab at this capability, but even IT uses a thread pool for the blocking calls (be default). Someday we'll have async databa
Read more

The difference between Scalability, Performance, Efficiency, and Concurrency explained

-
I thought I'd give some explanation of the difference between scalability, performance, Efficiency, and concurrency. To make things more accessible (and because I'm watching a cooking show on TV) I'm going to use a baking metaphor to help explain the differences. In our case, the system in question is baking cookies for consumers. Scalability, is how well something responds to adding more resources. If we can double our capacity by doubling the number of resources, we've got linear scalability. In our baking example, if we add more ingredients, we can scale to a larger number of cookies. To contrast with our other metrics, this doesn't actually increase the performance of creating a single cookie (they still take 10 minutes to bake), and the efficiency is the same (we just have more capacity). We also cannot bake more cookies in a fixed time period, but over a larger time period we can produce more cookies. Performance is how fast something can get done, u
Read more