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Way easier, way cheaper, and way less risky to buy a tool that a less-skilled person can use. I see too much of this. Blog posts that attack a business's decis

Way easier, way cheaper, and way less risky to buy a tool that a less-skilled person can use. I see too much of this. Blog posts that attack a business's decisions, without demonstrating adequate insight into the tradeoffs involved in making that decision. Skilled people are rare and valuable and hard to keep, even if you do everything right. Obviously the most effective choice in any situation is to use and develop human capital. But if and when that person leaves your company, your investment just went up in smoke. Fungibility of skilled labor is something we should all learn to love. We want it to be easier to jump around and not harder. We want more options and not less.

code4tee 11y ago
Well I would assume the point is to build up expertise more broadly... not just one person that knows everything and screws you when he/she leaves.
- vinceguidry 11y ago
  Way, way more difficult. Companies are made up of lots of different people with differing kinds of expertise. You don't want everyone learning everything. Derek Sivers wrote about how he had everyone in his company answer customer phone calls. There were phones everywhere, and there were incentives for employees to pick them up. It took a lot of work, but it paid off.
- crispyambulance 11y ago
  bingo. a high level of expertise has to be cultivated over a long time as part of the culture of the organization. this is not easy and not common but it can be done. in almost 20 years i have not seen this myself except at 2 companies that do manufacturing. i hesitate to use the words "big data" or "data science". really, unless you're talking about terabytes+ and/or sophisticated stats, it should just be called " analysis". The picture of success in manufacturing orgs is having domain experts (manufacturing/operations/quality folks) routinely performing tasks with data that are NOT sporadic one-offs (for PowerPoint slides). The actual tools don't matter too much but rather the fact that people are proficient at using them. It means, for instance, that you should see non-software-engineers able to work with databases and reporting tools directly (eg sql-server management/report studio), comfortable with basic scripting, and skilled in some programmable analysis tool like jmp, R, or even excel macros. This doesn't seem strange until you actually see it. This can exist ONLY because leadership has made it an actual priority and backed that up with serious resources. As expensive as enterprise BI tools are, they're still cheap compared to training and cultivating expertise in the staff. Sadly, however, the norm is organizations that say they do data-intensive stuff like 6-sigma but in fact the core of their data practice is nothing more than an overworked database guy and a bunch bs-artists whose main tool is PowerPoint.
avmich 11y ago
> But if and when that person leaves your company, your investment just went up in smoke. That means you'd like to get results from that person earlier than he/she leaves. Developing a person takes a measurable time and efforts; can you estimate how badly you need those results from data science? Maybe those anticipated benefits aren't that big? That's not to say you can always incentivize the person to stay longer. Of course if you need the results.
- vinceguidry 11y ago
  > That means you'd like to get results from that person earlier than he/she leaves. You don't invest in capital just to solve problems. You hire a specialist / contractor / consultant. You invest in capital if you expect the problem to recur often enough to make the investment worthwhile.
sharemywin 11y ago
The company I used to work for had 3 or 4 tools that did pretty much the same thing as far as moving data around. A lot of it sits in various SQL tables. Problem is unless you know which field and what the codes mean in the fields the tool is pretty useless. In fact in one of our system it contained 90% of our look up data in one table. It just had a field called table that determined which lookup value and name, etc. Since it was designed and built before I was in High School the design was pretty much set in stone.
GFK_of_xmaspast 11y ago
> Fungibility of skilled labor is something we should all learn to love As someone who does skilled labor, I don't see why this is true.

code4tee 11y ago

Well I would assume the point is to build up expertise more broadly... not just one person that knows everything and screws you when he/she leaves.

vinceguidry 11y ago
Way, way more difficult. Companies are made up of lots of different people with differing kinds of expertise. You don't want everyone learning everything. Derek Sivers wrote about how he had everyone in his company answer customer phone calls. There were phones everywhere, and there were incentives for employees to pick them up. It took a lot of work, but it paid off.
crispyambulance 11y ago
bingo. a high level of expertise has to be cultivated over a long time as part of the culture of the organization. this is not easy and not common but it can be done. in almost 20 years i have not seen this myself except at 2 companies that do manufacturing. i hesitate to use the words "big data" or "data science". really, unless you're talking about terabytes+ and/or sophisticated stats, it should just be called " analysis". The picture of success in manufacturing orgs is having domain experts (manufacturing/operations/quality folks) routinely performing tasks with data that are NOT sporadic one-offs (for PowerPoint slides). The actual tools don't matter too much but rather the fact that people are proficient at using them. It means, for instance, that you should see non-software-engineers able to work with databases and reporting tools directly (eg sql-server management/report studio), comfortable with basic scripting, and skilled in some programmable analysis tool like jmp, R, or even excel macros. This doesn't seem strange until you actually see it. This can exist ONLY because leadership has made it an actual priority and backed that up with serious resources. As expensive as enterprise BI tools are, they're still cheap compared to training and cultivating expertise in the staff. Sadly, however, the norm is organizations that say they do data-intensive stuff like 6-sigma but in fact the core of their data practice is nothing more than an overworked database guy and a bunch bs-artists whose main tool is PowerPoint.

avmich 11y ago

> But if and when that person leaves your company, your investment just went up in smoke. That means you'd like to get results from that person earlier than he/she leaves. Developing a person takes a measurable time and efforts; can you estimate how badly you need those results from data science? Maybe those anticipated benefits aren't that big? That's not to say you can always incentivize the person to stay longer. Of course if you need the results.

vinceguidry 11y ago
> That means you'd like to get results from that person earlier than he/she leaves. You don't invest in capital just to solve problems. You hire a specialist / contractor / consultant. You invest in capital if you expect the problem to recur often enough to make the investment worthwhile.

sharemywin 11y ago

The company I used to work for had 3 or 4 tools that did pretty much the same thing as far as moving data around. A lot of it sits in various SQL tables. Problem is unless you know which field and what the codes mean in the fields the tool is pretty useless. In fact in one of our system it contained 90% of our look up data in one table. It just had a field called table that determined which lookup value and name, etc. Since it was designed and built before I was in High School the design was pretty much set in stone.

GFK_of_xmaspast 11y ago

> Fungibility of skilled labor is something we should all learn to love As someone who does skilled labor, I don't see why this is true.