4 ms·

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.

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.