71–78 of 78 posts
"BREAKING: Hugely successful big co has ex employees not fully satisfied, willing to criticize it. It also has current employees who appreciate it. Will keep you posted."
To top it off, written by a company that has clear conflict of interest in context of eCommerce (NYTimes is an eCommerce player) and publishing (Washington Post) with Mr. Bezos. I am not in any way associated with the wunderkind of Seattle and/or his company. Just noting an interesting fact.
Earlier quoted context omitted.
To top it off, written by a company that has clear conflict of interest in context of eCommerce (NYTimes is an eCommerce player) and publishing (Washington Post) with Mr. Bezos. I am not in any way associated with the wunderkind of Seattle and/or his company. Just noting an interesting fact.
Doesn't Bezos own Washington Post?
Amazon's success at being a grinding place but reaping major business results align with what I've seen of my previous employer (a transportation icon) that tightened the screws, re-engineered processes, encouraged thousands to leave. Revenue, profit, stock price, and bonuses soared. All of what we've been told about the war for talent, desire for caring work places, etc. seems to be exaggerated. Companies can litera…
The data-driven management you're describing is based on the significant assumption that you are able to gather accurate data, and interpret that data accurately. For example, if you are making a management decision based on how much time employees are spending on X, you are assuming that you actually know how much time is being spent on X and what the value of that time is. Simply measuring data is not sufficient. Y…
The culture of having data collection and an ability to challenge it (whether it is flawed or not) indicates a desire for a fact-driven culture vs. a power-driven culture.
They might be over-confident, but one would think that reality in the market will eventually catch up to that.
I wonder if this is related to H1 demand and lack of women in tech. Think about it for a minute. H1 would have to leave (the country) if they lose their job, so they are stuck working for a bad boss. Women can sense that, hey, this makes no sense faster than man who is somewhat expected to 'solider on'. If there was less H1 (but more legal immigration other ways), than that would force a turn around and encourage mor…
My experience with women at Amazon is this: my ex-girlfriend had an internship with Amazon in the summer of 2013. While there her manager friended her on Facebook then sent her some messages suggesting that if she slept with him he would make sure she got a full time offer and explicitly describing his fantasies about her. She ended sleeping with him and true to his word he got her the full time position. About a mon…
Earlier quoted context omitted.
The data-driven management you're describing is based on the significant assumption that you are able to gather accurate data, and interpret that data accurately. For example, if you are making a management decision based on how much time employees are spending on X, you are assuming that you actually know how much time is being spent on X and what the value of that time is. Simply measuring data is not sufficient. Y…
Most companies I have consulted or work for make decisions based on politics and conjecture, with data being invented at a bare minimum to back the argument up, but no one doing serious analysis of it. The culture of having data collection and an ability to challenge it (whether it is flawed or not) indicates a desire for a fact-driven culture vs. a power-driven culture. They might be over-confident, but one would th…
Even just the "time worked on X" example is too complex to track. It is deceptively "simple." It seems like (especially to managers) employees should be able to work on a task for awhile, and afterward record how much time they spent working on it. However, it isn't that simple.
In reality, "working on X" might actually mean working on X along with several other things such as email or web browsing or talking to a co-worker or answering the phone. With reliance on self-reporting, and without some sort of monitoring system, it is unreasonable to expect this metric to be accurate, yet this is how many (most?) time reporting systems work.
Managers are making large-scale decisions based on this data. It looks like accurate data, it has fancy graphs and charts and reporting...but it isn't actually very accurate. An employee might report an hour spent on a project, when really only 40 minutes of that hour were spent on the project work, while the remaining 20 minutes were spent on various interruptions and extra tasks. This inaccuracy isn't much of an issue for informal uses, such as sticking to a personal schedule, but for driving decisions as part of a greater pool of data, it is misleading.
At my organization, upper management is trying to use time reporting data to come up with a total cost for various initiatives. This is the kind of scenario I am talking about. Managers don't typically sit and watch everyone work, nor do they discuss time reporting entries individually, so all they end up seeing is the data. This separates them from how that data is generated and leads to inappropriate reliance on that data. From their perspective, it feels like reliable data that can be used to assign a cost to various projects, but the data probably doesn't adequately support that use case.
Earlier quoted context omitted.
Most companies I have consulted or work for make decisions based on politics and conjecture, with data being invented at a bare minimum to back the argument up, but no one doing serious analysis of it. The culture of having data collection and an ability to challenge it (whether it is flawed or not) indicates a desire for a fact-driven culture vs. a power-driven culture. They might be over-confident, but one would th…
This is entirely possible. However, when the data being measured is about complex human behavior (i.e. time allocation, productivity, work habits, etc...), I don't think we are even a little bit close to accurately representing reality. If we were "pretty close," then sure you could justify making decisions based on that data, but I don't think we are close enough for that. Even just the "time worked on X" example is…
But sometimes there are ways to make it work. At Pivotal Labs for example, you are allocated to a pair daily and then to a project or product over a few weeks. There are no interruptions or meetings beyond townhalls and daily stand ups... Unless you and your pair occasionally want a ping pong break. Productivity and resourcing becomes a bit more reasonable in this culture as its related to stories/requirements in Pivotal Tracker being completed relative to the number of pairs rather than tying it to individual worth. It's like an incredibly kind and collaborative culture that also gets good data.
Stepping back to a general management perspective, To me the most effective data is if you keep it relatively simple and do controlled experiments that are systemically tied to an external market result. A lot of companies in the logistics (and now software services) space get good at this.
Earlier quoted context omitted.
This is entirely possible. However, when the data being measured is about complex human behavior (i.e. time allocation, productivity, work habits, etc...), I don't think we are even a little bit close to accurately representing reality. If we were "pretty close," then sure you could justify making decisions based on that data, but I don't think we are close enough for that. Even just the "time worked on X" example is…
I feel you. Time tracking analytics is God awfully complicated and mostly impossible to get right unless you have a very disciplined culture. I've railed against such poor systems in my younger years. But sometimes there are ways to make it work. At Pivotal Labs for example, you are allocated to a pair daily and then to a project or product over a few weeks. There are no interruptions or meetings beyond townhalls and…
So, I guess my original sentiment is more like: "data-driven" is not a good thing on its own, because you need more than just a bunch of data, you need analytics.
It may seem more semantic than anything else, but I think there are some real differences in how people (especially managers) perceive "data-driven" vs. some other term like "analytics-driven". "Data is not magic" should be a catch phrase spread far and wide among the non-technical business world.