dworin

It's very hard to find people with both deep domain knowledge and deep math/statistics knowledge, in the same way that it's often hard to find people with deep programming knowledge and deep business knowledge.

We solve the latter problem by having business analysts or product managers that "get" the technology enough to provide direction, even if they wouldn't be effective implementing it themselves. I think there's a next phase where, as we try to do data-science at scale, we look for a similar role that deeply understands the business and knows enough about the analytical techniques to define the problem and work with a team of specialists to figure out the best analytical approach.

People talk about data science teams being multifunctional - with programmers, data engineers, data scientists, and designers - but we always leave out the role for someone with deep business expertise and shallow but meaningful data science expertise.

The best business practices to emulate come from successful in boring and fiercely competitive industries, where you can see if those practices are really making a difference. When a company has a single massively profitable product that's mostly protected from competition, it's hard to know if anything they're doing makes sense at all. But boring companies don't make exciting Fast Company headlines.

I think this is a fairly common pervasive, semi-conscious but unspoken feeling throughout many marketing and communications measurement organizations.

>>> the change is mostly about moving this risk (and potential reward related to it).

This is exactly right! And the theory behind it is actually one of the papers behind this year's economics Nobel Prize.

Different billing models aren't about paying for outcomes - they're about how to both incentivize and compensate when 1) there is uncertainty about the effort and ability required and 2) there is uncertainty about the degree to which effort and ability will lead to outcomes and 3) outcomes are costly or difficult to measure.

Different ways of billing are really about who takes on the risk (and gets both the upside or the downside), as well as how to align incentives between the service provider and the recipient. Oftentimes risk-sharing and incentive-alignment are in conflict with each other, which is why this isn't an easy topic.

This echoes some of my recent frustrations with customer success organizations to a T. I'll also add in that many customer success organizations feel like the Post-Sell Upsell Sales Team rather than the Make My Customer Successful Team (upsells might be a goal, but it shouldn't feel that way to customers).

At most SaaS companies, customer success is designed as a slightly-better support function, rather than a value-added consultative function. This is actually an evolution from the old model, where you'd have a more expensive professional services function that accompanied enterprise software purchases, usually because the implementation itself required a great deal of technical sophistication that the cloud has made obsolete.

Customer success managers tend to be lower paid than consultants who have domain expertise and strategic thinking skills. They also typically handle a much larger client load, which makes it hard to invest time in relationships, and have automated their work to the point of annoyance, which makes it hard for them to individualize.

On the other hand, it's not always such a great idea for a company, especially a SaaS company looking to make an exit, to have their own professional services function, let alone a paid services function. Consultants are expensive and have much lower margins than software. They also add headcount and bring down valuations when it's time to sell the business. That's why most software companies a partner ecosystem around their software, rather than trying to do it in house.

Based on my experience, there are a handful of customer success organizations that get this right. But as a discipline, customer success is still meandering around trying to figure out what it really is. At most companies, the legacy is in a customer support function, not a professional services, consultative sales, or account management function. So that's the level of service you get. I'll be interested to see if they'll respond to feedback like the posters and evolve towards a more consultative model.

In my experience "expertise in making sense of data" is only one piece of the puzzle, and often not even the most important one.

Domain expertise is hugely important at making sense of data. Self-service allows domain experts to quickly look at data themselves. They may have to learn skills in data-sensemaking, but the expert in data will have to learn about the specific domain (often much harder).

I'm noticing that more and more people in a variety of fields have at least a passable understanding of how to make sense of data. For quick questions, self-service access to data makes the process much faster with little risk.

I've been in organizations that tried to put data behind gatekeepers who would protect users from making mistakes. In those cases, we made a lot more mistakes because not enough analysis was done, or people didn't have access to data.

I've been in other organizations where we let everyone look at the data. Sure, some people made mistakes, but we used that as an opportunity to teach.

If I had to bet on which type of firm would win, I'd bet on the latter. I'm deeply skeptical of the promises made by BI vendors, but self-service analytics isn't one of them.

As a user more than an engineer, most of the apps I use have 'one feature,' but the one feature that's important to me is different than the one feature that's important to other people. My 'one feature' may also be a unique combination of smaller features that, when brought together, solve one very important problem for me, and some different combination of smaller features in the same tool will solve a different problem for someone else.

There's actually a lot of research about confidence (and an entire pop-psychology book on the subject).

The basic finding is similar to what the article says: too much confidence is bad because it makes you miss things, but so is too little confidence if it makes it hard for you to do anything. The trick is calibrating confidence and how you react to under/over-confidence.

In a situation of low confidence, you want to take action to grow your confidence by getting feedback, learning new skills, or collaborating to get new ideas. When you have too much confidence, you need to figure out what you're missing or get a more realistic perspective on where you are.

For better or worse, there's also social value in projecting confidence (not arrogance, but confidence) in certain situations.

"2. Internal employees - Stack Overflow said this has been available internally for a bit, but when employees find out what others are making they are inclined to compare their own efforts/abilities vs others. It can lead to people either asking for raises to match their co-workers, or perhaps feeling slighted and seeking other employers."

One nice thing about being transparent and consistent with salaries is that you can have an objective conversation with someone about the reasons that they're making less, versus having to rely on vague, irrelevant, or harmful explanations like "he was making more at another company," or "he negotiated harder." If someone thinks they should be making what another developer is being paid, they need to make the case based on clearly laid out criteria.

There's no compensation system that makes everyone happy, and there shouldn't be. You want a system that leaves people knowing where they stand, what it takes for them to make more, and management that encourages them to grow into that amount.

>> Good HR means three things: a clear management structure, a way for people to talk about workplace issues and concerns, and pathways for people to evolve in their careers.

I've seen a number of startups who think the only part of HR where they need to invest resources is recruiting, right up to the point where the wheels start coming off. Once you're feeling the pain points - disgruntled employees, people leaving, communication problems, etc... - it takes a lot more time to get things working again.

It's hard because these issues aren't the things recruiters are good at solving, so they need more specialized knowledge, but at 20-50 people don't have the capacity to bring on the right expertise.

I've said the same thing to leaders at multiple startups (some well known) and they all gave me a look like I was a neophyte who didn't understand the motivating power of ping pong tables.

From the article: "If the Rock And Roll Hall of Fame wants to be taken seriously, they need to put their books out in the public. They need to fucking become transparent"

Am I missing some deeper story? They have an 82 page form 990 on their own website. Most non-profits don't disclose anywhere near as much, and few post it on their own site.

https://rockhall.com/media/assets/files/form990.pdf

The missing part of the advice is that you need to hire the best _for your company_. But there isn't an objective definition of 'best.' People can be great at one job and not right for another, great in one company and not right for another. Hiring and job hunting is about fit.

I've worked with people who were A players, hired into a new firm, and quickly spun out. Other people were C players, found a new job, and quickly became A players.

If you're a company who's great at training people, you can hire for energy and eagerness to learn. If you expect people to know everything on day one, hire for experience. The same people who succeed in one of those companies will fail in the other. A big part of hiring is knowing yourself and knowing what makes people successful.

Michael Lewis had an article a few years ago where he described this effect using Shane Battier as the example - none of his stats were spectacular, but somehow his team played better when he was on the court. http://www.nytimes.com/2009/02/15/magazine/15Battier-t.html

This is just a hypothesis, but maybe for viewability for ads? There's a big controversy among advertisers/publishers over the difference between "was my ad shown" and "was my ad seen." Clicking Read More at least says you're going to look a little down the page. Some evidence for it is that right below the "Read More" button, there's usually an annoying little ad square.

This sounds more like having a distributor than having a landlord.

Imagine you create a craft beer. You can't build a relationship with all of the bars, restaurants, nightclubs, gas stations, convenience stores, and grocery stores that sell beer. So you sell through a distributor, who has all of those relationships. The amount of product you sell is now at the whim of that distributor.

You own the product. The distributor owns the means of getting it to the audience. You can sell some direct, but not as much as the distributor can. So you go to them for volume.

You can increase sales by creating a better product, so consumers demand it (i.e. high quality content). You can provide incentives to the distributor (i.e. paid ads on FB). You can do some of your own marketing to make it appear more valuable. But ultimately, you have to sell to the distributor as much as the end customer.

And if you're in a popular category (content/craft beer), the more people that enter, and the more product they have, the less share of shelf-space (which is like the facebook feed) any one product is going to get.

There are some other great explanations in this thread (survivor bias seems very plausible), but I'll throw out another based on my experience with other construction trades: economics.

If you look at old buildings, you tend to notice that they also have a lot of intricate plaster-work that you never see anymore. Why? Because it used to be much cheaper to hire skilled labor than it is today. You can see a similar trend every year in the Christmas Price Index, which tracks the cost of the items in the 12 Days of Christmas song. The prices of goods tend to stay stable, while the price of labor tends to increase significantly.

For our brick buildings, I tried to find the best numbers I could, and here's what I came up with: In 1894, bricks cost about $5.70/thousand [1], which is $165.51 in today's dollars Today, you can get bricks wholesale for $220/thousand - and that's what I found online, I imagine an actual wholesaler is less. [2] That's an increase of about 37%

For the bricklayer, the average wage in 1891 was $4/day, which is about $110 in today's dollars [3]

Today, the median bricklayer pay is $24/hour [4], which is $192 per 8 hour day.

That's an increase of 75% in the real wages of the bricklayer, and it means that the rate labor costs have increased is double the rate of material costs.

In 1891, it may have made financial sense to pay for a bricklayer to make intricate, high quality buildings. In the past few decades, it's likely that's no longer the case.

[1] https://books.google.com/books?id=Oo4oAAAAYAAJ&pg=PA953&lpg=...

[2]http://brickbroker.com/brick.html [3] https://books.google.com/books?id=cNdEAQAAMAAJ&pg=PA722&lpg=... [4] http://www.payscale.com/research/US/Job=Bricklayer/Hourly_Ra...

>>> Wish I could tell all of this to my 14-year-old self.

I often think this about any number of things, and then I realize there were probably a lot of people in my life who were telling them to me, and I just didn't want to listen to them. Being 14 is really hard.

I use my Surface Pro 3 on my lap a fair amount. It's not the best, but it's functional if I'm in a seat at the airport, or a hotel lobby, or on the couch. I've also realized that a lot of the work that I do in those situations involves more reading than intensive typing, and I'll just switch to using it as a tablet.

I would say that "lapability" comes up with about the same frequency that "I need to use my laptop while standing and it's a pain" and "this laptop is getting really hot on my lap" have happened with previous laptops I've owned.

It's a Nespresso machine - it's not even that expensive.

http://www.theguardian.com/lifeandstyle/wordofmouth/2013/apr...

I could see any of the large professional services firms or government/defense contractors being interested.

From my experience with companies that sell consulting/technology to the government, 'blowing the whistle' is more likely to lead to people questioning how the government spent so much money on something so rudimentary, rather than discovering something sinister.

A large number of my clients have been research companies and consulting firms, and one thing I've seen is that 'research based consulting' is a phrase that research companies frequently use to sell themselves as more than researchers, but that is completely unimportant to clients. On the client side, it's assumed that most consultants will use research in some form or another.

In my experience, Data Science isn't replacing statistics, it's replacing Business Intelligence. And a lot of that is driven by PR and marketing departments that saw that business intelligence was boring and trapped them in the IT department, so they needed new buzz words. You can see the relationship on Google Trends as well, and it's stronger than comparing it to 'statistician', which looks flat more than declining:

https://www.google.com/trends/explore#q=data%20science%2C%20...

I've seen a huge range in the people calling themselves data scientists. Some have very analytically intensive academic degrees, others just finished a data science boot camp, and there are a lot of people that used to be called 'business analysts' who are basically doing the same job with a fancier title. In every group, I've had people tell me that what they're doing is really data science, because data science needs the (academic|integrated|business) perspective that they have, and what the other people are doing isn't really data science.

The vision of self-driving cars using special roads has actually been around since the 50s. Here's a commercial GM put together about it (admittedly using different technology at the time): https://www.youtube.com/watch?v=F2iRDYnzwtk

Making phones right now is really a game of supply chain mastery and scale. Nokia didn't have an particular advantage in either area, especially when it came to smart phones. If they started making android phones, it's more likely they'd be another HTC or LG than another Samsung or Apple.

I disagree. I'm not promising someone a job for life and they're not promising to work for me forever. Part of the value I offer them as an employee is what I can do for their career going forward. Sometimes that's a prestigious name, other times it's by giving them better opportunities to grow. It's one thing to say "I want people who want to work here," it's another to discount people who might want one day want to work someplace else.

I love that he uses "Spacely's Sprockets' as his example customer. I used to always use companies from cartoons in the case studies I made up for trainings, but I reached a point a few years ago where the people in the trainings had never heard of the companies I was using (Spacely's Sprockets, Omni Consumer Products, Slate Construction Company).

I use onenote on my surface for this, although I've still found myself going back to real notepads, mostly because the battery life is better.