Big Data, Plainly Spoken (aka Numbers Rule Your World)

Know your data 24: trust eroding

Big Tech is in serious danger of losing our trust when it comes to our data. For the longest time, they have sold consumers on a bargain: in return for providing convenience and services, they claim our personal data and sell them to the highest bidders.

That bargain requires consumers to trust Big Tech as good custodians of the data.

A number of recent revelations has eroded that trust. And I'm not talking about Cambridge Analytica.

Here are two recent examples that have not received the attention that they deserve.

Android phones found to track users even if they turn off Location History.

The Associated Press first broke this story, which was later verified by Princeton researchers. Android users who choose to turn off Location History obviously do so intending that they not be tracked.

In the same story, it is also disclosed that iPhone users who have installed Google apps are also being tracked at all times. When these users disable location tracking for the Google app, the app stops saving locations in the folder called Location History and saves the location data in a different folder instead. (Note also: the phone itself would not know how to do this; software engineers wrote code to enable this feature.)

This revelation is highly damaging. There have always been suspicion that your phone (or TV or other devices) is spying on you even if it is turned off. There are no technical obstacles to making this capability. The only reason why customers are not worried is that they trust Big Tech when they claim their phones (or other devices) do not have that capability.

Google does not deny this is happening, and in fact, argues that it is transparent in dealing with users.

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Facebook found to have taken users' phone numbers provided for "two-factor" authentication and sold them to advertisers.

For a number of years now, Big Tech and small tech alike have bombarded us with security warnings, and made claims that "two-factor" authentication is the ultimate solution to online security issues. Setting up two factor requires the user to provide a cell phone number, which immediately removes any semblance of online anonymity (unless you get your hands on a burner phone).

It's been known in the marketing world forever that the key to unlocking anyone's data is your phone number. That's because the cell phone is almost always a personal device.

I don't typically consent to two factor for fear that the phone number could then become marketing fodder. This story, by Gizmodo, confirms that my worst fear is real. Similar to Google above, Facebook confesses to coopting those phone numbers, and even maintains that users have consented to such use.

This revelation is also highly damaging. In fact, it damages the reputation of the security industry. How are we to trust them when they tell us to use things like phone numbers, fingerprints, eye scans, etc. for security purposes when we cannot trust that such private data would not be transferred to other entities?

In the same story, it is disclosed that Facebook also harvests phone numbers for advertisers under a variety of other pretexts. For example, when you provide your phone number to receive alerts about new log-ins to your account, that number ends up on marketing lists. Further, when you upload your contact list to Facebook, you have exposed all of your friends' phone numbers, turning them into fair game for Facebook's advertising clients!

This further relevation is even more damaging. Lots of websites ask for our phone numbers under the pretext of servicing our accounts, but we can no longer trust any of them that they would not turn our personal data over to advertisers.

 

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Judging by their subsequent pronouncements when caught red-handed, we fear that Big Tech is not sensing the importance of "trust" in their business model. If the trust goes away, these businesses will be in for a nasty surprise.

It's hard to tell apart business journalism and PR: the Wework edition

Most of business journalism reads like PR put out by companies. Still, some pieces are more blatant than others. The NY Times just printed an example of such gibberish, this opinion piece about Wework.

Wework is one of those "unicorn" startups that investors are hoping to sell to the public before the impending stock market collapse for gazillion dollars. Its main business is renting office space to mostly small businesses, entrepreneurs, or small divisions of large companies. The primary "innovation" is the short-term lease, as short as month-by-month. (Disclosure: my startup was a sub-tenant of Wework through the Harvard Business School Startup Studio. I am a satisfied customer.)

As with many successful startups, Wework offers a product to a segment of customers who find it irresistible. For a startup business like mine, it's much too risky to commit to a 10- or 20-year commercial lease. Most startups fail within a short few years, and the entrepreneurs are already taking on a lot of other risks. So they have an attractive product but does it have a viable business model to deliver that product profitably?

With few exceptions, Wework does not own the buildings it leases out - it has signed long-term leases with landlords, and then rent to startups and smaller businesses who sign short-term leases, thus the risk of the long-term lease shifts from the entrepreneurs to Wework.

The Times article presents an argument by analogy to refute an obvious weakness of Wework's business model. This same weakness has felled many bankrupt companies in the past. When you have long-term obligations and short-term revenue sources, you're highly vulnerable to the "credit crunch." This scenario played out in the Great Recession when many companies suddenly found that banks were unwilling to roll over their debt, which immediately put them out of existence. In the case of Wework, critics also worry that even the revenue sources will disappear since many startups will fold due to lack of customers and/or funding during a recession.

The reporter (Andrew Ross Sorkin) now builds a very flimsy argument based on "too big to fail." It cites some absolute numbers, such as square feet of space it has signed leases for, number of workers using the space, number of employees, etc. Then he arrived at the conclusion that: "when the next economic downturn comes — and it will — WeWork’s landlords will actually be less likely to evict the company if it doesn’t pay its rent."

For an argument based on big numbers, this one is particularly unmoored. None of those numbers are provided in context. What proportion of occupied commercial real estate has Wework leased out? What proportion of revenues does Wework contribute to the biggest landlords? The 1,300 employees may make Wework "one of the biggest architecture firms in the world" but the number does not qualify it as "too big to fail". (Fact check: the largest architecture firm is Gensler, according to Architectural Record, which employs over 5,000, according to Wikipedia. So Gensler is about 4 times the size of Wework.)

In addition, the argument misleads readers about what happened during the Great Recession. The banks who were the creditors did not renegotiate contracts with the debtors who no longer could pay. Far from it, they got bailed out by the U.S. government, using the argument that the failure of those loans would "bring down the entire global economy as we know it". 

This brings me to the final, and most salient, issue. What would those generous landlords be saving in this case? Not the global economy, but a business yet to find a self-sustainable business model - as Sorkin himself cited earlier in the article. Wework is currently running losses of over a billion dollars a year. According to Bloomberg, Wework made ~$800 million in 2017, and spent $1.8 billion. We also know Wework has sold junk bonds but I'm not sure we know the full amount of debt it is carrying on its books.

Wework has made some small steps to reducing its risk. It has indeed attracted some larger companies to rent space with longer leases. But those companies are doing it because the "longer" leases are still much shorter than what they could get from traditional landlords. Usually, the space is used by short-term, even experimental, business units.

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Now on to something of interest only to number geeks. I have questions about this statement: "the company says its average customer has a lease of seven to eight months and new customers are signing leases that average 20 months."

I wonder how this "average" is computed. Note that most of the little guys are on month-by-month leases, which renew until cancelled. This is "right-censored" data in which we don't know the end date yet, so a lot of assumptions and projections go into that average. It's got to be an average of projections as opposed to an average of observed data.

Also, I expect that the distribution of the size of customers will be heavily "right-skewed": a vast majority of customers will be freelancers, and small offices (who sign very short leases), with a few larger tenants (signing longer leases). It's challenging to interpret an "average" unless we also know the "median".

 

Exercising fails to stop obesity, doh

Axios has an informative article about obesity, and the various remedies such as exercising, diets, and so on. Their headline is: "Health and wellness are booming, but we're fatter than ever." They have compiled some data, shown in a triplet of graphs:

The problem of obesity is complex, and fascinating from a data perspective. I devoted an entire chapter of Numbersense (link) to issues around measuring obesity.

There is much more underneath the surface than what is presented here. Let me unpack the layers of complexity.

Correlation is not Causation

The simplest issue to explain - just because statisticians have been screaming about it forever. If you look at the obesity chart and the gym chart, it is entirely accurate to say that gym membership has been rising in lock step with obesity rate during this decade. Both metrics rose by roughly 20%; and so it is very tempting to argue that going to gyms makes you fatter.

Of course, if you draw that conclusion, you've just been disinvited from the party of statisticians.

Ecological Fallacy

Here's the disturbing bit: the charts are also compatible with the opposite conclusion - that gym membership reduces obesity. This is an example of why it's so hard to interpret observational data.

Note that the data analyst collapsed a 2x2 matrix into two aggregate rates. Imagine four types of people: those with or without gym membership, crossed with those who are obese or not obese. When you're aware of the four types, you should realize that the rate of obesity, aggregated across gym membership, is not a great metric. It's pretty obvious that the obesity rate of those who are gym members is lower than that of those who do not have membership. The average rate paints them with the same brush.

In the same way, gym membership, aggregated across obese and not obese people, is not a great metric.

You can reasonably assume that obesity rate for the gym members should be lower than the average obesity rate, for example, if the average is 25%, then perhaps the obesity rate for non-gym members is 15%.

It's possible that the 15% rate has not changed over time but if the obesity rate of the non-gym-members increases, the overall obesity rate will increase (note that there are five times as many non-gym-members as there are gym members). The 15% rate for gym members could even have improved, and the overall obesity rate could still decline to 30% - it just requires the non-gym-members to get even more obese.

When aggregating the rates, some information is lost, and that weakens our ability to draw conclusions about individuals.

Indirect Metrics

Gym membership is not the same as gym usage. The gym's ability to influence obesity would require usage, not just membership.

 

CDC Diet Recommendation

The bit about the CDC complaining that people don't consume the recommended levels of fruits and vegetables makes me wonder if their problem formulation is overly simplistic. The dietary guidelines appear to be an optimization of nutritional benefits. But the real problem is to maximize nutritional benefits under a budget constraint. Each item in the basket of recommended foods delivers an amount of benefits at a level of cost. The total cost can't exceed the household budget.

For anyone taking a traditional class on optimization, "the diet problem" is often the first problem discussed. Here is one exposition of the diet problem.