There is a dearth of people who can analyze data properly in this world.
If you need an example, check out this article regarding the internet blackout last week. Well, not the entire internet, but a decent amount of internet traffic was disrupted, including Amazon. An update by a single user triggered a bug, resulting in an outage with content provider Fastly, who detected the change within minutes and was back to 95% capacity within an hour. That's an admirable resolution time, folks.
Buried in the article is this statement:
"SEO agency Reboot estimates that the downtime cost Amazon $32M in sales."
There is no way Amazon lost $32 million dollars in sales during the outage. Let's break this down.
- First, unless it is Amazon reporting the number, then it's a just a wild guess as to how much money was lost.
- In this case, the guess is a result of taking Amazon's 2019 revenue numbers and doing some math.
- Why 2019, and not use the first quarter results from 2021? No idea, it's not as if they aren't available.
- Seriously, this SEO agency, which for some reason is the expert on revenue losses for Amazon, could not be bothered to locate more current revenue totals.
- Life isn't linear, and neither is revenue. Website sales to not happen at a constant rate. The have peaks and valleys. We have no idea, unless Amazon tells us, what a "normal" amount of sales looks like for that day and hour of the year. For all we know it is a historically slow sales day.
- Finally, if you were surfing Amazon trying to shop for grill accessories and you hit an error, you didn't go make a purchase from somewhere else. You waited 49 minutes and tried again later that day. Would be great for Amazon to report if there was a slight surge in the hours after the incident, considering they have the historical data.
But here's what I really want you to understand about all of this.
It's easy to poke holes in any particular statement or factoid. In this case, it is a statement being made solely for the purpose of getting your name into a press release, taking advantage of a situation. The points above are simple questions that I would expect any data analyst to think about, or be prepared to answer if questioned about their analysis.
The real takeaway here is that when you examine Amazon's revenue data, and you take the hourly average year over year, you find a company that earns 30+ millions of dollars each hour (on average) and yet cannot afford decent healthcare for their warehouse workers.
That's the real story here.
That's the piece of data you want from anyone analyzing revenues. Not "how much Amazon suffered" from the outage, but how much the workers suffer when the lights are on.
It's easy to take numbers and do the math. It's harder to find the connections and draw conclusions. That's the part that requires thought and consideration.
And there's a dearth of people in the world with those skills.
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Community Links
Query Store Hints Preview - Microsoft Tech Community
Microsoft announced the public preview of the Query Store hints feature, providing an easy-to-use method for shaping query plans and behavior without changing application code. Currently available in Azure SQL Database, and eventually making its way to Earthed versions of SQL Server.
The Environmental Toll of NFTs
Just because you can digitize something and sell it for large sums of money doesn't mean you should be doing that. Not without understanding the true cost of producing these items. Maybe we could create an NFT of an NFT being made and sell that for carbon offsets.
Raw Data Podcast
Shishir Mehrotra
Shishir is as ahead of the technology curve as it gets, some of his ideas have revolutionized the way that tech giants like Microsoft, Google, and YouTube operate. Now, he’s innovating again as the founder and CEO of Coda-an amazing integrated system that centers around creating Docs that are as powerful and actionable as Apps. He’s also one of the most down to Earth human beings we’ve ever had the pleasure of sitting down with!
Events
We are moving forward with plans to host Live! 360 this November in Orlando.
Live! 360 brings the IT, Developer, and Data communities together for six days of training, knowledge sharing, and networking. With unlimited access to Live! 360’s five co-located events, you and your team will get the training you need to keep you and your business competitive and future-ready.
Send any questions about the event to me at SQLRockstar@thomaslarock.com
Data Janitor Roundup
FBI sold phones to organized crime and read 27 million “encrypted” messages
These days, adversaries are not hacking their way through firewalls or breaking encryption codes. Their methods are more aligned with compromising trust. Well, turns out law enforcement is using the same techniques.
Microsoft’s Kate Crawford: ‘AI is neither artificial nor intelligent’
Artificial Intelligence, by definition, can be nothing more than a PowerShell script, a collection of IF THEN ELSE statements. On top of that, the algorithms have an inherent bias, as any piece of programmatic code would. But in the case of AI, users are led to believe the algorithms are pure. We need to raise awareness this is not true, and to understand the risks associated with blind trust of the algorithms in use today.
New system cleans messy data tables automatically
A new machine learning system from MIT uses probabilistic programming to clean dirty datasets, filling in blank cells accurately and quickly. Because it’s Bayesian, the artificial intelligence system can also tell you how confident it is in its answers. And, as we learned from Kate Crawford in the previous article, this is likely a system we shouldn't trust, either.
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