If predictions are like baseball, I’m bound to have a bad year in 2019, given how well things went the last time around. And given how my own interests, work life, and physical location have changed of late, I’m not entirely sure what might spring from this particular session at the keyboard.
But as I’ve noted in previous versions of this post (all 15 of them are linked at the bottom), I do these predictions in something of a fugue state – I don’t prepare in advance. I just sit down, stare at a blank page, and start to write.
One observer dubbed it “the Exxon Valdez of security breaches”: Yahoo revealed that information for a billion user accounts — including names, addresses, hashed passwords, phone numbers, birthdays, and security-question answers — was stolen from its servers in 2013 (Krebs on Security). No, you’re not having deja vu: This is a separate incident from the previously disclosed hack of another 500 million Yahoo accounts.
Let’s survey the damage. Yahoo’s deal to be acquired by Verizon looks far less likely. Countless individuals and organizations will now be that much more vulnerable to being hacked. On some broader level, our trust in the cloud-based systems that now run our lives is tattered, if not shattered.
Kelsey McGillis Interviews Matt Mayes, Director of Data Intelligence, Welcome
Why use KPIs when you can use KVIs? Welcome’s Head of Data Intelligence Matt Mayes spoke with us about value-based design, a new approach to data analysis that gives businesses a better look into why you are seeing specific results, and how to improve. It focuses on Key Value Indicators (KVI), which go a step further than Key Performance Indicators, giving businesses a clear path to act on the data mined.
Questioning Amazon’s algorithms. Amazon famously puts customers first. But a new report by ProPublica suggests that the company’s code pushes its own merchandise, even when other listings on the site offer better deals. The algorithm that chooses which seller to feature in the rectangular orange “buy box” seems to favor Amazon itself, or the “Fulfilled by Amazon” partners who pay the company to handle inventory and shipping. Read closely, though, and ProPublica’s argument turns out to be almost entirely about shipping costs. For Amazon’s favored Prime customers (who pay $100 a year for free shipping and other perks), and for anyone whose order tops $50, the shipping costs nothing, and the deal Amazon highlights really is the cheapest one. That’s still arguably a problem, but hardly the capital offense the story implies. Watchdogging algorithms is the investigative journalism of the future, and ProPublica does great work. But in its eagerness to tar Amazon it has obscured the real lessons here: Platform owners are always going to give themselves an edge. Amazon deserves credit for running an open platform that lets alert consumers find good deals and gives outside merchants access to its vast market. It has also earned a rap on the knuckles for tilting its listings to goose Prime signups. Instructive, for sure. Scandalous? Probably not.
Nuggets of gold in piles of user comments. Of course you care about user feedback and customer reviews! But who has time to read them all? Now there’s a machine-learning-style data analytics tool that will read them for you and tell you what to do (Buzzfeed). This service is called Metis, and for the moment its eyes are trained on the products of luxury merchants. For instance, it told a high-end hotelier that its guests really, really cared about customizing their breakfasts. We can assume this sort of analysis will quickly move down market as well, where the volume of feedback is even more overwhelming, and the potential payoff for small incremental improvements is that much higher. Anything that helps businesses listen better is valuable — as long as the process still allows individual human voices to make themselves heard.
“There will always be plenty of things to compute in the detailed affairs of millions of people doing complicated things.” — V. Bush
Quiet magic happens when an at-scale platform emerges unexpectedly — things previously thought impossible, or more aptly, things never imagined become commonplace faster than we can get used to them. Think of your first Google search. Your first flush of connection on Facebook. The moment a blue dot first guided you to a red destination. Coding before GitHub. Taxis before Uber. AR before Pokemon Go.
When a platform is built that allows for unexpected adjacencies, magic is unleashed and the world sparkles for a moment or two.