Big data is here and unavoidable
For years, we’ve written about big data and showcased the progression of business intelligence available now to brands of every size, in fact, most businesses have a feel for this type of data – open a spreadsheet of your sales data and you already know it’s just a bunch of numbers unless they are analyzed and filtered. Today, I want to review what big data is, how it is currently being used, what this means for the future, and most importantly, how it can be cherry picked and why it can upset entire industries.
[ba-pullquote align=”right”]”Big data” is typically consisting of at least dozens of terabytes in a single data set.[/ba-pullquote]“Big data” is defined as large data sets which cannot be managed with simple, common software that captures and processes the data, and is typically consisting of at least dozens of terabytes in a single data set. The challenges of big data are really big. It is described by Gartner analyst, Doug Laney as being three-dimensional, i.e. increasing volume (amount of data), velocity (speed of data in/out), and variety (range of data types, sources).
Let’s talk about how BIG this really is
Let me illustrate. The University of Nebraska physics department has 1.6 petabytes of data – that’s 1.6 million gigabytes in one department at one school. Boeing jet engines can produce 10 terabytes of operational information for every 30 minutes they turn. As of 2012, the average smartphone user has 736 pieces of personal data collected every day, stored for one to five years by service providers.
[ba-pullquote align=”right”]By 2020, there will be 5,200 gigabytes of data for every human on Earth.[/ba-pullquote]IBM’s chief executive, Virginia Rometty said, “By one estimate there will be 5,200 gigabytes of data for every human on the planet by 2020. And powerful new computing systems can store and make sense of it nearly instantaneously.” It has also been predicted that in the coming years, over 200,000 big data specialists will be required to make sense of the barrage of data being collected.
Big data is already being used today in a big way
Big data is a big deal and it’s not just because there’s a lot of it. In fact, today alone, SumAll raised $4 million and DataSift raised a whopping $42 million to help businesses make sense of their data as it relates to social media.
[ba-pullquote align=”right”]Retailers are analyzing your facial expressions on camera to tell if you’re a happy shopper, and tracking your gender, age, and size as you walk in the door.[/ba-pullquote]Big data is already used in amazing ways by the retail industry by analyzing shopper height and size as they walk in the door to determine age, gender, and more, and even have cameras analyzing facial expressions while you’re shopping to gauge your experience. If that doesn’t impress you, there’s already a seasoned company that is tracking “visual mentions” online so if you share a picture of your Starbucks cup on Instagram, even if you don’t say Starbucks or use GPS, Starbucks can see that their logo, even if curved, was used online on a social network.
Predicting the future with big data
But it’s not just that data is having a tremendous impact on life today, it is still a young sector with many startups yet to pop up to solve the data conundrums. SiftScience fights fraud using machine-learning that learns from data to recognize patterns of fraudulent behavior based on past examples, and Hadoop helps companies analyze massive amounts of generating about user behavior and their own operations while Recorded Future uses algorithms that unlock predictive signals based on web chatter to determine a brand anticipate risks and capitalize opportunities.
[ba-pullquote align=”right”]Intel is working on technology using big data to allow you to see three cars ahead, behind, and beside you.[/ba-pullquote]There are already projects in the works that allows forecasters to predict weather up to 42 days in advance, potentially saving lives and billions of dollars a year.Intel is working on a big data project that allows cars to communicate so drivers will be able to see three cars in front of, behind, and to the left and right – simultaneously. Ford is developing vehicle-to-vehicle and vehicle-to-infrastructure systems to warn drivers of potentially hazardous traffic events, like cars going through red lights.
But big data has some really big problems
First, and least upsetting, is that there are big problems with demographics, leaving brands with a lot of data that doesn’t yet mean much. Why? Incomplete self reporting is a huge issue because brands are still focused on using social networking profile data to gather intelligence on their site users, fans, and the like, but when they rely on this data, people may not be completely truthful (they may say they are 32, but they’re 12, and so forth). Additionally, privacy does protect users to a certain extent, blocking intelligence gathering by brands. Lastly, data is still largely inconsistent and unconnected – you may have a Twitter account and Facebook account, but a third party doesn’t know that unless (a) you use the same username consistently or (b) you grant access to both accounts through that third party.
While other problems exist (like how will we ever store all of this data, disseminate it, and make sense of it, and does it all really matter?), the biggest one we see is the potential for cherry picking, because when you look at a data set, it still takes a human to actually determine what is important to garner from that data set.
[ba-pullquote align=”right”]Big data may mean more information, but it also means more false information.[/ba-pullquote]Industry expert Nassim Taleb opined in February, “With big data, researchers have brought cherry-picking to an industrial level. Modernity provides too many variables, but too little data per variable. So the spurious relationships grow much, much faster than real information. In other words: Big data may mean more information, but it also means more false information.”
Taleb addresses something that could lead one to think that big data is faulty and bad, but perhaps Taleb is really pointing out the human nature that is still required in some instances of analyzing big data – and most people would not typically question a researcher or their methods, leaving analysis in its youngest phase subjective.
Chris Treadaway, CEO and Founder of Polygraph Media which is famous for data-driven analytics said, “To analyze big data, you have to know when you have enough data, know that you’re looking at the right data, and know how and when to draw conclusions from the data using methods developed from statistics theory and data science. That’s the great irony of “big data” – it’s as much of an art as a science, which is why the best efforts are multidisciplinary.”
“Big data can find tremendous hidden relationships,” Treadaway continued, “but you have to make sure your bias isn’t to find conclusions that don’t exist. Bias can cause the situation Taleb describes, and will cause disinformation as he says. If you’re cautious, discerning, and careful, you can make the most of big data. But there are pitfalls for the careless.”
And the coup de gras
[ba-pullquote align=”right”]Your performance data, finances, company info and more are already being repackaged for public consumption and monetization.[/ba-pullquote]The coup de gras is that professionals are being threatened by new ways big data is being used, but they are not recognizing it as a big data issue.
Several industries are seeing data about them individually, their performance, their company, their finances, all analyzed and repackaged for public consumption or monetization.
Imagine a site launches tomorrow based on publicly available data and you’re a social media consultant. Let’s say that this new site looks at who has recommended you on LinkedIn, Yelp, Angie’s List and so on, and has determined that the people recommending you are clients of yours, based on the assumption that it is the only reason they’d recommend you or review you. The new site also analyzes words and pictures used in your online bios to determine characteristics about you.
Then, they take those reviews and characteristics and quantify you into a score, giving you more points if someone from Coca Cola reviewed you than if the local dentist reviewed you, implying that you’re a higher quality consultant if you’ve worked with a major brand like Coca Cola than if you worked with a local dentist (God forbid you specialize in social media for independent medical professionals).
Then, Google gets interested in this new site and they invest, and later, they want to use that data to populate your Google+ profile, so now you, the social media consultant, has a score next to their face to determine how good you are at your job.
What’s wrong with that?
[ba-pullquote align=”right”]You must understand that data requires a human to determine what is relevant, which doesn’t always allow for the full context of the data points.[/ba-pullquote]Data is subjective, even when raw – it takes humans to determine what data points in the sea of data are relevant, and it doesn’t always take into account the context surrounding that data. You, the social media consultant, could have taken a two year sabbatical to execute social media strategies pro bono for three tiny charities, four local restaurants, two African orphanages, and a spa, earning a reputation for your high quality of work and compassion that can’t possibly quantified by a computer.
This scenario is fake. For now. But with every human generating billions of data points every year, evaluations are just the first of many steps in what is to come with big data – the data is now generated, and it is a race to see what can be displayed about you and your business so that companies can sell to you or repackage your data and sell it to someone else. Even your brand will be using big data to gain insights into your customers so you can better serve them.
[ba-pullquote align=”right”]The race is on to see what can be displayed online about you and your business, which is being repackaged and resold.[/ba-pullquote]There are pros and cons to big data, but the reality is that it is unavoidable, even if you ignore it or misunderstand it. Consumers need to begin to recognize when they see big data, and understand that it may not be the true context of that data, as it is ripe with humans’ decisions regarding what is important about a data set. This is just the beginning.
Airbnb has blocked 50K+ bookings for being too big during COVID-19
(NEWS) Airbnb has cancelled a huge number of reservations as a security precaution during COVID-19 in the past year or so.
In the last year or so, Airbnb has purposefully prevented at least 50,000 people from making irresponsible reservations on their properties, in many cases blocking those people from the platform itself. This prevention, at least in theory, helped cut down on the number of COVID parties during the pandemic.
According to The Verge, Airbnb’s head of trust and safety communication, Ben Breit, acknowledged blocked reservations in several cities across the United States, including Dallas, San Diego, and New Orleans. Breit confirmed that this response was an attempt to prevent large gatherings and parties during the height of the COVID-19 pandemic during which many areas banned group activities involving more than a few people.
While some requests for reservations were simply denied or “redirected”, many users were blocked from using Airbnb entirely. Airbnb noted that the number of blocked requests outpaced the number of people who were blocked, signifying that some accounts attempted to make more than one reservation before being removed from the platform.
Airbnb reportedly stated that “Instituting a global ban on parties and events is in the best interest of public health” prior to enacting a total ban on rentals at the beginning of 2020, a decision that gave way to the blocks and redirections in the last 12 months.
The evaluation system used to flag problematic reservations is relatively simple, according to Breit: “If you are under the age of 25 and you don’t have a history of positive reviews, we will not allow you to book an entire home listing local to where you live.”
But Airbnb didn’t entirely remove multiple-body listings or large rentals. The Verge reports that flagged users with the aforementioned criteria were still able to book both small rentals in local locations and larger rentals in reasonably distant locations.
Regardless of the optics here, Airbnb’s policy efficacy can’t be ignored. Multiple cities reported comparatively “quiet” holiday seasons–something that may contribute to Airbnb’s decision to extend their policy through the end of this summer.
The hosting company is also offering increased security measures, such as noise detection and a 24-hour hotline, at a discounted rate to property owners.
As both the vaccine gap and the proliferation of the Delta variant of COVID-19 continue to contribute to outbreaks, one can reasonably expect Airbnb to hold to this policy.
TL;DV summarizes video meetings so folks can catch up in quickly *with* context
(TECHNOLOGY) TL;DV makes catching up on video team meetings slightly more tolerable and easily digestable.
2021 was the year of virtual meetings, and while there are some perks associated with remote collaboration (I’m looking at you, pair of work pants that I didn’t have to wear once this year), these meetings often feel exponentially more arduous than their dressed-up counterparts. TL;DV, a consolidation app for Google Meet, looks to give back a bit of your time.
TL;DV (an acronym for “Too Long; Didn’t View”) is a Google Chrome recording extension that helps users specify important sections of meetings for anyone who needs to view them asynchronously. Users can tag specific segments in Google Meet sessions, transcribe audio, and leave notes above tagged sections for timestamp purposes, and the subsequent file can be shared via a host of both Google and third-party apps.
While the extension is only available for Google Meet at the time of writing, the TL;DV team has included a link to a survey for Zoom and MS Teams users on their site, thus implying that the team is looking into expanding into those platforms in the future.
The mission behind TL;DV is, according to the website, to empower users to “control how we spend our precious time” in the interest of combatting FOMO and meeting fatigue. By dramatically shortening the amount of time one must spend perusing a meeting recording, they seem well on their way to doing so.
Of course, the issue of human oversight remains. It seems likely that meeting facilitators will drop the ball here and there while tagging sections of the recording, and employees who miss crucial information in a recorded session are sure to be frustrated in the process–just not as frustrated as they might be if they attended the entire meeting live.
The current (free) version of TL;DV is in Beta, so users will have a three-hour cap on their videos. The development team promises a professional version by the end of 2021, with the added bonus of leaving prior recordings available for free for anyone who used the Beta. This is certainly an extension to keep an eye on–whether or not you’re remaining remote in 2022, virtual conferencing is no doubt here to stay.
Hiding from facial recognition is a booming business
(TECH NEWS) ‘Cloaking’ is the new way to hide your face. Companies are making big money designing cloaking apps that thwart your features by adding a layer of make up, clothing, blurring, and even transforming you into your favorite celebrity.
Facial recognition companies and those who seek to thwart them are currently locked in a grand game of cat and mouse. Though it’s been relentlessly pursued by police, politicians, and technocrats alike, the increasing use of facial recognition technology in public spaces, workplaces, and housing complexes remains a widely unpopular phenomenon.
So it’s no surprise that there is big money to be made in the field of “cloaking,” or dodging facial recognition tech – particularly during COVID times while facial coverings are, literally, in fashion.
Take Fawkes, a cloaking app designed by researchers at the University of Chicago. It is named for Guy Fawkes, the 17th century English revolutionary whose likeness was popularized as a symbol of anonymity, and solidarity in V For Vendetta.
Fawkes works by subtly overlaying a celebrity’s facial information over your selfies at the pixel level. To your friends, the changes will go completely unnoticed, but to an artificial intelligence trying to identify your face, you’d theoretically look just like Beyonce.
Fawkes isn’t available to the general public yet, but if you’re looking for strategies to fly under the radar of facial recognition, don’t fret; it is just one example of the ways in which cloaking has entered the mainstream.
Other forms of cloaking have emerged in the forms of Tik Tok makeup trends, clothes that confuse recognition algorithms, tools that automatically blur identifying features on the face, and much more. Since effective facial recognition relies on having as much information about human faces as possible, cloaking enthusiasts like Ben Zhao, Professor of computer science at the University of Chicago and co-developer of Fawkes, hope to make facial recognition less effective against the rest of the population too. In an interview with The New York Times, Zhao asserts, “our [team’s] goal is to make Clearview [AI] go away.”
For the uninitiated, Clearview AI is a start-up that recently became infamous for scraping billions of public photos from the internet and privately using them to build the database for a law enforcement facial recognition tool.
The CEO of Clearview, Hoan Ton-That, claimed that the tool would only be improved by these workarounds and that in long run, cloaking is futile. If that sounds like supervillain talk, you might see why he’s earned himself a reputation similar to the likes of Martin Shkreli or Ajit Pai with his company’s uniquely aggressive approach to data harvesting.
It all feels like the beginning of a cyberpunk western: a story of man vs. machine. The deck is stacked, the rules are undecided, and the world is watching. But so far, you can rest assured that no algorithm has completely outsmarted our own eyeballs… yet.
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