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Big data – buzz word or businesses’ wildest dream?

What exactly is big data? What is a petabyte? Why does it matter to a small business or independent contractor, and is it just a buzz word or is it a marketer’s dream?



What is big data?

“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, well, big. It is described by Gartner analyst, Doug Laney1 as being three-dimensional, i.e. increasing volume (amount of data), velocity (speed of data in/out), and variety (range of data types, sources).

The term is being leveraged in conferences across the globe, but most are talking about the challenges of big data as it pertains to social networking sites like Facebook or location based services like Foursquare, and the idea of the volume of data being collected on every single person and how that data is becoming so large that it is a logistical challenge to manage and harvest with any meaning.

In fact, big data was a popular theme at the recent South by Southwest Conference in Austin, with technologists and marketers bringing their unique backgrounds to the conversation, each addressing the collection of and processing of the unprecedented data being collected, for the first time outside of the government, and the concerns that go along with consumers blindly offering up the data.

How BIG is big data?

Strictly speaking to social data, consider for a moment that in a 60 second period, 23,148 apps are downloaded from the App Store, and 208,333 minutes of Angry Birds is played via smartphone3. Additionally, over 28,000 text messages are sent every second, and the average mobile phone user has 736 pieces of personal data collected every day and service providers store this information for one to five years3.

The University of Nebraska physics department4 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 turn4.

Twitter produces 20 gigabytes of data every day

Twitter serves more than 200 million users who produce over 800 tweets per second, each of which is roughly 200 bytes in size4, so on an average day, this traffic equals over 12 gigabytes, and throughout the Twitter ecosystem, the company produces a total of eight terabytes of data per day, compared to the New York Stock Exchange’s single terabyte of data daily.

Like a Boeing jet records every move, every time you interact with Facebook, it records data. It stores information on who clicks what and not just the name of the person, but that person’s profile information like hobbies, high school, family members, ethnicity, religion, employer, etc. Then, that data connects that one click with all other clicks you make within Facebook or any website with the Facebook plugin. That is a lot of information to store for a few simple clicks, especially given how much more demographic information is recorded and tracked than Twitter which creates 20 times the amount of data in a single day than the NYSE.

What to do with the data?

The big challenge behind the scenes is how to process and manage this volume of information in an era where consumers voluntarily offer thousands of data points through social networks, smartphones and the like. Social data is where the buzz is at conferences, but it is being referred to solely as big data, which clearly is much more complex than just what someone clicked on Facebook.

IT expert, Andrew McCafee recently shared the story5 of an Allstate-sponsored contest wherein a small team of data scientists quickly achieved a 340 percent improvement in Allstate insurance’s ability to predict bodily insurance claims based on car characteristics – without any expertise in insurance or automobiles, and without consulting Allstate’s well paid mathematicians who build and maintain these prediction models.

McAfee asks, “So how can it be that a small team of people who don’t even work for the company was able, in three months, to achieve a 340% improvement over Allstate’s ability to predict bodily injury insurance claims based on car characteristics? And how was the team able to do this while working only with disguised data — without, in other words, knowing the true makes and models of the cars? Welcome to the weird new world big data.”

What this means for business

All of the social data collected in recent years is finally becoming useful for more than buying a Facebook ad. True demographics are now evident, but moreover, real consumer behavior is being studied based on tremendously large amounts of data. If just one simple text message records a dozen pieces of data, imagine the depth of information a Facebook user transmits in a single day, with all of their personal data attached to each move.

Social data (or big data) is a nightmare to manage due to the sheer volume and velocity of transmission, but for businesses, it means a legitimate understanding of consumer behavior, not just what someone shared during a focus study or web poll, but really being able to track and understand how each consumer ticks. What’s next is being able to translate that behavior into an isolated profile of a specific type of buyer – this is the kind of data that marketers’ have been dreaming about for decades.

13D Data Management PDF
2Mobclix study
3How phone carriers track private data
4Information Management study
5Allstate prediction model

Lani is the Chief Operating Officer at The American Genius - she has co-authored a book, co-founded BASHH and Austin Digital Jobs, and is a seasoned business writer and editorialist with a penchant for the irreverent.

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  1. Matthew Hardy

    March 19, 2012 at 6:53 pm

    > consumers blindly offering up the data

    > … to translate that behavior into an isolated profile… marketers’ have been dreaming about for decades.

    Remember Soylent Green? Big data is us. 😉

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Tech News

Career consultants help job seekers beat AI robot interviews

(TECH NEWS) With the growth of artificial intelligence conducting the job screening, consultants in South Korea have come up with an innovative response.



job screening by robot

When it comes to resume screenings, women and people of color are regularly passed over, even if they have the exact same resume as a man. In order to give everyone a fair try, we need a system that’s less biased. With the cool, calculating depictions of artificial intelligence in modern media, it’s tempting to say that AI could help us solve our resume screening woes. After all, nothing says unbiased like a machine…right?


I mean, if you need an example of what can go wrong with AI, look no further than Microsoft’s Tay, which went from making banal conversation to spouting racist and misogynistic nonsense in less than 24 hours. Not exactly the ideal.

Sure, Tay was learning from Twitter, which is a hotbed of cruelty and conflict, but the thing is, professional software isn’t always much better. Google’s software has been caught offering biased translations (assuming, for example, if you wrote “engineer” you were referring to a man) and Amazon has been called out for using job screening software that was biased against women.

And that’s just part of what could go wrong with AI scanning your resume. After all, even if gender and race are accounted for (which, again, all bets are off), you’d better bet there are other things – like specific phrases – that these machines are on the lookout for.

So, how do you stand out when it’s a machine, not a human, judging your work? Consultants in South Korea have a solution: teach people how to work around the bots. This includes anything from resume work to learning what facial expressions are ideal for filmed interviews.

It helps that many companies use the same software to do screening. Instead of trying to prepare to impress a wide variety of humans, if someone knew the right tricks for handling an AI system, they could potentially put in much less work. For example, maybe one human interviewer likes big smiles, while the other is put off by them. The AI system, on the other hand, won’t waver from company to company.

Granted, this solution isn’t foolproof either. Not every business uses the same program to scan applicants, for instance. Plus, this tech is still in its relative infancy – a program could easily be in flux as requirements are tweaked. Who knows, maybe someday we’ll actually have application software that can more accurately serve as a judge of applicant quality.

In the meantime, there’s always AI interview classes.

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Tech News

Google chrome: The anti-cookie monster in 2022

(TECH NEWS) If you are tired of third party cookies trying to grab every bit of data about you, google has heard and responded with their new updates.



3rd party cookies

Google has announced the end of third-party tracking cookies on its Chrome browser within the next two years in an effort to grant users better means of security and privacy. With third-party cookies having been relied upon by advertising and social media networks, this move will undoubtedly have ramifications on the digital ad sector.

Google’s announcement was made in a blog post by Chrome engineering director, Justin Schuh. This follows Google’s Privacy Sandbox launch back in August, an initiative meant to brainstorm ideas concerning behavioral advertising online without using third-party cookies.

Chrome is currently the most popular browser, comprising of 64% of the global browser market. Additionally, Google has staked out its role as the world’s largest online ad company with countless partners and intermediaries. This change and any others made by Google will affect this army of partnerships.

This comes in the wake of rising popularity for anti-tracking features on web browsers across the board. Safari and Firefox have both launched updates (Intelligent Tracking Prevention for Safari and the Enhanced Tracking Prevention for Firefox) with Microsoft having recently released the new Edge browser which automatically utilizes tracking prevention. These changes have rocked share prices for ad tech companies since last year.

The two-year grace period before Chrome goes cookie-less has given the ad and media industries time to absorb the shock and develop plans of action. The transition has soften the blow, demonstrating Google’s willingness to keep positive working relations with ad partnerships. Although users can look forward to better privacy protection and choice over how their data is used, Google has made it clear it’s trying to keep balance in the web ecosystems which will likely mean compromises for everyone involved.

Chrome’s SameSite cookie update will launch in February, requiring publishers and ad tech vendors to label third-party cookies that can be used elsewhere on the web.

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Tech News

Computer vision helps AI create a recipe from just a photo

(TECH NEWS) It’s so hard to find the right recipe for that beautiful meal you saw on tv or online. Well computer vision helps AI recreate it from a picture!



computer vision recreates recipe

Ever seen at a photo of a delicious looking meal on Instagram and wondered how the heck to make that? Now there’s an AI for that, kind of.

Facebook’s AI research lab has been developing a system that can analyze a photo of food and then create a recipe. So, is Facebook trying to take on all the food bloggers of the world now too?

Well, not exactly, the AI is part of an ongoing effort to teach AI how to see and then understand the visual world. Food is just a fun and challenging training exercise. They have been referring to it as “inverse cooking.”

According to Facebook, “The “inverse cooking” system uses computer vision, technology that extracts information from digital images and videos to give computers a high level of understanding of the visual world,”

The concept of computer vision isn’t new. Computer vision is the guiding force behind mobile apps that can identify something just by snapping a picture. If you’ve ever taken a photo of your credit card on an app instead of typing out all the numbers, then you’ve seen computer vision in action.

Facebook researchers insist that this is no ordinary computer vision because their system uses two networks to arrive at the solution, therefore increasing accuracy. According to Facebook research scientist Michal Drozdzal, the system works by dividing the problem into two parts. A neutral network works to identify ingredients that are visible in the image, while the second network pulls a recipe from a kind of database.

These two networks have been the key to researcher’s success with more complicated dishes where you can’t necessarily see every ingredient. Of course, the tech team hasn’t stepped foot in the kitchen yet, so the jury is still out.

This sounds neat and all, but why should you care if the computer is learning how to cook?

Research projects like this one carry AI technology a long way. As the AI gets smarter and expands its limits, researchers are able to conceptualize new ways to put the technology to use in our everyday lives. For now, AI like this is saving you the trouble of typing out your entire credit card number, but someday it could analyze images on a much grander scale.

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