big data

Big data is useful, scary, and more subjective than you know

December 4, 2013
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big data Big data is useful, scary, and more subjective than you know

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.

“Big data” is typically consisting of at least dozens of terabytes in a single data set.
“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).



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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.

By 2020, there will be 5,200 gigabytes of data for every human on Earth.
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.

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.
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.

Intel is working on technology using big data to allow you to see three cars ahead, behind, and beside you.
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.

Big data may mean more information, but it also means more false information.
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

Your performance data, finances, company info and more are already being repackaged for public consumption and monetization.
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?

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.
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.

The race is on to see what can be displayed online about you and your business, which is being repackaged and resold.
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.

AGBeat Chief Operating Officer: Lani, named 100 Most Influential, as well as 12 Most Influential Women in Blogging, Bashh Founder, Out and about in Austin A Lister, is a business and tech writer and startup consultant hailing from the great state of Texas in the city of Austin. As a digital native, Lani is immersed not only in advanced technologies and new media, but is also a stats nerd often buried in piles of reports. Lani is a proven leader, thoughtful speaker, and vested partner at AGBeat.



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