What is big data analysis?

In mathematics, quantum and traditional physics don’t really match up. Tiny atoms don’t follow the known laws of gravity that govern the movement of the stars and the trajectory of a falling apple. These present two very separate mathematical rules that describe the natural world on two different scales. Understanding human behavior follows a similar pattern.

Ask an individual how he thinks or feels about a certain topic, object or other person and you’ll probably get a pretty clear answer. Go on Amazon and read even just a dozen product reviews — things quickly become more muddied. One person thought it worked great, another had every technical problem imaginable. One loved the design but another found it too childish. How do we make sense of this? There seems to be a different set of laws governing the tastes and behaviors of individuals and groups.

Big data analysis is essentially a way to observe and make sense of the behaviors and opinions of large groups — the more the merrier. The first step is gathering the data in “big data”. Today’s technology makes it possible to build algorithms that scan the entire internet — news sites, blogs, YouTube videos, social media, Yelp reviews — basically anywhere where members of the general public can post their own opinions and thereby make their beliefs and other data available.

These sites are part of what’s known as Web 2.0, the now dominant part of the internet where average users have input and voice their minds. Big data analysis is all about gathering public input on these sites and using this content to reveal truths about public opinion and individual behaviors that would otherwise be hidden from view, the same way scientists use quantum mechanics to understand a world we would otherwise have no access to.

And how do you turn all that data into useful information? Algorithms fulfill this role as well, analyzing words, phrases, tones and even facial expressions in videos to analyze how each author felt when writing a post, comment or review. These algorithms use machine learning to constantly improve upon themselves with experience. That means they’re constantly getting better at correctly interpreting complex texts and gathering relevant information.

Taking this scientific, machine approach to gathering big data has already proven to be far more effective than reaching out to people through surveys or other forms of direct outreach. First of all, algorithms do the job far faster than any human — already a huge plus. Second, this process reveals truths that wouldn’t be apparent by talking to an individual or conducting a survey. Individuals lie, exaggerate and generally don’t know themselves that well.

Big data analysis makes it possible to interpret the sentiment of the entire general public determine what’s true and what’s not. Are complaints on a topic the rule or the exception? Not only that, the practice of sentiment analysis using big data provides statistics that help determine why people feel one way or another on any topic.

That’s because big data analysis lets you filter and narrow down and filter through all the info on Web 2.0. Want to know how a specific demographic feels or behaves? How about the residents of a specific city? Observing all this data reveals patterns that you wouldn’t otherwise know about.

One of the biggest problems with big data is that consumers have no control over where their information goes and no incentive to make it available while businesses that want to use big data have no way of knowing if what they collect is accurate or if they are violating privacy laws. Senno is changing the name of the game by offering consumers a platform where they can choose what kind of information is made available to advertisers and others while giving them an incentive to do so. Everyone owns their personal data. That’s why Senno acts as a conduit to reimburse individuals for the data they agree to share. This also ensures that businesses have access to real and reliable data that doesn’t infringe on any privacy laws. Isn’t technology amazing?

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