Today, almost every large company collects data about its customers — reams and reams of raw, unstructured data. And they aren’t storing it for posterity. They are using it to do what businesses always try to do: Sell more widgets. More specifically, companies are using big data to identify new customers, advertise more effectively, and develop new products and services.

An apocryphal-sounding story about how companies are using big data to find customers recently made its way around cyberspace. Target tracks customers’ purchases to determine what products to advertise. According to the New York Times, an enterprising researcher at Target thought to analyze purchase data for women and developed a list of 25 products that, when considered together, generated a pregnancy-prediction score. The researcher analyzed every woman in Target’s database, and Target sent women with high scores baby-related coupons and advertisements.

A teenage girl, using her parents Target Value Card, bought four items — scent-free soap, extra-large bag of cotton balls, hand-sanitizer, and washcloths. Target concluded that she was pregnant and sent baby-related coupons to her home address. The girl’s father called the local Target store manager to complain. The manager apologized. But when the manager followed up a few days later, the father said he had learned that Target was right. His daughter was pregnant.

To the extent that big data is being used to identify the right customers — it is also being used to identify the wrong ones. Kreditech, a European company, uses more than 8,000 sources including social media, to create a unique credit score for consumers, which is then sells to banks and other lenders. And they have discovered some surprising correlations between social media behaviors and financial stability. For example, if your Facebook friends use all capital letters, your score is docked. Title insurance underwriters are using the same tools to evaluate specific risks.

In the same way, big data analytics are being used reinvent advertising. The name of the game used to be broadcasting, which worked on a carpet-bomb theory: The bigger the audience, the more likely you are to hit a target. Big data has turned that theory on its head. Now it is all about narrowcasting: Knowing exactly who your target consumer is and how to reach them as efficiently as possible.

Google and Facebook have built multibillion-dollar business on their ability to provide narrowcasting services. Google, of course, is free. But through its search engine and email services, Google collects a vast amount of data that it uses to create a data profile for each visitor. These profiles allow companies to strategically place their ads in front of individuals who are likely to be interested in their products. Facebook operates the same way. It encourages individuals to use its site by offering a free service. Then it tracks all user activity and creates an incredibly valuable profile for marketing purposes.

One example of narrowcasting is President Obama’s 2008 reelection campaign. The campaign hired more than 50 data analysts and behavioral sciences experts and housed them in a bunker-like room called “the Cave” located in the Chicago campaign headquarters. These experts worked 16-hour days over 16 months analyzing traditional campaign data from call-centers with publicly available and social media data about voters in swing states. They identified roughly 15 million undecided voters who might be persuaded to vote for the President. Using data from cable boxes, the analysts determined what those 15 million voters watched on TV. (Turns out they watched “Judge Joe Brown” in the afternoon and late-night repeats of “Inside Edition.”) Obama targeted his TV advertising to those viewers. Because Romney was not using this system, Obama’s ads went uncontested. And, because he eschewed the standard prime-time slots, the Obama campaign was able to purchase 40,000 more advertising spots than Romney and spend $90 million less.

Big data is not only driving sales, it is also driving innovation. In their book, Big Data: A Revolution That Will Transform How We Live and Work, Kenneth Cukier and Viktor Mayer-Schoenberger describe how this process of “datification,” taking something that has never been treated as data before and transforming it into a numerically quantified format, is drastically changing how we conduct R&D.

For example, one researcher figured out a way to assign a numerical value to 360 pressure points on a person’s back, hips, and buttocks. Using software that can read these values, a car company could teach a car to recognize who is sitting in the seat. This could prevent auto theft, by requiring a code for someone whose “seat” is not recognized. In another case, IBM patented a special type of floor that datafies foot traffic. By digitizing a footprint, the floor can determine when a person walks into the room. This information could be used to automate lights or initiate security protocols. Retailers, convention centers, and sports arenas could also use this technology to measure, analyze, and manage foot traffic.

And big data is not just about big profits. In their book, Cukier and Mayer-Schoenberger discuss ways in which our ability to manage full data sets is improving government services and public safety. For example, the Center for Disease Control (CDC) has historically had to rely on doctor reports of influenza outbreaks in order to track epidemics. This process had obvious drawbacks. Patients usually waited a few days before they go to the doctor with symptoms, and doctors usually waited a few days before they report to the CDC. The CDC was typically weeks behind a flu outbreak in providing vaccines.

A couple years ago, Google proposed a different approach. Using historical data from the CDC, Google compared search term queries against geographical areas that were known to have had flu outbreaks. Google found spikes in certain search terms where flu outbreaks occurred and identified forty-five terms that were strongly correlated with the outbreak of flu. Google then started tracking the use of those terms and is now able to accurately predict when a flu outbreak is occurring in real time. Using this data, the CDC can act immediately.

Big data a big deal for businesses. The use of big data will continue to increase exponentially — as will its applications. The promise of big data extends to other areas including health care and education. These new uses will raises a host of legal issues that will impact the way that lawyers litigate and advise clients — and how lawyers run their own law firms. These topics will be discussed in the next article in this series.