Big Data Case Studies: Parking to Media to HR

You expect to see Big Data analytics used in the technology sector for all types of applications, including social media, sales, user logins, and security violations. You also expect to see Big Data applied to medicine, travel, retail business, demographics, and the television and film industries.

But there are a few places that you might not expect to find the application of Big Data and analytics – situations that the uninitiated might refer to as mundane. These so-called mundane Big Data applications result in billions of dollars in revenue to start-up companies as well as blue chips. Big Data is big business and data collectors are everywhere.

Big Data is one of those terms that appears so often in technology articles that it could have its own analytics realm associated with it. One can imagine a data warehouse query that asks how many unique appearances of the term “Big Data” have there been since January 1, 2000. One can also imagine a numerical result in the hundreds of thousands.

This article offers case studies of some of the unusual places that you’ll find Big Data and analytics. Some of them will surprise you, while others will make you wonder why it’s important to know what the data offers. But Big Data proponents maintain that all these collected data points form patterns that, in some cases, promise to improve the quality of your life, while others may not be so beneficial.

These six Big Data applications are in no particular order.

1. Parking

With at least two parking spaces available for every man, woman, and child in the US, you’d think that parking wouldn’t be a problem, but it is. It’s a big problem—a big problem that companies like Smarking want to solve with Big Data analytics.

You might find it surprising, but parking facilities lose large amounts of money due to unused spaces. During busy times most of these business lack a demand control tool.

Smarking’s data allows parking businesses to improve their operations, boost their revenues, and increase their customer satisfaction. Its data helps parking managers to optimize staffing by reducing the number of staff during predictably slow times and by increasing staff during busy times.

Optimizing staff numbers at parking facilities helps reduce overall operational costs for the facility. Its data helps boost revenue by allowing facility managers to foresee demand in order to purchase advertising for event parking. Its data also allows parking facility managers to share availability information with customers through apps or web sites as well as add promotional information.

Smarking’s other Big Data information allows facility managers to access competitive pricing data, customer segmentation data, demand predictions, staffing tables, and even a revenue simulator. The next time you park, you might want to consider the science behind what you’re doing and your contribution to it.

2. Skiing

In 2003, SKI magazine conducted a study about ticket fraud at ski resorts and estimated that eight percent of ticket revenue is lost due to theft. Now, ski resorts use RFID tags inserted into the tickets. The result is that ski resorts can gather data on the busiest slopes, help track skiers, supply skiers with their own statistics, and have decreased ticket fraud to near zero. The new tickets also speed up the often long lift lines by allowing automated scanners to track tickets.

Big Data doesn’t just happen with tickets. Ski resort managers rely on complex data from weather prediction sources to tell them when and where to make snow or to not make snow on a daily basis during ski season. Without this continuous stream of data, managers would have to rely on weather reports that might have a several hour lag time.

Big Data analytics also informs resorts of how often skiers visit the resort in order to make discount offers to them.

But the most surprising use of Big Data and skiing comes from ski equipment manufacturers who use Big Data analytics to manage purchasing and production. Production cycles have been reduced to a few weeks. And Tecnica Group, one of Europe’s largest sportswear brands, and maker of Blizzard Skis, Nordica skis and boots, Moon Boots and Rollerblade uses IBM Cognos to reconfigure its production lines. Tecnica Group reports that its Big Data analytics has helped it become 48 percent more accurate in predicting demand and has resulted in 30 percent less idle time on the production line.

3. Outdoor media

Outdoor media, the outdoor industry, out-of-home, and outdoor advertising are all terms that describe an entire marketing genre that includes billboards, posters, digital screens, signage, and mobile ads that you see as “wraps” on cars, busses, and other types of transportation. The most successful research company to date is Route in the UK. Route has conducted the largest and most thorough outdoor media research project that interviewed and tracked, via GPS, 28,000 people across the UK.

Route’s initial research project includes 160 million records and 1,600 towns. This vast amount of data was gathered from interviews, eye-tracking information, and GPS information from participants. Some of the data points extracted from the survey and study provide insight into travel times, distances, age ranges, day of week information, and peak travel times.

But how does all this data help advertisers? It’s simple really. When companies plan a marketing campaign that includes outdoor advertising, this research tells them when and where people will be, what they’ll be doing, and most important of all, what they’ll be looking at. No longer will agencies purchase ad space on park benches, billboards, or on the sides of tour busses; what they’ll purchase is an audience.

For example, if you know that a particular demographic, say affluent people between the ages of 45 to 54 years of age walk along a particular path in the early mornings on their way to work, you’d place your outdoor advertising in visible places where they stop, where they wait for traffic signals, and where they look. It seems simple when you read the description, but the data itself is quite complex. The beauty of Big Data is that no matter how big it is, there’s a way to tame it into usable and actionable information.

4. Traffic

It makes sense to think that cities track the number of cars on roads, how fast we travel, and which routes are the most traveled. But did you know that these data points provide more than just how fast we wear out blacktop surfaces?

Big Data analytics supplies wear and tear data to rock-dropping truck drivers  and flag waving construction crews. Traffic data assists city planners in creating alternate routes during events. It also provides valuable feedback in timing traffic lights that seem to spend more time on red than green.

You might be surprised that clever city engineers are actually using your Bluetooth, Wi-Fi, GPS, and cellular data to gather information about commuter travel and traffic habits. That’s right, they’re using your transmissions to gather data. And they use it because setting up equipment to monitor major roadways is very expensive – and monitoring every tributary along the way would empty the coffers of the country’s financially flushest cities.

Using relatively inexpensive sensors allows city planners and engineers to divert traffic in cases of accidents, construction, events, funerals, or weather conditions. But the solution doesn’t stop at simply monitoring and diverting traffic to less congested routes. The future of connected car technology will allow messages to be sent directly to drivers during their commutes. In the meantime, apps such as Waze are providing communities with real-time traffic information from the app users themselves.

5. Employee hiring

Cornerstone, formerly Evolv, helps companies to recruit, train, and manage their employees. The company also assists government agencies, schools, non-profits, and medical facilities manage the entire employee life cycle. Yet it’s how Cornerstone helps companies manage employees that’s so interesting.

Its analysts pore through millions of human resources records to find out who succeeds at work and who does not. The company can also tell you who will stay longer, miss less work, and better adhere to company protocols.

Some Cornerstone clients develop their own hiring exams that focus on the traits that the companies desire in their new employees. Interesting findings that have come from Cornerstone’s research include:

·  Older employees have about half the attrition rate as younger ones.

·  Rehired employees leave companies 44 percent faster than new hires.

·  Employees with criminal backgrounds perform better than their non-criminal counterparts.

Big Data analytics plays a key role in using millions of records to extract personality traits, job success, retention information, job satisfaction, job mobility, and other data points relevant to the employee life cycle.

Cornerstone is one of three leaders, for the third year in a row, in Gartner’s Magic Quadrant for Talent Management Suites. The other two companies in the Leaders Quadrant are SAP (SuccessFactors), and Oracle (Talent Management Cloud).

6. Gambling

It might not surprise you to know that not all uses of Big Data are, shall we say, completely savory. Casinos and online gambling site produce a lot of data on customers and their gambling habits, favorite games, time spent on particular games, amount spent on a game, and many more related activities and habits. But apart from pure gambling-related information, these customers also purchase other products and services such as taxi rides, mass transit passes, food, drinks, hotels, rental cars, souvenirs, clothes, and various services inside and outside of the casinos. Online gamblers have habits too, for example, which sites they viewed before they switched to online gambling, and which sites they viewed after gambling. Online gamblers also click on advertisements and that behavior is recorded and used as well.

In the early days of online gambling, site managers didn’t know what to do with the gigabytes of data per day that they accumulated. Most of the data had to do with predictions, odds, and serving customer’s needs, but the customer behavior data was especially intriguing for marketing and advertising.

Big Data analytics is also used to find out what makes gamblers engage with a game and what makes them stay longer at a machine or at a table. And online gambling Big Data analytics are used for the same purpose—engagement, which translates to spending more time and money with a game. The more engagement a user has, the more likely he or she is to spend, and lose, to the house.

Big Data and Big Data analytics have become staples for businesses—all types of businesses. There’s almost no aspect of our lives that isn’t measured, collected, or recorded in some way, for advertisement, targeted marketing, or product engagement. Data is power. Knowledge is power. The only problem with all of the data we generate is determining ownership of that data. Be careful what you sign or accept as “acceptable use” for services and products that you use, because the data you generate and your privacy might both be sold to the highest bidder.

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