Top Big Data Trends and Global Success Cases

[Big Data Series] Chapter 1. Reading Trends with Big Data, Top Big Data Trends and Global Success Cases

What is Big Data?

Big data has multiple definitions.
According to Wikipedia, big data is “a term for data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage and process data. It also refers to the use of analytics to extract value from data sets that are diverse, complex, and of a massive scale”.
The IDC (Industrial Development Corporation) defines big data as “a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high-velocity capture, discovery, and/or analysis”.
Based on the mainstream definitions of industry-leading businesses, we can define big data as below:

“Big data is not only the high-velocity capture and processing of vast volumes of complex data, but also the discovery of business value through analysis and recognition of uncertain data.”

In other words, analyzing big data is a process required to discover business value in large volumes of complex data. So even if complex analysis of data is performed, unless it creates business value, it cannot be classified as big data.

So why did big data suddenly emerge?
First, the cost of computer hardware such as CPUs, storage, and memory fell, reducing data storage and processing costs.
Another reason is because open source technology such as Hadoop and R has advanced.
Thanks to these advancements, large volumes of unstructured data can now be quickly analyzed and visualized with Hadoop’s distributed processing and R’s statistical software.
The third reason is because businesses are beginning to pay attention to previously unmanaged data and are attempting to find hidden business value in it. Companies like Facebook, Twitter, Google, Dell, and Target have inspired this sudden spark of interest in unmanaged data by successfully discovering business value with big data and applying it to their operations. The details of these success cases will be discussed in further detail in the next page.

As the use of social media, smart phones, IoT (Internet of Things), and wearable devices has become more prominent, big data is set to play a bigger role in the ICT industry. Many are looking to big data for new business opportunities, with businesses in Korea and abroad launching big data driven projects. Experts forecast the big data industry to grow by 27.9% and 52% annually in Korea and abroad, respectively.

Big data (advanced, pervasive and invisible analytics) was also chosen as one of Gartner’s “Top 10 Strategic Technology Trends for 2015”.
Data has been consecutively recognized in Gartner’s top 10 strategic technology trends for the past five years. As a result, more businesses have been focusing their efforts on utilizing data. With advances in analytics, the analysis of vast pools of structured and unstructured data is now possible. So while in the past businesses were simply interested in the concept of big data, today businesses are able to put big data to actual use with more advanced and sophisticated analytics.

Big Data Success Cases in Korea and Abroad

Big data is being used across many industries including biotechnology, social networking, manufacturing, finance and telecom.
  Many global businesses have already embarked on big data projects. For example, Amazon has developed an “anticipatory shipping” system that delivers products to customers before they place an order by analyzing their consumption patterns.

Google Flu Trends is also big data tool.
Google Flu Trends is a web service that provides estimates of influenza activity across the United States by aggregating Google search queries and user location based on the likelihood that many patients search their symptoms online before visiting a doctor or pharmacy.
Google Flu Trends is able to detect the spread of the flu by highlighting regions with high volumes of flu-related queries such as “common cold” or “flu”. While most health agencies only update their estimates weekly, Google Flu Trends was able to provide upgraded services by updating their estimates 18 countries daily.

Google Flu Trend analysis sreenshot (Image source: Google) : Google Flu Trends is able to detect the spread of the flu by highlighting regions with high volumes of flu-related queries such as 'common cold' or 'flu'. Google Flu Trend analysis (Image source: Google)

Another company that utilizes big data analytics is fast fashion retailer Zara. Zara’s marketing strategy is based on frequent distributions of low volumes of manufactured goods. Zara develops twice the variety of goods compared to the industry average and it takes the company less than six weeks to develop a new product and get it to stores. For all of this to be possible, Zara needed to be able to forecast demand, view store inventory, determine optimized product prices, and manage shipment in real time. That is why Zara partnered with an MIT research team to develop an inventory management system that utilized big data.
In Korea, SK Telecom, one of Korea’s leading telecom providers, utilizes their own big data analytics solution to identify user and social trends and assess their corporate image for marketing purposes using social media, social data and search keywords. Based on this experience, SK Telecom recently unveiled their “Smart Artist Marketing” business which utilizes social media and keywords to analyze a celebrity’s image and determine their future career path. SK Telecom is currently also in the process of developing new big data-based service models such as mobile advertising analysis and social data analysis.

Major credit card companies are also using big data to analyze consumer patterns for activities such as marketing, new product development and recommendation. KB Kookmin Card, one of Korea’s leading financial groups, uses big data analysis to provide their services and additional benefits.
KB Kookmin Card developed “KB Wise Wallet”, an application that analyzes a user’s consumption pattern over the past years to make restaurant and store recommendations. In addition, the company developed a “real-time marketing system” which has been applied to their business operations.
Clients are able to receive personalized and optimized card benefits based on their needs and location.
In the past, the focus of marketing was reaching out to as many people the general public, as possible but today, the goal is to strategically provide personalized services to the right customer at the right place and time. In these changing times, KB Kookmin Card has actively been utilizing big data analytics to process vast volumes of structured and unstructured data.

NCSOFT, famous for creating the highly popular online role-playing franchise Lineage, has also developed and deployed a big data-based user data analysis system.
NCSOFT now uses big data to crack down on illegal activities including theft and fraud. They are able to monitor illegal activities by applying an advanced fraud detection algorithm that analyzes vast amounts of user activity and log data.
Whenever users play Lineage, an astronomical amount of log data is created. In order to analyze this structured and unstructured data in real-time, utilizing big data is inevitable. The company also widely uses big data in data mining and machine learning as below to develop sophisticated fraud detection algorithms.

In this chapter, we covered big data trends and global success cases.
In the upcoming chapters, we will take a more in-depth look into big data success cases across various industries from finance, biotechnology and video to IoT and deep learning. In the next chapter, we will discuss big data success cases in finance.

※ This is an article from the 2016 spring edition of KIIE’s (Korean Institute of Industrial Engineer) IE magazine, and is republished with the KIIE’s approval.

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Senior Engineer, Seoyeon Kim
Senior Engineer, Seoyeon Kim AI/Analytics
Samsung SDS Smart Factory Business Division

After receiving her Ph.D for industrial engineering from Gorgeia Tech in 2009, Dr. Seoyeon Kim worked as an industrial engineering researcher at the National University of Singapore before joining Samsung SDS in September 2010. As a data scientist, she leads multiple big data projects while also working as an instructor for an in-house data scientist training program she helped create. Dr. Kim regularly shares her in-depth big data expertise as a contributor for CommonSDS and IE magazine and also actively participates in various industry seminars.