Big Data has officially lived up to its namesake, with a recent report suggesting the market is currently valued at a whopping $46 billion according to a report published by SNS Research.
Enter Big Data
Data sets have continued to grow larger and larger with each passing year. Take the original Apple Macintosh computer, for instance. Released January 24, 1984, it features an 8 MHz 68000 processor, 128 KB of random access memory (RAM), and just 400 KB of disk storage space. In comparison, some of the newer desktop computers available on the market boast 1-2 GB of disk storage space. Of course, there's a good reason why companies are developing computers with large disk storage: data sets grow exponentially larger over time.
According to some reports, the global data storage capacity has doubled every 40 months since the 1980s. In 2012, 2.5 exabytes of data was generated every day. From high-definition photos and videos to applications, software and entire databases, there's A LOT of data floating around in cyber space. Computer and device manufacturers must adapt to this ever-evolving environment to meet the needs of users.
Whether you're an IT professional or someone who simply enjoys reading about new-age digital technology, you've probably heard of Big Data before. But do you really know what it is? The truth is that most people are unfamiliar with the definition. So, what is Big Data exactly?
Big Data is best described as excessively large data sets that are so big and/or complex that traditional processing applications cannot handle them. As data sets grow, it often strains the applications and systems responsible for processing them. Such challenges often include analysis, search, sharing, storage, maintenance, security and information privacy.
There are a few characteristics that large data sets must have in order for it to be classified as Big Data. Professional Big Data analyst Doug Laney laid out these characteristics back in the early 2000s, and they have since become the universal standard for classifying Big Data.
According to Laney, Bug Data must have the following:
Volume – Big Data is collected from a variety of sources.
Velocity – Big Data can be streamed at fast speeds.
Variety – Big Data isn't limited to a single format. Rather, it's available in a multitude of different formats, ranging from text files and emails to social media posts, photos, videos, financial transactions and more.
It's important to note, however, that some analysts include other factors when classifying Big Data, such as variability and complexity. The bottom line, however, is that Big Data is large (very large) sets of structured and unstructured data that traditional applications can not effectively process or otherwise handle.
Big Data: Looking Towards the Future
The report published by SNS Research suggests the Big Data market will reach $46 billion by the end of this year. SNS Research goes on to predict the Big Data market will balloon to a staggering $72 billion, growing a compound annual growth rate (CAGR) of 12% through the forecast period.
“Despite challenges relating to privacy concerns and organizational resistance, Big Data investments continue to gain momentum throughout the globe. SNS Research estimates that Big Data investments will account for over $46 Billion in 2016 alone. These investments are further expected to grow at a CAGR of 12% over the next four years,” wrote SNS Research in a report summary.
Market drivers include increased adoption of Big Data processing on mobile devices and social media, as well as the introduction of Big Data in new verticals like scientific research and development and fraud protection.
The American Bankers Association Deposit Account Fraud Survey found that U.S.-based banks halted 8 out of 10 fraudulent deposits that were attempted in 2014, which totaled some $11 billion. Nevertheless, fraudulent deposits still costed banks $1.9 billion in 2014, up from $1.7 billion in 2012.
The most common form of bank fraud is debit card fraud, accounting for 66% of industry loss. Coming in second is check fraud at 32%, followed by online banking and ACH transactions at 2%.
But banks have since taking a proactive approach towards protecting their business from fraudulent activity, including the use of Big Data. As explained in this Forbes article, banks and other financial institutions are now using Big Data analytics to identify patterns of user behavior.
These automated systems, for instance, can when and where a user makes a purchase. If a user normally buys products online using his or her smartphone, and typically spends less than $100, the system may flag both the account holder and the financial institution if a purchase is made from over a dialup Internet connection for $200 or more. Big Data analytics looks at behavior such as this, comparing it to the account holder's past behavior.
Another way in which Big Data is being used to protect against fraud in the financial industry is identifying trends in cyber attacks. Whether it's a brute-force attack or phishing attempt, financial institutions are often the target of hackers, largely because they offer a treasure trove of data. In response to this disturbing trend, banks are now using Big Data to identify commonly used cyber attacks so they can better defend against them.
Regarding scientific research and development, Big Data plays a key role in this industry. The Sloan Digital Sky Survey (SDSS) began collecting data on the Milky Way galaxy in 200. According to Wikipedia, it collects some 200 GB of data per night. In order to process all of this information, Big Data analytics is used. Big Data can even be used to decode the human genome. Before, this process took a full decade to complete. Thanks to Big Data, however, it can be done in less than 24 hours, which is pretty impressive to say the least.
Thanks for reading and feel free to let us know your thoughts in the comments below regarding Big Data.