Big data is the buzzword of the century, it seems. But, why is everyone so obsessed with it? Here’s what it’s all about, how companies are gathering it, and how it’s stored and used.
What is it?
Big data is simply large data sets that need to be analyzed computationally in order to reveal patterns, associations, or trends. This data is usually collected by governments and businesses on citizens and customers, respectively.
The IT industry has had to shift its focus to big data over the last few years because of the sheer amount of interest being generated by big business. By collecting massive amounts of data, companies, like Amazon.com, Google, Walmart, Target, and others, are able to track buying behaviors of specific customers.
Once enough data is collected, these companies then use the data to help shape advertising initiatives. For example, Target has used its big data collection initiative to help target (no pun intended) its customers with products it thought would be most beneficial given their past purchases.
How Companies Store and Use It
There are two ways that companies can use big data. The first way is to use the data at rest. The second way is to use it in motion.
At Rest Data – Data at rest refers to information that’s collected and analyzed after the fact. It tells businesses what’s already happened. The analysis is done separately and distinctly from any actions that are taken upon conclusion of said analysis.
For example, if a retailer wanted to analyze the previous month’s sales data. It would use data at rest to look over the previous month’s sales totals. Then, it would take those sales totals and make strategic decisions about how to move forward given what’s already happened.
In essence, the company is using past data to guide future business activities. The data might drive the retailer to create new marketing initiatives, customize coupons, increase or decrease inventory, or to otherwise adjust merchandise pricing.
Some companies might use this data to determine just how much of a discount is needed on promotions to spur sales growth.
Some companies may use it to figure out how much they are able to discount in the spring and summer without creating a revenue problem later on in the year. Or, a company may use it to predict large sales events, like Black Friday or Cyber Monday.
This type of data is batch processed since there’s no need to have the data instantly accessible or “streaming live.” There is a need, however, for storage of large amounts of data and for processing unstructured data. Companies often use a public cloud infrastructure due to the costs involved in storage and retrieval.
Data In Motion – Data in motion refers to data that’s analyzed in real-time. Like data at rest, data may be captured at the point of sale, or at a contact point with a customer along the sales cycle. The difference between data in motion and data at rest is how the data is analyzed.
Instead of batch processing and analyzation after the fact, data in motion uses a bare metal cloud environment because this type of infrastructure uses dedicated servers offering cloud-like features without virtualization.
This allows for real-time processing of large amounts of data. Latency is also a concern for large companies because they need to be able to manage and use the data quickly. This is why many companies send their IT professionals to Simplilearn Hadoop admin training and then subsequently load them up on cloud-based training and other database training like NoSQL.
Big Data For The Future
Some awesome, and potentially frightening, uses for big data are on the horizon. For example, in February 2014, the Chicago Police Department sent uniformed officers to make notification visits to targeted individuals they had identified as potential criminals. They used a computer-generated list which gathered data about those individuals’ backgrounds.
Another possible use for big data is development of hiring algorithms. More and more companies are trying to figure out ways to hire candidates without trusting slick resume writing skills. New algorithms may eliminate job prospects based on statistics, rather than skillsets, however. For example, some algorithms find that people with shorter commutes are more likely to stay in a job longer.
So, people who have long commutes are filtered out of the hiring process quickly.
Finally, some insurance companies might use big data to analyze your driving habits and adjust your insurance premium accordingly. That might sound nice if you’re a good driver, but insurers know that driving late at night increases the risk for getting into an accident. Problem is, poorer people tend to work late shifts and overnights or second jobs just to make ends meet. The people who are least able to afford insurance hikes may be the ones that have to pay them.