Big data is nothing more than just a ‘buzzword’. It’s a term used to describe the process of analyzing huge & complex data to make better decisions and to find patterns that were previously unknown.
Here’s a definition of Big Data on Wikipedia:
Wikipedia: “Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search sharing, storage, transfer, visualization, querying and information privacy.”
It’s one of those ‘terms’ that startups and big corporations talk about when being asked about the future of tech & business. Despite its hype, many still don’t really know what big data is, so in this article, I’d like to explain big data and its uses to you.
The 3 V’s Characteristics That Defines Big Data
In the early 2000s, an analyst by the name of Doug Laney defined big data as the three Vs.
Here are what the three Vs mean.
Volume: Volume refers to the amount of data. A corporation collects data from all types of sources, from social media activity, information from polls, sensors, business transactions and more. This data is called ‘Big Data’. Back then, storing such huge amount of data would be impossible. Today, new technologies such as Hadoop or MongodB has made storing possible.
Velocity: Velocity refers to the speed of data processing. Computers will require some time to process data and update databases.
Think about the speed of which data is created today. Every minute, 240,000 photos are uploaded on Facebook, 500 hours of video uploaded on YouTube and almost 34,851GB of internet traffic happens every second.
See how much data is being transferred in a minute on the internet.
Over time, more Vs have been added on top of the 3 traditional Vs that defined big data. Together, they more accurately define big data.
Veracity: Having lots of data could prove worthless if the data is incorrect. In data automation where no humans are involved in, it’s important to make sure that the data and analysis are correct.
Variability: Variability refers to data that meaning is constantly changing. Brian Hopkins, a Forrester principal analyst refers to Watson, a supercomputer that had to “dissect an answer into its meaning and to figure out what the right question was”. The computer had to understand the context by just analyzing words. The meaning of a word can vary differently according to the context.
Take, for example, a drink company trying to measure success using social media updates data.
“Love the new drink by @drinkcompany – Such a good way to quench my thirst”
“Don’t really enjoy the new drink by @drinkcompany. Don’t know how some love drinking it”
“Must say that the advertising campaign by @drinkcompany is amazing, can’t say I love drinking something so sweet, though!”
So in this context, the word ‘love’ is not sufficient enough to show positive success for the company. Sophisticated programs that can understand the context and analyze the meaning of words have to be developed.
Visualization: Often the toughest part of big data, visualization is turning all the data into a manner that is easily read & understandable. Without proper visualization, raw data collected would simply remain useless.
Visualization isn’t the most technological difficult process in big data, but it’s challenging to find a way to present information that makes data collection clear. There are lots of visualization software are appearing all the time, example like Tableau Public.
Tableau is a big data visualizing software
Value: Big data in itself is not valuable at all. The true value lies in analysis and being able to turn data into information and eventually into knowledge. This new found knowledge then is then used by organizations to make smart decisions based on the insights found in data analyses. Only then data is valuable.
What Big Data Means For You
Big data isn’t just only data. It has actual real world implications. On the downside, it also raises a concern as massive volumes of information are stored in mere databases that can be sold or traded should the database owner wanted to.
As an example, think about how Facebook mines data from your uploads, status updates, and activity. It’s not difficult to describe your habits, purchasing power, activities and what you like. That is the scary dark side to big data.
However on the other side, big data can prove very useful for you as an individual or even for an organization. There are many types of big data usages, but here are three simple examples of real big data usage in the real world:
Consumption Habits
Whenever you go shopping, you’ll share details about your consumption habits to retailers. The retailers would make use of this data to figure out what products you prefer, which shopping coupons would attract you and which discounts would make you happier.
There is a remarkable story of how Target, a retail company found out that a girl was pregnant before her parents did. What’s even creepier is that they were able to somewhat tell the date of her delivery and whether the baby was a boy or girl.
Target started sending coupons related to baby items to the customer based on the pregnancy scores. Read about Target and big data here.
Business Insights
Corporations are able to gain business insights with big data.
Procter & Gamble (P&G), a large consumer goods company used information from transactions to know about customers from where they engaged on the brand and why they are engaged with it.
They created a conceptual architectural framework that allows them to detect key indicators to create a predictive model and to use data to create new products. P&G uses data to have better insights & prediction of what’s going to happen in the future instead of analyzing what happened in the past. This is also known as business intelligence.
Better Hiring
A HR company has better opportunities to be more analytical and accurate in hiring work candidates with big data. Big data can also help them avoid hiring bad employees.
The activities of an application from records of employment, internet activity, tests, games and online resume databases can be used by employers to determine the talent of an application. No more for the need of having to rely on feeling and feel good factors when it comes to hiring.
Data Is Information – Worry About Who Uses It
At the end of the day, big data is simply information used by companies do better business. They are also giving birth to greater science, technology innovations, and medicine improvement.
More and more information is being available every day for processing to study all sorts of things from climate, genetics, disease, habits, physics and more.
As a business or startup owner, it’s crucial that you take the step to utilize big data to make good decisions, knowing the market and to understand your customers better.
As a consumer, you can expect more of your lifestyle to be used to make decisions about you. Big data is growing and as it does, more industries would become useful for each other. Insurance companies would want to know your social media habits to see what motivates you and your browsing habits would be useful for a retailer. That is until you take steps to protect your privacy.
Conclusion
Big data is not a single market. Rather, it is a combination of data management technologies that have evolved over time. It’s important for you as a consumer or a business owner to keep an eye on big data because it’s not going away.
As companies begin to evaluate and form new types of big data solutions, many new opportunities would unfold. It’s important for you as a consumer or a business owner to keep an eye on big data because it’s not going away.
In fact, it’s going to grow so much that it’s inevitable that everyone should learn or at least know something about it.
What is your take on Big Data technologies? Please share in the comment below as I’d love to hear from you.