Have you ever wondered: where does your information go? What is its use? And who might be interested in it?
In relation to these questions, Big Data has a lot to tell us. Below, we invite you to learn what this term consists of, what its history is and in which areas it is being implemented.
What is Big Data?
When we talk about Big Data , we refer to the process of collecting, analyzing and interpreting large volumes of data that would take too much time and be very expensive to load into a database for analysis.
Also, Big Data is considered as a large amount of data ranging from 30-50 terabytes to Petabytes.
In this way, the concept of Big Data applies to the set of data that cannot be processed or analyzed using traditional tools, so a more complex process is needed to “clean” said data and extract elements that can be interpreted.
Gartner defines Big Data as data that contains greater variety, which is presented in increasing volumes and at a higher speed.
IBM, in its Performance and Capacity Implications for Big Data report published qatar number for whatsapp in 2014 , agrees with this statement by stating that Big Data solutions are distinguished from traditional ICT solutions by considering four dimensions:
Volume: Big Data solutions must manage and process large amounts of data.
Speed: They must process data that is recorded at high speed.
Variety : They must be responsible for processing different types of data and structured in multiple ways.
Truthfulness: they must find inconsistencies in the information that is collected.
The complex data that Big Data has to process is extracted daily not only from users who use digital platforms and smart devices such as cell phones, tablets, TVs, Smart Watches, but also from inanimate objects.
With the advent of the Internet of Things (IoT), there are a growing number of objects and devices connected to the Internet that generate data related to usage patterns. This makes Big Data more important than ever to monetize the data that is collected.
History of Big Data
Although Big Data as a concept is relatively new, its origins date back to the 1960s and 1970s, when the world of data was just beginning.
As a result of the Baby Boom demographic growth in the United States, systems were created that could store and analyze data from the large number of people who were being born.
However, it was not until 1999 that the term Big Data was first used in an academic paper called “Visually Exploring Gigabyte Datasets in Realtime,” written by Steve Bryson, David Kenwright and other contributors.
In 2001, Doug Laney established the “3 V's that are part of Big Data”, which we already explained in the previous lines.
In 2005, researchers, scientists and companies began to realize the amount of data generated by users through mass consumption social networks Facebook, YouTube and other online platforms.
That same year, Hadoop was created, an open source system used to store, process and analyze large volumes of data, saving time for developers who programmed algorithms to analyze information.
Hadoop has greatly accelerated the use of Big Data, since multiple data analyses can be performed on this platform, including linear regressions.
Globally, 9.57 zettabytes (9,570,000,000,000 gigabytes) of information were processed in 2008. Due to the rapid growth of data, it was estimated that 14.7 exabytes of new information would be produced this year, 2020.
In 2009, the McKinsey Global Institute’s “ The Next Frontier for Innovation, Competition and Productivity” study found that the average US company with more than 1,000 employees stores more than 200 terabytes of data.
In this sense, the development of open source software, such as Hadoop and Spark, was fundamental to the growth of Big Data , as they made this process accessible and economical for companies.
In the years since, the volume of big data has skyrocketed. In 2010, Google CEO Eric Schmidt told a conference that the amount of data now being created every two days is greater than that created since the beginning of human civilization up until 2003. Impressive, isn't it?
Why is Big Data so important?
Data alone is not valuable unless it is organized and transformed into useful information aimed at achieving a goal. In this sense, Big Data comes into play as the process that makes this data meaningful and more useful at a business level.
Large companies in the world not only use data as a commodity (let's look at the example of Facebook ads) but they also use it to create better products and services that can have more value and recognition in the market.
In fact, the term “data commodities” is a concept recently used to summarize this economic phenomenon where data is a very valuable commodity in the business world.
If you're wondering how Facebook makes its business model possible if its app is free, well, data has a lot to do with it. According to the BBC's How Much Money Does Facebook Make From You and How Does It Do It? report on 11/04/16, between July and September 2016 alone, Facebook's revenues exceeded US$7 billion.
A figure so large that it exceeds the Gross Domestic Product of more than 40 countries. Of the US$7 billion in revenue that Facebook announced that year, US$6.82 billion corresponds to advertising.
So, if it weren't for Big Data, Facebook wouldn't be able to process all the information to sell its advertising services and classify its users according to:
Geographic locations.
Degree of academic instruction.
Professions.
Interests.
Purchasing behavior, among others.
Uses of Big Data in the world
Since Big Data is simply a process, its uses and applications are diverse and countless. Below are some examples where Big Data has been of great relevance:
Applied to product development and analysis
Companies like Netflix and Procter & Gamble use Big Data to predict customer demand. In the case of Netflix, the company uses subscriber data to recommend personalized content based on their tastes and viewing history.
This also allows it, within its business model, to produce original content, knowing in advance what topics to address, what series to produce and what talents to sign.
According to a study by the analytics firm Orcan Intelligence , “It took Netflix about six years to gather enough data to be sure they had all the ingredients necessary to make a successful series based on what Big Data was telling them.”
Using data on viewer habits, Netflix designed content that had all the elements to become a successful phenomenon, demonstrating how to combine Big Data with creativity.
The success of the House of Cards series was the result of implementing Big Data as a strategy to give its subscribers what they were looking to see.
In the case of P&G, the company uses data analysis on interest groups, social networks, test markets and in-store exit previews to plan, produce and launch new products.
Applied to health