There is no doubt that Big Data is a very interesting concept to learn about, especially when we listen or read, which is the new wheel that is changing the world.
Have you ever wondered: where does your information go? What use is it? And who might be interested in it?
In relation to these questions, Big Data has a lot to tell us. Next, we invite you to learn what this term consists of, what its history is and in what areas it is being implemented.
What is the 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 upload to a database for analysis.
Also, Big Data is considered 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.
The company Gartner defines Big Data as data that contains a greater variety, that is presented in increasing volumes and at a higher speed.
IBM, in its report Performance and Capacity Implications for Big Data published 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.
- Veracity: they must find inconsistencies in the information that is collected.
The complex data that Big Data has to process is extracted daily not only through users who use digital platforms and smart devices such as cell phones, tablets, TVs, Smart Watches, but also from inanimate objects.
With the arrival of the Internet of Things (IoT), there are a greater number of objects and devices connected to the Internet that generate data related to usage patterns. This makes Big Data more important than ever today to monetize the data that is collected.
History of Big Data
Although Big Data as a concept is relatively new, its origin dates back to the 1960s and 1970s, when the world of data was just beginning.
As a consequence of the demographic boom of the Baby Boom in the United States, systems were created that could store and analyze the data of this 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 work called “Visually Exploring Gigabyte Datasets in Realtime”, written by Steve Bryson, David Kenwright and other collaborators.
In 2001, Doug Laney established the “3 V’s that are part of Big Data”, which we already explained to you in the previous lines.
In 2005, researchers, scientists and companies began to realize the amount of data that users generated through mass consumption social networks Facebook, YouTube and other online platforms.
So that same year, Hadoop was created, an open-source system used to store, process and analyze large volumes of data, saving time for developers programming algorithms to analyze information.
Hadoop greatly accelerated the use of Big Data, since multiple data analyses can be performed on this platform, including linear regressions.
Globally, in 2008, 9.57 zettabytes (9,570,000,000,000 gigabytes) of information were processed. 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 study “ The Next Frontier for Innovation, Competition and Productivity” conducted by McKinsey Global Institute noted that the average American 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 essential for 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, Eric Schmidt, CEO of Google, stated at a conference that the amount of data now being created every two days is greater than that created from the beginning of human civilization until 2003. Impressive, right?
Why is Big Data so important?
Data alone is not valuable unless it is organized and transformed into useful information oriented toward achieving a goal. In this sense, Big Data comes into play as the process that makes this data make sense and can be more useful at a business level.
Large companies in the world not only use data as a commodity (let’s see the example of Facebook advertisements) but 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 are wondering how Facebook makes its business model possible if its application is free? Well, data has a lot to do with it. According to the report, “How much money does Facebook make from you and how does it do it?” carried out by the BBC 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 corresponded to advertising.
So, if it were not for Big Data, Facebook would not 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, we show you some examples where Big Data has had great relevance:
Applied to product development and analysis
Companies like Netflix and Procter & Gamble use Big Data to forecast customer demand. In the case of Netflix, this company uses its subscribers’ data to recommend personalized content according to their tastes and viewing histories.
This also allows it, within its business model, to produce original content, knowing in advance what themes to address, what series to produce and what talents to sign.
According to a study by the analytical company Orcan Intelligence. “It took Netflix about six years to gather enough data to be sure they had all the ingredients needed to make a successful series based on what the 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-in-data analytics across stakeholders, social media, test markets, and in-store previews to plan, produce, and launch new products.
Applied to health
According to a study conducted by IBM, the University of Ontario Institute of Technology (UOIT) together with the Toronto Hospital, use IBM InfoSphere, a Big Data platform that allows monitoring premature babies in neonatal wards to determine any changes in the blood pressure, temperature, alterations in electrocardiogram and electroencephalogram records.
The objective is to detect, up to 24 hours before, those conditions that may be a threat to the life of newborns.
On the other hand, the State University of New York (SUNY) is applying Big Data tools to contribute to the research, diagnosis, treatment, and perhaps even the possible cure of multiple sclerosis: a disease of the nervous system that affects the brain. and the spinal cord.
Applied to fault verification
The Pacific Northwest National Labs (PNNL) laboratories also use IBM InfoSphere Streams to analyze meter events in their electrical network and in real-time verify those exceptions or failures in the network components, managing to communicate almost immediately to the consumers about the problem to help them manage their electricity consumption.
In this sense, Big Data has so many applications and uses that IBM develops software and technologies so that companies, scientific researchers and large organizations have access to programs that allow them to apply Big Data to process large volumes.
If it were not for this process, advertising business models would not be as successful. Neither would artificial intelligence technologies.
