What is Big Data: Definition, Characteristics, and Benefits
What is Big Data: Definition, Characteristics, and Benefits
Lately the term 'Big Data' has been in the limelight, but not many people know what Big Data is. In the internet era, companies and organizations around the world have collected a large amount of data. IBM states that businesses around the world generate nearly 2.5 trillion bytes of data every day! Nearly 90% of global data has been produced in the last 2 years alone.
Forbes also reports that every minute, users watch 4.15 million YouTube videos, send 456,000 tweets on Twitter, post 46,740 photos on Instagram and there are 510,000 comments posted and 293,000 statuses updated on Facebook! Just imagine the huge amount of data generated by such activities. This constant creation of data using social media, business applications, telecommunications and various other domains leads to the creation of Big Data and it impacts all of our lives.
What is Big Data?
Big Data is a high-volume, high-speed, and diverse information asset that demands an innovative, cost-effective form of information processing for enhanced insight and decision making. Big Data refers to complex and large data sets that must be processed and analyzed to uncover valuable information that can benefit businesses and organizations. This data set is so large that ordinary data processing software cannot manage it.
History of Big Data
Although the concept of big data itself is relatively new, the history of big data dates back to the 1960s and 70s when the world of data was just getting started with the first data centers and the development of relational databases. Around 2005, people started to realize how much data users were generating through Facebook, YouTube and other online services. Hadoop (an open source framework created specifically for storing and analyzing big data sets) was developed the same year. NoSQL also started gaining popularity during this time.
The development of open-source frameworks, such as Hadoop (and more recently, Spark) is critical to the growth of big data because they make big data easier to work with and cheaper to store. In the years since, the volume of big data has skyrocketed. Users still generate large amounts of data, but it's not just humans who do it.
With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data about customer usage patterns and product performance. The rise of machine learning is still generating more and more data. While big data has come a long way, its use is only just beginning. Cloud computing has expanded the possibilities of big data even further. The cloud offers truly elastic scalability, where developers can easily spin up ad hoc clusters to test subsets of data.
Characteristics of Big Data
The Big Data model refers to structured, unstructured and semi-structured data collected from various sources. While in the past, data could only be collected from spreadsheets and databases, today data comes in many forms such as emails, PDFs, photos, videos, audio, SM posts and many more.
Velocity basically refers to the speed at which data is being generated in real-time. In a broader perspective, it consists of rate of change, connecting incoming data sets at varying rates, and exploding activity.
We already know that Big Data denotes huge 'volumes' of data generated daily from various sources such as social media platforms, business processes, machines, networks, human interaction etc. Huge amounts of data are stored in data warehouses.
What is Big Data Technology?
Suppose our PC can only manage a small amount of data. Just imagine all the possible information to fit into one spreadsheet. Database software is capable of handling higher volumes of information. These devices could put that information onto a single data hard drive which would otherwise require a shelf filled with boxes full of notebooks and folders. But these tools are not enough to handle all the volume of information we call big data.
Examples of Using Big Data
Internet of Things
The internet that we know today is the internet of people. This is where people interact with each other, with the machines that facilitate that communication. We see sites that people designed. We read the words that people type.
The Internet of Things are devices that communicate directly with each other without human involvement. Examples include weather monitoring devices. Smart thermostats access that information and make adjustments to the temperature in our homes. Big data and the Internet of Things are interdependent. These devices can take action on their own thanks to all the data available to them. The more devices work in this way, the more data they generate.
Machine Learning refers to the ability of computers to learn from data. Machine Learning is also behind content recommendations on YouTube. This prediction is due to the algorithm. Google search algorithm? The algorithm that determines what we see on Facebook's news feed? It's all machine learning at work.
Artificial Intelligence or artificial intelligence is the next step after machine learning. Here, not only does the computer learn from the data, but uses that information to make its own decisions and shape its own behavior. Microsoft and Google have both demonstrated efforts to build humanoid robots. Facebook uses artificial intelligence to help prevent suicide. This technology is developing at a rate where there have been several instances where computer thinking has outperformed humans.
What is Big Data Analytics?
Big data sources tell us nothing about themselves. One has to understand all that information. This is what big data analytics is all about: looking at the enormous volumes of information and seeing what we can learn. Today, more and more organizations are embarking on new big data projects, and companies are racing to offer their specialized forms of big data analytics in various fields. Through these actions, big data has an effect on our lives.
Benefits of Big Data
Big Data In Health Services
The healthcare industry isn't the fastest at adopting new technologies. Some providers are still migrating from paper to digital storage devices. Nonetheless, there are areas where big data makes a difference. One of them is the area of integration. Insurers and providers work to combine data from multiple sources, such as claims, X-rays, doctor's notes, and prescriptions.
Many believe that if healthcare data was better integrated, it could provide better care at a lower cost. When Amazon, Berkshire Hathaway, and JP Morgan announced earlier this year that they were teaming up on healthcare, they named technology as their area of focus, as covered by The Guardian.
Big Data In Finance
The financial industry has embraced the idea of making decisions based on computer analysis. Like an Automated trading system that uses a machine to sell shares without human intervention, based on what is happening in the market. This is called high frequency trading. Now, scientists are using big data to predict which stocks will succeed and when future crashes are likely to occur. Banks also see big data as a way to increase their revenue.
Big Data In Ecommerce And Marketing
We definitely generate a lot of information when shopping. Online, we must create an account before shopping, allowing the site not only to track what we buy, but every item we view. Stores base their layouts around consumer interests and behavior. Online sellers decide what to see based on demographic information and other metrics.
There is a huge demand for the kind of insight that comes from monitoring online interests and behavior. Facebook and Google are profitable tech giants because of their ability to sell ads that are better able to target specific groups of consumers than other advertising methods and platforms. They can do this thanks to all the information we provide when we use their services.
Is Big Data Dangerous?
Big data comes with promise, but it also comes with risks. First is the erosion of privacy. More people know more about each of us than at any other point in human history. Not only is it easy to find where we live, but where we go, who we love, how we live, and what we think.
This makes individuals and society more open to manipulation. We can be tricked into giving up our passwords or credit card numbers. More data offers advertisers and media companies more ways to shape our desires and values. There is more data about us than ever before, and it is being stored in various places. It creates more attack targets. It's not enough to get there, to protect our own machines. Data breaches are now a regular occurrence, with what happens to our data beyond our control.
Even companies that may be doing a decent job of protecting our data from outside attacks are often doing questionable things with the data itself. Finding ways to keep our data safe, our privacy respected, and our values preserved will be an ongoing challenge as the trend towards big data continues. But no matter how we feel about it, for better or for worse, we all live in a big data world.
So What Is Big Data? Big Data refers to large amounts of data flowing from various data sources and having different formats. Even before there was big data stored in databases, but due to the varied nature of this Data, traditional relational database systems were unable to handle this Data. Big Data is more than just a collection of datasets of different formats, it is an important asset that can be used to derive quantifiable benefits.
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