Big data (BD) refers to huge complicated data set (either semi-structured, structured or unstructured) that are quickly generated and transmitted by a vast array of sources.
These characteristics constitute three Vs of big data:
- Volume the huge amount of information that are stored.
- Velocity The high speed that data streams need to be processed and analysed.
- Variety Different forms and sources through which data is gathered like text, numbers, video audio, images, and text.
Nowadays data is generated constantly whenever we launch an app, browse Google or simply go from around with phones. The result? Huge amounts of data that organizations and companies control, manage, visualise and analyse.
The traditional data applications aren’t able to handle this level of volume. Complexity that’s led to numerous specialized large-scale data platforms and solutions for architecture specifically designed to manage the burden.
WHAT are the big data platforms?
Big data platforms are made to handle large amounts of data that enter the system at high speeds and in many varieties. These big data platforms typically comprise a variety of servers as well as database systems. Business intelligence tools which allow data scientists to work with data to discover patterns and trends.
Big data is basically the process of combining the three Vs in order to discover insights and to make predictions Therefore, it’s beneficial to look more closely at each one of the aspects.
Volume
Big data is huge. Although traditional data is measured using familiar sizes such as gigabytes, megabytes and terabytes, the big information is stored as petabytes as well as Zettabytes.
To appreciate the magnitude of the scale difference take a look at this chart of the Berkeley School of Information One gigabyte is equivalent to seven minutes of video in HD One Zettabyte is equivalent to the equivalent of 250 billion DVDs.
This is only the beginning of the ocean. According to Statista the production of data is expected to double within a five-year period. It is expected to reach 180 zettabytes generated globally in 2025.
Big data offers the structure for to handle this type of data. Without the proper options for processing and storage the data, it is impossible to gain insights from the data.
Velocity
From the time data is created to the time it takes to process it, everything related to big data is speedy. Many have described the process as attempting to drink from an open fire.
Businesses and organizations need the ability to tap into the data they collect. Extract insights from it instantly otherwise it’s not beneficial. Real-time processing lets decision makers act fast, giving them a advantage over their competitors.
While some types of data are processed in batches and are useful over time, the majority of the data that is big is flowing into companies at a rapid pace which requires prompt action to achieve optimal results. Health device sensor data is one instance. The capability to immediately process health data can provide patients as well as physicians with life-saving information.
Variety
About 80-90 percent of the data is not structured which means it isn’t able to be incorporated into a simple, conventional model. All kinds of data from videos and emails to meteorological. Scientific data can be considered a large streaming of data, all with its distinctive characteristics.
The benefits from Big Data
Although the sheer size of big data could seem overwhelming, the quantity of data can provide a wealth of data that professionals can make use of to their advantage. Large data collections can be used to identify patterns in their sources providing insights for improving business efficiency.
A few notable areas in which big data can be beneficial include:
- Cost optimization
- Customer retention
- Making decisions
- Process automation
USES OF BIG DATA
What is Big Data Utilized?
The variety of big data is what makes it extremely complicated and requires to have systems capable of processing the various semantic and structural variations.
Big data demands specific NoSQL database that are able to store data in a manner that doesn’t require adhering to a specific model. This gives you the freedom to analyze in a cohesive manner seemingly unrelated sources of data to get an overall view of what is happening, what needs to be done, how to respond and when to take action.
Operational systems store huge amounts of information across multiple servers . They include inputs like inventory, customer data , and purchases — the day-today information that an organization needs.
Big Data Examples
- Experiences for shopping online that are personalized.
- Modeling of the financial market.
- Enhanced medical research based on Data point collection.
- Media recommendations for streaming services.
- Forecasting yields of crops for farmers.
- Studying patterns of traffic to ease congestion in cities.
- Recognition of shopping habits in retail and optimizing product placement.
- Maximizing the effectiveness of sports teams and the value of their work.
- Recognition of the Education habit for students, schools and districts.
Here are some illustrations of sectors in which the big data revolution is already in full swing:
Financial Data
The insurance and finance industries make use of large datasets as well as predictive analytics to aid in the detection of fraud, risks assessment credit scores brokerage services, blockchain technology, to name a few applications.
Financial institutions also use big data to boost their cybersecurity and tailor customer financial decisions.
Health IT Big Data
Researchers, hospitals and pharmaceutical companies make use of big data strategies for improving and enhance health care.
With the access to huge volumes of population and patient information, healthcare professionals are enhancing treatments, conducting more efficient studies on diseases such as cancer. Alzheimer’s creating new medications and getting crucial insights into the health of the population.
Media & Entertainment
If you’ve had the pleasure of using Netflix, Hulu or any other streaming service that offer suggestions you’ve seen massive information being used.
Media companies study our reading, watching and listening habits in order to provide personalized experiences. Netflix even analyzes data about titles, graphics and colorsto determine the preferences of its customers.
Agriculture
From the engineering of seeds to predicting yields of crops with incredible precision, big data, and automation are rapidly improving the agriculture industry.
Due to the massive increase in data that has been generated in the past two decades, data is more plentiful that food, in many places, prompting scientists and researchers to make use of big data to combat malnutrition and hunger.
With organizations like GODAN Global Open Data for Agriculture & Nutrition (GODAN) that promote open and unrestricted access to information on global agriculture and nutrition Some progress is being made to end hunger around the world.
In addition to the areas mentioned above the field of big data analytics is all industries to alter the way companies are running on a contemporary scale. It is also possible to see big data being used in the areas of marketing and advertising, retail, business, e-commerce as well as the education sector, Internet of Things technology and sports.
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