What’s Big Data?
Big Data
In other words, the term “Big Data” was first coined by O’Reilly Media in 2005 to describe a vast amount of data due to its complexity and size, because of its inability to manage and process traditional data with management techniques. Introduced by Roger Magoulas.
A study on the Evolution of Big Data as a Research and Scientific Topic showed that research on “Big Data” began in the 1970s. However, it began appearing in publications in 2008. Today, the Big Data concept is handled differently in applications in many areas.
Data, personal information fragments have been collected and used throughout history. The recently changing outlook is the advancement of digital technologies with markedly increasing capabilities in data collection, storage and analysis. Big Data has many circulating definitions around the world, but every company is in favor of defining itself as it cares. For example, according to MIKE 2.0 (Open Source Standard for Data Management), Big Data is defined by interactive, very large, complex, and independent data sets and size. According to Ed Dumbill at the O’Reilly Strata Conference, Big Data can be described as “data that exceeds the processing capacity of traditional database systems.” The data is too large, very fast, it cannot fit into the configuration of your database architecture. To derive value from this data, we must choose alternative ways to process it. Every leader has to define new concepts to give the company a competitive edge.
Concept
The storage, access to information, processing, and storage of non-structural/fluid high volume, speed and variety of data that exceed the processing capacity of traditional database management systems as a result of technological advances, analysis and meaning: it can be expressed as a process of extracting the necessary information from the data collected.
In organizations’ own data warehouses – all data stores – and data silos – subdata warehouses under the control of a department independent of other departments – local data accumulates structured. The data in the local data warehouse targets strategic decisions, while data in data silos targets immediate purpose or tactical decisions. However, this local data is insufficient to help institutions and businesses make accurate and strategic decisions in this era.
Today, apart from this local data, billions of interactions of computers, GPS devices, mobile phones, medical devices and blogs, data shared on social media, data collected from various sensors, “Data Flood” is not configured or semi-structured by platforms such as e-mails, photos, videos, and blogs. Many of these interactions are mainly made up of mobile devices used by people whose needs and habits are insufficiently understood so far.
In this respect, the data that continues to increase but is not taken into account as structural data has been analyzed and evaluated as structural data accumulation. It can be expected that such data will bring implicit or confidential information from institutions and organizations to a content and wealth that will further strengthen the structural or institutional knowledge obtained by combining it with clear information. The result of such an application will bring with it the possibility that strategic decisions taken by institutions and organizations may expand the scope and influence of the target audience.
Accordingly, as data volume grows in the modern era, businesses and other organizations are forced to deal with “Big Data”, which is much more broadly defined than traditional data methods. Big Data means difficulty for many organizations and companies, but also means opportunity. On the other hand, many industry trends put pressure on traditional data management, business intelligence platforms and tools.
“Big Data” analysis in creating investments, problem solutions, process improvements, customer satisfaction studies, sales policies and general corporate strategies made by many voluntary organizations and organizations, private or non-profit for profit they should take advantage of it.
However, researchers and politicians can also use data for the benefit of low-income people to anticipate and prevent crises, provide various services, identify needs, and he’s starting to realize the potential difficulty of his downpours.
Governments, development organizations and companies that help the individuals and communities that create data through co-compliant action need to benefit from this data.
Today, Big Data is used to refer to data sets far beyond single data stores (databases or data warehouses). It is too large and very complex to be processed by traditional data management processes and processing tools. Big Data can include information such as transactional data, social media, corporate content, sensors, and mobile devices.
According to MIKE 2.0 (Open Source Standard for Data Management), Big Data is defined by interactive, very large, complex, and independent data sets and size. In addition, an important aspect of Big Data is the fact that it cannot be addressed with traditional data management.
Features
Big data platforms try to categorize different, discrete, and contradictory ones on digital networks while at the same time reducing costs by enabling more data to be added to virtual environments. In this context, the so-called 5V (initials of the components’ English names) has gained importance (Gurbeal, 2013; Aegean, 2013; Wikipedia, 2012).
So let’s just say there are five components to the formation of the Big Data platform. It is called 5V for short, using the initials of english expressions.
Variety
The data can be configured, unconfigured, or semi-structured, and all three types of data can be frequently and intensely interchangeable. Structured data accounts for only 20% of the large data stored in databases. Data on the Internet, social networks, physical sensing devices by users is dynamic and unstructured.
80 percent of the data generated and stored in databases is not structured and each newly produced technology can produce data in different formats. A variety of “Data Type” from phones, tablets and integrated circuits must be dealt with. And if you think that this data can be non-Unicode in different languages, they need to be integrated and transformed into each other.
Imagine all the data, resources you can think of, such as social media, sensor data, CRM files, documents, images, videos, etc. It’s not possible and costly to store all of this in a relational database, not even a database, even on a file system that we know of. If the diversity of data has increased and we want to process, analyze and store all this data, the concept of Big Data is the perfect one for this.
Velocity
Speed refers to the high requirements for part-time and real-time processing in big data analytics. Real-time requirements for traditional data warehouse and business intelligence are lower.
The rate at which large data is produced is very high and increasing. The faster the data produces, resulting in the increase in the number and diversity of transactions in need of that data at the same rate.
Volume
Due to the rapid increase in the data produced, the worldwide data volume is enormous. Mobile devices and wireless data sensors everywhere generate data every minute, and bulk data exchanges continue to occur every second across billions of internet services. Scientific applications, video surveillance, medical records, operational commercial data and e-commerce data are big data sources.
In 2011, the International Data Agency IDC claimed that data in the world doubled every two years. According to IDC statistics, the amount of data to be reached in 2020 is projected to be 44 times that of 2009.
It is necessary to think about the capacities and “large systems” that are currently being used, which we call “large”, and imagine how they will cope with data 44 times the size. These huge increases pose significant problems for storage. Data archiving, processing, integration, storage, etc. technologies of this magnitude need to be set up how to deal with the volume of data. In the 2010s, total IT expenditures in the world increased by 5% per year, but the amount of data generated increased by 40%.
Verification
Another component is that it is “secure” during the flow of data in this information intensity. During the stream, it must be monitored at the security level, visible or confidential by the right people, without the correct inclusion.
Value
Traditional data in a business can be used for static and archival analysis. However, Big Data is more of an important factor that will affect future trends and important decisions. However, Big Data has a lower value density. For example, continuous surveillance data can be generated from a video recorder, but only a few seconds of these images may actually be useful.
Therefore, the most important component is that large data creates a value. Big Data described with all the above components must be a plus value for the organization after your data production and processing layers.
It needs to have an immediate impact on decision-making processes and should be at hand in making the right decision.
For example, if you’re in A government institution that makes strategic decisions about health should be able to see disease, medicine, doctor distributions in the details of region, province, county etc. instantly. The Air Force should be able to see the instantaneous location seits and conditions of its vehicles in all its volatile inventory, and be able to follow retrospective maintenance histories. A bank should be able to track not only the demographic information of the borrower, but also the habits of eating and vacationing, and, if necessary, be able to see what they are doing on social networks.
Summary
With the rapid development, spread of the Internet and information tools and the introduction of all areas of our lives, there is a huge amount of “Data Generation” from a lot of sources, unlike classical data.
This data, generated by humans through machines and technological devices used, provides information about users’ geographical locations, habits, expenditures, likes, likes, watches, and so on. Provides.
Internationally referred to as “Big Data” and thought to be a new phenomenon of the information age , “Big Data” analysis, industrial investments, problem solutions, process improvements, customer satisfaction analysis, sales policies and general company strategies.
“Big Data” analysis has the opportunity to be applied in many areas such as e-commerce, financial services, public services, education and health. In the most clear way, e-commerce comes with too many customized offers and forms of communication. “Big Data” now stands before us as an inevitable phenomenon.
