What Is Big Data?
When you hear “big data,” you probably have an image of “a huge amount of data.” Sure, it’s part of big data, but it’s not the only one. Advantages And Utilization Of Big Data.
Big data consists of three Vs.: “Volume,” “Variety,” and “Velocity.” In other words, it refers to data of various types and formats that are generated and accumulated in massive amounts daily.
There are several types of data, such as “quantitative data/qualitative data” and “flow data/stock data,” depending on how they are classified. However, in big data, it is classified as “structured data / unstructured data (semi-structured data).”
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Data that has the concept of “columns” and “rows” that can be expressed in Excel files, CSV files, fixed-length files, etc., is called structured data.
Examples include sales and customer data used in databases for business software such as SCM, ERP, and CRM.
■Unstructured data (semi-structured data)
Unstructured data refers to data that cannot be defined by “columns” and “rows” like structured data.
Specific examples include documents, e-mail, design, audio, video, e-books, web pages (HTML), SNS, and data obtained from GPS and sensors.
With the advancement of IT, the amount of unstructured data handled by companies is increasing, and the ratio of unstructured data to structured data is also increasing.
Even before the concept of big data was born, measured numerical values were used as data in business and natural phenomena. Computers have made this possible.
Computers, which originated from computers and were mainly used for arithmetic processing, dealt with only structured data in databases, but with the advent of personal computers and the evolution of programs, not only numbers but also documents, images, sounds, etc. We now deal with unstructured data such as videos.
However, “structured data / unstructured data” was not used initially. There is a history that came to be called data.
RDB cannot handle this unstructured data. After RDB, databases have changed in response to the demands of the times, and now more and more companies are adopting NoSQL, which Google and Amazon implement.
NoSQL is an abbreviation for Not only SQL and is a general term for database management systems other than the RDB system. It can be roughly classified into four types: key-value type, broad column store type, document type, and graph type, and it is a database that can handle unstructured data.
The emergence of in-memory databases has also boosted the speed of database processing and the spread of big data.
To utilize big data, it is necessary to generate, collect, store and analyze data. The transition of the above database corresponds to the “accumulation” part.
It can be said that the use of big data has progressed due to the combination of conditions such as advances in ICT that are responsible for “generation and collection,” the price reduction of hardware, and the spread of the cloud that is responsible for processing vast amounts of data for “analysis.”
Advantages Of Big Data
Here are two benefits of using big data.
- high real-time
- Leads to a reduction in the cost of collecting information
I will explain each one in detail.
Real-time, one of the building blocks of big data, gives you an edge over your competitors. Real-time performance means high-speed processing on large-scale data and immediately analyzing the constantly flowing data.
Big data has an element called integrity (accuracy) and is characterized by having real-time data. Real-time performance enables us to quickly detect market needs and utilize them in marketing and management strategies, leading to the creation of accurate business.
Immediate response to the ever-changing market establishes a competitive advantage over others.
It Leads To A Reduction In The Cost Of Collecting Information
Big data, a high-quality data collection, can reduce the cost of collecting information. In the past, information gathering through interviews and questionnaires, for example, was subject to time constraints and required labor costs.
However, big data makes it possible to collect a large amount of information in a short period on the Internet, and it is possible to reduce the person’s restraint time and labor costs.
Big data enables companies to collect information at a low cost, and the saved costs can be invested in necessary businesses such as development and marketing.
Big Data Utilization Examples By Industry
Here are five examples of how big data can be used in different industries.
- Food industry
- medical industry
- tourism industry
- Education industry
- agricultural industry
Let’s check the specific usage of each.
Examples Of Use In The Food And Beverage Industry
In the food and beverage industry, big data is used as “ID receipts” and “BI tools.”An ID receipt is data recorded when a product is purchased at a store cash register.
Analyzing the attributes of the purchased products and the people who purchased them can be used for practical sales activities. BI tool is an abbreviation for Business Intelligence and supports the utilization of the analysis results of the enormous amount of data accumulated daily for management decision-making.
Using ID receipts and BI tools contributes to accurate market research and analysis, enabling companies to formulate effective marketing strategies. Decision-making based on consumer behavior data can be expected to improve business performance and corporate growth.
Examples Of Use In The Medical Industry
DPC and NDB are typical examples of big data in medicine. DPC accumulates information that categorizes patients by a combination of diagnosis and medical treatment performed as data. NDB is a nationwide database that summarizes medical facility receipts (medical fee statements).
DPC and NDB are used to build an information infrastructure for clinical research. Clinical research is medical research that elucidates the causes and treatment methods of diseases and is necessary to continue protecting health. However, proper elucidation requires large-scale clinical studies, which require vast amounts of information.
DPC and NDB accumulate a large amount of medical data in Japan, which helps conduct effective clinical research. Advances in effective clinical research are expected to improve the adequacy of domestic medical administration and, in turn, raise the welfare level of the nation as a whole.
Examples Of Use In The Tourism Industry
In the tourism industry, location information big data is being used.
In this modern age, where many people own smartphones and tablets, the GPS function has accumulated many location information data.
It is possible to analyze people’s behavior by extracting behavioral trajectories and understanding their characteristics from the accumulated big data of location information.
In recent years, due to the increase in the number of SNS users, there have been cases where location information and post content added to SNS posts are accumulated as information and analyzed to understand tourist behavior patterns. By analyzing the place of stay and the number of posts, we can expect to judge accurately whether the location is suitable as a tourism resource. In addition, there are cases where
The recognition patterns of each region are acquired, and the region’s image is grasped and used for promotion. Specifically, we use big data to understand the image and perception of a specific region. As a result, you realize, “I haven’t put much effort into promoting the goodness of the region that many people have in mind.”
Effective promotion can be achieved by utilizing market needs obtained from big data. The use of big data in the tourism industry can be expected to create new businesses by grasping the current situation and discovering needs.
In this way, capturing customer information quantitatively and qualitatively makes it easier to identify customer trends and reasons for behavior, enabling a deep understanding of customers.
Use Cases In The Education Industry
In the education industry, big educational data is utilized to improve the quality of education. “E-portfolio” is spreading mainly in universities and institutions of higher education in education big data, which collects a large amount of educational data. An e-portfolio is a collection of meaningful and helpful learning records in the learning process, which is stored as data and utilized.
Furthermore, the e-portfolio can be used in two different ways.
- Broad definition
- “All portfolios handled in electronic form.”
- Characteristic: Superiority in processing that takes advantage of digital data
- Narrow definition
- “Software for creating portfolios or systems for managing portfolios”
- Features: Networks enable close mutual and collaborative learning and evaluation activities
The use of e-portfolios enables the processing of diverse and large-scale data and is expected to realize new education using mechanical analysis technology. In other words, big educational data in the education industry has the effect of supporting the learning of individual learners and promoting learning by converting proper learning records into data and utilizing them.
Examples Of Use In The Agricultural Industry
The agricultural industry is aiming for new developments through advances in sensor innovation. Sensor innovation refers to IoT that combines network technology and sensor technology. Expectations are rising in the agricultural industry as
A means of efficiently collecting data and constructing big data. Data generated at agricultural production sites can be broadly classified into environmental, biological, and cultivation management data. Technological development is underway to collect each of these data efficiently.
In particular, biometric data is a type of data for which there are growing expectations for technological development in recent years. Biological data can be used to know the state of the crop itself; conventionally, information has been collected visually or manually.
Therefore, due to the difficulty of time-series measurement and the fact that it is a destructive measurement, there is a background that data collection has been significantly delayed.
However, we are now seeing the acceleration of technological development, such as technology that speeds up image analysis and a glove-type sugar content sensor that incorporates an ultra-compact FTIR that applies MEMS technology. Furthermore, with the advent of inexpensive biosensors that can be easily used outdoors, it has become possible to collect a wide range of biometric data efficiently.
In the agricultural industry, advances in building big data have enhanced information collection and analysis capabilities, driving new developments.