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    Demystifying the Definition of Big Data: Exploring How to Harness its Power in Data Analytics

    The application of big data has developed rapidly with Industry 4.0. In recent years, the collection of data has become easy, not only to collect information but also to analyze different data and apply it in various fields. Data collection must rely on the assistance of other technologies, such as the Internet of Things, system integration, network security, etc., the complementarily of various technologies creates today's big data. You may ask a question, What kind of applications for Big Data?

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    The beginning of big data

    People upload information to the Internet every day, such as photos, documents, videos, etc. With the advancement of technology, the cost of storing data has dropped significantly, therefore, the amount of data in various industries has increased rapidly. For example, Facebook processes hundreds of millions of photos every day. YouTube users watch hundreds of millions of hours online every day, and the amount of data that can only be obtained within ten years in the past can be collected overnight today, and whether the data can be successfully analyzed is the most important thing.


    The term "big data" was originally from "The Third Wave" written by the futurist Alvin Toffler and called "big data" the Cadenza of the third wave. 《Nature》 magazine also published a special column called "Big Data" in 2008 which became a buzzword in the Internet technology industry.


    McKinsey & Company was the first to apply big data. They found that the information recorded by various online platforms has commercial value and released a report about "big data" in June 2011. Therefore, "big data" is gradually being valued by all walks of life. Facebook also digitizes interpersonal interaction, bringing about great commercial value. Now, we are in Internet of Things generation, and many machines have begun to be digitized, such as watches, cars, and motorcycles. The information collected will be important foundation of Analysis.

    What is Big Data?

    The data is too large to be stored, calculated, and analyzed with the method in the past, therefore, it is not only for data processing, but also a business model now. In the past, META Group analysts have defined big data as Volume, Velocity and Variety, which means that new processing methods are needed to facilitate greater decision-making, insight, and optimization.


    • Volume: As the name implies, the amount of data is quite big. Usually, more than 1TB of data can be generated in one day. Taking Excel as an example, the file size usually does not exceed 4,000KB, and 1TB is one billion times of KB which shows how huge the amount of data information it is.


    • Velocity: The processing speed of data is very fast. The point of big data is to analyze high-value information from various types of data.


    • Variety: The types of data are very diverse, including basic personal information (name, phone number, behavior, and habits, etc.), pictures, videos, audio files or text, and combine comprehensive information can provide high reliability data.

    Why is big data needed?

    Big data is akin to the "lifeblood" of humanity, permeating the entire lifecycle. It has deeply influenced both businesses and individuals. For businesses, it has transformed current technology and management processes, enabling various methods or decisions that were previously unverifiable. It provides solutions for automating optimization in areas such as marketing, sales, and services. In the past, decisions were often made based on data, involving minor adjustments and alerts for anomalies in processes. Enterprises would evaluate their performance and propose improvement plans, sometimes planning years ahead before implementation.


    Big data disrupts previous evaluation methods. In an unstable and constantly changing market, the ability to swiftly grasp information and take action is crucial for gaining a competitive edge. The key to success for businesses lies in whether they can extract valuable insights from big data and are willing to undergo reform and take action. This is the way to effectively enhance service quality, management efficiency, and create new business models.

    The Operation of Big Data

    Proper utilization of big data can bring new opportunities to businesses and even establish new operational models. The following three key factors are involved:


    • Integration: Big data is formed by aggregating multiple application sources. Traditional integration methods such as extraction, transformation, and loading (ETL) are mostly insufficient for handling big data tasks. Newer strategies and technologies are required to analyze datasets larger than terabytes (TB).


    • Management: Big data needs to be stored, and the data can be placed in the cloud, within the company's infrastructure, or deployed on both sides simultaneously. The appropriate storage and access methods should be chosen based on the company's needs, along with the establishment of necessary processing standards and engines.


    • Analysis: When analyzing and processing data, significant returns can be obtained from accumulated data over the long term. Visualizing analysis using different data sets provides different insights and deeper information. Leveraging machine learning and artificial intelligence can further unleash the potential of big data, leading to actionable insights.

    Risks of Big Data

    With more and more information, the security issue of personal information is gradually magnified, but this is not a problem that only exists with big data. In fact, there is no information security problem from the data itself, but how companies use this data. Another risk is data dictatorship. If big data is used blindly, it may label things or even stigmatize certain things finally. 

    Advantages of Big Data

    In addition to risks, big data has many advantages in fact, including that companies can make more effective decisions, reduce investment risks and costs, improve productivity and revenue, and optimize customer experience. Although there are many successful cases of data analysis, it also needs to be adjusted with the operation and decision-making of the company in practical application, rather than ignoring the possible impact on the company for the sake of benefit.

    Application of big data

    Big data is not only used in enterprises, but also in daily life. There are seven most common applications as follows:

    • Analyze customer requirements: Many companies will use big data to analyze customer behavior in order to understand customer preferences, purchasing behaviors and extend corresponding strategies.


    • Manufacturing process and SOP optimization: Through big data, the process of the company or the manufacturing industry can be optimized, and the problems encountered in the process can be improved, thereby reducing costs, and finding the most efficient model, such as logistics industry distribution routes, human resources management, recruitment analysis, workflow optimization, etc.


    • Improve the quality of life: Not only for enterprises, but also track and analyze the situation of the body or the car body for big data through mobile phones, watches, cars, and other devices, and propose solutions for the human body or the car, thereby improving the quality of life.


    • Medical research, technology research and development: Through the prediction and calculation of big data, it can help doctors to propose better and more accurate treatment methods for patients and can also record and analyze premature birth or sick babies, then they can predict the situation and propose follow-up countermeasures.


    • Improve sports performance: By wearing measurement equipment, athletes can understand sports performance, posture, and angle, and then analyze various improvement suggestions to improve sports performance.


    • Improve security and law enforcement: In recent years, many countries have used big data to identify and track faces. Although it has caused serious personal information problems, there are also some positive effects. For example, the United States uses big data to prevent or respond to terrorism, and fraudulent transactions, etc.


    • Finance: The use of big data to analyze and predict financial transactions has become a common application. It can even discuss community and website news information to decide whether to buy or sell.

    Trends and developments of Big Data

    With the rapid development of AI, many industries have invested resources in big data and AI, such as banking, e-commerce, and delivery industries. The public has also begun to change their lifestyle due to the impact of the epidemic, forcing various industries to conduct Digital Transformation. Expanding new business models is not only for the civilian production industry, but general enterprises also rely on big data analysis to make the most effective decisions, and to look for speed and innovation in production and R&D to meet people's requirements. 

    Conclusion

    From the past to the present, the application fields of data have become more and more extensive, and because of the advancement of technology, it has become easier to keep data. After the data is accumulated and analyzed, it can be improved and managed according to various problems, and finally, provide more effective decisions, reduce costs or increase productivity and revenue to meet the needs of various markets.

    Main photo by photographer: Markus Spiske, link: Pexels

    Source 1on1

    References INSIDE / BNEXT / 1on1Blog / KKNEWS

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