Overcoming Difficulties in Modern Big Data Analysis for Business: Strategies and Implications

This paper aims to provide an understanding of the challenges associated with Big Data. With the advent of Big Data and its impact on business performance, organizations have witnessed a transformation in data management practices. In today's era of business growth, the accumulation of raw data and various types of information, such as employee mobile data recorded for security reasons, emails, blogs, and more, has become inevitable. As businesses expand, the volume of data continues to increase. For instance, multinational e-commerce stores like Amazon need to store vast amounts of information, including warehouse data, employee data, and user data, which falls under the realm of big data. Effectively handling this data is crucial for business improvement. The key challenge lies in extracting valuable insights from raw data and refining it. Big data exhibits a variety of formats, including structured, unstructured, and semi-structured data. Unstructured data, which is predominant in big data, necessitates the utilization of advanced business analytics techniques. This paper provides a comprehensive overview of big data in the business context, highlighting its significance, available tools, challenges in data management, and the potential competitive advantages it offers to businesses.

Keywords – Big Data, Big Data Analysis, Business Analysis, Difficulties of Big Data, Literature review, Modern Large Data, Varieties of Big Data.

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I. ?INTRODUCTION

Big data analysis poses significant challenges due to the time deviation of data. Extracting relevant information from vast datasets requires the use of decision-making procedures, often referred to as data mining or knowledge discovery from data (KDD) [2]. The indispensability of data in today's world is evident; without proper data storage, transactions, businesses, and various other processes would be severely hindered. As data continues to grow, the challenges of handling large-scale big data become even more pronounced, necessitating advanced analytical techniques to derive meaningful insights from unstructured data.

The process of big data analysis involves several crucial phases[5]:

1) Data Acquisition

2) Data Storage

3) Data Analysis

The evolution of big data is closely linked to the rapid growth of the internet and advancements in technology. With built-in storage capabilities and diverse methods of data gathering, enormous volumes of data have become easily accessible within minutes. Each second, the world generates millions of records over the internet, with data continuously being created both online and offline, requiring efficient storage and analysis methods to extract valuable insights from these vast repositories. As a result, organizations strive to derive as much meaningful data as possible from the massive amounts of stored information.

II. BIG DATA

Big Data refers to the availability of an enormous amount of data, which poses challenges in terms of storage, filtering, and analysis due to its large, unstructured, and constantly changing nature [2]. This is why big data first gained attention in e-commerce businesses and platforms like Google, Twitter, LinkedIn, and Facebook.

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The objective of big data is to enable efficient resource utilization, storage, and reduce the time taken for analysis and business decision-making. Big data represents a large volume of data that cannot be effectively analyzed using traditional database management systems [7].

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Business analytics encompasses techniques, processes, technologies, and methods for data analysis. It involves transforming data into valuable insights to assist organizations in understanding their operations and facilitating the decision-making process. Business analytics goes beyond mere reporting; it aims to provide rapid business responses and results. Business intelligence, on the other hand, involves storing data in a data warehouse, enabling business employees to easily perform reporting, querying, and predictive analytics. The decision-making process requires considering various aspects of data, and business analytics incorporates perspectives and methods from statistics, machine learning, and data mining.

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The definition also extends to the tools and technologies employed to handle big data. In recent years, learning environments have generated vast amounts of data, which has led to the development of specific tools and technologies to manage such data [1].

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Big Data: The Characteristics of the Six Vs [6]

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Big data is characterized by six key dimensions, often referred to as the six Vs: Volume, Variety, Velocity, Veracity, Variability, and Value.

III. SECTION HEADING

A. Volume

In today's world, data continues to grow exponentially, leading to a vast volume of big data. For instance, a few years ago, mobile phones had a storage capacity of around 256KB, while modern smartphones offer up to 256GB of storage space, and the need for even more space persists, exemplifying the significance of data volume.

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B. Variety

Data exhibits various formats, including structured, semi-structured, and unstructured data. For instance, mobile sales reports and call records are structured, while formats like JSON, web pages, and XML fall under the category of semi-structured. On the other hand, unstructured data comprises collections of sentiments, smileys, and natural language.

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C. Velocity

Velocity refers to the speed at which data is generated, processed, and accessed. For example, when we search for a specific file on a smartphone and receive results within microseconds, seconds, or minutes, it showcases the importance of data velocity, with a particular focus on real-time streaming of data.

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D. Veracity

Veracity pertains to the reliability and accuracy of the insights derived from data analysis. When meaningful information is obtained through various analysis techniques, individuals or organizations may harbor doubts or lack trust in the results, leading to concerns regarding the veracity of the data.

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E. Volatility

Volatility relates to the time period during which data remains relevant or valid. For instance, banking passwords may have an expiration date, and once that date passes, they become obsolete, highlighting the aspect of data volatility.

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F. Value

This characteristic emphasizes the importance of data in terms of its value to the organization. For instance, businesses may prioritize profitability, making profit a critical value. Alternatively, other businesses may prioritize customer engagement, satisfaction, and relationship-building, considering these aspects as essential values.

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IV. BUSINESS & BIG DATA

Businesses are increasingly recognizing the importance of understanding what big data is and how it impacts their systems, as well as the advantages it can bring to organizations. According to a survey, only 12% of businesses have currently implemented a big data strategy, while 71% are in the planning phase [6].

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Analysis refers to the process of breaking down complex obstacles into smaller parts for analytical examination at an appropriate level [4].

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It is evident that businesses require a deep understanding of consumers, products, and regulations. With the help of big data, organizations can discover new ways to compete with others. Effective implementation of big data can significantly impact an organization's decision-making process [9]. Big data analytics can facilitate smoother and more informed organizational decisions. However, it is important to note that decisions based on the execution of past and present data may not capture the full picture, as there is non-linear and less organized data, such as weblogs, social media, emails, and photographs, which can also contribute to the decision-making process.

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The process involves the following steps:

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Decision norms are based on social, technological, and economic aspects.

Candidate situations refer to various scenarios that organizations can select for big data implementation, such as high demand with cautious optimism.

Candidate technologies include data repositories, cloud analytics, embedded analytics, and the conceptualization of big data.

Technology evaluation pointers consider global market size, enterprise acceptance ratio, entry constraints, and industry assets.

Technology planning implications encompass technology development implications for high-demand situations and technology planning inferences for cautiously optimistic scenarios.

Organizations are actively seeking ideas to enhance their products and processes in order to increase returns on investment. The approach of product or process innovation requires extensive research to develop predictive models that can drive growth and profitability [11].

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V. SOURCES OF BIGDATA

Massive amounts of data are generated from various sources, particularly through social media platforms like Facebook, Google forums, and search engines.

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These data are produced through online transactions, blogs, emails, posts, videos, search queries, and Internet of Things (IoT) applications and devices, contributing to the ever-increasing volume of data.

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The three primary sources of Big Data are as follows:

Social data: This data includes likes, posts, tweets, retweets, comments, video uploads, and other social media interactions on popular platforms. Analyzing social data can provide valuable insights into consumer behavior and sentiment, making it highly valuable for digital marketing analytics.

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Public web data: The public web serves as another significant source of data. Tools like Google Trends can be leveraged to effectively gather and augment the volume of big data.

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Machine data: Machine data refers to information generated by industrial tools, sensors embedded in machinery, websites, and web logs that capture user activity. With the proliferation of the internet and the advent of the Internet of Things (IoT), machine data is expected to grow exponentially. Sensors in medical devices, smart meters, road cameras, games, and other IoT devices contribute to the high velocity, value, veracity, volume, and variety of data in the present and near future.

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Additionally, transactional data is generated from daily online and offline transactions, including invoices, payment orders, return orders, storage records, and delivery receipts. While transactional data alone may be meaningless, organizations strive to extract meaningful insights from the data they generate to derive value and make informed decisions.

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VI. BIG DATA APPLICATION

Big data encompasses information generated by various applications, and some of the key application areas that require and utilize big data include:

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1.????? Health Data: Health-related applications gather extensive data, such as step count, heart rate, temperature, and other biometric information, to monitor and track individuals' health and fitness levels.

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2.????? Search Engine Data: Search engines accumulate vast amounts of data by retrieving information based on characters and keywords input by users. This data is used to enhance search results and improve user experience.

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3.????? Stock Exchange Data: Data related to stock exchange activities, including the buying and selling of shares, is crucial for monitoring market trends, analyzing investments, and measuring profitability.

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4.????? Social Media: Social media platforms like Facebook, Instagram, Twitter, and Snapchat handle significant volumes of data. These platforms manage user-generated content, such as images, posts, videos, job-related information (LinkedIn), and facilitate social interactions, generating massive amounts of data.

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These applications represent just a few examples of how big data is utilized across various domains to gather, analyze, and extract valuable insights for different purposes.

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VII. IMPOTANCE OF BIGDATA IN BUSINESS

Difficulties in Business Intelligence (BI) arise due to its technology-driven approach to data analysis and presentation, aimed at assisting scientists, business executives, managers, and other users in making informed business decisions. BI encompasses a range of tools, applications, and technologies that enable businesses to gather data from internal and external sources, prepare it for analysis, execute data processing for effective insights, and generate reports and visualizations to facilitate decision-making. Statistical and quantitative analysis methods are often employed in business intelligence.

The growing importance of data analysis in various industries has sparked significant interest in business intelligence, which refers to the techniques and technologies that enhance market understanding and enable timely decision-making.

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Big data analysis plays a crucial role in improving business effectiveness and accuracy, leading to greater customer satisfaction, improved outcomes, and other business benefits. The primary objective of big data analytics is to assist data scientists, analysts, and business professionals in making effective and swift decisions by analyzing vast amounts of transactional and other data that were previously inconceivable with traditional business approaches. Companies are leveraging analytical tools and techniques to extract more value from the available data and are hiring data scientists skilled in handling big data to gain meaningful insights. As big data continues to evolve, it has the potential to reshape the way we think, make decisions, and conduct business. Leveraging big data effectively empowers companies to make faster, more informed and intelligent decisions.

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VIII. HOW IT USEFUL FOR BUSINESS (PROCESS)

The process of digging into big data is an ongoing phase that continues to evolve. An effective big data analytics approach encompasses key factors such as speed of analysis, scalability, the ability to handle large volumes of data, and efficient data management.

The proliferation of data and its significance to businesses has seen a tremendous growth in recent years. For instance, in 2010, social media platform Twitter had only four data experts, but today there are thousands of employees dedicated to data analytics, particularly within their Hadoop cluster node data centers. Twitter recognized the importance of harnessing the power of analytics ahead of its time, and businesses that fail to prioritize this aspect may face significant challenges.

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Big data is increasingly crucial for decision-makers. The vast amount of highly detailed data derived from various sources such as scanners, cell phones, loyalty cards, credit cards, the web, and social media platforms presents significant opportunities for organizations. However, these opportunities can be realized only if the data is properly analyzed to uncover valuable insights. Decision-makers can then leverage these insights to capitalize on the wealth of historical and real-time data generated by supply chains, production processes, customer behaviors, and more.

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IX. INNOVATION IN BIG DATA TECHNOLOGY TILL 2020

In the pre-2005 era, Google pioneered the map reduce technique as a means to organize data. Around the same time, Yahoo introduced Hadoop, a framework that incorporated map reduce for efficient processing of large-scale data. These advancements made searching and retrieving data easier and faster. In the present era, numerous businesses rely on Hadoop to manage their data using the Hadoop Distributed File System (HDFS). Some of the notable tools utilized in the realm of Big Data include [5].

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Based on extensive literature surveys conducted in various journals and books, it has been revealed that the volume of data generated in the entire year of 2011 is equivalent to the data generated in just one day in 2020. This exponential growth of data has prompted many businesses to transition towards big data solutions. In the present era, multinational companies are actively engaging in learning about big data and preparing themselves for its implementation. Interestingly, the usage of Hadoop has seen a decline as more companies are opting for Spark as their preferred big data processing framework. This shift can be attributed to the remarkable speed advantage of Spark, which is approximately 99 times faster than map reduce. Moreover, Spark offers the flexibility of coding in popular programming languages such as Java, Python, and R.

X. DIFFICULTIES WHEN HANDLING BIG DATA TILL 2020

Every evolution brings along its own set of challenges, and the implementation of big data in business is no exception. As the applications and usage of big data continue to expand, various difficulties arise [5]. Some of the common challenges encountered while managing big data are as follows:

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1.????? Storage Problems: The sheer volume of data generated requires adequate storage solutions. As data sets grow rapidly over time, it becomes increasingly challenging to manage and maintain them effectively.

2.????? Uncertainty in Tool Selection: Companies often face confusion when selecting the most suitable tools for analyzing and storing big data. Decisions such as choosing between HBase and Cassandra for data loading or determining whether Hadoop MapReduce or Spark is the optimal choice for data analytics and storage can be daunting.

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3.????? Lack of Data Specialists: To effectively leverage the potential of big data technologies, companies require skilled data specialists such as data scientists and data analysts. However, there is often a shortage of professionals with expertise in working with these tools and technologies, creating a gap that needs to be addressed.

4.????? Data Security: Safeguarding large amounts of data poses significant challenges. Companies may focus on data acceptance, loading, and processing without giving due attention to data security, leaving potential vulnerabilities for malicious hackers to exploit.

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5.????? Integration of Data from Various Sources: Business data originates from diverse sources such as social media, websites, ERP applications, customer logs, financial reports, emails, and employee-generated content. Consolidating and integrating data from these disparate sources to generate meaningful reports can be a complex task.

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6.????? Storage Capacity: The volume of data generated daily, both online and offline, far exceeds the capacity of traditional storage solutions. Conventional relational database management systems (RDBMS) may struggle to store, manage, and analyze such large-scale data. SQL-based queries may not be sufficient for handling big data effectively.

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1.????? 7.Data Analysis: Big data encompasses a variety of data formats, including structured, semi-structured, and unstructured data. Additionally, data is often fragmented into smaller pieces, making the analysis of big data a challenging task.

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Addressing these challenges requires organizations to invest in appropriate storage infrastructure, develop strategies for tool selection, bridge the skills gap through training and recruitment, prioritize data security measures, implement robust data integration techniques, explore scalable storage solutions, and adopt advanced analytics approaches suited for handling big data.

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XI. LIMITATION OF BIG DATA

The limitations of big data are evident in various aspects. For instance, a recent recruitment policy that disregards any correlation between academic achievements and work performance can lead to the misuse and misinterpretation of big data, rendering it unstructured. Additionally, when data transmission occurs, it can introduce confusion and complicate the handling and management of data. It is important to note that user-level outcomes cannot be directly inferred, as user-level data is susceptible to more noise and may not be easily transferable.

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XII. FUTURE RESEARCH AND INFERENCE

The generation of data is experiencing exponential growth on a daily basis, making the task of analyzing this data a challenging endeavor. Big data has become an integral part of businesses, even for those that are well-established and of significant size. Governments, IT companies, e-commerce organizations, and other relevant sectors have begun grappling with the implications of big data. Large businesses are embracing the data economy by merging big data analytics with traditional analytics, resulting in impacts on organizational benefits, leadership, structures, and technologies.

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However, despite recognizing the potential benefits such as cost reduction, improved decision-making processes, increased customer satisfaction, and operational efficiency, there is still a lack of precise understanding regarding the costs and benefits associated with big data implementation in businesses. This indicates a gap in the understanding of the concept of big data and its impact on business analysis.

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As a result, this paper aims to take the first step towards providing a comprehensive understanding of big data by highlighting the relevant factors and challenges associated with its management. Big data refers to a massive volume of data that exceeds the analytical capabilities of traditional data systems in terms of organization and analysis within specific time frames. Advanced methods are required to effectively analyze and store such large volumes of data. This paper examines the different phases of data processing, the nature of big data, its challenges, and potential solutions, including the utilization of the Hadoop MapReduce V2 YARN framework for processing big data in real-world scenarios.

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Acknowledgement

?I am appreciated and thankful to my beloved family.

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