The Big Data Technology

The Big Data Technology

In an era characterized by extensive technological advancements, Big Data technology

remains competitive in addressing contemporary challenges affecting modern businesses and

industries. At its core, Big data entails integrating structured, semi-structured, and unstructured

data sets that organizations collect, scrutinize, and mine to acquire more information and

understanding. Aligned with the concept of big data, Big Data technology encompasses the

software tools utilized to manage all types of datasets and transform them in a manner that makes

sense for business analysis. According to Volk et al. (2020) statistics, approximately 97.2% of

the companies thriving in developing nations are investing heavily in adopting and applying Big

Data technology in their business analysis. Rahman et al. (2021) further assert that 58% of the

organizational performance in companies that have seemingly integrated Big Data technology is

directly associated with this technology. Such findings illustrate that in an era where technology

is a critical driving force to business and industries’ success, Big Data technology remains the

most effective solution. Concisely, the analysis of the Big Data technology informs how well to

apply the technology for the best outcome, the services offered through the technology, the

threats limiting the successful application of the technology, its opportunities, and its future

outlook.

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Reasons for Choosing the Topic, “Big Data Technology”

The choice to explore the topic of “Big Data Technology” is primarily driven by the

extensive influence that this technology currently has on the thriving of businesses and top-

performing organizations worldwide. Mainly, internalizing this topic informs on the best

strategies that a company should apply when incorporating the technology into its systems.

Aligned with Mikalef et al. (2020) suggestions, understanding the complex functioning of big

data analytics is a revolutionary step toward mastery of epic data analysis dimensions. With such

knowledge, entrepreneurs and organization managers make the process of decision-making more

flexible and informed. Apart from that, the topic resonates well with the digital age's exponential

growth in data generation, where understanding how big data technology works is an important

step for the organization seeking to remain competitive and relevant to the increasingly data-

centric digital environment (Volk et al., 2020). Given the relevance of this technology as a

catalyst to the company’s data analytic systems and machine learning, this topic remains a

guiding principle towards the optimistic application of modern technology. Essentially,

embracing this topic is a groundbreaking approach towards maximum revolutionizing

organizations’ data analysis systems to meet the extensive data analysis requirement.

Applications of Big Data Technology in Businesses and Industries

    Big data technology has immeasurable positive impacts on the retail and e-commerce

business sector through its unique way of transforming business transactions and business

information from a rigorous exercise to a simple and transparent process. When integrated into

the business sector, big data analytics informs the entrepreneur about the commodities with the

highest value across the market spectrum as well as shows the competitive index in the supply

chain. According to Sestino et al. (2020), big data technology modernizes the retail sector by

tracking online browsing patterns to analyzing purchase histories. In doing this, the technology

helps retailers to personalize customers' experiences, optimize product offerings, and drive sales.

In this case, platforms using extensive data analysis, like Amazon and Alibaba, exemplify this

approach to business analysis through vast analysis of datasets to tailor product

recommendations and streamline purchasing (Volk et al., 2020). Apart from that, big data

technology helps business entrepreneurs in inventory management, demand forecasting, and

supply chain optimization (Rahman et al., 2021). Appropriately analyzed data set makes the

retailers more informed on the correct decisions to make that best address the situations in the

market.

    The advancement in how data are analyzed and interpreted is an approach toward more

efficient service delivery in the financial sector. When big data technology is epically applied in

bank digital systems, the analysis of a wide range of data from every basic unit of a bank

becomes simpler and more realistic. With quick monitoring of how the financial systems of the

institution are functioning, the financial institution’s employees are able to determine sources of

fraud activities and stop them (Kushwaha et al., 2021). Likewise, Mikalef et al. (2020) indicate

that the technology is an optimistic approach for the banks to monitor real-time customer

feedback from every branch of the organization and deliver a quick response. Rahman et al.

(2021) show that 70% of the positive customer feedback in the financials sector is primarily

driven by the ability of the bank to address and respond to customers' demands immediately after

they raise a concern. In such a case, big data technology allows the customer service providers to

quickly access the information requested by the client and amicably address their issue. As such,

applying big data analytic systems in the financial sector makes the analysis of information more

efficient and improves the customers’ trust in the financial institution.

Gathering vast information, organizing data into more relatable information and offering

interpretation of abstract datasets makes the big data system the most efficient technology in the

health sector. As the information flows from different healthcare departments, it is usually

composed of several medically written data sets that are technically challenging to relate.

However, after entering the information into the big data system, one can easily interpret whether

the information translates to improvement of the health organization’s service delivery or

diminishing performance. Galetsi et al. (2020) research indicates that in health organizations that

have adopted big data analytic technology, the technology helps reduce costs by minimizing

unnecessary diagnostics and enabling proactive disease detection through early symptom

analysis and predictive modeling. Supporting the adoption of a big data analytic approach in

medical research, Mikalef et al. (2020) stipulate that epidemiological studies harness Big Data to

forecast and mitigate the impact of epidemics. On top of that, Rahman et al. (2021) case analysis

shows that patient care witnesses a remarkable enhancement as evidence-based medicine

becomes more accessible through a more informed analysis of past medical results. As such,

applying the technology of Big Data represents an essential technological innovation that directly

helps to streamline health service delivery by relaying the areas that need improvement.

Services and Products Offered by Big Data Technology

Data Mining and Data Storage

Big data technology is comprised of advanced algorithms that offer more advanced data

mining as well as data storage. In the aspect of data storage, the software tools of data analytics

are composed of rigorous infrastructure solutions designed to fetch, store, and manage vast

datasets efficiently (Maheshwari et al., 2021). For instance, Apache Hadoop stands out as a

leading open-source platform designed with big data analytic technology to offer scalable storage

and processing capabilities across distributed computing environments. Apart from that,

MongoDB is another essential platform designed in big data analytics dimensions to offer offers

flexibility in handling large volumes of unstructured data through its NoSQL database

architecture. As per Volk et al. (2020), big data technology is equally utilized in data mining,

especially in extracting valuable insights and patterns from raw data. In this case, the Rapidminer

platform stands out as the most recognized modernized platform utilizing big data technology to

perform extensive data analytics (Rahman et al., 2021). Through constructive data mining, this

technology offers constructive predictive models for extracting actionable insights through its

well-informed data processing capabilities. In utilizing any of these platforms that apply big data

analytic procedures in data mining and data storage, business organizations stand a chance of

interpreting their data correctly and storing it for further reference.

Data Analytics and Data Visualization

Data analytics and visualization are crucial components of big data technology, which

helps businesses and organizations meet rapidly changing organizational and business demands.

Digital platforms constructed to address the need for extensive data analysis primarily apply the

big data analytical approach to extract (Rawat & Yadav, 2021). In doing this, these digital

platforms enable the organization to make informed decisions and gain a competitive edge.

Mikalef et al. (2020) further illustrate that data analytics platforms like Apache Spark and Splunk

offer well-thought-out solutions for cleaning, transforming, and analyzing data. To achieve this,

these platforms have advanced algorithms and machine-learning techniques that uncover

valuable patterns and trends. Supporting Mikalef et al. findings, Rahman et al. (2021) contend

that the platforms constituted with the algorithms of big data technology allow businesses to

access powerful analytics capabilities on a scalable and give room for adopting a pay-as-you-go

basis business model. With such an optimistic approach to service delivery, the business

becomes more flexible to suit diverse organizational needs and preferences. On the other end,

data visualization tools adopting big data analytic approaches like Tableau and Looker empower

users to create appealing visual representations of data (Volk et al., 2020). Importantly, the data

analytic tools are primarily made available as cloud-based services that enable correct

collaboration and real-time sharing of visualizations across teams and as standalone products for

installation within enterprise environments. As such, the data analytics platforms and data

visualization tools are essential components of the big data ecosystem that greatly empower the

success of businesses and industries through correct data interpretation.

Critical Factors for the Success of the Big Data Technology

Clear Vision and Organization’s Readiness

Successful application of big data technology is primarily determined by how well-

prepared the organization is and how well the proposed analytical technology aligns with the

institutional visions and goals. If the company is well prepared and its goals clearly align with

the intended technological framework, then its chances of successful implementation are

relatively high. Contrary to this, when an institution seeking to incorporate big data technology

fails to choose the best analytical approach that best fits its capability, it's likely to result in

extensive losses and unwarranted compromising of the information (Kushwaha et al., 2021).

Comprehending this issue, Sestino et al. (2020) specify that aiming to enhance customer

relationships, optimize operations, or drive strategic decision-making calls for deliberate

decisions for implementing big data initiatives. For instance, if an organization wishes to become

a data-driven entity in which the information analysis guides the company’s strategic moves, its

goal should primarily be tailored to a steady stream of actionable information derived from big

data. Generally, aligning the adoption of big data analytic procedures with the company’s

readiness and goals ensures seamless technology implementation.

Establishing Key Performance Indicators

Establishing key performance indicators (KPIs) is a promising approach to ensuring that

big data technology is amicably applied and adopted by a particular corporation. Rawat & Yadav

(2021) speculate that defining clear and measurable KPIs grounded on the organization's

objectives and use cases is a unique way of helping the company's managing directors to

effectively gauge the efficacy of their big data initiatives and track progress toward predefined

goals. Rahman et al. (2021) further state that in a scenario where the organization aims to

optimize operational efficiencies, enhance customer experiences, and drive revenue growth

through strategic adoption of big data technology, KPIs stand impactful in making a meaningful

decision. Mainly, the KPIs include metrics such as cost reduction percentages, improvements in

data processing speed, increases in customer acquisition rates, and enhancements in user

experience metrics like reduced clicks to purchase on e-commerce platforms.

Strict Adherence to Project Management Processes

Adherence to the rigorous project management processes during the implementation of

big data technology initiatives is the driving force toward the profitable adoption of the

technology. Centered on Kushwaha et al. (2021) analysis, efficient project management ensures

that Big Data projects are delivered within scope, time, and budget. Concurring with Kushwaha

et al. analysis, Volk et al. (2020) note that for the organization to ensure the implementation

process does not face any unwarranted resistance, the managing directors should conduct

empirical and rational inter-organization research to determine the possible limiting factors.

After acquiring such findings, the organizational managers should devise policies and procedures

to address every form of limitations determined. Upon preparing the ground for technology

application, gather necessary resources like skilled human capital, adequate resources, and means

of risk management, and offer channels for consistent communication. To a considerable extent,

such disciplined project management facilitates a smoother execution by mitigating delays and

technical challenges as well as enhancing the adaptability of the team.

Upcoming Threats and Opportunities of Big Data Technology

Upcoming Threats

   As businesses and companies worldwide embrace big data technology to unlock the

extensive abilities of data utilization, the alarming issue of data security breaches remains a

devastating limitation. Giving digital platforms operating with the big data analytic system

access to vast sensitive information poses a risk of manipulation of the data (Maheshwari et al.

2021). Such cases are often rampant whenever a platform entrusted with the data is hacked or

faces malfunctioning. Mikalef et al. (2020) raises similar complaints by highlighting that big data

analytics gives potential of high scale data breaching especially from unauthorized access to

customer information and other business data that is illegal. On top of that, failure to control the

extent and the kind of data the big data analytic technology is required to analyze poses a threat

to the uncontrollable flow of data that likely results in heavy penalties (Volk et al., 2020). When

dealing with such issues, organizations should only conduct a proper technological check on the

big data system they opt to use. Failure to do so is likely to compromise the organization's

legitimacy and risk tremendous losses for the company.

Ethical concerns remain at the forefront of extensive data evaluations using advanced

technology. The more technology is allowed to handle people’s information, the greater the risk

of using unauthorized personal information. Research findings from Kushwaha et al. (2021)

illustrate that overreliance on machines to analyze information makes it difficult to determine the

difference between unauthorized personal information and authorized information. If the owner

of the information realizes their information was utilized without their consent, then the company

faces the risk of legal liabilities. Besides that, the ability of big data to analyze vast amounts of

information is prone to the use of data for profiling and targeting. With statistical profiling

considered one of the most authentic ways of predetermining who a person is, using big data for

such a purpose creates societal divisions and amplifies discrimination. Comprehending this,

Rahman et al. (2021) claim that how big data technology’s algorithms operate remains unclear

and has no way of verifying the authenticity of the data collection methods. As these

technological tools advance, the biases raises at an alarming rate.

Maintaining trust among the stakeholders calls for open, transparent and easy verification

of the dataset and the information offered. In this case, big data technology involves formulation

and interpretation of the data through the use of advanced technology. To a significant degree,

only individuals well-skilled in computers understand the information processed by big data

analytics. To give meaning to this information to stakeholders who have less computer skills,

ICT individuals are forced to simplify the information for better understanding. If, by any

chance, the person simplifying the information commits a breach of the information, the final

data is likely to reflect something different (Rahman et al. (2021). A similar risk arises as a result

of several data sets channeling in the same platform, thereby making the software tools more

vulnerable to manipulation of the data. Volk et al. (2020) further illustrate that the rapidly

increasing machine learning poses a risk of crashing and modifying the technology’s algorithms,

resulting in the analysis of information in contradicting dimensions. With this in consideration,

the advancement of big data technology should be moderated with more customized ways of

limiting data misuse and their intended use.

Opportunities for Big Data Technology

Innovative insights driven by the strategic application of big data technology present a

promising avenue for the technology to unlock unutilized business opportunities. Through

rigorous analysis of the vast opportunities existing in different business loopholes, big data

analytics spells out the barely noticed idea and opportunity for the entrepreneur to venture into

(Kushwaha et al., 2021). Similarly, meticulous analysis of vast troves of structured and

unstructured data informs the companies on how best to discern patterns and trends illuminating

previously unseen pathways to success. Mikalef et al. (2020) suggest that big data analytics

empowers organizations to optimize operational efficiency, enhance customer experiences, and

drive innovation across various facets of their operations. On the aspect of personalized product

recommendations, the data analytic findings inform the investors on how best to conduct targeted

marketing campaigns and agile decision-making. As businesses and corporations seek to

understand the current trends of the market in real time, big data technology remains the ultimate

solution.

The increase in the demand for expertise in data analysis makes big data technology the

most preferred technology by experts who wish to maximize the monetization of datasets. Given

that most business corporations recognize the essence of utilizing the correct data sets for

profitability, experts in data analytics are likely to keep utilizing big data analytics to offer such

services (Mikalef et al., 2020). To do this, data analytical experts stand a chance to utilize this

technology for vastly analyzing structured and unstructured information for businesses intending

to utilize such information for market preparedness (Sestino et al., 2020). In utilizing

personalized recommendations and predictive analysis, some businesses will become more

competitive than others, thereby making big data technology a solution for corporations that wish

to increase their competitiveness.

The urgent need for every sector and businesses to modernize the data analysis methods

from the traditional cumbersome and less effective strategies to a more advanced and effective

data analytical technology is a promising avenue for big data technology. Among other modern

technologies that makes data analysis more efficient and time-saving, big data technology

remains the best. Mainly, the sectors still heavily relying on traditional data analytical systems,

such as the agricultural sector, manufacturing industries, and transportation industries, are

promising clients for the big data system (Maheshwari et al., 2021). For instance, in

manufacturing, predictive analytics is an essential tool in forecasting equipment failures,

minimizing downtime, and optimizing maintenance schedules. In the agricultural sector, data-

driven insights optimize crop yields through precision farming techniques. Fundamentally, the

likelihood for the companies, sectors, and industries currently utilizing traditional analytical

approaches to adopt modernized approaches is an epic opportunity for big data technology.

Comparison of Big Data Technology with Other Technologies

Volume Data Handling

Big data technology is characterized by handling a large volume of data compared to

traditional data analytic methods that barely handle bulky data in a short time. Whereas

traditional data analytical methods were initially considered the best prior to big data analytics,

they only manage to handle moderate-sized datasets. When confronted with the immense scale of

data characteristic of big data applications, spanning from terabytes to petabytes and beyond, the

traditional analytical method becomes ineffective (Sestino et al., 2020). On the other end, Big

data solutions like Hadoop, Apache Spark, and NoSQL databases are purpose-built to tackle

large data volumes through distributed computing architectures that distribute data processing

tasks across multiple clusters. Rahman et al. (2021) discredit traditional technologies in

analyzing data sets by arguing that traditional technologies struggle to maintain performance and

reliability when tasked with processing and storing massive amounts of data. Comparatively, big

data technology remains more advanced and efficient in analyzing and interpreting massive

datasets.

Variety of Data

In analyzing the dataset based on structured, semistructured as well as unstructured, the

big data analytics remains as the most effective compared to other technologies. The algorithms

features in the big data systems is more advanced to decipher information from many forms

unlike traditional technologies that only relies on typped data set (Kushwaha et al. (2021). Some

of the digital platforms utilizing big data analytic model like Hadoop Distributed File System

(HDFS) as well as the NoSQL databases have the capability to capture and analyze the

information in the image form (Volk et al., 2020). Likewise, these platforms are interconnected

with other digital platforms, social media, clickstream channels, and IoT devices. With such

connections, these big data analyzing platforms collect real-time information from each of these

channels at the same time and offer a real-time analysis of the data.

Speed of Processing

The high speed of processing data and analysis is a crucial aspect of big data technology

that differentiates it from other data analyzing technologies. Some data analysis tools, like

traditional technologies, rely on batch processing, which usually struggles with the velocity of

data influx (Rawat & Yadav, 2021). On the other hand, big data solutions such as Apache Kafka,

Apache Storm, and Apache Flink facilitate instantaneous analysis and decision-making. In most

instances, organizations benefit from big data technology through its highly advanced algorithms

that analyze the data as it arrives. With timely data analysis, the organization extracts essential

ideas promptly thus enhancing operational efficiency and responsiveness. Remarkably, high

processing speed is particularly important in financial trading, cybersecurity, and online

advertising applications.

Flexibility and Scalability

The aspect of flexibility and scalability reveals big data technology as a technology

offering unique, unparalleled horizontal scaling and resource elasticity capabilities. As per

Maheshwari et al. (2021), big data digital platforms, especially those deployed in the cloud,

allow organizations to scale, compute, and store resources according to fluctuating workloads

and data volumes. Such an elasticity helps businesses optimize costs by paying for the resources

they consume while avoiding upfront investments in hardware and infrastructure. On the other

end, traditional technologies primarily rely on vertical scaling, which has impending limitations

that are usually cost-prohibitive for large-scale data processing needs (Mikalef et al., 2020). In

general, the flexibility and scalability of big data technology empower corporations to manage

and analyze vast amounts of data efficiently.

Future Outlook of Big Data Technology

Exponential Growth

The exponential growth of the data illustrates how big data technology is likely to remain

in high demand. Kushwaha et al. (2021) utilize 328.77 million terabytes of data created each day

and an annual production reaching 120 zettabytes to interpret that the scale of data generation is

monumental. According to Maheshwari et al. (2021), such a state of affairs reflects a trend that

has intensified over the last two years. Mikalef et al. (2020) likewise indicate that the exponential

growth with a positive deviation translates to the transformative potential of big data technology,

as businesses and industries increasingly rely on data-driven insights for strategic decision-

making. As big data continues to grow and become more practical to most businesses, its

exponential growth keeps shaping every area in which it is applied.

Machine Learning Integration in Big Data Technology

Advancements in the complementing technologies to big data technology, like machine

learning, are promising avenues for technology to become more advanced in addressing more

complex aspects. As businesses continue to struggle with an ever-increasing volume of data,

incorporating machine learning algorithms with big data algorithms in a very optimistic and

technical dimension will likely make the technology more effective than it is now (Sestino et al.,

2020). In reality, modifying the technology with machine learning will likely enhance the

efficiency of data analysis and empower businesses to forecast trends, understand customer

behavior, and optimize operations in real-time.

Data Democratization

Data democratization is a promising avenue for big data to reshape and revolutionize data

utilization and decision-making processes. Through adopting this approach, data analysis will

become more inclusive, decision-making will extend beyond specialized IT departments, and

non-technical staff within organizations will be empowered. Volk et al. (2020) posit that with the

adoption of the approach of data-as-a-service (DaaS) platforms simplifying challenging data

analysis tasks and offering intuitive interfaces, employees across various departments have a

chance to engage in decision-making processes based on real-time data information. Such a trend

will likely create a more inclusive work environment as well as make the analyzed dataset more

essential.

Conclusion

In summary, big data technology is an important technological advancement that

positively impacts businesses and industries through its seamless data analysis capabilities.

When well utilized, the corporation stands a chance of realizing more profit and increasing its

performance. Notably, to successfully implement big data technology, organizations should be

well prepared financially, equipped with skilled personnel, and well-informed on how

technology operates.

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