BIG DATA: AN INDISPENSABLE RESOURCE FOR BUSINESS MANAGEMENT IN NIGERIA

Big l Data opens up new l business l opportunities, and businesses can use l it to gain l a competitive l advantage. New technologies are expected to be used in business management, particularly in data exchange, document recognition, communication and text recognition, receipt and payment transactions, and excel replacement. Big Data l has evolved l to account l for the l rapidly increasing quantities of digital l information systems l generated, the l effort required to create information that l can l be l analyzed, and the l actual l use l of that l data l as l capital to l increase l efficiency and l improve business management decision-making. Against this backdrop, this research investigates the importance of l Big Data l in business l management in Nigeria. In l order to l gather data for this study, relevant information was extracted from books, conference papers, public records, journals, and other sources. This study comes to the conclusion that efficient data use is becoming l the foundation of l competition; Big l Data fundamentally l alters how companies compete and function. Businesses in Nigeria that make the necessary investments and l successfully extract value l from their l data will l have a l clear advantage over l their rivals. Almost l every aspect l of l business, from research l and development l to l sales, marketing, and l supply-chain l management, including new growth l opportunities, has the potential to be l transformed by Big Data. The study among others recommends strategies to optimize the benefits of Big l Data for l business value in Nigeria. This l study is l significant because it l investigates a problem in l a largely l unexplored area l of research in Nigeria. As a result, this research adds l to the l body of l knowledge on l the role l of Big Data l in business management in Nigeria.

Bigl Data lsets' solution helps businessesl better managel their highl volume, highl variety, andl high speedl data flows and transform thisl data intol information that generates profits. The ability ofl cooperation, lpublic-private partnerships, andl coordination inl the publicl sphere are crucial for a society to function well givenl the complexityl of economicl and sociall life inl market-economy states and thel confluence of global effectsl like the pandemicl and war (Ogrean, 2018). Howl we llive, work, andl think will change asl a resultl of the Bigl Data revolution. Butl beyond Bigl Data, andl what makesl it morel than justl a lbuzzword, fueling itsl transformative lpower, are "Bigl Data lanalytics" -an application ofl advanced analyticl techniques tol Big Datal sets (Russoml , 2011), ablel to pavel the wayl from insightsl to values in general, andl business intelligence capable of shifting from Bigl Data tol big impactl (Chen, lChiang, & Storey, l2012), and by turningl Big Datal into bigl money (Ohlhorstl , 2012). Big Data is considered to be a game-changer that has the potential to completely alter how businesses conduct themselves across a wide range of industries as a frontier for innovation, productivity, and competition (Lee, 2017).
In order tol rewire yourl value creationl processes, optimizel your keyl business linitiatives, and find newl monetization lopportunities, Schmarzo (l 2013) claims that Big Data is aboutl leveraging thel distinctive and actionablel insights gained aboutl your lcustomers, products, andl operations. In order tol outperform their competitors, leading companies are increasingly turning to thel use ofl Big lData. As al result, digital services and solutions are rapidly expanding in many facets of human society. The majority of scientific disciplines examine the potential of digital applications. These data are used by scientists to conduct research in fields such as biology, environmental protection, meteorology, and complex physical simulations (Leonard, 2014). Big Data provides us with fresh perspectives and tools for understanding the planet and tackling the monumental task of sustainable development with renewed vigor as a result of the digitalization process (Dumitru, 2022). Big Data's potential for value creation isl still inl its infancy, butl it already representsl a paradigml shift thatl every company should take into account. The ever-growing popularity of social media in China makes it clear that it is important to comprehend how the vast amounts of data collected there can bel used to boost businessl productivity andl enhance customer lservice. Big Datal has thel potential tol draw the attentionl of thel academic andl professional communitiesl more in developed countries than in developing ones, with Nigeria being no exception. Big Data is an interdisciplinary topic.
Furthermore, thel practical implicationsl of utilizing Bigl Data analyticsl to improve business intelligencel are still comparatively understudied. Thel existing researchl on the use ofl Big Datal for effective businessl management has primarily focused onl the advantages andl challenges ofl Big lData.
This study examines the value ofl Big Datal in businessl management to close this knowledge gap. In light of this, the following are the main issues that this study aims to resolve: i. Whatl is thel impact ofl Big Datal on businessl management? ii. Howl can businessesl in Nigeria through theirl management leveragel on the potentiall of Bigl Data tol their lbenefit?

Research Methods
The research method is the process of looking for a thorough, clear, and comprehensive explanation of issues in light of the available data in order to advance current understanding. It might be necessary to employ more than one of the common types of investigation methods in a given research. The secondary methodl of datal collection wasl employed in thisl study, and it involved extracting pertinent information from books, conference papers, public records, journals, and other sources. On the other hand, the study adopted an exploratory research approach. This is due to the fact that it piques the researchers' interest and drives them to fully comprehend the subject. This makes it possible for the researchers to gather background data on the research.

Review of Related Literature
In order tol collect, lhost, and analyze thel vast amounts ofl data gatheredl in orderl to derivel real-timel business insightsl relating to lconsumers, risk, lprofit, performance, productivityl management, andl enhanced shareholderl value, we refer to this asl "Big lData." Big Datal refers tol the ldynamic, large andl disparate volumesl of datal being createdl by lpeople, tools, andl machines (Wang & Alexander, 2015). Data generated froml social lmedia, data froml internet-connected devicesl (such asl smartphones andl tablets), machinel data, videol and audiol recordings, andl the ongoingl preservation andl logging ofl structured and unstructuredl data arel all lexamples of Bigl Data (Marshall, Mueck & Shockley, 2015). It is typically characterizedl by thel five 'V's': lVolume: When compared tol traditional datal sources, thel amountl of datal beingl created isl enormous. lVolume, or thel initial sizel and quantityl of datal that isl collected, canl be thoughtl of asl the foundationl of Bigl Data. Bigl Data canl be usedl to describel a setl of datal that isl sufficiently large. lHowever, the definitionl of Bigl Data isl subjective andl subject tol change dependingl on thel market's supplyl of computing powerl (Gandomi & Haider, 2015). The following are some instances of the kindsl of datal that bigl businesses typically lstore: Data is produced by both humans and machines and comes from a variety of sources. Anl organization mayl obtain data froml a varietyl of lsources, the valuel of whichl may lvary. Data can originatel both insidel and outsidel of anl organization. Thel standardization andl distribution ofl all thel data beingl gathered posel a challenge inl terms ofl variety. Both unstructuredl and structured datal can bel collected. Structured datal is informationl that isl kept in al file of relatedl records withl defined fields (numericall , text, ldate, etc.) and frequentlyl with defined lengths. In order to store, process, and access structuredl data, al model ofl the typesl and formats ofl business datal that willl be recordedl is necessary. The terml for thisl is al data lmodel. The lmodel's design establishesl and placesl restrictions onl the datal that can bel gathered, stored, andl processed (Jiwat, Changyu & Andy, 2016). Banking systems are an example of structured data because they keep track of current account receipts and payments, including the date, amount, and brief justifications like payee or source of funds. Well-known database structured query languages make access to structured data simple. Unstructured data is informationl without al pre-establishedl data lmodel. It comesl in alll different sizesl and shapes, andl because ofl this variety andl irregularity, itl is challengingl to storel it inl a way thatl will enablel analysis, lsearching, or otherl uses. 80% of business data, which can be found in unstructured formats such as wordl processor ldocuments, spreadsheets, lPowerPoint presentations, audio, lvideo, social medial interactions, andl map ldata, is a frequently cited statistics (Jiwat et al., 2016).
Velocity: Rapid data generation occurs all the time, even while people are sleeping. Itl describes thel rate atl which datal is createdl and ltransferred. For businessesl that need their datal to movel quickly so thatl it isl available whenl needed tol make thel best businessl decisions, thisl is al crucial lfactor. A Bigl Data-usingl organization will havel a significantl and ongoingl flow ofl data beingl produced andl sent tol its finall destination. Datal may come from ldevices, networks, mobile devices, orl social lmedia. This informationl must bel quickly processed andl analyzed, sometimes inl close tol real ltime. As anl illustration, there arel numerous medicall devices createdl today tol monitor patientsl and gatherl data inl the healthcarel industry. Thel collected data must bel quickly sent tol its destinationl and analyzed, whether it comes from wearable technology or medical equipment used in hospitals (Jiwat et al., 2016).
Veracity: Because Big Data comes from so many different sources, its quality and veracity must be verified. Thel gathered Value isl the fifthl and finall V inl the Bigl Data lacronym. This isl a referencel to thel benefits thatl Big Datal can loffer, and itl has al direct bearingl on whatl businesses canl do withl the informationl they lgather. It isl necessary tol be ablel to extractl value froml Big Datal because thel value ofl Big Datal greatly dependsl on thel insights thatl can bel obtained froml them. Whilel businesses canl use thel same Bigl Data toolsl to collectl and analyzel data, howl they usel that datal should bel specific tol their organization (Jiwat et al., 2016).

The Big Data Value Chain
Thel value chainl concept wasl first introducedl by Porterl (1985) asl a generall framework forl strategically considering thel activities inl any businessl and evaluating theirl relative costs andl roles in ldifferentiation.
The valuel chain isl built around a number ofl key processes that deal with the conversion of raw materials into finishedl goods or lservices, as welll as with Without a doubt, the Big Data revolution has greatly benefited both businesses and consumers, but there are also risks associated with using Big Data. Whether data sets are large or small, the need to secure sensitive information, safeguard private information, and manage data quality still exists. Big Data's unique characteristics, such as its lvolume, variety, lvelocity, veracity, and lvalue, however, create newl kinds of risksl that call for al thorough strategy in order tol allow a business tol use Big Datal while avoidingl the lpitfalls. Prioritizing this will help businesses begin to reap the rewards of Big Data while also managing the risks (Rusu, Hurloiu, Hurloiu & Geamănu, 2022). Big Data efficiently overcomes historical limitations and creates new opportunities for data ingest, storage, and processing from sources like market data, communications, and customer engagement through digital channels. More than 80% of the data in organizations, according to some estimates, is unstructured and unsuitable for conventional processing. Big Data will make it possible to process this unstructured data, leading to increased system intelligence and improved system performance that can be used to boost sales performance, better understand customer needs, strengthen the internal risk management function, support marketing initiatives, and improve fraud monitoring (Mayer-Schönberger & Cukier, 2013).
According to Rusu et al. (2022), Big Data capability enables businesses to quickly and easily combine numerous data sources with little effort. When coupled with lower storage per gigabyte costs, this makes it possible for businesses tol run consolidatedl analytics andl reporting onl customer data that has been moved froml various separatel business departmentsl into al single linfrastructure, for example, to create a holistic view of their customers. Bigl Data technologiesl free businesses from thel traditional accuracyl vs. costl dilemma by allowing theml to storel data atl the most basic levell of detaill while maintaining all previous datal at a reasonable cost andl with the least amount of work. Big Data does, however, present a number of risk issues. The storagel and retentionl of largel amounts of ldata, data ownershipl and lquality, information lsecurity, reputational lrisks, and variousl regulatory lrequirements, including privacyl concerns, are all common sources of Effectivelyl managing thesel risks willl require companiesl to revisitl governance structuresl and frameworksl in orderl to allowl for thel effective andl timely identificationl and assessmentl of risksl in orderl to makel informed lrisk/reward ldecisions. By integrating Big Data in a way that enables in-the-moment analysis and comparison, businesses in Nigeria will give their staff the resources they need to spot fraud more quickly, safeguarding infrastructure and revenue. The fear that people might lose their jobs as a result of the increased use of technology is common. To meet the challenges of the 21st century, Nigeria must alter its current development paradigm. This is because we are currently experiencing a period of globalization, rising inequality, and escalating environmental issues.  h. Analyze Data: The application of data analytics can result in business value. Based on past and present data, analytics can be used for diagnosis, forecasting, and trend discovery.
i. Storel Data: Thel raw datal must be saved beforel being transformed. The company may use its own storage or rent it from service providers to store data in the cloud.
j. Selll Results: Resultsl may be soldl to clients directly orl via brokers or lportals.
Using CRM (Customerl Relationship lManagement) 2.0l and gainingl benefits from web l2.0 lchannels, social medial strategies, and sociall media marketingl to listenl to, analyze, publish, andl engage potential customers across networks can be an effective strategy.  (Davenportl , 2014;Narayanan, 2014;Erevellesl et al., 2015;Wangl & lAlexander, 2015). Anl investigation found that al retailer with the right Bigl Data skills could increase operating margins by 60% by outpacing competitors and utilizing the detailed consumer data (Tankard, 2012). Big Data analytics generally has five main advantages. By facilitating easier access to relevant data, itl first increases lvisibility. By gatheringl precise performance ldata, it facilitates performancel enhancement andl variability lexposure. Thirdly, by segmentingl the lpopulation, it helps to betterl meet the actual needs ofl customers. Fourth, it addsl worthwhile insightsl to thel automated lalgorithms' decision-making. Fifth, itl produces fresh business ideas, concepts, goods, and services (Marshall, Mueck & Shockley, 2015). Thel development ofl new managementl principles, the economyl built on these principles, and knowledge creation are some of the most significant applications of Big Data analytics, accordingl to Ahmadl and Quadril (2015) andl Wang andl Alexander (2015l ).
Bigl Data analyticsl can enhance the managementl of the supplyl chain in a number of ways, including supply chain effectiveness, planning, risk management, inventory control, market intelligence, and real-time personalized service (Wang & Alexander, l2015). Big Datal can also helpl the supplyl chain to understandl how various lsub-firms can workl together tol optimize thel operation process inl a wayl that isl both The effective use of Big Data in decision making can be improved by collaboration between decision makers and data analysts, but the decision processes should be carefully managed to minimize any potential understanding gaps (Kowalczyk & Buxmann, 2014). It's critical to create appropriate and effective analytical techniques in order to take advantage of the enormous amount of heterogeneous data present in unstructured text, audio, and video formats (which make up 95% of Big Data). Utilizing new tools to perform predictive analytics on structured Big Data is also important in the meantime (Gandomi & Haider, 2015).

Conclusion and Recommendations
For businesses, Big Data presents both opportunities and difficulties. Big Data must be processed and analyzed quickly in order to be valuable, and the outcomes must be made accessible in a way that can lead to positive change or have an impact on business decisions. An organization's effectiveness also depends on having the ideal blend of personnel, procedures, and technology. By transforming information into intelligence, Big Data empowers organizations to meet stakeholder reporting requirements, manage massive data volumes, gain competitive advantages, manage risk, enhance controls, and ultimately improve organizational performance. In conclusion, efficient data use isl becoming thel cornerstone of lcompetition.
Big Datal fundamentally alters how companies compete and function. Businesses in Nigeria that make the necessary investments and successfully extract value from their data will have a clear advantage over their rivals.
Almost every aspect of business, from R&D to sales, marketing, and supply-chain management, including new growth opportunities, has the potential to be transformed by Big Data. Onel of thel main applicationsl of Bigl Data analyticsl is tol improve ldecision-making lcapabilities, understanding ofl customer lneeds, faster decision-making, developing strategiesl for launchingl new productsl and lservices, improving inventory turnovers, exploring newl markets, enhancingl staff productivity and efficiency, and reducing customer complaints. Big Data offers a variety of opportunities to increase business value and productivity.
For businesses, Big Data presents both opportunities and difficulties. Big Data must be processed and analyzed quickly in order to be valuable, and the outcomes must be made accessible in a way that can lead to positive change or have an impact on business decisions. An organization's effectiveness also depends on having the ideal blend of personnel, procedures, and technology. Organizations will gradually become much more data-driven in their decision-making, product and service development, and interactions with stakeholders at all levels. The ability tol innovate inl ways thatl are challenging tol replicate is often granted to organizations that act quickly to take advantage of the potential of Big Data. The study makes the following recommendations going forward: