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34Методология использования больших данных в сфере финансовых услуг для повышения эффективности принятия решений
Мухтаров Иззатулло Шухратович,
Старший финансовый аналитик; Badu Furniture LTD E-mail: I.MUXTAROV91@mail.ru
Эта методология охватывает ключевые аспекты анализа и использования данных для принятия обоснованных решений, учитывая меняющийся ландшафт аналитики больших данных. Центральное место в этой методологии занимает признание данных стратегическим активом, служащим основой для получения информации, способствующей принятию эффективных решений. В ней описан системный подход, начинающийся с выявления рассматриваемой проблемы, за которым следуют тщательный сбор данных, уточнение, исследование и моделирование. Эффективность этого подхода подкрепляется интеграцией методов статистики и машинного обучения, точно адаптированных к аналитическим задачам. Кроме того, эта методология решает проблемы, присущие интеграции аналитики больших данных в процессы принятия решений, подчеркивая важность целостности данных, устранения разрозненности данных, развития экспертных знаний, организационной культуры и соблюдения нормативных требований. Масштабируемость и устойчивость являются неотъемлемой частью этой методологии, пропагандирующей адаптивные стратегии, управление данными, безопасность и расширение прав и возможностей персонала. Реальный пример - методология Netflix, основанная на данных, иллюстрирует практическое применение этих принципов, демонстрируя преобразующий потенциал анализа данных в точной персонализации. В конечном счете, эта методология служит компасом для организаций, ориентирующихся в ландшафте, основанном на данных, позволяя им использовать весь потенциал данных для принятия обоснованных, стратегических и устойчивых решений в эпоху, обогащенную данными.
Ключевые слова: Управление данными, Принятие решений, Фреймворк, Большие данные, Аналитика, Интеграция, Вызовы, Масштабируемость, Устойчивость, Пример.
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I. Introduction
1. Statement of the Problem
In the epoch of digital transformation, financial sectors grapple with an overwhelming deluge of data. This data, which encompasses transactional metrics, client interactions, market variables, and diverse, unconventional data subsets, falls under the ambit of 'Big Data'. Intrinsically, Big Data holds latent capabilities for enriching decision matrices in finance. Yet, there's an evident incongruity. A considerable faction of financial entities remain entangled in the challenge of optimizing Big Data's benefits. Predominantly, the mammoth scale, unrelenting influx, and heterogeneity of Big Data pose analytical quandaries. Conventional data algorithms falter, accentuating the need for innovative analytical paradigms. Moreover, siphoning actionable intelligence from such data isn't linear. The multifaceted nature of financial sectors coupled with intricate decision matrices demands meticulous interpretation strategies. A consequential outcome: institutions, awash with data, might be skimming just the surface of its inherent value. In essence, a lacuna exists. A methodological void which, if filled, could empower these entities to harness Big Data optimally. This inquiry delves into devising such an instrumental methodology.
2. Purpose of the Study
Central to this exploration is the formulation of a robust, methodical framework. A blueprint aimed squarely at capacitating financial entities to exploit Big Data, refining their decisional acumen. Addressing the tribulations associated with data's magnitude, dynamism, and diversity stands paramount. The impending methodology emphasizes judicious data curation, the application of avant-garde analytical protocols, and the metamorphosis of raw insights into tangible financial actions. A salient feature: empirical scrutiny in extant scenarios, assessing the methodology's pragmatic resonance. Foreseen deliverables encompass a modular, malleable, and cogent methodological toolset, capable of buttressing decisional infrastructures. Enhanced stratification of risks, amplified client rapport, incisive investment modulations, and an uptick in fiscal outcomes. Culminating objective: augment scholarly dialogues on Big Data's fiscal implications whilst bequeathing the sector with a pivotal analytical tool, ensuring its seamless transition into an era marked by data-centric.
3. Significance of the Study
In the present landscape, financial institutions grapple with an exponentially expanding pool of multifaceted datasets. A paramount concern emerges: devising efficacious techniques to channel this voluminous 'Big
Data' for decision-making matrices. Though awash with data assets, a multitude of these institutions are ensnared in a quandary - efficient management, incisive analysis, and actionable insight extraction remain elusive. Intervening into this chasm, the imminent research endeavors to blueprint a holistic methodology to harness Big Data's potential in the realm of financial services. Foreseen ramifications include the reinvigoration of decision-making architectures, culminating in superior fiscal outcomes, risk stratification enhancements, astute investment modalities, and enriched client interfaces. Pedagogically, this venture is poised to be seminal - infusing the academic interplay of finance and data science with a coherent modality for Big Data integration. Not merely a terminal investigation, it paves avenues for future explorations, notably, synergies of machine learning and artificial intelligence within financial precincts. Extending its ambit, the research's methodology might exhibit malleability, lending itself to applications in diverse sectors and amplifying the canvas of data-informed decision frameworks.
4. Empirical Observations: The Netflix Paradigm
Netflix, an exemplar in leveraging Big Data, finetunes its promotional strategies, targeting precise user cohorts. This stratification facilitates Netflix's penetration into its envisioned audience, metamorphosing viewers into staunch subscribers.
Metrics elucidate the success narrative: Come 2022, a global subscriber base surpassing 222 million coalesced with revenues soaring beyond $30 billion (Figure 1). A symbiosis of tailored recommendations, elevated user interactions, and precision-targeted promotions catalyzed this trajectory.
Figure 1 - Netflix's annual revenue from 2002 to 2021
Here are some specific examples of how Netflix has used big data to increase sales:
In 2017, Netflix launched a new feature called "Netflix Roulette." This feature randomly recommends a movie or TV show to users based on their viewing history. Netflix found that users who used Netflix Roulette were more likely to watch the entire movie or TV show than users who chose their own content. This led to an increase in watch time and sales.
In 2018, Netflix released a new original series called "Stranger Things." Netflix used big data to target its marketing campaign for "Stranger Things" to specific groups of users who were likely to be interested in the show. This led to a record-breaking number of viewers for "Stranger Things" and a significant increase in subscriptions.
In 2020, Netflix launched a new feature called "My List." This feature allows users to save movies and TV shows that they want to watch later. Netflix found that users who used "My List" were more likely to watch the content that they had saved, which led to an increase in watch time and sales [1].
II. Literature Review
1. Overview of Big Data in Financial Services
Within the financial sector's purview, Big Data denotes the labyrinthine conglomerates of datasets, emanating from the multifarious undertakings intrinsic to the industry. Apart from conventional data founts - such as transaction-centric or client-centric data - contemporary data categories, encompassing social media imprints, ge-ospatial markers, and mechanized data manifestations, have emerged. The digital metamorphosis transpiring in financial realms has engendered an amplification in data's volume, velocity, and heterogeneity, offering to financial entities. The latent capability of Big Data in this milieu is prodigious. Proffering perspicacious discernments on client dynamics, market oscillations, and risk constituents, it furnishes a foundation for an eclectic array of decisions - spanning from investment paradigms and risk modulation to client engagement and product ideation. Illustratively, dissecting transactional imprints permits financial entities to extrapolate client expenditure trajectories and fiscal reliability. Analogously, market data scrutiny can unearth emergent trends and lucrative investment niches. Notwithstanding its latent prowess, the actuation of Big Data within financial spheres is fraught with intricacies. Navigating the voluminous and multifac-eted data milieu can be an arduous endeavor. Conventional data management modalities frequently falter when confronted with Big Data's enormity, prompting the quest for innovative alternatives. Compounding this, the proprietary essence of financial information augments data confidentiality and fortification apprehensions, mandating judicious stewardship. In the temporal backdrop of recent technological strides, the realization of Big Data's potential in finance has been rendered increasingly plausible. With the advent of powerful data processing tools, advanced analytical paradigms and sophisticated machine learning, financial institutions have at their disposal deep data analytics tools. However, an effective combination of these unusual technologies requires a clear operational plan. Under these conditions, the upcoming research is aimed at developing an integrated approach that will help financial organizations understand the complexities and opportunities of the digital age.
2. Review of Existing Methodologies for Decision-Making in Financial Services
• Traditional Financial Analysis: The financial domain has long been anchored in established an-
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alytical techniques. These encompass practices such as ratio diagnostics, cash flux scrutinies, and assessments grounded in net present value dynamics. Such techniques conventionally pivot on the examination of fiscal chronicles to glean insights into an entity's operational vigor and fiscal wellness [2].
• Risk Assessment Models: Risk modulation has witnessed the deployment of a plethora of models geared towards the delineation and quantification of risk elements. Prototypes such as Value at Risk (VaR), Expected Shortfall (ES), and regimes centering on stress testing, collectively, become critical in shaping determinations pertaining to investment trajectories, credit endorsements, and risk averting stratagems [3].
• Econometric Constructions: Econometric models are often used in financial decision-making to understand the relationship between different economic variables. Such constructions are key when discussing investment projects, forecasting excessive market returns and making decisions in the field of fiscal policy.
• Machine Learning Algorithms: Concomitant with the Big Data surge, algorithmic learning frameworks have gained pronounced momentum in financial resolutions. These mechanisms, adept at sifting through expansive and multifaceted data agglomerates, serve to discern latent trends and proffer predictions. Notable exemplars span decision arboreal structures, neural nexus configurations, and segmentation algorithms [4].
• Behavioral Finance Models: Recognizing that financial choices can sometimes be influenced by inherent human characteristics, certain methods lean towards behavioral finance principles. These paradigms explore concepts related to cognitive biases and group behavioral tendencies, aiming to decipher and predict financial outcomes [5].
• Simulation Models: Tools like Monte Carlo simulations, amongst assorted simulative models, find utilization across finance's diverse spectrums, ranging from portfolio harmonization to risk delineation. These models adeptly mirror a plethora of situations, enlightening verdicts in the face of inherent ambiguities [6].
Upon reviewing the outlined methodologies, a clear gap surfaces. A unified approach, capable of channeling the breadth and complexity of Big Data within financial decision-making, remains notably lacking. While conventional methods may struggle with the sheer scale and intricacy of Big Data, newer models often demand profound technical knowledge, potentially compromising clarity. Thus, leveraging the full potential of Big Data and crafting theoretical models that seamlessly merge simplicity with comprehensibility is vital to refining financial institutions' decision-making processes.
3. Gaps in Current Knowledge and Practice
• Lack of Comprehensive Methodology: Notwithstanding the conspicuous strides in mobilizing big data analytics within financial services, there pre-
vails a conspicuous void pertaining to a circumscribed, ubiquitously ratified, and exhaustive paradigm for embedding big data intricacies within fiscal verdict matrices. Methodologies in action often undergo iterative customizations, inherently spawning pronounced heterogeneity in methodologies and outcomes.
• Inadequate Tools for Handling Complex Data: Conventional analytic implements, albeit efficacious for structured data manifestations, tend to be sub-optimal vis-à-vis Big Data's vast tapestry encompassing both unstructured and semi-structured data forms. An exigency surfaces for robust instruments and paradigms adept at adeptly managing and culling insights from these labyrinthine data conglomerates.
• Data Privacy and Security Concerns: Given the intrinsically sensitive nature of fiscal data, palpable trepidations ensue regarding the sanctity and fortification paradigms in the Big Data milieu. Prevalent paradigms might potentially fall short in fortifying these vertices, precipitating prospective data compromise and regulatory infringements.
• Difficulty in Translating Data to Actionable Insights: A tangible fissure often manifests between the extraction of discernments from Big Data and the metamorphosis of said discernments into im-plementable stratagems. Dominant paradigms might ostensibly lack lucid directives to seamlessly span this chasm.
• Need for Skilled Personnel: The realm of Big Data analytics invariably necessitates a mastery over intricate technical acumen, spanning a spectrum inclusive of data processing implements and coding lexicons. Such prerequisites might emerge as deterrents, stymieing the assimilation of Big Data paradigms within fiscal entities deficient in such expertise.
• Lack of Transparency in Machine Learning Models: despite the growing trend towards the introduction of algorithmic learning models into the financial decision-making process, these components, as a rule, remain mysterious objects, which leads to a lack of clarity regarding their internal mechanisms of operation. Such opacity becomes a difficult problem, especially if it is associated with tightly regulated financial sectors.
These delineated lacunae underscore the imperative for an all-encompassing paradigm, envisioned to empower fiscal entities in optimally harnessing Big Data within their determinative frameworks, whilst adeptly navigating the aforementioned challenges.
III. Research Design and Methodology
1. Explanation of the Research Paradigm (qualitative, quantitative, or mixed methods)
Starting with a detailed presentation of the methodological components inherent in our research, it becomes necessary to realize a comprehensive research paradigm, which serves as a guiding basis guiding our
efforts to solve urgent research tasks. In the following paragraphs of the methodology, we will discuss research paradigms specifically selected in the context of our research project. The methodological scheme adopted by us is characterized by the simultaneous use of both quantitative and qualitative methods, thereby providing a holistic understanding of the subject under consideration [7].
Quantitative Facet: Given the inherent characteristics of big data juxtaposed with financial realms, the prodigious volume of numerical and categorial datasets align seamlessly with a quantitative scrutiny matrix. Within this ambit, one can marshal statistical paradigms, algorithmic learning blueprints, and prognostic modeling stratagems to decipher said vast datasets. This measurement provides a platform for testing hypotheses, identifying networks of associations, inferring patterns, making predictions for the future and evaluating the efficiency coefficient of the combined approach. A typical question of quantitative research: "How much does the described methodology improve the accuracy of budget forecasts compared to traditional methods? To do this, it is necessary to conduct a rigorous quantitative assessment by comparing the effectiveness of the two methods.
Qualitative analysis: Quantitative data is also important, but qualitative analysis complements the research matrix, providing a more detailed understanding of the complexity of the decision-making process, the orientation of financial professionals to big data, as well as the confusion that arises when integrating big data into existing processes. Qualitative data can be collected during an interactive exchange of opinions, discussion sessions and text study of relevant documents and notes. A plausible suggestion for a qualitative study may be the question: "What is the level of awareness of the community of financial professionals about the specific problems associated with the implementation of the planned approach?" A dialogue with these experts can reveal some illuminating facts.
The mixed paradigm combines the advantages inherent in both quantitative and qualitative research matrices. This not only expands the possibilities of integrating methods based on big data analytics (quantitative), but also provides a real opportunity to take into account the problems of participants in the financial industry (qualitative).
2. Selection of Research Design
Selection of Research Design
Observational iQÛâsijêxpêrimêntâil Case Analysis
Blueprint Blueprint Blueprint
Figure 2 - Selection of Research Design
Observational Blueprint: The first attempt is to monitor the current state of big data applications in the financial sector. This exploration would encapsulate delineating data typologies, understanding data trans-
mutations, and discerning the application of resultant intelligence in decisional matrices. Employing textual evaluations, dialogic engagements, or query-driven engagements would yield the requisite insights. The non-intrusive essence of this segment ensures a lucid comprehension of the present milieu sans any interventionist distortions.
Quasi-experimental Blueprint: In accordance with modern methods using big data, it can be assumed that quasi-experimental trajectories can converge to non-equivalent configurations of consortia. This might comprise dual clusters of fiscal aficionados or entities. One group (the treatment group) would utilize your newly developed methodology, while the other group (the control group) would continue to use the standard practices. You could then measure key outcomes of interest, like accuracy of financial forecasts, speed of decision-making, etc. It's called "quasi-experimental" because participants are not randomly assigned to groups.
Case Analysis Blueprint: In the aftermath of oper-ationalization, one could deep dive via meticulous case analyses of specific institutions that have on-boarded the postulated methodology. Such pursuits would render intricate qualitative cognizance, shedding light on the pragmatic dimensions of deployment, perceived merits, and inherent challenges. Furthermore, these case dissections could elucidate adaptability paradigms across diverse fiscal contexts [8].
It is paramount to ensure that the elected configuration is vindicated vis-à-vis the investigative propositions, aims, and the practicable constraints of the investigative milieu. Recognizing the limitations and implicit biases endemic to each modality and contemplating countermeasures is imperative.
3 . Explanation of Research Methods (case studies)
Netflix is a great example of a company that has successfully leveraged big data for enhanced decision-making. The company collects a vast amount of data on its users, including what they watch, how long they watch it for, and what they rate. This data is used to create a profile of each user's interests. Upon user portal ingress, Netflix mobilizes these avatars, deploying algorithms to curate filmic and televisual suggestions aligned with anticipated user proclivities. The outcome? Amplified user gratification and prolonged platform fidelity.
Delving deeper, we unveil the granular modalities through which Netflix maneuvers these expansive datasets to fortify its operational decisional matrix (fig. 3):
Marketing
Figure 3 - Netflixs Methods of Using Big Data
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• Personalized recommendations: At the heart of Netflix's user experience lies its formidable ability to harness vast datasets to generate bespoke content suggestions. This user-centric algorithmic modulation ensures that each user's content landscape is a reflection of their unique viewing idiosyncrasies. Such meticulous personalization elevates user engagement metrics, given the elevated propensity for users to engage with content that resonates with their predilections.
• Content selection: Netflix's content acquisition and creation matrix is intricately tied to the analytical prowess of its vast datasets. Evaluating user engagement vectors juxtaposed with external content landscapes from rival platforms, the platform extrapolates content lacunae and saturation points. This analytical exercise underpins Netflix's strategic decisions regarding content production and procurement, always calibrated according to the user's expected preferences.
• Pricing: Harnessing vast datasets, Netflix embarks on a dynamic pricing optimization journey. By synthesizing data vertices reflecting user's monetary thresholds for divergent content genres, the platform calibrates its subscription tariffs, ensuring a harmonious balance between competitiveness and fiscal sustainability.
• Marketing: Netflix uses big data to help it target its marketing campaigns. The company looks at data on its users' demographics, interests, and viewing habits. This helps Netflix to create marketing campaigns that are more likely to reach its target audience.
While Netflix's tryst with vast datasets stands as a testament to the transformative potential of this digital arsenal, it's merely a precursor. As the digital data deluge intensifies, the horizon beckons with even more avant-garde deployments of this formidable toolset, poised to redefine enterprise architectures.
IV. Data Collection
1. Gather relevant data from various sources, such as historical financial data, customer behavior data, market data, and external datasets.
Leveraging Big Data for Enhanced Decision-Making in Financial Services necessitates a systematic approach to sourcing and interpreting diverse datasets [9]. These datasets, when harnessed effectively, yield actionable insights to drive financial strategies and operations:
• Historical Financial Data: This encompasses chronological records of a company's financial activities, which allow analysts to discern trends across sales, profits, expenses, and other pivotal metrics. By delving into these records, one can juxtapose a company's performance with that of its competitors or even the overarching market.
• Customer Behavior Data: This dataset provides granular insights into the engagements customers have with financial establishments. It captures a gamut of interactions ranging from purchasing
patterns and frequency to spending behavior. Further, it sheds light on responses to marketing endeavors, facilitating the evaluation of customer satisfaction and loyalty indices.
• Market Data: A holistic view of the market landscape is indispensable for astute decision-making. Data pertaining to supply-demand dynamics, pricing fluctuations, and emerging market trends equip decision-makers with the tools to calibrate pricing strategies, steer marketing campaigns, and foster product innovation.
• External Datasets: data sets that have been collected by third parties and made available for public use. These data sets can be a valuable source of information for data analysis, as they can provide data that is not available from other sources. For instance:
- The United States Census Bureau disseminates pivotal data on U.S demographics, economic indicators, and housing statistics.
- The World Bank champions the cause of global economic development by sharing metrics on economic progression and poverty metrics.
- The Pew Research Center serves as a repository for public sentiment on an array of subjects.
The judicious selection of data sources is contingent on project objectives and data accessibility. By synthesizing information from these datasets, financial institutions can foster an environment of informed decision-making, enhancing their strategic prowess in an ever-evolving market landscape [9].
2. Ensure data quality and accuracy through data cleaning and validation processes.
Maintaining impeccable data quality and accuracy is indispensable for effectuating informed decisionmaking in financial services. A meticulous approach involves several phases, each tailored to bolster the integrity of data [10].
• Data Quality Stipulations: Commence by ascertaining the criteria that earmark data as high caliber. Key attributes to consider encompass accuracy, completeness, consistency, timeliness, and pertinence.
• Diagnosis of Data Quality Discrepancies: With established benchmarks, embark on discerning anomalies within the data, encompassing voids, incorrect data formats, repetitive entries, and statistical outliers.
• Data Rectification:
- Imputation: Offset missing entries by interpolating them with either statistically deduced estimates or prevalent values.
- Transformation: Adapt the data by revising its type or transmuting alphanumeric entries into numerical ones.
- Normalization: Reframe data to a uniform scale to ensure consistency across datasets.
• Validation Regimen: Post-rectification, it's imperative to reassess the data against the predetermined quality benchmarks. Deploy rigorous checks to en-
sure there's no reoccurrence of duplicates, outliers, and other inconsistencies. • Continuous Quality Vigilance: Upon validation, institutionalize a surveillance mechanism to incessantly monitor data quality. This proactive approach identifies and rectifies emerging discrepancies, ensuring sustained data integrity. Figure 4 provides recommendations for maintaining data quality
Figure 4 - Recommendations for Upholding Data Quality
3. Address data privacy and security concerns, adhering to regulatory guidelines.
Maintaining rigorous data privacy and security is paramount, especially when navigating the intricate landscape of financial services. Adherence to regulatory guidelines not only ensures operational integrity but also fortifies institutional repute [11].
At the heart of this imperative lies the need to discern potential privacy and security vulnerabilities inherent to an organization. Threat vectors such as unauthorized access, susceptibility to breaches, and potential data loss necessitate vigilant attention.
Several robust strategies can be adopted to fortify defenses:
• Access Management: Restrict data access strictly to individuals whose roles necessitate it.
• Data Cryptography: Safeguard data through encryption techniques, rendering it inaccessible to unauthorized entities.
• Regular Data Backups: A systematic backup regimen ensures data integrity, even in adverse circumstances.
• Security Enlightenment: Equip staff with a thorough understanding of best practices in data privacy and security, making them formidable first lines of defense.
• Persistent Control Surveillance: Post-implementation, perpetually reassess the efficacy of protective measures, refining them to align with evolving threat dynamics.
• Breach Response Strategy: A contingency blueprint for data breach scenarios enables swift, decisive action, encompassing breach delineation, stakeholder communication, breach etiology exploration, mitigation initiatives, and coherent public communication.
Additional strategic imperatives include:
Leadership Endorsement: To imbue a culture of security, executive endorsement and active participation are essential.
Regulatory Vigilance: As regulatory paradigms continually evolve, staying abreast ensures perpetual compliance.
Opt for Privacy-centric Cloud Solutions: Leveraging cloud providers that prioritize robust privacy and security mechanisms is instrumental. Periodic Security Audits: External security evaluations provide unbiased insights into organizational fortifications, highlighting areas for augmentation. Investment in Security Education: A well-informed workforce is a bulwark against potential threats.
Breach Response Readiness: An organization's resilience is often gauged by its preparedness and response to unforeseen adversities. By meticulously implementing these strategies and continually refining them, organizations not only safeguard their data assets but also buttress their commitment to stakeholders, reinforcing trust and ensuring sustained operational excellence.
V. Data Analysis
1. Apply appropriate statistical and machine learning techniques to analyze the collected data.
In the realm of big data analytics, deploying apt statistical and machine learning methods is pivotal for deriving actionable insights. The selection of these methods, profoundly influenced by the analytical objectives, ensures precise interpretation and modeling of data patterns [12, 13]:
• Descriptive statistics serve as foundational tools for data characterization. They afford concise summaries of datasets through parameters like mean, median, and standard deviation. Visual tools, such as histograms and boxplots, further elucidate the underlying data distribution, offering a panoramic view of the data landscape.
• Inferential statistics, on the other hand, venture beyond mere data description. They facilitate extrapolations about broader populations based on sample data. Instances include hypothesis testing, often employed to discern significant disparities between data groups, or confidence intervals, which yield estimations of population parameters.
• Machine learning is the vanguard of predictive and prescriptive analytics. It crafts algorithms capable of prognostications and recommendations. Whether it's anticipating customer attrition or suggesting pertinent products, machine learning models epitomize data-driven decision-making.
Ultimately, the choice of these techniques is contingent upon the analytical aims. Engaging with experts in statistics or data science can provide nuanced guidance in method selection, ensuring that the analytical approach aligns seamlessly with the intended objectives.
When selecting statistical and machine learning approaches for data analysis, several pivotal considerations come to the fore (table 1).
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Table 1. Factors influencing technique selection in data analysis
Characteristic Description Influence on Technique Selection
Dataset Characteristics Both volume and nature of data impact technique selection. Massive datasets may require more efficient algorithms. Determines the choice of algorithms and computational resources.
Data Integrity The quality of data input is crucial. Data cleaning and validation processes are necessary for reliable outcomes. Affects data preprocessing steps and ensures trustworthy results.
Analytical Objectives End goals (predictive, descriptive, inferential) guide tool selection. Determines the choice of analytical methods and modeling techniques.
Given these considerations (table 1), one can aptly
tailor their methodology for optimal results. A litany of
statistical and machine learning techniques, each with
its unique strengths, are available:
• Linear Regression: Apt for predicting a continuous outcome from multiple predictors, such as estimating house prices from attributes like area, bedroom count, and locale.
• Logistic Regression: Suitable for binary or categorical predictions, it can, for instance, ascertain customer churn from purchasing behavior and demographics.
• Support Vector Machines (SVM): Versatile in nature, SVMs excel in classification tasks, and even regression or outlier detection, especially when data exhibits linear separability.
• Decision Trees: These provide rule-based decisions and can segment customers for promotional offers, for example.
• Neural Networks: Capable of discerning intricate variable relationships, they find applications from stock market forecasting to medical diagnoses.
• Random Forests: An ensemble technique, leveraging multiple decision trees, ideal for both classification and regression analyses.
• K-Nearest Neighbors (KNN): This non-parametric method predicts labels for new data points based on the similarity to known data, commonly employed in classification and regression.
• Naive Bayes Classifiers: Rooted in probability, these classifiers are especially adept at text categorization tasks.
• Deep Learning: Using layered artificial neural networks, deep learning has revolutionized fields like image recognition, natural language understand-
= ing, and voice recognition. © While this compilation is by no means exhaustive, S3 the technique or combination thereof chosen should ° resonate with the analysis's overarching objectives, en-sb suring analytical robustness and precision.
2. Identify patterns, trends, and correlations in the data that can be used for decision-making.
To make astute decisions based on data, it's imperative to discern patterns, trends, and correlations inherent within the data set (table 2) [14].
Table 2. Key aspects of data analysis techniques
Aspect Description Role in Data Analysis
Visualization Representation of data through charts, graphs, or maps can reveal hidden patterns. Helps in visual exploration and pattern discovery.
Statistical Examination Utilizing statistics, including descriptive, inferential, and machine learning techniques, to find significant patterns. Unveils statistically pertinent insights.
Field Expertise Leveraging domain-specific knowledge to enhance data analysis. Enhances analysis by considering contextual insights.
Once you've spotlighted patterns, correlations, and trends, they become invaluable assets for decisionmaking. Patterns in consumer tendencies might refine marketing strategies. Sales trends can be precursors to demand predictions. Correlations between disparate variables can be harbingers of either risks or opportunities.
However, it's crucial to approach with caution: not every detected pattern or trend necessarily holds actionable value. Evaluating the data critically and applying judicious reasoning can aid in distinguishing genuinely impactful patterns from mere coincidences. Strategies for Pattern Recognition:
• Employ a Diverse Toolkit: Engaging a multifacet-ed approach can both reinforce conclusions and mitigate the risk of erroneous observations.
• Spot the Unconventional: Outliers, or those data points that deviate from the norm, can either signal emergent trends or pinpoint areas of concern.
• Lean on Expertise: Domain-specific knowledge can be a beacon, spotlighting nuances a novice might miss.
• Exercise Patience: Unearthing meaningful insights is seldom instantaneous. It necessitates persistence and a commitment to the analytical process.
3. Utilize data visualization techniques to present the findings effectively.
To effectively convey research findings, it's essential to adeptly harness data visualization techniques [15]:
• Selecting the Apt Visualization: Among the myriad of visualization tools available, such as charts, graphs, and maps, the optimal choice hinges on the nature of the data in question and the target audience.
• Clarity is Key: Strive for simplicity in your visual representations. Minimize extraneous colors and
intricate designs. The primary objective of any visualization is to relay information lucidly and succinctly.
• Labeling and Legends: Ensure each element of your visualization is distinctly labeled, aiding in audience comprehension. Position legends strategically to provide context without obscuring the main content.
• Narrative Through Numbers: Instead of merely displaying quantitative values, let your visualizations narrate the tale encapsulated within your data, spotlighting notable trends, patterns, or anomalies.
• Preliminary Reviews: Prior to unveiling your findings to a broader audience, test your visualizations among a select group. This preliminary feedback can pinpoint any visualization shortcomings, ensuring effective communication.
• Effective Use of Color: While color can vivify a visualization, it's crucial to strike a balance. Deploy color to emphasize pivotal information and augment visual allure, but refrain from overwhelming the viewer.
• Strategic Whitespace Utilization: Whitespace isn't merely a void but serves to enhance the legibility of visualizations, guiding the viewer's focus towards salient points.
• Font Finesse: Opt for legible and pertinent fonts for your data presentation. An overabundance of diverse fonts can introduce unnecessary chaos to your visualizations.
• Maintain Stylistic Uniformity: To exude a professional aura, ensure a consistent stylistic approach across all visual elements.
By heeding these guidelines, one can craft visualizations that not only convey the essence of the data but also engage and inform the audience.
VI. Framework for Decision-Making
1. Explanation of How the Analyzed Data Can Inform Decision-Making
Through the intricate examination of data, pivotal insights can be harnessed to inform strategic decision-making [16]:
• Unearthing Trends: A meticulous data assessment often reveals underlying trends and patterns, unobservable with a cursory glance. For instance, a surge in sales data might imply escalating product popularity, guiding decisions to bolster production or augment marketing strategies.
• Anticipatory Steps: Data analytics can facilitate forecasts regarding forthcoming events, empowering organizations to either strategically prepare for or adeptly sidestep potential challenges. Consider weather analytics; predicting a looming hurricane could influence pivotal calls like area evacuation or other preventive measures.
• Comparative Analytics: Contrasting your data metrics against industry counterparts enables performance benchmarking. This comparison can spotlight strengths and areas needing augmenta-
tion. Let's say, surpassing competitors in customer satisfaction metrics can steer initiatives to further enhance client interactions.
• Decision-making Amid Ambiguity: Often, decisions are shrouded in uncertainty. Leveraging data can elucidate these ambiguities, equipping organizations to make more enlightened choices. For example, before venturing into a new product domain, sales projections based on data can dictate the probable success, influencing the investment decision.
Beyond these, data-driven insights can be instrumental in diverse realms:
- Process Enhancement: Data scrutiny can pinpoint process bottlenecks or inefficiencies, offering pathways to streamline operations.
- Marketing Precision: Data insights can refine marketing strategies, delineating accurate target demographics, fine-tuning messages, and optimizing delivery timings for maximal impact.
- Elevated Customer Service: Data analytics can offer insights into problem resolution, avenues for amplifying customer delight, and preemptive strategies to curb potential issues.
- Financial Prudence: Financial data analytics can guide optimal resource allocation, risk management strategies, and astute investments, fostering improved fiscal outcomes.
In summation, data serves as a compass in the complex maze of decision-making. With burgeoning data at one's disposal, decisions can be progressively more informed, enhancing the probability of desired outcomes.
2. Development of a Proposed Framework for Decision-Making Based on Big Data
• Problem Definition: Initially, crystallize the predicament at hand. Ascertain the objective behind the impending decision. A lucid grasp of the challenge paves the way for effective data collection.
• Data Accumulation: Procure data pertinent to the identified problem. Drawing from multifarious sources - be it internal repositories, external databases, or social media streams - enriches the data pool, laying a robust foundation for decision-making.
• Data Refinement: Post-collection, the data necessitates purification. This encompasses duplicate removal, error rectifications, and reformatting for subsequent analyses.
• Data Exploration: Delve into the curated data employing statistical methodologies or cutting-edge machine learning algorithms. The goal is to unveil latent patterns and trajectories that will underpin the decision.
• Result Dissemination: Post-analysis, it's imperative to relay the findings to the stakeholders. A transparent presentation of insights ensures that decisions rest on an informed foundation.
• Decision Execution: With the decision crystallized, the next phase is its implementation, which
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could entail modifications to existing processes, protocols, or guidelines.
• Decision Appraisal: Conclusively, reflecting on the decision's efficacy is paramount. Such retrospection fosters learning, enabling improved decisions in subsequent ventures [17].
Though delineated above is a broad blueprint, the particulars might oscillate contingent on the unique challenges encountered. Nonetheless, it serves as a foundational guide to navigate data-driven decision-making.
Guidelines for a Robust Data-Centric Decision Framework:
• Engage Stakeholders: Integrating stakeholders' perspectives from the outset ensures decisions align with the organization's ethos.
• Diversify Data Streams: Relying on a singular data source might lead to tunnel vision. Embracing diverse data streams provides a holistic overview.
• Optimal Tool Selection: The big data realm is replete with analytical instruments. Handpick those congruent with your objectives and financial constraints.
• Adaptive Decision-making: Recognize that the trajectory towards a decision might necessitate re-calibration based on evolving insights.
• Perspicuous Communication: Ensure that your analytical conclusions are communicated lucidly, empowering stakeholders to take well-founded actions.
Adherence to the aforementioned guidelines amplifies the potential of forging an efficacious big data-centric decision-making framework, steering the organization towards judicious choices.
3. Justification for the Proposed Framework The pervasive influx of big data is reshaping the information landscape. As its ubiquity intensifies, enterprises are privy to an unprecedented data volume, serving as a reservoir for enhanced decision-making. The analytical prowess inherent in data processing techniques unveils obscured patterns within this voluminous data, aiding in more insightful resolutions across sectors like marketing, product innovation, and client engagement [17].
In the contemporary, high-stakes commercial arena, expeditious and astute decisions are paramount. Big data extends a granular comprehension of clientele, markets, and operational intricacies, fostering a more enlightened decision-making process. The advocated model for decisions predicated on big data delineates a systematic trajectory towards harnessing this potential. This paradigm, while being rooted in rigorous data analysis and decision-making tenets, offers the malleability to cater to distinct organizational requisites. Adherence to this model accentuates several piv-„ otal advantages:
= • Precision Augmentation: Employing data-driven © analytical tools, predictions regarding impending g events or trends achieve heightened accuracy, fa-~ cilitating superior decisions in realms from marketz ing strategies to product innovation.
• Operational Efficacy: The automation of decisionmaking, underpinned by big data, economizes both time and capital, liberating resources for alternate organizational pursuits.
• Client Relations Enhancement: Deciphering client behavior through big data insights paves the way for superior client services, culminating in bolstered client contentment and allegiance.
• Risk Attenuation: Proactively discerning potential pitfalls through big data insights empowers enterprises to devise strategies, attenuating prospective hazards and safeguarding organizational assets. In summation, the propounded big data-centric
decision-making paradigm presents an invaluable asset, poised to elevate organizational outcomes. Embracing this framework equips businesses to optimize their operations, amplify their revenue streams, and carve out a distinctive competitive edge.
VII. Testing the Framework
1. Implementation Strategy for the Decision-Making Framework within Financial Entities
Incorporating a decision-making framework rooted in big data within financial entities entails a sequence of meticulous steps [17]:
• Problem Identification: Pinpoint the specific challenge or question the institution intends to address. Recognizing the objective underpinning the decision is paramount.
• Data Acquisition: Procure pertinent data congruent with the identified problem. This data can originate from diverse sources, encompassing internal repositories, external databases, and digital media platforms. A comprehensive data set augments the depth and accuracy of the ensuing analysis.
• Data Refinement: Post-acquisition, the data necessitates purification. This includes the eradication of redundant entries, rectification of inaccuracies, and transmutation into an analyzable format.
• Data Exploration: Delve into the curated data, seeking discernible patterns or trends. Visualization utilities can enhance this exploratory process.
• Model Formulation: Post exploration, formulate a predictive model to envisage decision outcomes, leveraging machine learning methodologies.
• Model Appraisal: Subsequent to model construction, validate its reliability by juxtaposing its predictions against archived data sets.
• Decision Execution: Harness the analytical outcomes to finalize and enact the decision.
• Decision Oversight: Post-execution, continually supervise the decision's ramifications, recalibrating when necessary [17].
Beyond these methodical stages, financial entities should also emphasize a few pivotal considerations for the efficacious materialization of the decision-making framework:
• Infrastructure Augmentation: Prioritize investments in robust data management infrastructures, encompassing the requisite hardware, analytical software, and specialized personnel.
• Personnel Capacitation: Institute training initiatives, acquainting staff with the intricacies of big data-driven decision-making, from data typologies to analytical procedures and ethical considerations.
• Cultivating Data-Reliant Culture: Champion a workplace ethos that venerates evidence-based decisions. Employees should be galvanized to pivot from intuition to a more empirical, data-grounded decision paradigm.
Adhering to this strategy equips financial entities with a robust blueprint, enabling them to integrate a data-centric decision-making framework seamlessly, ensuring decisions are both well-informed and optimally beneficial.
2. Navigating Obstacles in the Integration of Big Data Analytics into Decision-Making
• Data Integrity Concerns: The vast expanse of big data can be riddled with noise and gaps, potentially leading to skewed analytical insights. It's essential to allocate resources for rigorous data cleansing and preparation to guarantee data's authenticity and relevance before proceeding with analysis.
• Fragmented Data Reservoirs: A prevalent challenge in many entities is the existence of data compartmentalized across different departments or systems. To foster comprehensive data insights, it's pivotal to dismantle these data silos, centralizing data in a unified repository or data lake for cohesive analysis.
• Expertise Shortfall: The specialized realm of big data analytics often surpasses the skill sets available within organizations. Filling this gap necessitates proactive endeavors in nurturing internal talents through targeted training, as well as harnessing a cadre of adept data scientists and analysts.
• Organizational Culture: The inherent inertia in some organizational cultures towards transformative methodologies can hinder the embrace of big data-driven strategies. Cultivating an environment that champions empirical decision-making and celebrates data-driven insights is crucial.
• Regulatory Landscapes: Navigating the intricate web of regulations, like the General Data Protection Regulation (GDPR) in the EU, is imperative. Awareness and adherence to these data-centric regulations ensure the lawful and ethical utilization of big data in decision-making processes [17]. Strategies for Efficacious Integration:
- Initiate Gradually: Leverage a phased approach to big data analytics. Commence with pilot projects, incrementally expanding the scope, drawing from the insights and lessons at each stage.
- Stakeholder Engagement: Earnest stakeholder buy-in is foundational. Ensuring alignment of all decision-making participants with the tenets and potential of big data analytics can catalyze its successful incorporation.
- Transparent Communication: Distill complex analytical outcomes into clear, comprehensible insights when conveying to stakeholders. This en-
hances their appreciation of big data's potential, fostering more enlightened decisions.
- Persistence: Cultivating a data-centric decision-making ethos is a journey, not a destination. Resilience and consistency are key, with the promise of a data-empowered future as the reward. Addressing these impediments with strategic, informed approaches will enable organizations to seamlessly weave big data analytics into their decisionmaking tapestry, resulting in enhanced, data-enlightened decisions.
3. Integrating Scalability and Sustainability in Big Data Implementation Strategies
• Scalability Considerations: The designed implementation blueprint should inherently possess the capacity to evolve, accommodating escalating data volumes and intricacies. Solutions might encompass cloud-driven strategies or the infusion of cutting-edge hardware and software capabilities.
• Sustainability Factors: The longevity of the implementation strategy in both financial and resource contexts is paramount. Leveraging open-source platforms or cultivating in-house expertise in big data analytics could be instrumental in achieving this.
• Data Governance Protocols: Instituting a robust data governance mechanism within the implementation ensures ethical and conscientious data utilization. Crafting comprehensive data privacy guidelines or designating data stewardship roles may cater to this need.
• Security Essentials: Implementing fortified security protocols safeguards data from illicit intrusions or leaks. A multi-pronged approach encompassing data encryption, fortified firewall protections, and stringent access controls would bolster data security.
• Capacitating Personnel: A pivotal aspect of the implementation roadmap involves endowing the workforce with the requisite competencies to harness big data tools and decode analytical outcomes. This can be orchestrated either through structured in-house training modules or by commissioning expertise from external consultants [18]. Strategies for Sustainable and Scalable Big Data Deployments:
- Embark with a Pilot: Instead of a blanket implementation across the entire operational landscape, initiate with a circumscribed pilot within a specific department. This allows for real-time learning and adjustments, facilitating a more nuanced large-scale rollout subsequently.
- Cloud Adoption: Embracing cloud infrastructure can serve as an adaptable and economically prudent avenue for scaling big data endeavors.
- Commitment to Capacity Development: Ensure an ongoing investment in personnel training, equipping them to adeptly navigate big data tools and derive actionable insights.
- Opt for Open-Source Platforms: Tapping into open-source platforms can be a cost-efficient method to embed big data analytical capabilities.
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- Fortress of Data Governance: Establishing a rigorous data governance architecture accentuates the commitment to principled and ethical data handling.
- Rigorous Encryption Protocols: Ensure data remains impervious to unsolicited access by encrypting it at both rest and transit stages.
- Incorporate Defensive Mechanisms: Integrate robust firewalls complemented by access control hierarchies to further elevate the security matrix.
- Backup Resilience: In the unforeseen event of data anomalies, having a contingency plan inclusive of robust data backup solutions is indispensable. By embracing these methodologies, enterprises
can confidently steer their big data endeavors, ensuring they remain scalable, sustainable, and impervious to threats.
VIII. Discussion of a real-world example
Netflix's journey in leveraging data to refine its recommendation algorithms serves as an emblematic representation of harnessing extensive data analytics in contemporary business scenarios. Beginning its data-centric journey in 2000, Netflix encouraged users to rate movies, subsequently using these ratings to tailor film suggestions. With the inception of its streaming feature in 2001, the data utilization was amplified. By 2006, in pursuit of refining its algorithms, Netflix shared over 100 million movie rental histories for enhancing its recommendation capabilities, famously termed as the Netflix Prize Challenge. The objective was a 10% improvement in the system [19].
While the winning solution from this challenge offered promising results, it never saw implementation, attributed partly to its intricate nature and Netflix's realization of inherent limitations within the 5-star rating method. This system's lack of precision signaled to Netflix the necessity for a more nuanced approach. The company initiated a comprehensive tagging system, classifying movies based on intricate criteria. By 2014, this meticulous method birthed an astonishing 76,897 hyper-specific genres. This intricate methodology was eloquently termed the Netflix Quantum Theory by Madrigal, post discussions with Netflix's VP of Product, Todd Yellin.
In essence, the Netflix Quantum Theory encapsulated strategies to meticulously tag multifarious movie elements. It analyzed the tone of movie endings, ranging from exuberance to melancholy, with a scope for ambiguity. Additionally, it methodically categorized various plot dimensions, professions of pivotal characters, and movie settings. This innovative methodology exemplifies how tangible attributes can be metamorphosed into quantitative or categorical data, catering to the evolving demands of precision-driven personalization.
£ IX. Conclusion
a.
e
« In conclusion, the methodology presented encapsu-
= lates a holistic approach to data-driven decision-making
<3 within the domain of big data analytics. It draws upon
a wealth of knowledge and insights to offer a systematic framework for organizations seeking to harness the transformative power of data in their decision-making processes.
Fundamentally, this methodology underscores the pivotal role of data as a source of invaluable insights and knowledge, urging organizations to recognize its inherent value. It delineates a structured sequence of steps, commencing with problem identification and culminating in continuous oversight, ensuring that decisions are not only informed but also systematically data-driven.
Moreover, the methodology acknowledges and addresses the challenges that organizations may face when integrating big data analytics into their decisionmaking. These challenges encompass issues related to data integrity, fragmented data reservoirs, expertise shortfalls, organizational culture, and regulatory compliance. Strategies to navigate these challenges are woven into the fabric of the methodology.
Scalability and sustainability are accorded paramount importance within this methodology. It emphasizes the need for solutions that can adapt to the ever-expanding volumes and complexities of data while also emphasizing the long-term viability of data-driven decision-making practices. Key aspects such as cloud adoption, personnel training, data governance, security, and resilience are integral components of this approach.
Illustrating the practical applicability of this methodology, the case study of Netflix's data-driven approach to refining recommendation algorithms serves as a compelling real-world example. This case study showcases how data analytics can be effectively employed to enhance precision-driven personalization, demonstrating the tangible benefits of data-centric decision-making.
Ultimately, this methodology serves as a comprehensive guide for organizations navigating the dynamic landscape of data-driven decision-making in the era of big data. It encapsulates the essence of contemporary data analytics practices, addressing challenges, and offering practical strategies for seamless integration. By adhering to this methodology, organizations can empower themselves to make informed decisions, optimize operations, enhance customer experiences, and gain a competitive edge in an increasingly data-centric world.
In essence, the journey from data collection to actionable insights represents a transformational voyage. It signifies the evolution of decision-making processes, where data, once dormant, emerges as the catalyst for innovation, growth, and strategic excellence. As organizations continue to embrace the possibilities of big data, the methodology presented here stands as a testament to the boundless potential that awaits those who embark on this data-driven odyssey.
Литература
1. https://www.start.io/blog/netflix-target-market-consumer-segmentation-the-complete-brand-analysis/
2. Damodaran, A. (2012). Investment Valuation: Tools and Techniques for Determining the Value of Any Asset. Wiley.
3. Jorion, P. (2006). Value at Risk: The New Benchmark for Managing Financial Risk. McGraw-Hill.
4. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
5. Thaler, R. H. (2016). Misbehaving: The Making of Behavioral Economics. W.W. Norton & Company.
6. Broadie, M., & Glasserman, P. (2004). A stochastic mesh method for pricing high-dimensional American options. Journal of Computational Finance, 7(4), 35-72.
7. Mayer-Schonberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.
8. Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. Sage publications.
9. Augmented Intelligence: The Business Power of Human-Machine Collaboration 1st Edition. by Judith Hurwitz (Author), Henry Morris (Author), Can-dace Sidner (Author), Daniel Kirsch (Author)
10. Data Cleaning and Validation: A Practical Guide by Michael C.J. Wu.
11. Data Privacy Law: A Practical Guide by Christopher Kuner.
12. Data Analysis: A Tutorial by Michael J. Kane.
13. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy.
14. Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, and Jian Pei.
15. Data Visualization: A Practical Introduction by Alberto Cairo.
16. Data Driven Decisions: The Role of Analytics in Business by Thomas H. Davenport and Jeanne G. Harris.
17. Big Data for Decision-Making: The Essential Guide to Using Data for Better Business Outcomes by Thomas H. Davenport and Jeanne G. Harris.
18. Big Data Analytics for Decision Making: Concepts, Techniques, and Applications by Galit Shmueli, Peter C. Bruce, and Nitin R. Patel
19. NETFLIX AND BIG DATA. 20-05-2016, Oslo. Department of Journalism and Media Studies (Faculty of Social Sciences) Oslo and Akershus University College of Applied Sciences.
METHODOLOGY OF LEVERAGING BIG DATA IN
FINANCIAL SERVICES FOR ENHANCED DECISIONMAKING
Mukhtarov I.S.
Badu Furniture LTD
This methodology encompasses key facets of data analysis and
utilization for informed decision-making, catering to the evolving
landscape of big data analytics. Central to this methodology is the
recognition of data as a strategic asset, serving as the bedrock of insights that fuel effective decision-making. It delineates a systematic approach, commencing with the identification of the problem at hand, followed by meticulous data acquisition, refinement, exploration, and modeling. The efficacy of this approach is underpinned by the integration of statistical and machine learning techniques, aptly tailored to analytical objectives. Furthermore, this methodology addresses challenges inherent in the integration of big data analytics into decision-making processes, emphasizing the importance of data integrity, the dismantling of data silos, expertise development, organizational culture, and regulatory compliance. Scalability and sus-tainability form an integral part of this methodology, advocating for adaptive strategies, data governance, security, and personnel empowerment. A real-world exemplar, Netflix's data-driven methodology, illustrates the practical applications of these principles, showcasing the transformative potential of data analytics in precision-driven personalization. Ultimately, this methodology serves as a compass for organizations navigating the data-driven landscape, enabling them to harness the full potential of data for informed, strategic, and sustainable decision-making in a data-enriched era.
Keywords; Data-driven, Decision-making, Framework, Big data, Analytics, Integration, Challenges, Scalability, Sustainability, Exemplar.
References
1. https://www.start.io/blog/netflix-target-market-consumer-segmentation-the-complete-brand-analysis/
2. Damodaran, A. (2012). Investment Valuation: Tools and Techniques for Determining the Value of Any Asset. Wiley.
3. Jorion, P. (2006). Value at Risk: The New Benchmark for Managing Financial Risk. McGraw-Hill.
4. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
5. Thaler, R. H. (2016). Misbehaving: The Making of Behavioral Economics. W.W. Norton & Company.
6. Broadie, M., & Glasserman, P. (2004). A stochastic mesh method for pricing high-dimensional American options. Journal of Computational Finance, 7(4), 35-72.
7. Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.
8. Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. Sage publications.
9. Augmented Intelligence: The Business Power of Human-Machine Collaboration 1st Edition. by Judith Hurwitz (Author), Henry Morris (Author), Candace Sidner (Author), Daniel Kirsch (Author)
10. Data Cleaning and Validation: A Practical Guide by Michael C.J. Wu.
11. Data Privacy Law: A Practical Guide by Christopher Kuner.
12. Data Analysis: A Tutorial by Michael J. Kane.
13. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy.
14. Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, and Jian Pei.
15. Data Visualization: A Practical Introduction by Alberto Cairo.
16. Data Driven Decisions: The Role of Analytics in Business by Thomas H. Davenport and Jeanne G. Harris.
17. Big Data for Decision-Making: The Essential Guide to Using Data for Better Business Outcomes by Thomas H. Davenport and Jeanne G. Harris.
18. Big Data Analytics for Decision Making: Concepts, Techniques, and Applications by Galit Shmueli, Peter C. Bruce, and Nitin R. Patel
19. NETFLIX AND BIG DATA. 20-05-2016, Oslo. Department of Journalism and Media Studies (Faculty of Social Sciences) Oslo and Akershus University College of Applied Sciences.
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