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Bigdata Analytics for Enterprise Risk Management: A Review
Rativardhan Sengar
University of Central Missouri, Warrensburg, MO, USA
Pratheep Paranthaman
Elon University, Elon, NC, USA
May 2021
Abstract:
Bigdata analytics integration in Enterprise Risk Management (ERM) features of the modern and
transformative impact and risk assessment models. The advent of Big Data and advanced analytics has
revolutionized the organizational identification, assessment, and risk mitigation model, hence the revolution
from static approaches to dynamic and data-driven models. This research paper examines the theoretical
frameworks such as Risk Analytics Frameworks, Data-driven Decision-Making models, and Cybersecurity
Risk models essential in enhancing the leverage of enhanced risk assessment. The research findings depict
data integration as the fundamental concept in risk identification, predictive capabilities, and dynamic risk
management alongside the multi-dimensional perspective on risk factors. The study concludes with the
illustration of integrating Big Data analytics as fundamental in modern risk management practices,
empowering the organizations' approaches to addressing emerging threats. The approaches enhance decision-
making, resource allocation, and organizational resilience. Hence, the research has expounded on the
significance of adaptive data-driven approaches in manoeuvring the complex business world.
Keywords:
Big Data Analytics, ERM, Risk Assessment, Data-driven Decision Making, Risk Mitigation.
I. Introduction
Enterprise Risk Management (ERM) forms a fundamental component of the organizational
strategy as it incorporates identifying, assessing, and mitigating the risks that are likely to affect
the success and achievement of the business objectives. Over the recent past, technological
advancement has culminated in the advent of Big Data alongside advanced analytics, which
has presented new avenues for the organization enhancement in risk management essential for
organizations in enhancing their risk management models (Gatzert & Schmit, 2016). The
business world has revolutionized with emerging modern markets; hence, businesses struggle
with market fluctuations, cybersecurity breaches, and the ultimate need for integrating big
analytics and enterprise risk management as transformative tools. The ultimate shift serves as
poignant of the ultimate drift from the traditional and static approaches to the modern and data-
driven approaches. Big data forms the fundamental tool, incorporating the immense data
pooling and the comprehensive real-time analytics essential for business operations (Machorro-
Cano et al., 2020). The integration forms the ultimate tool in identifying the potential risks in
the business operations, alongside the enhanced prediction and proactive mitigation with a
correspondent resilience and reliability. An extensive analysis of the Big Data analytics within
the ERM examines the theoretical frameworks that underline the integration and the challenges
alongside the possible solutions to challenges and navigation of the complex hurdles within the
dynamic and evolving business landscape, especially in risk management.
Research Objectives
Based on the business landscape and technological advancement, this research objectives
include;
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a. Examine the theoretical framework of incorporating Big Data analytics within
Enterprise Risk Management (ERM).
b. Investigate the essential models for enhancing the data collection, processing, and
analysis for effective risk management.
c. Assess the outcomes and implications of incorporating the Big Data analytics within
the ERM.
II. Literature Review
Big Data and It’s Relevance to ERM.
Modern business operations have transitioned to data-driven decision-making, including "Big
Data" as a representative of the immense datasets with complex, extensive, and high-generation
velocities as their definitive characteristics (Dash et al., 2019). The datasets have transitioned
to overpass the traditional data processing tools' capabilities (De Broe et al., 2019). "Big Data"
has fallen under three categories: Volume, Velocity, and Variety. The ERM context is
significant in three particular capabilities in revolutionizing risk assessment models and
mitigation strategies (see Figure 1). The traditional models entailed the ERM overlying on
structured data sources such as financial reports and historical performance data, which often
have remained limited to capturing emerging and non-linear risks (De Broe et al., 2019). The
Bigdata comprises unstructured and variant data sources such as social media interactions,
customer feedback, market sentiment, sensor data, and comprehensive and real-time data
provisions. Technological advancement has revolutionized the data industry by incorporating
the various theoretical models that guide the integration of Big Data into ERM. These
frameworks have enhanced the capabilities of harnessing the data analytics power (Giannakis
& Louis, 2016).
Figure 1. Usableness of Bigdata in ERM
Risk Analytics Framework.
This approach seeks to leverage the Big Data capabilities and advanced analytics within
incorporation to enterprise risk management, enhancing the risk assessment models. The model
posits the business transition from traditional statics models to real-time and data-driven
approaches (Araz et al., 2020). The Risk Analytics framework has revolutionized data analysis
by emphasizing new models such as machine learning algorithms. These new approaches have
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culminated in enhancing the business processes and analysing the extensive data volumes
extending beyond conventional data processing. Incorporating machine learning algorithms
has influenced the identification of intricate patterns alongside the data correlations,
consequently identifying new insights and likely insights.
The Risk Analytics framework provides a new model for data analysis and risk assessment,
emphasizing real-time risk assessment (see Figure 2). This model downgrades the traditional
approaches through continuous data collection and analysis, unlike traditional ones, which
depend on periodic assessment. The approach presents various advantages in risk assessment
as it facilitates prompt detection and response to ever-emerging risks and provides efficient
data vigilance and real-world operations (Ivanov et al., 2018). This approach assures enhanced
decisions and proactive mitigation of potential threats.
The new Risk Analytics Framework has acknowledged the modern-day technologies that have
taken the shape of the dynamics and static risk assessment models, making some easily
obsolete. The real-time approaches suit the modern market, whose technologies and regulations
require swift organizational adaptations (Yang et al., 2020). The approach, therefore,
emphasizes the utilization of historical data and fixed models as likely to culminate in myopic
risk comprehensions with a likelihood of exposure to unforeseen challenges.
Figure 2. Risk Analytics Framework
Data-Driven Decision-Making Model.
A data-driven approach in business enterprise risk management forms the ultimate model
featuring new industries and their corresponding approach to risk management. The theoretical
framework emphasizes the systematic approach, which demands overreliance on data evidence
for decision-making. This data analysis model anchors risk management decisions with
empirical data-driven analysis, enhancing comprehensive data insights (Lu et al., 2019). Hence,
this approach enhances the organizations in making informed and proactive decisions on risk
assessments and management.
The key approaches in Data-Driven Decision-Making entail continuous data collection and
analysis. The model correlates to the Risk Analytical Framework emphasizing real-time data
analysis. The organization has revolutionized to easily respond to emerging risks and adapt to
risk management strategies (Visvizi et al., 2021). Besides, the organization recognizes the
data–driven approaches with unprecedented access to large data volumes and variety, hence
providing extensive insights on the risks and opportunities. Hence, the approaches enhance the
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interdependence of the factors contributing to the risk profiles (Lu et al., 2019). The data-driven
Decision-Making Model (see Figure 3) nurtures the proactive approach in risk management
through the leveraging of data-driven insights for the identification of the likely risks before
escalation, hence implementing the preventive measures. The approach downgrades the
capability of negative outcomes, positioning organizations on the approaches essential to
capitalize on emerging opportunities.
Figure 3. Data-driven decision-making process in ERM
Cybersecurity Risk Model.
Modern society has witnessed a revolution in cyber risk, which threatens the organizational
landscape in the era of the digital age. The Cyber Security Model features the strategic
utilization of Big Data and advanced analytics, emphasizing the organization's security posture.
The model has emphasized the cyber risk, which entails the collection and the vast data analysis
with the utilization of immense data sources to help unmask the hidden anomalies that cause
potential security breaches (Wang et al., 2020). The advanced analytics featured in this model
incorporate sophisticated algorithms, machine learning techniques, and artificial intelligence
essential for data sifting and identifying the irregularities that signal the various cyber threats.
The approaches further meanders through adaptive, responsive mode to emerging cyber risks.
The cybersecurity Risk Model seeks to unravel modernized cyber threats by rapidly deploying
models combating evolving cyber adversaries. Incorporating Big Data and advanced analytics
provides insights against evolving cyber threats. The model also signifies the need to initiate
timely detection and responses to the threats to cyber threats (Shin & Lowry, 2020). The
leveraging of Big Data and advanced analytics has facilitated organizations in detecting and
responding to cyber threats in real-time, significantly reducing the likely damages accrued to
the risk breach.
III. Methodology
This research paper incorporates a multidisciplinary approach comprising various domains to
address the complex challenges inherent in modern risk assessment and mitigation, especially
in Big Data analytics. The major components within the multifaceted framework include
machine learning algorithms, data-driven decision-making processes, and predictive analytics.
These methodologies guide the development of robust predictive models in data forecasting,
resource allocation, and inventory management alongside routine optimization in the context
of risk management. The research framework also incorporates data science principles in
guiding the research data collection, processing, and analysis using the various risk
assessments. The approach includes the incorporation of historical, real-time, and IoT essential
for Big Data analytics. The incorporation of logistics constitutes the third guiding framework
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comprising supply chain management, inventory control, and last-mile optimization. The
logistical principles integration assures the streamlining of the goods and information pertinent
to the risk assessment within the enterprise assessment. The sustainability approach includes
introducing principles to reduce excess energy consumption and emission during production
by incorporating sustainable practices to mitigate environmental risks and contribute to a
resilient enterprise ecosystem.
Data Collection, Processing and Analysis
This research incorporates a systematic approach to expand the data collection from vast
sources on enterprise risk management and integration within Big Data analytics. For instance,
the research incorporated traditional sources of data collection comprising financial reports to
enhance the comprehension of the historical performance and financial risks, market research
from market studies and industry reports to assess trends, customer behaviour, and competitive
landscapes alongside the surveys and questionnaires issued to stakeholders, experts, and risk
management practitioners. Further, the research incorporated data collection from big data
sources such as social media for public sentiment, consumer feedback, and emerging trends
assessment. The incorporation of the Internet of Things (IoT) included the interconnection of
the system, hence gathering real-time across various operational practices.
Data Processing and Analysis
The data analysis for this research study incorporated the various aspects that helped provide
insights into big data analytics for enterprise risk assessment. The descriptive analytics
included summarizing and exploring the data to provide insights into the various patterns,
distributions, and key characteristics. The incorporation of predictive analytics included
utilizing machine learning algorithms in nurturing the models essential for predicting future
risks according to historical and real-time data using regression, classification, and time series
analysis. The prescriptive analytics incorporated the specific risk mitigation strategies
according to analytical insights such as incorporation of simulations and techniques
optimization.
IV. Results and Discussion
Regarding the three research objectives, the Big Data analytics integration in Enterprise Risk
Management (ERM) provided the organizations with profound insights and transformative
capabilities.
Enhanced Risk Identification and Assessment.
Incorporating Big Data analytics has systematically helped enhance risk identification and
assessment. The approach includes utilizing diverse data sources such as social media
interactions, customer feedback, and market sentiment, enhancing the likely risk identification.
This approach proved a viable proactive approach in addressing the emerging threats often
overlooked by traditional approaches.
This is especially crucial in cases where specific regulatory risks related to data privacy have
been imposed by regulatory requirements like the US Privacy Act and the General Data
Protection Regulation (GDPR). Therefore, another issue that businesses must deal with is how
to apply Big Data Science without compromising customer privacy. For instance, one might
use big data analytics to examine an individual's political preferences, purchasing patterns, and
other personal information through posts or content that is posted online (Saltz & Lahiri, 2020).
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Figure 4. Risks Assessment
Predictive Capabilities.
The advanced analytics techniques exemplified by machine learning have provided the ultimate
approach to predicting future risks. This approach leverages historical and real-time data to
develop accurate models to anticipate potential threats (Wang et al., 2018). This approach
enhances the implementation of the pre-emptive measures essential to reducing the likelihood
of adverse outcomes.
Dynamic Risk Management.
As influenced by Big Data analytics, continuous data monitoring sought to enhance the
institution of dynamic approaches in risk management. The organizations incorporate the
adaptation to real-time strategies to enhance the response to emerging risks, hence the enhanced
risk management practices relevant to evolving business environments (Choi et al., 2017).
Multi-dimensional Risk Perspective.
Incorporating the traditional ERM and Big Data sources (see Figure 5) forms a foundation for
enhancing the multi-dimensional perspectives of the risk landscape (Sharma & Dash, 2020).
The comprehensive view includes the analysis of risk factors and impacts on business
objectives, hence a holistic approach to risk assessment and mitigation (Al-Zobbi et al., 2017).
Figure 5. Essential Elements of ERM controlled through Bigdata Analytics
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The research findings have indicated that integrating Big Data analytics is fundamental in
enhancing risk management practices. This approach includes harnessing the Big Data
potential, which supports organizations in overcoming the traditional models essential in
enhancing the proactive and data-driven approaches. This shift correlates to the modernized
and evolving landscape with diverse, dynamic, and unpredictable risks (Choi et al., 2017).
Machine learning has consequently influenced predictive capabilities through accurate risk
forecasts; hence, effective mitigation models are essential for enhancing organizational
resilience and improving decision-making processes.
Continuous data monitoring using real-time approaches helps in enhancing risk management.
The approach supports the swift response to emerging threats, mitigating the likely damages
and enhancing business continuity. The multi-dimensional risk perspectives accrued from the
diverse data sources integration provide the comprehension of the complex risk factors
integration (Wang et al., 2016). The holistic approach enhances the organizations' operation in
initiating informed and strategic decisions within risk management.
V. Conclusion and Recommendations
Integrating Big Data Analytics within Enterprise Risk Management (ERM) features the
advancement in risk assessment and mitigation models. The approach seeks to enhance the
proactive identification, assessment, and mitigation of risks, supporting their resilience and
adaptability to ever-evolving business landscapes. Incorporating vast data sources such as
social media interactions and customer engagements is fundamental in enhancing the
mitigation efforts against the emerging threats often overlooked in traditional approaches. The
predictive capabilities facilitated by machine learning enhance the organizational capabilities
in anticipating and mitigating future risks, optimizing resource allocation, and decision-
making. Risk management facilitates dynamic risk management by swiftly responding to
emerging threats and minimizing the likely damages. Blending traditional and Big Data sources
offers a multi-dimensional perspective on comprehending the various risk factors' interaction
and impact on business objectiveness.
Big data analytics for risk management will inevitably make it possible for enterprises to
employ increasingly sophisticated fraud detection methods. In order to identify suspicious
patterns, trends, and anomalies, it enables precise analysis of transactional, social media, and
geolocation data, consumer behaviour, and other relevant information. Future research will
examine how big data analytics and AI are being used to transform the digital environment of
contemporary ERM procedures and methods.
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