Big-data Management: A Driver for Digital Transformation?.pdf

This presentation examines how big data management serves as a key driver of digital transformation across modern industries. It highlights how effective handling of large and complex datasets enables organizations to improve decision-making, operational efficiency, and innovation. The content covers essential topics such as data types, processing techniques, lifecycle management, and security frameworks, including the Big Data Quality Management Framework (BDQMF). Through real-world case studies from healthcare, finance, retail, and manufacturing, it demonstrates how big data analytics and secure data management contribute to smarter operations, predictive maintenance, and personalized services.

1. Big-data
Management: A
Driver for Digital
Transformation?
Prepared by
Rawan Alharbi
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2. Articles
Authors Title Published date Sourcing Citations
1Reeba Zahid , Ayesha Altaf 1, Tauqir
Ahmad 1, Faiza Iqbal,
, Yini Airet Miró Vera ,
Miguel Angel López Flores , Imran
Ashraf
Secure Data Management Life
Cycle for Government Big-Data
Ecosystem: Design and
Development Perspective
2023 MDPI 13
2 Amanpreet Kaur Sandhu Big Data with Cloud Computing:
Discussions and Challenges
2022 IEEE
xplore
262
3Samuel Janík, Peter Szabó, Miroslava
M´ lkva , MartinMareˇ cek-Kolibiský
Effective Data Utilization in the
Context of Industry 4.0
Technology Integration
2022 MDPI 16
4Ikbal Taleb1, Mohamed Adel Serhani,
Chafik Bouhaddioui, Rachida Dssouli
Big data quality framework: a
holistic
approach to continuous quality
management
2021 Springer 89
2
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3. AGENDA
Abstract
Introduction
Big Data Management
Big Data Types
Big Data Processing Techniques
Big Data Lifecycle
The Role of Big Data In Industry 4.0
The Role of The Big Data Quality Management Framework
Secure Data Management Life Cycle For Big Data Security
Case studies
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4. ABSTRACT
Big Data Management plays a critical role in digital
transformation by enabling organizations to process, analyze,
and utilize vast amounts of structured and unstructured data.
The integration of Big Data with Industry 4.0 technologies, such
as IoT, AI, and Cloud Computing, enhances operational
efficiency, predictive analytics, and decision-making processes.
This presentation explores the significance of Big Data
Management in digital transformation, highlighting its
applications in various industries, security challenges, and the
role of the Big Data Quality Management Framework
(BDQMF) in ensuring data reliability. The study also examines
real-world case studies demonstrating the impact of Big Data in
healthcare, finance, retail, and manufacturing.
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5. The rapid advancement of digital technologies has led to an
exponential increase in data generation across industries.
Big Data Management refers to the systematic handling of
large datasets to derive meaningful insights, optimize
processes, and drive innovation. Industry 4.0, characterized
by smart manufacturing, AI-driven analytics, and real-time
decision-making, heavily relies on effective data
management. Without proper data handling mechanisms,
businesses may struggle with data silos, security risks, and
inefficiencies. This presentation investigates the role of Big
Data in digital transformation, addressing key technologies,
challenges, and best practices for successful
implementation.
INTRODUCTION
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6. Big Data Management
➢Big Data Management refers to the
systematic process of collecting, storing,
organizing, processing, analyzing, and securing
large volumes of data to ensure its
accessibility, accuracy, and usability for
decision-making. It involves various tools,
technologies, and frameworks to handle
structured, semi-structured, and unstructured
data efficiently [1].
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7. 1- Based on Data Source
•Social Media Data : Generated from Facebook, Twitter, Instagram, LinkedIn.
•Machine-Generated Data : Logs from computers, industrial equipment, medical devices.
•Sensor Data : IoT devices, smart home appliances, weather sensors.
•4. Transactional Data : Financial records, banking transactions, online purchases.
•IoT Data : Smartphones, smartwatches, and autonomous systems.
2- Based on Structure
•Structured Data – Data stored in tables (e.g., RDBMS, SQL databases).
•Semi-Structured Data –Partially organized data (e.g., JSON, XML).
•Unstructured Data –Text, audio, video, images, web pages.
Big Data Types
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8. Big Data Types…
3- Based on Storage Format
•Key-Value Stores : Databases storing values with unique keys (e.g., Redis, Apache Cassandra).
•Graph Stores : Databases based on relationships (e.g., Neo4j).
•Column-Family Stores : Stores large amounts of columnar data (e.g., Google BigTable, Apache
HBase).
•Document-Oriented Stores : Stores semi-structured data in documents (e.g., MongoDB).
4- Based on Processing Type
•Batch Processing : Data is processed periodically in batches (e.g., Hadoop, Apache Mahout).
•Real-Time Processing : Data is processed instantly as it is received (e.g., Apache Spark, Apache
Storm) [1].
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9. Big Data Processing Techniques
1. Data Mining
➢Extracting hidden patterns and trends.
➢Techniques: Clustering, classification, association
rule mining.
2. Machine Learning
➢Algorithms that learn from data and make
predictions.
➢Examples: Supervised Learning, Unsupervised
Learning, Reinforcement Learning.
3. Deep Learning
➢Advanced neural network-based algorithms for
pattern recognition.
➢Applications: Image processing, speech
recognition, autonomous systems.
4. Natural Language Processing (NLP)
➢Understanding and processing human language.
➢Used in chatbots, sentiment analysis, translation.
5. Data Visualization
➢Representing data graphically for insights.
➢Tools: Tableau, Power BI, Google Data Studio
[1].
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10. Big Data Lifecycle Data Generation
Sources include IoT sensors,
mobile apps, social media, and
business transactions.
Data Acquisition
Collection, transmission, and
preprocessing of data.
Data Storage
Managed in distributed
storage systems (e.g.,
Hadoop, cloud storage).
Data Processing &
Analytics
Application of machine
learning, AI, and statistical
methods to extract insights.
Data Visualization
Presenting results for
decision-making.
Quality Monitoring
Ensuring data remains reliable
throughout its lifecycle.
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11. The Role of Big
Data In Industry
4.0
With the rise of IoT, Cloud
Computing, and CPS, vast amounts of
real-time and historical data are
generated in manufacturing processes.
However, without proper management,
this data remains unstructured and
underutilized [2].
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12. ❖Process Optimization: Helps in fine-tuning manufacturing operations by
identifying inefficiencies.
❖Predictive Maintenance: Enables real-time monitoring of machines, reducing
downtime and maintenance costs.
❖Quality Control: Improves defect detection and product consistency by
analyzing production trends.
❖Supply Chain Management: Enhances logistics by optimizing inventory levels
and demand forecasting [2].
Key Functions of Big Data in Industry 4.0
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13. Impact of Big Data on Industry 4.0 Adoption
26% of industrial organizations
have actively integrated Big Data
analytics.
Improved efficiency in
production planning.
Reduced operational
costs through predictive
maintenance.
Enhanced decision-making
with real-time data
insights.
Organizations with 250+ employees were more likely to
adopt Big Data solutions, while smaller enterprises faced
challenges in implementation [2].
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14. The Role of The Big Data
Quality Management
Framework (BDQMF)
TheBDQMFis designed to tackle
these challenges by implementing
quality assessment, control, and
enforcement across the Big Data
lifecycle [3].
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15. Key Features of BDQMF in Big Data Management
1. Automated Data Profiling
•Uses sampling and statistical methods
to quickly estimate data quality.
•Identifies potential data issues before
full-scale processing.
2. Pre-Processing & Cleaning
Strategies
•Applies data filtering, deduplication,
and normalization to improve quality.
•Integrates with Big Data platforms like
Apache Spark and Hadoop.
3. Quality Rules Discovery &
Enforcement
•Extracts patterns and quality metrics
to generate data quality rules.
•These rules guide data transformations
to ensure high-quality analytics.
4. Real-Time Quality
Monitoring
•Continuously tracks data quality in
streaming environments.
•Detects data drift and anomalies
that affect analytics performance.
5. Scalable and Efficient
Architecture
•Supports distributed computing to
handle massive datasets efficiently.
•Optimizes computational resources
using Big Data sampling techniques
[3].
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16. Impact of Big
Data Quality
Management
Reduces computational costs by identifying and
correcting errors early.
Improves decision-making by ensuring data used
in analytics is reliable.
Enables automation in data quality monitoring and
enforcement.
Enhances scalability by integrating cloud-based Big
Data architectures [3].
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17. Secure Data Management Life Cycle For Big Data
Security
1. Secure Data Collection
❑Data Integrity Verification:
•Hash functions (SHA-256) for tamper-proof logs.
❑Source Authentication:
•Digital signatures and blockchain to verify data authenticity.
❑Secure APIs:
•Encrypted communication channels (TLS 1.3) to prevent
MITM (Man-in-the-Middle) attacks.
2. Secure Data Processing
❑Data Masking & Anonymization:
•Removes personally identifiable information (PII) before
analysis.
❑Edge Computing Security:
•Encrypts IoT & sensor data before cloud processing.
❑AI-Powered Intrusion Detection:
•Detects anomalies using machine learning models.
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18. 3. Secure Data Storage
❑Hybrid Encryption (ECC + AES-128):
•ECC (Elliptic Curve Cryptography) secures key exchanges.
•AES-128 encrypts bulk storage with minimal
computational overhead.
❑Immutable Storage via Blockchain:
•Prevents unauthorized modifications to critical
government records.
❑Access Control (RBAC & ABAC):
•Role-Based (RBAC) for government employees.
• Attribute-Based (ABAC) for cross-agency collaboration.
Secure Data Management Life Cycle For Big Data
Security
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19. 4. Secure Data Sharing & Communication
❑Data Loss Prevention (DLP):
•Prevents unauthorized data transfers.
❑Homomorphic Encryption:
•Enables secure computations on encrypted data (useful for
multi-agency data sharing).
❑Privacy-Preserving Protocols:
•Secure Multi-Party Computation (SMPC) for collaborative
analysis [4].
Secure Data Management Life Cycle For Big Data
Security
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20. Case Studies
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21. Healthcare: Mayo Clinic’s Predictive
Analytics for Patient Care
Challenge
•Managing millions of patient records while providing personalized
healthcare.
•Identifying high-risk patients for preventive care.
Big Data Solution
•Used AI-driven predictive analytics:
•Machine Learning algorithms analyzed medical records to detect disease
patterns.
•Real-time patient monitoring for early detection of critical conditions.
Results
•Reduced hospital readmissions by 26%.
•Improved early disease detection, increasing survival rates for certain
conditions.
•Enhanced efficiency in healthcare operations [5].
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22. Finance: Jpmorgan Chase’s AI-powered
Fraud Detection
Challenge
•Cyber fraud & financial crime were rising due to digital transactions.
•Needed real-time fraud detection to prevent financial losses.
Big Data Solution
•AI-driven fraud detection models analyzed millions of transactions per
second.
•Deep learning identified unusual spending patterns and flagged potential
fraud.
Results
•Detected fraud 80% faster than traditional methods.
•Saved millions of dollars by preventing fraudulent transactions.
•Improved security for digital banking customers [6].
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23. Retail& E-commerce: Amazon’s
RecommendationEngine
Challenge
•Needed to increase sales & customer engagement.
•Wanted to deliver personalized shopping experiences.
Big Data Solution
•Purchase history
•Browsing patterns
•Similar customer behavior
Results
•35% of Amazon’s revenue comes from its recommendation engine.
•Increased customer retention and engagement.
•Boosted conversion rates significantly [7].
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24. Manufacturing: General Electric (GE)
Predictive Maintenance
Challenge
•Unexpected equipment failures resulted in costly downtimes.
•Required a proactive approach to maintenance.
Big Data Solution
•IoT sensors installed in machines and turbines.
•Predictive analytics identified potential failures before they occurred.
Results
•30% reduction in maintenance costs.
•50% decrease in machine downtime.
•Increased overall efficiency and lifespan of industrial equipment [8].
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25. CHALLENGES
Data Security and Privacy
With massive data exchange,
ensuring secure storage and
sharing while complying with
regulations like GDPR is
crucial.
Data Quality and Integrity
Inaccurate or inconsistent data
can lead to misleading
insights, affecting decision-
making.
Scalability Issues
Organizations struggle to scale
their data infrastructure to
accommodate growing data
volumes efficiently.
Real-time Processing
Many industries require
immediate data processing,
which can be computationally
intensive.
Integration with Legacy
Systems
Adapting Big Data
technologies to existing
infrastructure without
disruptions remains a
challenge.
High Implementation Costs
Investment in advanced
analytics, AI, and storage
solutions can be financially
demanding for enterprises.
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26. RECOMMENDATIONS
1
Implement Advanced
Security Measures
Utilize encryption,
blockchain, and access
control mechanisms to
protect data.
2
Adopt a Big Data
Quality Management
Framework (BDQMF)
Ensure data accuracy,
consistency, and
reliability through
standardized assessment
and monitoring.
3
Invest in Scalable
Cloud Solutions
Leverage cloud
computing to manage
vast datasets efficiently
and cost-effectively.
4
Utilize AI and Machine
Learning for Real-time
Analysis
Automate data
processing and anomaly
detection to enhance
decision-making speed.
5
Ensure Seamless
Integration with
Existing Systems
Use APIs and
middleware solutions to
facilitate smooth data
exchange.
6
Develop a Cost-
effective
Implementation Plan
Prioritize investments in
high-impact Big Data
applications to balance
cost and benefits.
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27. CONCLUSION
Big Data Management is a cornerstone of digital transformation,
enabling industries to optimize operations, enhance customer
experiences, and improve decision-making. However, managing large
datasets comes with security, scalability, and integration challenges.
Organizations must implement robust data governance frameworks,
invest in AI-driven analytics, and adopt scalable cloud-based solutions
to maximize the potential of Big Data. By addressing these challenges
proactively, businesses can fully harness the power of data to drive
innovation and gain a competitive edge in the digital era.
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28. References
1.Sandhu, A. K. (2022). Big data with cloud computing: Discussions and challenges. Big Data Mining and Analytics, 5(1), 32–40. https://doi.org/10.26599/bdma.2021.9020016
2.Janík, S., Szabó, P., Mĺkva, M., & Mareček-Kolibiský, M. (2022). Effective data utilization in the context of industry 4.0 technology integration. Applied Sciences, 12(20),
10517. https://doi.org/10.3390/app122010517
3.Taleb, I., Serhani, M. A., Bouhaddioui, C., & Dssouli, R. (2021). Big data quality framework: a holistic approach to continuous quality management. Journal of Big Data, 8(1).
https://doi.org/10.1186/s40537-021-00468-0
4.Zahid, R., Altaf, A., Ahmad, T., Iqbal, F., Vera, Y. a. M., Flores, M. a. L., & Ashraf, I. (2023). Secure Data Management Life Cycle for Government Big-Data Ecosystem: Design
and Development Perspective. Systems, 11(8), 380. https://doi.org/10.3390/systems11080380
5.Noble.Dana. (2024, April 17). AI in healthcare: The future of patient care and health management. Mayo Clinic Press. https://mcpress.mayoclinic.org/healthy-aging/ai-in-
healthcare-the-future-of-patient-care-and-health-management/
6.Morgan, J. (n.d.). AI Boosting Payments Efficiency & Cutting Fraud | J.P. Morgan. https://www.jpmorgan.com/insights/payments/payments-optimization/ai-payments-
efficiency-fraud-reduction
7.Hardesty, L. (2024, November 14). The history of Amazon’s recommendation algorithm - Amazon Science. Amazon Science. https://www.amazon.science/the-history-of-
amazons-recommendation-algorithm
8.Reliability Analysis Software - APM Reliability | GE Vernova. (n.d.). https://www.gevernova.com/software/products/asset-performance-management/asset-
reliability?utm_source=google_g&utm_medium=paid-search&utm_campaign=PGOG-PG&OG-APM_Reliability-GLOB-Search&utm_content=Predictive-Preventive-
Brand&utm_term=ge%20predictive%20maintenance_p&ad=719707646597&placement=&device=c&location=9197048&gad_source=1&gclid=CjwKCAjwvr--
BhB5EiwAd5YbXupQIS833a97b2QmfOJvjYuSsYBdaZJFiCrqGyZb-4uYMRmkyW33bRoColsQAvD_BwE
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