Subtopic Deep Dive

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Big Data Applications in Industry
Research Guide

What is Big Data Applications in Industry?

Big Data Applications in Industry refers to the deployment of big data analytics technologies across industrial sectors like manufacturing, healthcare, agriculture, and oil and gas to drive operational efficiency, decision-making, and competitive advantage.

This subtopic covers sector-specific implementations such as predictive maintenance in oil and gas (Nguyen et al., 2020, 139 citations) and analytics in agriculture (Sonka, 2014, 93 citations). Key papers span healthcare (Bahri et al., 2018, 100 citations), supply chain (Ittmann, 2015, 69 citations), and business intelligence (Ram et al., 2016, 92 citations). Over 1,000 papers explore ROI and challenges in these applications.

Curated Papers

Key Challenges

Why It Matters

Big data applications enable predictive maintenance in oil and gas, reducing downtime as shown by Nguyen et al. (2020). In agriculture, analytics improve yield predictions (Sonka, 2014), while healthcare benefits from patient data processing (Bahri et al., 2018). Supply chain optimization via analytics cuts costs (Ittmann, 2015), guiding corporate investments and policies on data-driven transformations.

Key Research Challenges

Data Volume Management

Processing high-volume data from sensors and logs overwhelms storage in industries like oil and gas (Nguyen et al., 2020). Almeida and Calistru (2012) highlight scalability issues across sectors. Real-time velocity adds computational strain (Watson, 2014).

Privacy and Surveillance Risks

Dataveillance emerges from analytics in personalization, raising ethical concerns (Degli Esposti, 2014). Yeung (2018) details fears in predictive personalization across industries. Balancing insights with data protection remains critical (Bahri et al., 2018).

ROI Measurement Barriers

Quantifying returns on big data investments challenges firms, especially in China-based studies (Ram et al., 2016). Implementation hurdles in supply chains complicate evaluation (Ittmann, 2015). Sector-specific metrics vary widely (Sonka, 2014).

Essential Papers

Tutorial: Big Data Analytics: Concepts, Technologies, and Applications

Hugh J. Watson · 2014 · Communications of the Association for Information Systems · 336 citations

We have entered the big data era. Organizations are capturing, storing, and analyzing data that has high volume, velocity, and variety and comes from a variety of new sources, including social medi...

When big data meets dataveillance: the hidden side of analytics

Sara Degli Esposti · 2014 · Surveillance & Society · 151 citations

Among the numerous implications of digitalization, the debate about ‘big data’ has gained momentum. The central idea capturing attention is that digital data represents the newest key asset organiz...

A Systematic Review of Big Data Analytics for Oil and Gas Industry 4.0

Trung Nguyen, Raymond G. Gosine, Peter Warrian · 2020 · IEEE Access · 139 citations

Big data (BD) analytics is one of the critical components in the digitalization of the oil and gas (O&G) industry. Its focus is managing and processing a high volume of data to improve operatio...

BIG DATA for Healthcare: A Survey

Safa Bahri, Nesrine Zoghlami, Mourad Abed et al. · 2018 · IEEE Access · 100 citations

International audience

Big Data and the Ag Sector: More than Lots of Numbers

Steve Sonka, Sonka, Steve · 2014 · AgEcon Search (University of Minnesota, USA) · 93 citations

It seems that one can’t go through a work day without seeing some mention of Big Data, its application and its potential to have unprecedented impact. The potential for Big Data application in the ...

The Implications of Big Data Analytics on Business Intelligence: A Qualitative Study in China

Jiwat Ram, Changyu. Zhang, Andy Koronios · 2016 · Procedia Computer Science · 92 citations

Social media has brought about a revolution and dictated a paradigm shift in the operational strategies of firms globally. It has resulted in collection of massive data from a variety of social med...

The impact of big data and business analytics on supply chain management

Hans W. Ittmann · 2015 · Journal of Transport and Supply Chain Management · 69 citations

Background: Change is inevitable and as supply chain managers prepare for the future they face many challenges. Two major trends over the last few years are the growing importance of ‘big data’ and...

Reading Guide

Foundational Papers

Start with Watson (2014, 336 citations) for core concepts across industries; Degli Esposti (2014, 151 citations) for early privacy risks; Sonka (2014, 93 citations) for agriculture applications.

Recent Advances

Nguyen et al. (2020, 139 citations) for oil and gas; Rahman and Reza (2022, 56 citations) for social media analytics; Bahri et al. (2018, 100 citations) for healthcare.

Core Methods

Volume-velocity-variety processing (Watson, 2014); predictive modeling in O&G (Nguyen et al., 2020); business intelligence pipelines (Ram et al., 2016).

How PapersFlow Helps You Research Big Data Applications in Industry

Discover & Search

Research Agent uses searchPapers and citationGraph to map 250M+ papers, starting from Watson (2014, 336 citations) to find sector applications like oil and gas. exaSearch uncovers niche implementations in agriculture; findSimilarPapers links Nguyen et al. (2020) to supply chain works.

Analyze & Verify

Analysis Agent employs readPaperContent on Nguyen et al. (2020) for O&G specifics, verifies claims with CoVe chain-of-verification, and runs PythonAnalysis with pandas to replicate efficiency metrics from Bahri et al. (2018). GRADE grading scores evidence strength in ROI claims from Ram et al. (2016).

Synthesize & Write

Synthesis Agent detects gaps in privacy coverage between Degli Esposti (2014) and Yeung (2018); Writing Agent uses latexEditText, latexSyncCitations for sector reports, and latexCompile for publication-ready docs. exportMermaid visualizes application workflows across industries.

Use Cases

"Analyze ROI data from big data in oil and gas papers using Python."

Research Agent → searchPapers('big data oil gas ROI') → Analysis Agent → readPaperContent(Nguyen 2020) → runPythonAnalysis(pandas on efficiency metrics) → CSV export of quantified impacts.

"Write a LaTeX review on big data in agriculture applications."

Synthesis Agent → gap detection(Sonka 2014) → Writing Agent → latexEditText(sector summary) → latexSyncCitations(Watson 2014) → latexCompile → PDF with diagrams.

"Find GitHub repos with code for supply chain big data analytics."

Research Agent → citationGraph(Ittmann 2015) → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect → curated code list for analytics pipelines.

Automated Workflows

Deep Research workflow conducts systematic reviews of 50+ papers on industrial applications, chaining searchPapers → citationGraph → structured ROI report. DeepScan applies 7-step analysis with CoVe checkpoints to verify claims in Nguyen et al. (2020). Theorizer generates hypotheses on privacy gaps from Degli Esposti (2014) and Yeung (2018).

Try Doxa for Big Data Applications in Industry Research

Frequently Asked Questions

What defines Big Data Applications in Industry?

Deployment of analytics on high-volume, velocity, variety data in sectors like manufacturing, healthcare, and agriculture for efficiency gains (Watson, 2014).

What methods dominate these applications?

Predictive analytics in oil and gas (Nguyen et al., 2020), business intelligence from social media (Ram et al., 2016), and operational processing in healthcare (Bahri et al., 2018).

What are key papers?

Watson (2014, 336 citations) tutorials concepts; Nguyen et al. (2020, 139 citations) reviews O&G; Sonka (2014, 93 citations) covers agriculture.

What open problems exist?

Privacy in personalization (Yeung, 2018), scalable management (Almeida and Calistru, 2012), and consistent ROI metrics across sectors (Ittmann, 2015).