Subtopic Deep Dive

Big Data Analytics in Business
Research Guide

What is Big Data Analytics in Business?

Big Data Analytics in Business applies large-scale data processing techniques to enhance business decision-making, customer insights, and operational efficiency within media and information systems.

Researchers develop analytics frameworks to extract value from massive datasets in business contexts (Fesenmaier et al., 2017, 5 citations). Studies address privacy in data value chains for marketing (Zenda et al., 2020, 8 citations). Over 10 papers in provided lists examine data-driven models in new economy business (Feng et al., 2001, 13 citations).

Curated Papers

Key Challenges

Why It Matters

Firms use big data analytics for micro-marketing in destination management, creating personalized customer experiences (Fesenmaier et al., 2017). Insurance industries apply data value chains while navigating POPI regulations for marketing, reducing misuse risks (Zenda et al., 2020). New business models leverage data abundance for competitive edges in media economies (Keane, 2013; Gentzkow, 2006). Analytics inform SEO-to-SEM transitions, boosting online visibility (Heinze et al., 2010).

Key Research Challenges

Privacy in Data Value Chains

Organizations misuse personal data in marketing, prompting regulations like POPI (Zenda et al., 2020). Insurance firms struggle with compliance in data processing. Balancing analytics value and privacy protection remains unresolved.

Measuring New Goods Complementarity

Demand models often assume no complementarity between new digital goods like online newspapers and traditional products (Gentzkow, 2006). Accurate valuation requires advanced econometric methods. Business analytics must quantify substitution effects.

Scalable Micro-Marketing Systems

Tourism organizations need information systems for 'markets of one' using big data (Fesenmaier et al., 2017). Co-creation via Internet demands real-time personalization. Implementing frameworks at scale challenges resource-limited firms.

Essential Papers

Democracy and Media Decadence

John Keane · 2013 · Cambridge University Press eBooks · 317 citations

We live in a revolutionary age of communicative abundance in which many media innovations - from satellite broadcasting to smart glasses and electronic books - spawn great fascination mixed with ex...

Valuing New Goods in a Model with Complementarities: Online Newspapers

Matthew Gentzkow · 2006 · 37 citations

Many important economic questions hinge on the extent to which new goods either crowd out or complement consumption of existing products.Recent methods for studying new goods are based on demand mo...

Towards a (Meta-)Sociology of the Digital Sphere

Hans Geser · 2002 · Social Science Open Access Repository (GESIS – Leibniz Institute for the Social Sciences) · 21 citations

Content: 1. Introductory considerations; 2. The functional universality of digital computer systems as a starting point; 3. Is Cyberspace spatial?; 4. Implications of Cyberspace for the level of So...

The role of Human Resource Management and the Human Resource Professional in the new economy

W. Rennie · 2005 · UpSpace Institutional Repository (University of Pretoria) · 15 citations

The world economy is currently dominated by information- and communication technology, and has consequently become increasingly competitive and globalised. The changing economy also impacts on our ...

A new business model

Hengyi Feng, Julie Froud, Sukhdev Johal et al. · 2001 · Econstor (Econstor) · 13 citations

The paper delivers an analysis of the “New Economy” focussing on the roles of new business models, the capital market and venture capital. The capital market created a double standard in the 1990s:...

Political and Media Leadership in the Age of YouTube

Stuart Cunningham · 2008 · ANU Press eBooks · 9 citations

‘Leadership’ is routinely admired, vilified, ridiculed, invoked, trivialised, explained and speculated about in the media and in everyday conversation. Despite all this talk, there is surprisingly ...

Protection of personal information: An experiment involving data value chains and the use of personal information for marketing purposes in South Africa

Benson Zenda, Ruthea Vorster, Adéle da Veiga · 2020 · South African Computer Journal · 8 citations

South Africa enacted the Protection of Personal Information Act 4 of 2013 (POPI) in an effort to curb the misuse of customers’ personal information by organisations. The aim of this research was to...

Reading Guide

Foundational Papers

Start with Keane (2013, 317 citations) for media data abundance context, then Gentzkow (2006, 37 citations) for business valuation models, followed by Feng et al. (2001, 13 citations) on new economy frameworks.

Recent Advances

Study Fesenmaier et al. (2017, 5 citations) for micro-marketing systems and Zenda et al. (2020, 8 citations) for privacy in data chains.

Core Methods

Core techniques: data value chains (Zenda et al., 2020), complementarity demand models (Gentzkow, 2006), and information systems for SEM (Heinze et al., 2010).

How PapersFlow Helps You Research Big Data Analytics in Business

Discover & Search

Research Agent uses searchPapers and exaSearch to find papers like 'Micro-Marketing and Big Data Analytics' by Fesenmaier et al. (2017), then citationGraph reveals connections to Keane (2013) on media abundance. findSimilarPapers expands to related works on data-driven business models.

Analyze & Verify

Analysis Agent applies readPaperContent to extract data chain methods from Zenda et al. (2020), verifies claims with CoVe against Gentzkow (2006) demand models, and runs PythonAnalysis with pandas to simulate complementarity metrics. GRADE grading scores evidence strength for privacy challenges.

Synthesize & Write

Synthesis Agent detects gaps in privacy-compliant analytics between Zenda (2020) and Fesenmaier (2017), flags contradictions in new economy models (Feng et al., 2001). Writing Agent uses latexEditText, latexSyncCitations for Keane (2013), and latexCompile to produce business analytics reports; exportMermaid diagrams data flows.

Use Cases

"Analyze citation networks for big data privacy papers in business marketing"

Research Agent → citationGraph on Zenda (2020) → Analysis Agent → runPythonAnalysis (NetworkX for centrality) → network diagram of 10+ papers with privacy focus.

"Draft LaTeX report on micro-marketing analytics frameworks"

Synthesis Agent → gap detection in Fesenmaier (2017) → Writing Agent → latexEditText + latexSyncCitations (Gentzkow 2006) + latexCompile → formatted PDF with sections on data complementarity.

"Find code implementations for big data business models from papers"

Research Agent → paperExtractUrls on Heinze (2010) → Code Discovery → paperFindGithubRepo → githubRepoInspect → Python scripts for SEM analytics pipelines.

Automated Workflows

Deep Research workflow conducts systematic review: searchPapers (50+ on media data analytics) → DeepScan (7-step verification on Fesenmaier 2017) → structured report with GRADE scores. Theorizer generates theory on data value chains from Zenda (2020) and Keane (2013), chaining gap detection to hypothesis diagrams via exportMermaid. DeepScan applies CoVe checkpoints to validate business model complementarities (Gentzkow 2006).

Try Doxa for Big Data Analytics in Business Research

Frequently Asked Questions

What defines Big Data Analytics in Business?

It applies large-scale data techniques for business decisions, customer insights, and efficiency, as in micro-marketing systems (Fesenmaier et al., 2017).

What methods are used?

Methods include data value chains for marketing (Zenda et al., 2020) and demand models for new goods complementarity (Gentzkow, 2006).

What are key papers?

Foundational: Keane (2013, 317 citations) on media abundance; Gentzkow (2006, 37 citations) on online newspapers. Recent: Fesenmaier et al. (2017, 5 citations) on micro-marketing.