Subtopic Deep Dive
Knowledge Management with Big Data
Research Guide
What is Knowledge Management with Big Data?
Knowledge Management with Big Data integrates big data technologies into knowledge management systems to capture, organize, and disseminate organizational knowledge using semantic analysis and collaborative platforms.
This subtopic combines big data analytics with KM processes to handle vast data volumes for knowledge extraction. Key papers include Ferraris et al. (2018) with 612 citations linking BDA capabilities to firm performance via knowledge management. Over 10 papers from 2011-2022 explore digital transformation impacts on KM.
Why It Matters
Big data-enhanced KM systems enable firms to analyze unstructured data for insights, improving decision-making and innovation as shown by Ferraris et al. (2018) where BDA capabilities boosted firm performance through knowledge processes. In manufacturing, Dubey et al. (2019) demonstrated BDA pathways to operational performance under entrepreneurial orientation. Côrte-Real et al. (2016) assessed BDA business value in European firms, highlighting competitive advantages from knowledge utilization.
Key Research Challenges
Data Quality in KM
Big data volumes introduce noise and inconsistencies challenging KM accuracy. Otto (2011) found telecommunications firms struggle with data governance for quality maintenance. Ferraris et al. (2018) note BDA requires clean data for effective knowledge categorization.
Semantic Knowledge Extraction
Extracting meaningful knowledge from unstructured big data demands advanced analytics. Tsai et al. (2015) surveyed platforms needed for high-performance big data analysis in knowledge contexts. Lycett (2013) discusses datafication complexities in making sense of complex data worlds.
Integration with Legacy Systems
Merging big data tools with existing KM systems faces compatibility issues. Parviainen et al. (2022) identify digitalization challenges in adopting technologies within organizational environments. Fatorachian and Kazemi (2018) propose frameworks for Industry 4.0 integration in manufacturing.
Essential Papers
Digital Transformation: An Overview of the Current State of the Art of Research
Sascha Kraus, Paul Jones, Norbert Kailer et al. · 2021 · SAGE Open · 1.1K citations
The increasing digitalization of economies has highlighted the importance of digital transformation and how it can help businesses stay competitive in the market. However, disruptive changes not on...
Tackling the digitalization challenge: how to benefit from digitalization in practice
Päivi Parviainen, Maarit Tihinen, Jukka Kääriäinen et al. · 2022 · International journal of information systems and project management · 997 citations
Digitalization has been identified as one of the major trends changing society and business. Digitalization causes changes for companies due to the adoption of digital technologies in the organizat...
Big data analytics: a survey
Chun‐Wei Tsai, Chin‐Feng Lai, Han‐Chieh Chao et al. · 2015 · Journal Of Big Data · 785 citations
The age of big data is now coming. But the traditional data analytics may not be able to handle such large quantities of data. The question that arises now is, how to develop a high performance pla...
Artificial Intelligence and Business Value: a Literature Review
Ida Merete Enholm, Emmanouil Papagiannidis, Patrick Mikalef et al. · 2021 · Information Systems Frontiers · 767 citations
Abstract Artificial Intelligence (AI) are a wide-ranging set of technologies that promise several advantages for organizations in terms off added business value. Over the past few years, organizati...
Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations
Rameshwar Dubey, Angappa Gunasekaran, Stephen J. Childe et al. · 2019 · International Journal of Production Economics · 712 citations
Big data analytics in E-commerce: a systematic review and agenda for future research
Shahriar Akter, Samuel Fosso Wamba · 2016 · Electronic Markets · 656 citations
Abstract There has been an increasing emphasis on big data analytics (BDA) in e-commerce in recent years. However, it remains poorly-explored as a concept, which obstructs its theoretical and pract...
Big data analytics capabilities and knowledge management: impact on firm performance
Alberto Ferraris, Alberto Mazzoleni, Alain Devalle et al. · 2018 · Management Decision · 612 citations
Purpose Big data analytics (BDA) guarantees that data may be analysed and categorised into useful information for businesses and transformed into big data related-knowledge and efficient decision-m...
Reading Guide
Foundational Papers
Start with Lycett (2013) for datafication concepts and Otto (2011) for data governance in organizations, as they establish pre-2015 bases for big data KM complexities.
Recent Advances
Study Ferraris et al. (2018) for BDA-KM performance links and Dubey et al. (2019) for operational applications in dynamic environments.
Core Methods
Core methods encompass BDA platforms (Tsai et al., 2015), analytics capabilities (Ferraris et al., 2018), and Industry 4.0 frameworks (Fatorachian and Kazemi, 2018).
How PapersFlow Helps You Research Knowledge Management with Big Data
Discover & Search
Research Agent uses searchPapers and citationGraph to map core literature starting from Ferraris et al. (2018), revealing 612-citation links to BDA-KM performance. exaSearch uncovers niche papers like Otto (2011) on data governance; findSimilarPapers extends to Dubey et al. (2019) for operational impacts.
Analyze & Verify
Analysis Agent applies readPaperContent to parse Ferraris et al. (2018) abstracts for BDA-KM metrics, then verifyResponse with CoVe checks claims against Tsai et al. (2015) survey data. runPythonAnalysis with pandas computes citation trends across 10+ papers; GRADE grading scores evidence strength for performance links.
Synthesize & Write
Synthesis Agent detects gaps in BDA-KM integration from Parviainen et al. (2022), flags contradictions with Lycett (2013) datafication views. Writing Agent uses latexEditText and latexSyncCitations to draft reports citing Dubey et al. (2019), with latexCompile for publication-ready output and exportMermaid for KM workflow diagrams.
Use Cases
"Analyze citation networks between BDA capabilities and KM firm performance"
Research Agent → citationGraph on Ferraris et al. (2018) → Analysis Agent → runPythonAnalysis (NetworkX for centrality) → network visualization of 612-citation impacts.
"Draft LaTeX review on big data challenges in organizational KM"
Synthesis Agent → gap detection across Tsai et al. (2015) and Otto (2011) → Writing Agent → latexEditText + latexSyncCitations → latexCompile → PDF with integrated bibliography.
"Find GitHub repos implementing BDA for knowledge extraction from papers"
Research Agent → paperExtractUrls on Dubey et al. (2019) → Code Discovery → paperFindGithubRepo → githubRepoInspect → list of analytics scripts for operational KM.
Automated Workflows
Deep Research workflow conducts systematic reviews of 50+ big data KM papers via searchPapers chains, producing structured reports with GRADE-scored sections on Ferraris et al. (2018). DeepScan applies 7-step analysis with CoVe checkpoints to verify BDA performance claims in Dubey et al. (2019). Theorizer generates theory on data governance-KM links from Otto (2011) and Lycett (2013).
Frequently Asked Questions
What defines Knowledge Management with Big Data?
It integrates big data technologies with KM systems for capturing, organizing, and disseminating knowledge using semantic analysis.
What methods improve BDA in KM?
Methods include high-performance platforms (Tsai et al., 2015) and analytics capabilities for knowledge categorization (Ferraris et al., 2018).
What are key papers?
Ferraris et al. (2018, 612 citations) links BDA to KM performance; Dubey et al. (2019, 712 citations) studies manufacturing impacts.
What open problems exist?
Challenges include data quality governance (Otto, 2011) and legacy system integration (Parviainen et al., 2022).
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