PapersFlow Research Brief
Big Data and Business Intelligence
Research Guide
What is Big Data and Business Intelligence?
Big Data and Business Intelligence is the study and practice of collecting, managing, and analyzing large-scale organizational data to support decision-making, improve performance, and generate actionable insights through information systems and analytics methods.
The Big Data and Business Intelligence literature in Management Information Systems spans 183,705 works in the provided cluster data, covering analytics-enabled decision support, data warehousing, predictive analytics, and performance impacts in areas such as supply chains and sustainability. "Business Intelligence and Analytics: From Big Data to Big Impact" (2012) framed BI&A as an important research and practice area driven by data-related organizational problems and the need to translate data into impact. Foundational IS and organizational knowledge perspectives used in this area are commonly grounded in models of IS success (DeLone and McLean (2003), "The DeLone and McLean Model of Information Systems Success: A Ten-Year Update") and knowledge management concepts (Alavi and Leidner (2001), "Review : Knowledge Management and Knowledge Management Systems: Conceptual Foundations And Research Issues1,2").
Topic Hierarchy
Research Sub-Topics
Big Data Analytics in Supply Chain Management
Researchers examine applications of big data for supply chain optimization, demand forecasting, and risk assessment. Studies integrate IoT data streams with predictive models for real-time decision-making.
Predictive Analytics for Business Performance
This sub-topic develops machine learning models to predict firm outcomes using big data sources like financial and market metrics. Research validates model accuracy across industries and time horizons.
Business Intelligence Decision Support Systems
Investigations focus on BI dashboard design, real-time analytics integration, and user adoption in executive decision processes. Studies measure impacts on decision quality and organizational outcomes.
Big Data and Organizational Sustainability
Studies explore big data applications for measuring ESG performance, sustainable supply chains, and circular economy models. Research includes metric development and benchmarking frameworks.
Knowledge Management with Big Data
This field integrates big data technologies with KM systems for capturing, organizing, and disseminating organizational knowledge. Research addresses semantic analysis and collaborative platforms.
Why It Matters
Big data and BI systems matter because organizations invest in analytics platforms and data stacks specifically to improve decision quality, operational efficiency, and measurable system outcomes, which MIS research often evaluates using established success and performance frameworks. DeLone and McLean (2003) in "The DeLone and McLean Model of Information Systems Success: A Ten-Year Update" provided a widely used structure for assessing system quality, information quality, use, user satisfaction, and net benefits—dimensions that directly map to how BI&A deployments are justified and audited inside firms. Chen et al. (2012) in "Business Intelligence and Analytics: From Big Data to Big Impact" positioned BI&A as addressing data-related problems in contemporary organizations, aligning BI practice with managerial goals such as decision support and performance impact rather than analytics for its own sake. Real-world market signals in the provided news show sustained investment pressure around analytics stacks and platforms, including a $10 million seed round for Definite to replace “clunky big-data stacks and business intelligence tools” ("Definite bags $10M in funding to replace clunky big-data ...", 2025) and Databricks reporting it closed a $1 billion funding round while projecting $4 billion in annualized revenue ("Databricks closes $1 billion round, projects $4 billion in annualized revenue on surging AI demand", 2025). These figures illustrate why research that connects BI&A capabilities to validated measures of IS success and organizational benefit remains practically consequential.
Reading Guide
Where to Start
Start with Chen, Chiang, and Storey’s "Business Intelligence and Analytics: From Big Data to Big Impact" (2012) because it defines BI&A as a research area and motivates why organizations pursue analytics for measurable impact.
Key Papers Explained
A coherent reading path begins with Chen et al. (2012), "Business Intelligence and Analytics: From Big Data to Big Impact", to anchor the BI&A problem statement and scope. You can then use DeLone and McLean (2003), "The DeLone and McLean Model of Information Systems Success: A Ten-Year Update", as the primary evaluation framework for BI systems and link it to user-level outcomes via Goodhue and Thompson (1995), "Task-Technology Fit and Individual Performance". For organizational learning and reuse of analytics outputs, Alavi and Leidner (2001), "Review : Knowledge Management and Knowledge Management Systems: Conceptual Foundations And Research Issues1,2", supplies the conceptual bridge between data products and knowledge as a resource. Finally, Gefen et al. (2000), "Structural Equation Modeling and Regression: Guidelines for Research Practice", and Saldaña (2025), "The Coding Manual for Qualitative Researchers", provide complementary quantitative and qualitative method scaffolding for building and validating BI impact studies.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Advanced work increasingly focuses on how BI&A architectures and governance adapt to cloud deployment expectations defined by Mell and Grance (2011), "The NIST definition of cloud computing", and how self-service discovery depends on retrieval foundations described by Manning et al. (2008), "Introduction to Information Retrieval". A practical frontier is evaluating BI stack consolidation and replacement pressures signaled by the $10 million seed funding for Definite ("Definite bags $10M in funding to replace clunky big-data ...", 2025) and platform-scale investment highlighted by Databricks’ $1 billion funding round and $4 billion annualized revenue projection ("Databricks closes $1 billion round, projects $4 billion in annualized revenue on surging AI demand", 2025), using success and fit frameworks rather than tool-specific benchmarks.
Papers at a Glance
| # | Paper | Year | Venue | Citations | Open Access |
|---|---|---|---|---|---|
| 1 | The Coding Manual for Qualitative Researchers | 2025 | — | 17.8K | ✕ |
| 2 | Research methods for business: A skill building approach | 1993 | Long Range Planning | 17.1K | ✕ |
| 3 | The NIST definition of cloud computing | 2011 | — | 11.5K | ✓ |
| 4 | The DeLone and McLean Model of Information Systems Success: A ... | 2003 | Journal of Management ... | 11.0K | ✕ |
| 5 | Introduction to Information Retrieval | 2008 | Cambridge University P... | 10.5K | ✕ |
| 6 | <i>Review</i> : Knowledge Management and Knowledge Management ... | 2001 | MIS Quarterly | 9.8K | ✕ |
| 7 | Structural Equation Modeling and Regression: Guidelines for Re... | 2000 | Communications of the ... | 6.3K | ✓ |
| 8 | Business Intelligence and Analytics: From Big Data to Big Impact | 2012 | MIS Quarterly | 5.8K | ✕ |
| 9 | Task-Technology Fit and Individual Performance | 1995 | MIS Quarterly | 5.5K | ✕ |
| 10 | An Introduction to Statistical Methods and Data Analysis | 1994 | Technometrics | 5.4K | ✕ |
In the News
Definite bags $10M in funding to replace clunky big-data ...
* * * * * * * * * * * * * * A startup called Definite reckons it can help businesses to do away with their clunky big-data stacks and business intelligence tools after raising $10 million in seed f...
Strategic Insights Into the $40+ Billion Business
one of the main drivers of market growth. June 2025: Snowflake said it would acquire Crunchy Data for an estimated USD 250 million to expand PostgreSQL database capabilities in its AI Data Cloud pl...
Databricks closes $1 billion round, projects $4 billion in annualized revenue on surging AI demand
Sept 8 (Reuters) - Data analytics firm Databricks said on Monday it was on track to hit $4 billion in annualized revenue on the back of booming demand for its artificial intelligence products, as i...
Analytics Startups funded by Y Combinator (YC) 2026
Y Combinator LogoS2025 •Active0] Automatic insights on all your company data. Speed up business workflows to visualizations with a 0 learning curve. generative-ai artificial-intelligence ai analyt...
Observe Secures $156M in Recent Funding Round to ...
Observe Inc., the AI-powered observability company, announced it secured $156 million in a Series C funding round, enabling Observe to continue investing in product development, AI innovation, and ...
Code & Tools
> > A curated list of tools, platforms, frameworks, and learning resources for **> Business Intelligence (BI) **> , covering reporting, dashboards,...
Helical Insight is world's first Open Source Business Intelligence framework which can help you derive insights out of your one or multiple datasou...
The easy-to-use open source Business Intelligence and Embedded Analytics tool that lets everyone work with data 📊 metabase.com ### Topics
Data Solutions Framework (DSF) on AWS is a framework for implementation and delivery of data solutions with built-in AWS best practices. DSF is an ...
development platform for the Hadoop ecosystem that provides developers with data and application abstractions to simplify and accelerate applicatio...
Recent Preprints
Exploring business intelligence in the time of big data
Keywords: Big data, Business intelligence, Competitive advantage, Data analytics, Data integration, Machine learning, Data warehousing, Predictive analytics, IT companies, Operational efficiency. ...
A Narrative Review of the Integration of Big Data Analytics ...
The integration of Big Data Analytics (BDA) and Business Intelligence (BI) has become increasingly vital for enhancing strategic decision-making within contemporary organizations. This narrative re...
Business Intelligence and Analytics: From Big Data to ...
Keywords: Business intelligence and analytics, big data analytics, Web 2.0 Introduction Business intelligence and analytics (BI&A) and the related field of big data analytics have become increas...
Measuring the success of business intelligence and ...
Technology-based solutions such as business intelligence and analytics (BI&A) systems have become indispensable for organizations due to their ability to support decision-making. Recent development...
Leveraging Big Data for Competitive Advantage
Accepted: August 05, 2025 Purpose: This study examines the strategic integration of big data analytics for achieving competitive advantage in business analytics. Research Method: This study employ...
Latest Developments
Recent developments in Big Data and Business Intelligence research as of February 2026 highlight the growing adoption of AI and machine learning for augmented analytics, real-time data analysis, and self-service tools to democratize data access (Improvado, MIT Sloan Review). Additionally, there is a focus on organizational use of generative AI, data governance, and the development of advanced BI tools and benchmarks to evaluate system effectiveness (Deloitte, OvalEdge).
Sources
Frequently Asked Questions
What is the difference between business intelligence (BI) and big data analytics (BDA) in MIS research?
"Business Intelligence and Analytics: From Big Data to Big Impact" (2012) treats BI&A as an umbrella that includes analytics practices and systems intended to turn organizational data into actionable impact. In this framing, “big data” primarily describes the scale and diversity of data and the resulting analytical and managerial problems BI&A must solve.
How do researchers evaluate whether a BI or analytics system is successful?
DeLone and McLean (2003) in "The DeLone and McLean Model of Information Systems Success: A Ten-Year Update" synthesized IS success research into constructs commonly used to evaluate systems, including system quality, information quality, use, user satisfaction, and net benefits. BI evaluations often operationalize success by measuring these constructs around dashboards, reporting, and analytics-enabled decision processes.
Which theories help explain when BI tools improve individual or managerial performance?
Goodhue and Thompson (1995) in "Task-Technology Fit and Individual Performance" argued that performance improves when technology capabilities fit the tasks users must perform. BI tool adoption and impact studies often use task-technology fit logic to explain why the same BI platform can yield different outcomes across roles, teams, or decision contexts.
Which methods are commonly used to test BI and big data hypotheses about performance and adoption?
Gefen et al. (2000) in "Structural Equation Modeling and Regression: Guidelines for Research Practice" provided guidance for selecting and applying SEM versus regression in IS research designs, which are frequently used to test relationships among BI capabilities, use, satisfaction, and performance outcomes. For qualitative components (e.g., understanding how analysts and managers interpret dashboards), Saldaña (2025) in "The Coding Manual for Qualitative Researchers" is a standard reference for coding and analytic memo practices.
How do knowledge management concepts connect to BI and analytics initiatives?
Alavi and Leidner (2001) in "Review : Knowledge Management and Knowledge Management Systems: Conceptual Foundations And Research Issues1,2" described knowledge as an organizational resource and outlined KM systems concepts that align with BI goals of capturing, organizing, and reusing insights. In BI contexts, this connection is often expressed as the translation of data outputs (reports, models) into shared organizational knowledge that supports decisions.
Which technical foundations are most relevant when BI systems rely on cloud platforms and large-scale retrieval?
Mell and Grance (2011) in "The NIST definition of cloud computing" is commonly cited to define cloud deployment models and service characteristics when BI architectures move to cloud infrastructure. Manning et al. (2008) in "Introduction to Information Retrieval" provides core retrieval concepts that underpin search, indexing, and text-based analytics features used in many BI and analytics workflows.
Open Research Questions
- ? How can IS success constructs in "The DeLone and McLean Model of Information Systems Success: A Ten-Year Update" (2003) be adapted to evaluate modern BI&A deployments where “use” is partly automated (e.g., scheduled pipelines) and “net benefits” are distributed across teams rather than individual users?
- ? Which task characteristics most strongly moderate the relationships proposed by "Task-Technology Fit and Individual Performance" (1995) when BI is embedded into operational workflows (e.g., supply chain decision support) rather than used as a standalone reporting tool?
- ? How should researchers choose between SEM and regression approaches from "Structural Equation Modeling and Regression: Guidelines for Research Practice" (2000) when BI constructs are formative (platform capabilities) but outcomes are reflective (satisfaction, perceived usefulness)?
- ? What mechanisms convert analytics outputs into organizational knowledge assets as conceptualized in "Review : Knowledge Management and Knowledge Management Systems: Conceptual Foundations And Research Issues1,2" (2001), and how can those mechanisms be measured without conflating “information quality” with “knowledge creation”?
- ? Which retrieval and indexing design choices from "Introduction to Information Retrieval" (2008) most affect the usability and perceived information quality of BI search and self-service analytics features in enterprise settings?
Recent Trends
The provided cluster data indicates a large research volume (183,705 works) focused on analytics-enabled decision support, performance, and organizational integration themes, while the most-cited conceptual anchors remain IS success (DeLone and McLean ) and BI&A impact framing (Chen et al. (2012)).
2003Recent signals in the provided news emphasize continued capital flow and platform consolidation pressures around analytics stacks, including a $10 million seed round aimed at replacing “clunky big-data stacks and business intelligence tools” ("Definite bags $10M in funding to replace clunky big-data ...", 2025) and Databricks reporting a $1 billion funding round and projecting $4 billion in annualized revenue ("Databricks closes $1 billion round, projects $4 billion in annualized revenue on surging AI demand", 2025).
Methodologically, the sustained prominence of Gefen et al. and Saldaña (2025) in the provided top-cited list reflects that BI research continues to rely on both causal modeling (SEM/regression) and rigorous qualitative coding to connect system capabilities to measurable outcomes and organizational practices.
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