Does big data mean big knowledge? KM perspectives on big data and analytics

2017 ◽  
Vol 21 (1) ◽  
pp. 1-6 ◽  
Author(s):  
David J. Pauleen ◽  
William Y.C. Wang
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Content Type ◽  
Guiding Principle ◽  
Proposed Model ◽  
Organizational Value ◽  
Practical Implications

Purpose This viewpoint study aims to make the case that the field of knowledge management (KM) must respond to the significant changes that big data/analytics is bringing to operationalizing the production of organizational data and information. Design/methodology/approach This study expresses the opinions of the guest editors of “Does Big Data Mean Big Knowledge? Knowledge Management Perspectives on Big Data and Analytics”. Findings A Big Data/Analytics-Knowledge Management (BDA-KM) model is proposed that illustrates the centrality of knowledge as the guiding principle in the use of big data/analytics in organizations. Research limitations/implications This is an opinion piece, and the proposed model still needs to be empirically verified. Practical implications It is suggested that academics and practitioners in KM must be capable of controlling the application of big data/analytics, and calls for further research investigating how KM can conceptually and operationally use and integrate big data/analytics to foster organizational knowledge for better decision-making and organizational value creation. Originality/value The BDA-KM model is one of the early models placing knowledge as the primary consideration in the successful organizational use of big data/analytics.

scholarly journals Ambidextrous organization and agility in big data era

2018 ◽  
Vol 24 (5) ◽  
pp. 1091-1109 ◽  
Author(s):  
Riccardo Rialti ◽  
Giacomo Marzi ◽  
Mario Silic ◽  
Cristiano Ciappei
Keyword(s):  
Big Data ◽  
Conceptual Framework ◽  
Data Analytics ◽  
Design Methodology ◽  
Business Process Management ◽  
Process Management ◽  
Big Data Analytics ◽  
Organizational Agility ◽  
Content Type ◽  
Practical Implications

Purpose The purpose of this paper is to explore the effect of big data analytics-capable business process management systems (BDA-capable BPMS) on ambidextrous organizations’ agility. In particular, how the functionalities of BDA-capable BPMS may improve organizational dynamism and reactiveness to challenges of Big Data era will be explored. Design/methodology/approach A theoretical analysis of the potential of BDA-capable BPMS in increasing organizational agility, with particular attention to the ambidextrous organizations, has been performed. A conceptual framework was subsequently developed. Next, the proposed conceptual framework was applied in a real-world context. Findings The research proposes a framework highlighting the importance of BDA-capable BPMS in increasing ambidextrous organizations’ agility. Moreover, the authors apply the framework to the cases of consumer-goods companies that have included BDA in their processes management. Research limitations/implications The principal limitations are linked to the need to validate quantitatively the proposed framework. Practical implications The value of the proposed framework is related to its potential in helping managers to fully understand and exploit the potentiality of BDA-capable BPMS. Moreover, the implications show some guidelines to ease the implementation of such systems within ambidextrous organizations. Originality/value The research offers a model to interpret the effects of BDA-capable BPMS on ambidextrous organizations’ agility. In this way, the research addresses a significant gap by exploring the importance of information systems for ambidextrous organizations’ agility.

Davenport and Prusak on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 7-11 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Early Years ◽  
Content Type ◽  
Potential Risks ◽  
The Relationship

Purpose Larry Prusak and Tom Davenport have long been leading voices in the knowledge management (KM) field. This interview aims to explore their views on the relationship between KM and big data/analytics. Design/methodology/approach An interview was conducted by email with Larry Prusak and Tom Davenport in 2015 and updated in 2016. Findings Prusak and Davenport hold differing views on the role of KM today. They also see the relationship between KM and big data/analytics somewhat differently. Davenport, in particular, has much to say on how big data/analytics can be best utilized by business as well as its potential risks. Originality/value It is important to understand how two of the most serious KM thinkers since the early years of KM understand the relationship between big data/analytics, KM and organizations. Their views can help shape thinking in these fields.

The framework of talent analytics using big data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Arnold Saputra ◽  
Gunawan Wang ◽  
Justin Zuopeng Zhang ◽  
Abhishek Behl
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Talent Management ◽  
Content Type ◽  
Using Data ◽  
Business Challenges ◽  
Practical Implications

PurposeThe era of work 4.0 demands organizations to expedite their digital transformation to sustain their competitive advantage in the market. This paper aims to help the human resource (HR) department digitize and automate their analytical processes based on a big-data-analytics framework.Design/methodology/approachThe methodology applied in this paper is based on a case study and experimental analysis. The research was conducted in a specific industry and focused on solving talent analysis problems.FindingsThis research conducts digital talent analysis using data mining tools with big data. The talent analysis based on the proposed framework for developing and transforming the HR department is readily implementable. The results obtained from this talent analysis using the big-data-analytics framework offer many opportunities in growing and advancing a company's talents that are not yet realized.Practical implicationsBig data allows HR to perform analysis and predictions, making more intelligent and accurate decisions. The application of big data analytics in an HR department has a significant impact on talent management.Originality/valueThis research contributes to the literature by proposing a formal big-data-analytics framework for HR and demonstrating its applicability with real-world case analysis. The findings help organizations develop a talent analytics function to solve future leaders' business challenges.

Dave Snowden on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 12-17 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theory And Practice ◽  
Content Type ◽  
Face To Face ◽  
The Relationship ◽  
A Company ◽  
Practical Implications ◽  
Scientific Officer

Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

Big data analytics capabilities and knowledge management: impact on firm performance

2019 ◽  
Vol 57 (8) ◽  
pp. 1923-1936 ◽  
Author(s):  
Alberto Ferraris ◽  
Alberto Mazzoleni ◽  
Alain Devalle ◽  
Jerome Couturier
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Structural Equation ◽  
Positive Impact ◽  
Big Data Analytics ◽  
Integrated Approach ◽  
Content Type ◽  
Decision Making Processes ◽  
External Sources

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-making processes, thereby improving performance. However, the management of the knowledge generated from the BDA as well as its integration and combination with firm knowledge have scarcely been investigated, despite an emergent need of a structured and integrated approach. The paper aims to discuss these issues. Design/methodology/approach Through an empirical analysis based on structural equation modelling with data collected from 88 Italian SMEs, the authors tested if BDA capabilities have a positive impact on firm performances, as well as the mediator effect of knowledge management (KM) on this relationship. Findings The findings of this paper show that firms that developed more BDA capabilities than others, both technological and managerial, increased their performances and that KM orientation plays a significant role in amplifying the effect of BDA capabilities. Originality/value BDA has the potential to change the way firms compete through better understanding, processing, and exploiting of huge amounts of data coming from different internal and external sources and processes. Some managerial and theoretical implications are proposed and discussed in light of the emergence of this new phenomenon.

How to measure knowledge management: dimensions and model

VINE ◽  
2015 ◽  
Vol 45 (1) ◽  
pp. 107-125 ◽  
Author(s):  
Mariano García-Fernández
Keyword(s):  
Knowledge Management ◽  
Literature Review ◽  
Design Methodology ◽  
Present Model ◽  
Content Type ◽  
Proposed Model ◽  
The Creation ◽  
And Storage ◽  
Practical Implications

Purpose – The aims of this paper are: to identify the dimensions of knowledge management (KM), and to propose a model for KM that will be useful for future researchers in carrying out KM measurement. Design/methodology/approach – The paper is based on a literature review of theoretical and empirical contributions to KM. Findings – The results obtained show that the creation, transfer and storage, and implementation and use are dimensions of the concept of KM. On the basis of these dimensions, this study proposes a model integrating these dimensions and operationalizes it using selected items, so that future researchers may carry out measurements using the proposed model. Practical implications – The study implies that companies and researchers use a smaller time in theoretical checks and can devote to measurements which develop improvements. Originality/value – The present model differs from other, previous models in that it integrates various approaches to the study of KM.

A knowledge management super-strategy fueled by big data

2019 ◽  
Vol 35 (1) ◽  
pp. 22-23
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Intellectual Capital ◽  
Management Strategy ◽  
Design Methodology ◽  
Business Value ◽  
Content Type ◽  
Pertinent Information ◽  
Conceptual Paper ◽  
Practical Implications

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings This conceptual paper proposes a model for growing company competitive advantage into the future by integrating a knowledge management strategy with progressive insights from Big Data and artificial intelligence. The ultimate strategic aim here is to create and codify intellectual capital that adds business value. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Analytics based decision making

2014 ◽  
Vol 6 (4) ◽  
pp. 332-340 ◽  
Author(s):  
Deepak Agrawal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Design Methodology ◽  
Historical Perspective ◽  
Big Data Analytics ◽  
Analytical Techniques ◽  
Content Type ◽  
Informed Decisions ◽  
Different Types

Purpose – This paper aims to trace the history, application areas and users of Classical Analytics and Big Data Analytics. Design/methodology/approach – The paper discusses different types of Classical and Big Data Analytical techniques and application areas from the early days to present day. Findings – Businesses can benefit from a deeper understanding of Classical and Big Data Analytics to make better and more informed decisions. Originality/value – This is a historical perspective from the early days of analytics to present day use of analytics.

Mediating effect of big data analytics on project performance of small and medium enterprises

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sachin K. Mangla ◽  
Rakesh Raut ◽  
Vaibhav S. Narwane ◽  
Zuopeng (Justin) Zhang ◽  
Pragati priyadarshinee
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Project Performance ◽  
Mediating Effect ◽  
Equation Modeling ◽  
Content Type ◽  
Operational Capabilities ◽  
Green Purchasing

PurposeThis study aims to investigate the mediating role of “Big Data Analytics” played between “Project Performance” and nine factors including top management, project knowledge management focus on sustainability, green purchasing, environmental technologies, social responsibility, project operational capabilities, project complexity, collaboration and explorative learning, and project success.Design/methodology/approachA sample of 321 responses from 106 Indian manufacturing small and medium-scaled enterprises (SMEs) was collected. Data were analyzed using empirical analysis through structural equation modeling.FindingsThe result shows that project knowledge management, green purchasing and project operational capabilities require the mediating support of big data analytics. The adoption of big data analytics has a positive influence on project performance in the manufacturing sector.Practical implicationsThis study is useful to SMEs managers, practitioners and government policymakers to develop an understanding of big data analytics, eliminate challenges in the adoption of big data, and formulate strategies to handle projects efficiently in SMEs in the context of Indian manufacturing.Originality/valueFor the first time, big data for manufacturing firms handing innovative projects was discussed in the Indian SME context.

Impacting Big Data analytics in higher education through Six Sigma techniques

2017 ◽  
Vol 66 (5) ◽  
pp. 662-679 ◽  
Author(s):  
Chad Laux ◽  
Na Li ◽  
Corey Seliger ◽  
John Springer
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Student Success ◽  
Research Design ◽  
Six Sigma ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Content Type ◽  
Conceptual Research

Purpose The purpose of this paper is to develop a framework for utilizing Six Sigma (SS) principles and Big Data analytics at a US public university for the improvement of student success. This research utilizes findings from the Gallup index to identify performance factors of higher education. The goal is to offer a reimagined SS DMAIC methodology that incorporates Big Data principles. Design/methodology/approach The authors utilize a conceptual research design methodology based upon theory building consisting of discovery, description, explanation of the disciplines of SS and Big Data. Findings The authors have found that the interdisciplinary approach to SS and Big Data may be grounded in a framework that reimagines the define, measure, analyze, improve and control (DMAIC) methodology that incorporates Big Data principles. The authors offer propositions of SS DMAIC to be theory tested in subsequent study and offer the practitioner managing the performance of higher education institutions (HEIs) indicators and examples for managing the student success mission of the organization. Research limitations/implications The study is limited to conceptual research design with regard to the SS and Big Data interdisciplinary research. For performance management, this study is limited to HEIs and non-FERPA student data. Implications of this study include a detailed framework for conducting SS Big Data projects. Practical implications Devising a more effective management approach for higher education needs to be based upon student success and performance indicators that accurately measure and support the higher education mission. A proactive approach should utilize the data rich environment being generated. The individual that is most successful in engaging and managing this effort will have the knowledge and skills that are found in both SS and Big Data. Social implications HEIs have historically been significant contributors to the development of meritocracy in democratic societies. Due to a variety of factors, HEIs, especially publicly funded institutions, have been under stress due to a reduction of public funding, resulting in more limited access to the public in which they serve. Originality/value This paper examines Big Data and SS in interdisciplinary effort, an important contribution to SS but lacking a conceptual foundation in the literature. Higher education, as an industry, lacks penetration and adoption of continuous improvement efforts, despite being under tremendous cost pressures and ripe for disruption.

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