scholarly journals A new theoretical understanding of big data analytics capabilities in organizations: a thematic analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Renu Sabharwal ◽  
Shah Jahan Miah
Keyword(s):  
Big Data ◽  
Thematic Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Development Theory ◽  
Research Model ◽  
Theoretical Understanding ◽  
The Past ◽  
Validity Assessment

AbstractBig Data Analytics (BDA) usage in the industry has been increased markedly in recent years. As a data-driven tool to facilitate informed decision-making, the need for BDA capability in organizations is recognized, but few studies have communicated an understanding of BDA capabilities in a way that can enhance our theoretical knowledge of using BDA in the organizational domain. Big Data has been defined in various ways and, the past literature about the classification of BDA and its capabilities is explored in this research. We conducted a literature review using PRISMA methodology and integrated a thematic analysis using NVIVO12. By adopting five steps of the PRISMA framework—70 sample articles, we generate five themes, which are informed through organization development theory, and develop a novel empirical research model, which we submit for validity assessment. Our findings improve effectiveness and enhance the usage of BDA applications in various Organizations.

Extending The Theoretical Understanding of Big Data Analytics Capabilities In Organizations: A Thematic Analysis

2021 ◽  
Author(s):  
Renu Sabharwal ◽  
Shah Jahan Miah
Keyword(s):  
Big Data ◽  
Thematic Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Development Theory ◽  
Research Model ◽  
Theoretical Understanding ◽  
The Past ◽  
Validity Assessment

Abstract Big Data Analytics (BDA) usage in industry has been increased markedly in recent years. As a data-driven tool to facilitate informed decision making, the need for BDA capability in organizations is recognized, but few studies have communicated an understanding of BDA capabilities in a way that can enhance our theoretical knowledge of using BDA in organizational domain. Big Data has been defined in various ways and , the past literature about classification of BDA and its capabilities is explored in this research . We conduct a literature review using PRISMA methodology, and integrate a thematic analysis using NVIVO12. By adopting five steps of PRISMA framework - , 70 sample articles we generate five themes, which informed through organization development theory, and develop a novel empirical research model which we submit for validity assessment. Our findings improve effectiveness and enhance the usage of BDA applications in various Organizations.

scholarly journals Extending Theoretical Understanding for Defining the Capabilities of Big Data Analytics in Organisations: a Thematic Analysis

2021 ◽  
Author(s):  
Renu Sabharwal ◽  
Shah Jahan Miah
Keyword(s):  
Big Data ◽  
Thematic Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Development Theory ◽  
Theoretical Understanding ◽  
Organisation Development ◽  
Validity Assessment ◽  
Academic Researchers

Abstract Big Data Analytics (BDA) have been proliferated to academic researchers and industry practitioners over the past few years. As a prominent data-driven decision application, the BDA capabilities in organisation have been recognised, but limited studies have successfully attempted to communicate an authentic understanding on BDA capabilities that may enhance the current theoretical knowledge. While big data have been defined in various ways with its characteristics of shared definitions, it is important to explore the classification of BDA and its capabilities considering its advantageous opportunities. This study conducts a review study adopting the well-known PRISMA methodology, integrating a thematic analysis approach using NVIVO12. The study analyses 70 elected sample articles for generating new insights of BDA, informing through organisation development theory and leading to this an empirical research model is outlined for further validity assessment. It is anticipated that the findings would be contributing to address dynamic clarity and relevance of adopting BDA application.

Classification of Pests for Rice Crop Using Big Data Analytics

2019 ◽  
Vol 8 (3) ◽  
pp. 27-31
Author(s):  
R. P. L. Durgabai ◽  
P. Bhargavi ◽  
S. Jyothi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Andhra Pradesh ◽  
Big Data Analytics ◽  
Rice Crop ◽  
Strategic Decisions ◽  
High Productivity ◽  
Almost All ◽  
Big Data Technology

Data, in today’s world, is essential. The Big Data technology is rising to examine the data to make fast insight and strategic decisions. Big data refers to the facility to assemble and examine the vast amounts of data that is being generated by different departments working directly or indirectly involved in agriculture. Due to lack of resources the pest analysis of rice crop is in poor condition which effects the production. In Andhra Pradesh rice is cultivated in almost all the districts. The goal is to provide better solutions for finding pest attack conditions in all districts using Big Data Analytics and to make better decisions on high productivity of rice crop in Andhra Pradesh.

scholarly journals Adopt Big-Data Analytics to Explore and Exploit the New Value for Service Innovation

2020 ◽  
Vol 9 (3) ◽  
pp. 29 ◽  
Author(s):  
Nopsaran Thuethongchai ◽  
Tatri Taiphapoon ◽  
Achara Chandrachai ◽  
Sipat Triukose
Keyword(s):  
Big Data ◽  
Mixed Methods Research ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Service Innovation ◽  
Development Theory ◽  
Service Development ◽  
Structured Interviews ◽  
Opportunity Identification ◽  
Service Channel

Big-data analytics is gaining substantial attention due to its contribution to the process of determining business strategy and providing valuable information for the design and development of service innovation. The principal objective of this research is to study the adoption of big-data analytics for service innovation. The focus will be on leveraging features of data analytics to capture genuine customer’s requirements from the communication data through the digital service channel. This study used mixed methods research of documentary research, with supplementary semi-structured interviews. The interviews were conducted with 11 executive managements who have more than ten years of experience in data analytics or service development. The result of the research found that organizations in the services industry are using big data analytics to build capabilities to gain competitive advantages as well as the ability to rapidly and accurately respond to the market’s demands. The process of adopting big-data analytics for service innovation described in this article consists of seven essential procedural steps that impact the success of the development of service innovation, and also considered with the objective of increasing effectiveness in opportunity identification and reduce complexity in the fuzzy frond-end service innovation development theory.

Quality dominant logic in big data analytics and firm performance

2019 ◽  
Vol 25 (3) ◽  
pp. 512-532 ◽  
Author(s):  
Samuel Fosso Wamba ◽  
Shahriar Akter ◽  
Marc de Bourmont
Keyword(s):  
Big Data ◽  
Firm Performance ◽  
Data Analytics ◽  
Online Survey ◽  
Information Quality ◽  
Big Data Analytics ◽  
Firm Strategy ◽  
Theoretical Understanding ◽  
Content Type ◽  
The Relationship

Purpose Big data analytics (BDA) gets all the attention these days, but as important—and perhaps even more important—is big data analytics quality (BDAQ). Although many companies realize a full return from BDA, others clearly struggle. It appears that quality dynamics and their holistic impact on firm performance are unresolved in data economy. The purpose of this paper is to draw on the resource-based view and information systems quality to develop a BDAQ model and measure its impact on firm performance. Design/methodology/approach The study uses an online survey to collect data from 150 panel members in France from a leading market research firm. The participants in the study were business analysts and IT managers with analytics experience. Findings The study confirms that perceived technology, talent and information quality are significant determinants of BDAQ. It also identifies that alignment between analytics quality and firm strategy moderates the relationship between BDAQ and firm performance. Practical implications The findings inform practitioners that BDAQ is a hierarchical, multi-dimensional and context-specific model. Originality/value The study advances theoretical understanding of the relationship between BDAQ and firm performance under the influence of firm strategy alignment.

scholarly journals Review on Big Data & Analytics – Concepts, Philosophy, Process and Applications

2017 ◽  
Vol 17 (2) ◽  
pp. 3-27 ◽  
Author(s):  
Kari Venkatram ◽  
Mary A. Geetha
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Review Article ◽  
Cutting Edge ◽  
Competitive Edge ◽  
The Past ◽  
Past Data ◽  
State Of Art

Abstract Big Data analytics has been the main focus in all the industries today. It is not overstating that if an enterprise is not using Big Data analytics, it will be a stray and incompetent in their businesses against their Big Data enabled competitors. Big Data analytics enables business to take proactive measure and create a competitive edge in their industry by highlighting the business insights from the past data and trends. The main aim of this review article is to quickly view the cutting-edge and state of art work being done in Big Data analytics area by different industries. Since there is an overwhelming interest from many of the academicians, researchers and practitioners, this review would quickly refresh and emphasize on how Big Data analytics can be adopted with available technologies, frameworks, methods and models to exploit the value of Big Data analytics.

scholarly journals General Psychology as Common Ground and Point of View: Enduring and Evolving Features

2020 ◽  
Vol 24 (1) ◽  
pp. 6-17 ◽  
Author(s):  
Lisa M. Osbeck
Keyword(s):  
Big Data ◽  
Philosophy Of Science ◽  
Data Analytics ◽  
Common Ground ◽  
Big Data Analytics ◽  
Point Of View ◽  
Critical Psychology ◽  
General Psychology ◽  
The Past

The article draws from historical and contemporary resources to articulate the enduring or persistent responsibilities of general psychology, suggesting “common ground” and “point of view” as useful concepts in line with these. It then explores three important developments in the discipline over the past several decades—big data analytics, methodological proliferation, and critical psychology—and considers the role of general psychology in relation to these developments. The point of the article is to claim and illustrate that general psychology includes a philosophy of science from within, and that it has lasting importance to the broader discipline, even as the discipline itself transforms.

An Analysis of Big Data Analytics

2021 ◽  
pp. 203-230
Author(s):  
Vijander Singh ◽  
Amit Kumar Bairwa ◽  
Deepak Sinwar
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Past Century ◽  
Data Generation ◽  
Microsoft Excel ◽  
Web Based ◽  
The Past ◽  
Digital World ◽  
World Information

In the development of the advanced world, information has been created each second in numerous regions like astronomy, social locales, medical fields, transportation, web-based business, logical research, horticulture, video, and sound download. As per an overview, in 60 seconds, 600+ new clients on YouTube and 7 billion queries are executed on Google. In this way, we can say that the immense measure of organized, unstructured, and semi-organized information are produced each second around the cyber world, which should be managed efficiently. Big data conveys properties such as unpredictability, 'V' factor, multivariable information, and it must be put away, recovered, and dispersed. Logical arranged data may work as information in the field of digital world. In the past century, the sources of data as to size were very limited and could be managed using pen and paper. The next generation of data generation tools include Microsoft Excel, Access, and database tools like SQL, MySQL, and DB2.

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