scholarly journals Characterizing Big Data Management

10.28945/2204 ◽  
2015 ◽  
Vol 12 ◽  
pp. 165-180 ◽  
Author(s):  
Rogério Rossi ◽  
Kechi Hirama
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Management ◽  
Large Volume ◽  
Process Management ◽  
Three Dimensions ◽  
Storage And Retrieval ◽  
Human Aspects ◽  
Business Approach ◽  
Support Decision Making

Big data management is a reality for an increasing number of organizations in many areas and represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial to facilitate the management of big data in any kind of organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management can be supported by these three dimensions: technology, people and processes. Hence, this article discusses these dimensions: the technological dimension that is related to storage, analytics and visualization of big data; the human aspects of big data; and, in addition, the process management dimension that involves in a technological and business approach the aspects of big data management.

scholarly journals Characterizing Big Data Management

10.28945/2192 ◽  
2015 ◽  
Author(s):  
Rogério Rossi ◽  
Kechi Hirama
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Big Data ◽  
Data Management ◽  
Process Management ◽  
Three Dimensions ◽  
Storage And Retrieval ◽  
Human Aspects ◽  
Business Approach ◽  
Support Decision Making

[The final form of this paper was published in the journal Issues in Informing Science and Information Technology.] Considering that big data is a reality for an increasing number of organizations in many areas, its management represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial dimensions to facilitate the management of big data in any organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management must be supported by technology, people and processes; hence, this article discusses these three dimensions: the technologies for storage, analysis and visualization of big data; the human aspects of big data; and, in addition, the process management involved in a technological and business approach for big data management.

scholarly journals Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: A dynamic capabilities view

2019 ◽  
Vol 56 (6) ◽  
pp. 103135 ◽  
Author(s):  
Saqib Shamim ◽  
Jing Zeng ◽  
Syed Muhammad Shariq ◽  
Zaheer Khan
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Management ◽  
Dynamic Capabilities ◽  
Chinese Firms

A Conceptual Model for Describing the Integration of Decision Aspect into Big Data

2022 ◽  
pp. 294-318
Author(s):  
Fatma Chiheb ◽  
Fatima Boumahdi ◽  
Hafida Bouarfa
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Competitive Advantage ◽  
Conceptual Model ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Modern Business ◽  
Definition Of ◽  
A Company ◽  
Support Decision Making

Big Data is an important topic for discussion and research. It has gained this importance due to the meaningful value that could be extracted from these data. The application of Big Data in the modern business allows enterprises to take faster and smarter decisions, achieving a real competitive advantage. However, a lot of Big Data projects provide disappointing results that don't address the decision-makers' needs due to many reasons. The main reason for this failure can be summarized in neglecting the study of the decision-making aspect of these projects. In light of this challenge, this study proposes the integration of decision aspect into Big Data as a solution. Therefore, this article presents three main contributions: 1) Clarify the definition of Big Data; 2) Presents BD-Da model, a conceptual model describes the levels that should be considered to develop a Big Data project aiming to solve a problem that calls a decision; 3) Describes a particular, logical, requirements-like approach that explains how a company develops a Big Data analytics project to support decision-making.

ISSUES AND CHALLENGES IN BUSINESS INTELLIGENCE CASE STUDIES

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Azizah Abdul Rahman ◽  
Nooradilla Abu Hasan ◽  
Norminshah A. Lahad
Keyword(s):  
Decision Making ◽  
Data Management ◽  
Case Studies ◽  
Business Intelligence ◽  
Qualitative Method ◽  
User Acceptance ◽  
Three Dimensions ◽  
Implementation Project ◽  
Organization Process ◽  
The Impact

Business Intelligence (BI) has embarked on decision-making setting. This has influenced many organizations from different industries that are located in diverse regions to implement BI. Critical Successful Factors (CSF) becomes the guideline for the implementer to adopt BI successfully. However, lack of BI knowledge and weak consideration of BI CSF led to the failure of BI implementation project. Several issues and challenges have been identified during the BI implementation. In addition, other researchers rarely discussed this subject. Thus, the objective of this paper is to recognize the issues and challenges on BI implementation. Through qualitative method, BI practices systematically described the purpose of BI execution on selected organizations, industries and regions. It has given the path towards the issues and challenges of BI implementation. The identified issues and challenges are defining the business goal, data management, limited funding, training and user acceptance as well as the lack of expertise issues. The findings categorized the issues and challenges into three dimensions of CSF for BI implementation, which are Organization, Process and Technology dimension. Limitation in this study requires future researchers to study in details of these issues and challenges including the solutions and the impact of BI implementation.

A Conceptual Model for Describing the Integration of Decision Aspect into Big Data

2019 ◽  
Vol 10 (4) ◽  
pp. 1-23
Author(s):  
Fatma Chiheb ◽  
Fatima Boumahdi ◽  
Hafida Bouarfa
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Conceptual Model ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Modern Business ◽  
Definition Of ◽  
A Company ◽  
Support Decision Making

Big Data is an important topic for discussion and research. It has gained this importance due to the meaningful value that could be extracted from these data. The application of Big Data in the modern business allows enterprises to take faster and smarter decisions, achieving a real competitive advantage. However, a lot of Big Data projects provide disappointing results that don't address the decision-makers' needs due to many reasons. The main reason for this failure can be summarized in neglecting the study of the decision-making aspect of these projects. In light of this challenge, this study proposes the integration of decision aspect into Big Data as a solution. Therefore, this article presents three main contributions: 1) Clarify the definition of Big Data; 2) Presents BD-Da model, a conceptual model describes the levels that should be considered to develop a Big Data project aiming to solve a problem that calls a decision; 3) Describes a particular, logical, requirements-like approach that explains how a company develops a Big Data analytics project to support decision-making.

Mining big data to support decision making in healthcare

2016 ◽  
Vol 18 (3) ◽  
pp. 141-154 ◽  
Author(s):  
Ajaya K. Swain
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Support Decision Making

scholarly journals BIG DATA AND DECISION-MAKING SUPPORT

2021 ◽  
Vol 2 ◽  
pp. 87-92
Author(s):  
Peter Procházka
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Methodological Approach ◽  
Timely Manner ◽  
Domestic Literature ◽  
Risk Definition ◽  
Similar Solutions ◽  
Support Decision Making ◽  
Big Data Technology

INTRODUCTION: Nowadays, Big Data is created in previously unimaginable quantities. Newly generated data from various Internet of Things (IoT) sensors and their use have never reached their current dimensions. Along with this trend, the availability of devices capable of collecting this data increases, the time for their evaluation is reduced and the volume of data collected at the same time increases. The most important task of research and development in this area is to bring solutions suitable for processing large amounts of data because our current storage and processing capabilities are limited and unable to compete with the storage, processing and publication of the resulting data. OBJECTIVES: Point out the importance of implementing Big Data technology. METHODS: To achieve the goal, the following methodological approach was chosen: study and processing of foreign and domestic literature, acquaintance with similar solutions for data processing, definition of Big Data and IoT, proposal for using Big Data solution to support decision-making, risk definition and evaluation. RESULTS: With the growing amount of disparate and incoherent data and the further growth of the Internet of Things, it is now almost impossible to evaluate all available information correctly and in a timely manner. Without this knowledge, the company loses its competitive advantage and is unable to respond in a timely manner to client requests. CONCLUSION: Implementing a solution for processing Big Data to support decision-making in the company is a complex process. As part of the implementation and use of the Big Data solution to support decision-making, the company must be prepared for the emergence of various problems. We can assume that Big Data technology will constantly be evolving in terms of streamlining analytical tools for obtaining information from large volumes of generated data. Therefore, it is appropriate to create space for the implementation of Big Data technology.

scholarly journals APPLICATION OF TRANSPORTATION BIG DATA TO SUPPORT DECISION-MAKING FOR ARCHITECTURE TEAMS: PROCESSES AND EXPERIENCES FROM TWO CASE STUDIES

2019 ◽  
Author(s):  
LADISLAVA FIALKA SOBKOVÁ ◽  
MICHAL ČERTICKÝ ◽  
ŠIMON JIRÁČEK
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Case Studies ◽  
Support Decision Making

scholarly journals Big data and risk management in business processes: implications for corporate real estate

2019 ◽  
Vol 26 (5) ◽  
pp. 1141-1155 ◽  
Author(s):  
Enrico Battisti ◽  
S.M. Riad Shams ◽  
Georgia Sakka ◽  
Nicola Miglietta
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Big Data ◽  
Real Estate ◽  
Business Strategy ◽  
Process Management ◽  
Business Processes ◽  
Content Type ◽  
Data Intensive ◽  
Corporate Real Estate

Purpose The purpose of this paper is to improve understanding of the integration between big data (BD) and risk management (RM) in business processes (BPs), with special reference to corporate real estate (CRE). Design/methodology/approach This conceptual study follows, methodologically, the structuring inter-textual coherence process – specifically, the synthesised coherence tactical approach. It draws heavily on theoretical evidence published, mainly, in the corporate finance and the business management literature. Findings A new conceptual framework is presented for CRE to proactively develop insights into the potential benefits of using BD as a business strategy/instrument. The approach was found to strengthen decision-making processes and encourage better RM – with significant consequences, in particular, for business process management (BPM). Specifically, by recognising the potential uses of BD, it is also possible to redefine the processes with advantages in terms of RM. Originality/value This study contributes to the literature in the fields of real estate, RM, BPM and digital transformation. To the best knowledge of authors, although the literature has examined the concepts of BD, RM and BP, no prior studies have comprehensively examined these three elements and their conjoint contribution to CRE. In particular, the study highlights how the automation of data-intensive activities and the analysis of such data (in both structured and unstructured forms), as a means of supporting decision making, can lead to better efficiency in RM and optimisation of processes.

The big data regime shift in real estate

2020 ◽  
Vol 38 (4) ◽  
pp. 363-395 ◽  
Author(s):  
James R. DeLisle ◽  
Brent Never ◽  
Terry V. Grissom
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Real Estate ◽  
Urban Areas ◽  
Regime Shift ◽  
Design Thinking ◽  
Future Research ◽  
Content Type ◽  
Data Movement ◽  
Support Decision Making

PurposeThe paper explores the emergence of the “big data regime” and the disruption that it is causing for the real estate industry. The paper defines big data and illustrates how an inductive, big data approach can help improve decision-making.Design/methodology/approachThe paper demonstrates how big data can support inductive reasoning that can lead to enhanced real estate decisions. To help readers understand the dynamics and drivers of the big data regime shift, an extensive list of hyperlinks is included.FindingsThe paper concludes that it is possible to blend traditional and non-traditional data into a unified data environment to support enhanced decision-making. Through the application of design thinking, the paper illustrates how socially responsible development can be targeted to under-served urban areas and helps serve residents and the communities in which they live.Research limitations/implicationsThe paper demonstrates how big data can be harnessed to support decision-making using a hypothetical project. The paper does not present advanced analytics but focuses aggregating disparate longitudinal data that could support such analysis in future research.Practical implicationsThe paper focuses on the US market, but the methodology can be extended to other markets where big data is increasingly available.Social implicationsThe paper illustrates how big data analytics can be used to help serve the needs of marginalized residents and tenants, as well as blighted areas.Originality/valueThis paper documents the big data movement and demonstrates how non-traditional data can support decision-making.

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