Philosophising Data

Big Data ◽  
2016 ◽  
pp. 302-313
Author(s):  
Jackie Campbell ◽  
Victor Chang ◽  
Amin Hosseinian-Far
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Collection ◽  
Data Visualisation ◽  
Business Decisions ◽  
Human Skill ◽  
Use Of Data ◽  
Market Trends ◽  
Scale Data ◽  
Current Practices

This chapter aims to critically reflect on the processes, agendas and use of Big Data by presenting existing issues and problems in place and consolidating our points of views presented from different angles. This chapter also describes current practices of handling Big Data, including considerations of smaller scale data analysis and the use of data visualisation to improve business decisions and prediction of market trends. The chapter concludes that alongside any data collection, analysis and visualisation, the ‘researcher' should be fully aware of the limitations of the data, by considering the data from different perspectives, angles and lenses. Not only will this add the validation and validity of the data, but it will also provide a ‘thinking tool' by which to explore the data. Arguably providing the ‘human skill' required in a process apparently destined to be automated by machines and algorithms.

Philosophising Data

2015 ◽  
Vol 5 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Jackie Campbell ◽  
Victor Chang ◽  
Amin Hosseinian-Far
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Collection ◽  
Data Visualisation ◽  
Business Decisions ◽  
Human Skill ◽  
Use Of Data ◽  
Market Trends ◽  
Scale Data ◽  
Current Practices

This paper aims to critically reflect on the processes, agendas and use of Big Data by presenting existing issues and problems in place and consolidating our points of views presented from different angles. This paper also describes current practices of handling Big Data, including considerations of smaller scale data analysis and the use of data visualisation to improve business decisions and prediction of market trends. The paper concludes that alongside any data collection, analysis and visualisation, the ‘researcher' should be fully aware of the limitations of the data, by considering the data from different perspectives, angles and lenses. Not only will this add the validation and validity of the data, but it will also provide a ‘thinking tool' by which to explore the data. Arguably providing the ‘human skill' required in a process apparently destined to be automated by machines and algorithms.

scholarly journals PENANAMAN NILAI-NILAI MORAL OLEH ORANG TUA DALAM KELUARGA MENURUT REMAJA DI JORONG KUBANG KENAGARIAN MAGEK KECAMATAN KAMANG MAGEK KABUPATEN AGAM

2014 ◽  
Vol 2 (1) ◽  
Author(s):  
Fajrin Alfarabi
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Moral Values ◽  
Use Of Data ◽  
The Family

This research in the background by the availability of good in JorongKubang.This study aims to describe the cultivation of moral values ​​in children by teenagers in the family in JorongKubangKenagarianMagekKecamatanKamangMagekKabupatenAgam. This research is descriptive quantitative. The population in this study were all teenagers at Jorongkubang totaling 26 people, A survey of data collection with the use of data collection tools and quisioner. While the techniques of data analysis using the percentage formula.From the results of the study found that the cultivation of moral values ​​in children in aspects: (1) through habituation, (2) by example, (3) through advice, (4) through attention and (5) through rulemaking. From the above findings it can be concluded that the cultivation of moral values ​​in children by adolescents has been running well this is evident from the results of the percentage of each variable is declared good. General suggestion that the cultivation of moral values ​​can be enhanced and become a major concern by the parents in the family

scholarly journals PERAN KEPUASAN KONSUMEN MEMEDIASI HUBUNGAN KUALITAS PRODUK DAN PERSEPSI HARGA DENGAN KEPUTUSAN PEMBELIAN ULANG

2020 ◽  
Vol 9 (4) ◽  
pp. 1379
Author(s):  
Ida Ayu Prayoni ◽  
Ni Nyoman Rsi Respati
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Customer Satisfaction ◽  
Product Quality ◽  
Consumer Satisfaction ◽  
Purchase Decisions ◽  
Price Perception ◽  
Analysis Techniques ◽  
Probability Methods ◽  
Scale Data

This study aims to determine the effect of product quality and price perception on consumer satisfaction and repurchase decisions on consumers who buy and use Pepsodent toothpaste. This research was conducted in Denpasar City with a sample size of 105 respondents with non-probability methods in the form of purposive sampling. Data collection was carried out through questionnaires with as many as 15 indicators measured using a Likert scale. Data analysis techniques used in this study are path analysis techniques with classical assumption test and sobel test. The results of the study stated that the variables of customer satisfaction, product quality variables and price perception variables had a positive and significant effect on repurchase decisions. Keywords: product quality, price perception, customer satisfaction, repeat purchase decisions

scholarly journals Influencing Factors of e-Commerce Enterprise Development Based on Mobile Computing Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yixue Zhu ◽  
Boyue Chai
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Big Data Analysis ◽  
Support Vector ◽  
Data Sets ◽  
Large Scale Data ◽  
Vector Machines ◽  
Physical Information ◽  
Scale Data

With the development of increasingly advanced information technology and electronic technology, especially with regard to physical information systems, cloud computing systems, and social services, big data will be widely visible, creating benefits for people and at the same time facing huge challenges. In addition, with the advent of the era of big data, the scale of data sets is getting larger and larger. Traditional data analysis methods can no longer solve the problem of large-scale data sets, and the hidden information behind big data is digging out, especially in the field of e-commerce. We have become a key factor in competition among enterprises. We use a support vector machine method based on parallel computing to analyze the data. First, the training samples are divided into several working subsets through the SOM self-organizing neural network classification method. Compared with the ever-increasing progress of information technology and electronic equipment, especially the related physical information system finally merges the training results of each working set, so as to quickly deal with the problem of massive data prediction and analysis. This paper proposes that big data has the flexibility of expansion and quality assessment system, so it is meaningful to replace the double-sidedness of quality assessment with big data. Finally, considering the excellent performance of parallel support vector machines in data mining and analysis, we apply this method to the big data analysis of e-commerce. The research results show that parallel support vector machines can solve the problem of processing large-scale data sets. The emergence of data dirty problems has increased the effective rate by at least 70%.

scholarly journals Peran Strategi Bisnis Dalam Meningkatkan Keunggulan Bersaing di Industri Fashion

2019 ◽  
Vol 5 (1) ◽  
pp. 48
Author(s):  
Linda Maryani ◽  
Harmon Chaniago
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Competitive Advantage ◽  
Business Strategy ◽  
Goodness Of Fit ◽  
Likert Scale ◽  
Creative Industry ◽  
Research Model ◽  
Goodness Of Fit Test ◽  
Scale Data

This research aims to analyze the influence of business strategy in increasing competitive advantage. Selected population is the Creative Industry SMEs in the fashion sector in Bandung, amounting to 507 population in the period 2010-2017 and the number of samples of 223 SMEs fashion business in Bandung. Data collection using questionnaires with Likert scale. Data analysis using Regression Linear with SPSS 22.0. The result of data analysis shows that the research model can be accepted with goodness of fit test, that is effect positive and significant to competitive advantage.

Big data for small and medium-sized enterprises (SME): a knowledge management model

2020 ◽  
Vol 24 (4) ◽  
pp. 881-897 ◽  
Author(s):  
Shouhong Wang ◽  
Hai Wang
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analysis ◽  
Qualitative Data ◽  
Project Planning ◽  
Content Type ◽  
Use Of Data ◽  
Proposed Model ◽  
New Knowledge ◽  
Synergistic Relationship

Purpose Big data has raised challenges and opportunities for business, the information technology (IT) industry and research communities. Nowadays, small and medium-sized enterprises (SME) are dealing with big data using their limited resources. The purpose of this paper is to describe the synergistic relationship between big data and knowledge management (KM), analyze the challenges and IT solutions of big data for SME and derives a KM model of big data for SME based on the collected real-world business cases. Design/methodology/approach The study collects eight well-documented cases of successful big data analytics in SME and conducts a qualitative data analysis of these cases in the context of KM. The qualitative data analysis of the multiple cases reveals a KM model of big data for SME. Findings The proposed model portrays the synergistic relationship between big data and KM. It indicates that strategic use of data, knowledge guided big data project planning, IT solutions for SME and new knowledge products are the major constructs of KM of big data for SME. These constructs form a loop through the causal relationships between them. Research limitations/implications The number of cases used for the derivation of the KM model is not large. The coding of these qualitative data could involve biases and errors. Consequently, the conceptual KM model proposed in this paper is subject to further verification and validation. Practical implications The proposed model can guide SME to exploit big data for business by placing emphasis on KM instead of sophisticated IT techniques or the magnitude of data. Originality/value The study contributes to the KM literature by developing a theoretical model of KM of big data for SME based on underlying dimensions of strategic use of data, knowledge guided big data project planning, IT solutions for SME and new knowledge products.

Geospatial Data Analysis: A Review of Theory and Methods

2017 ◽  
Author(s):  
Prof. Rajagopalan S ◽  
Yogalakshmi Jayabal
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Collection ◽  
Geospatial Data ◽  
Data Generation ◽  
Vast Amount ◽  
Temporal Aspect ◽  
Geospatial Data Collection

A vast amount of data is generated and collected every moment and often, data has a spatial and/or temporal aspect. This increasing data generation and collection is resulting in increasing volume and varying formats of data being collected and the geospatial data collection is no exception. This posses challenges in storing, processing, analyzing and visualizing the geospatial data. This paper discusses the big data paradigm of the geospatial data and presents a taxonomy for analysis of the geospatial data. The existing literature is studied and discussed based on the proposed taxonomy for analysis of geospatial data.

First of All, Understand Data Analytics Context and Changes

2019 ◽  
pp. 92-124
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Processing ◽  
Large Data ◽  
Big Data Analysis ◽  
Raw Material ◽  
Use Of Data ◽  
Broad Term ◽  
Major Turning Point ◽  
Data Collections

Big data marks a major turning point in the use of data and is a powerful vehicle for growth and profitability. A comprehensive understanding of a company's data, its potential can be a new vector for performance. It must be recognized that without an adequate analysis, our data are just an unusable raw material. In this context, the traditional data processing tools cannot support such an explosion of volume. They cannot respond to new needs in a timely manner and at a reasonable cost. Big data is a broad term generally referring to very large data collections that impose complications on analytics tools for harnessing and managing such. This chapter details what big data analysis is. It presents the development of its applications. It is interested in the important changes that have touched the analytics context.

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