scholarly journals A Big Data Architecture to Support Bank Digital Campaign

2019 ◽  
Vol 8 (2S7) ◽  
pp. 25-29
Keyword(s):  
Big Data ◽  
Credit Card ◽  
Latent Dirichlet Allocation ◽  
Time Windows ◽  
Shopping Center ◽  
Computing Power ◽  
Point Of Interest ◽  
Data Pipeline ◽  
Data Platform ◽  
Data Architecture

Bank marketers still have difficulties to find the best implementation for credit card promotion using above the line, particularly based on customers preferences in point of interest (POI) locations such as mall and shopping center. On the other hand, customers on those POIs are keen to have recommendation on what is being offered by the bank. On this paper we propose a design architecture and implementation of big data platform to support bank’s credit card’s program campaign that generating data and extracting topics from Twitter. We built a data pipeline that consist of a Twitter streamer, a text preprocessor, a topic extractor using Latent Dirichlet Allocation, and a dashboard that visualize the recommendation. As a result, we successfully generate topics that related to specific location in Jakarta during some time windows, that can be used as a recommendation for bank marketers to create promotion program for their customers. We also present the analysis of computing power usages that indicates the strategy is well implemented on the big data platform.

2. Why is big data special?

2017 ◽  
pp. 14-25
Author(s):  
Dawn E. Holmes
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Statistical Analysis ◽  
Rapid Growth ◽  
Credit Card ◽  
Fraud Detection ◽  
Small Data ◽  
Computing Power ◽  
Data Volume ◽  
And Storage

The rapid growth in computing power and storage has led to progressively more data being collected. Big datasets are certainly large and complex, but in order to fully define ‘big data’ we need first to understand ‘small data’ and its role in statistical analysis. ‘Why is big data special?’ considers the four main characteristics of big data: volume, variety, velocity, and veracity, which present a considerable challenge in data management. The advantages we expect to gain from meeting this challenge and the questions we hope to answer with big data can be understood through data mining. The use of big data mining in credit card fraud detection is discussed.

How strategists use “big data” to support internal business decisions, discovery and production

2014 ◽  
Vol 42 (4) ◽  
pp. 45-50 ◽  
Author(s):  
Thomas H. Davenport
Keyword(s):  
Big Data ◽  
Financial Services ◽  
Data Sets ◽  
Supporting Evidence ◽  
Business Decisions ◽  
Computing Power ◽  
Content Type ◽  
Product Service ◽  
Continuous Approach ◽  
Data Architecture

Purpose – The author, an internationally known IT expert, aims to explain how big data is being used by leading corporations to promote better decision making, especially about innovation. Design/methodology/approach – Big data is the collection and interpretation of massive data sets, made possible by vast computing power that monitors a variety of digital streams – such as sensors, marketplace interactions and social information exchanges – and analyses them using “smart” algorithms. It offers a promising new way to discover new opportunities to offer customers high-value products and services. Findings – Big data […] resembles not so much a pool of statistics as an ongoing, fast-flowing stream of information about customer choices. Therefore, a more continuous approach to sampling, analyzing and acting on data is necessary. Practical implications – A number of major financial services firms are using “customer journeys” through the tangle of websites, call centers, tellers and other branch personnel to better understand the paths that customers follow through the organization, and how those paths affect attrition or the purchase of particular financial services. Originality/value – The desired outcome of data discovery is an idea – a notion of a new product, service, or feature, or a hypothesis – with supporting evidence – that an existing model can be improved. Increasingly, corporate strategists are recognizing that big data architecture and management should be designed so that discovery and analysis is the first order of business.

scholarly journals A Proposed Integration Architecture for University Research Data Repository to Support University and University Hospital on Medical Digital Image Management and Analytics using Hadoop

2021 ◽  
Vol 12 (3) ◽  
pp. 2077-2082
Author(s):  
Iskandar Ishak Et.al
Keyword(s):  
Big Data ◽  
Research Data ◽  
University Hospital ◽  
Data Repository ◽  
Digital Image Management ◽  
Integration Architecture ◽  
Data Platform ◽  
Data Architecture ◽  
Site Access ◽  
The University

Big Data has been used in university and hospital due to its enormous potential in managing large volume and many types of data. However, university that also has hospitals may need to integrate their data repository to have a single site access for easier system administration and management. The needs of image analytics for both researchers in the university and physicians in the university hospital demand the need of Big Data platform such as Hadoop framework. Based on the literatures, there are no papers that describe in detail the integration of big data for university, which include its own teaching hospital. Therefore, this paper focuses on the proposed research data architecture for university and university hospital to support data repository for both with capability of image analytics using Hadoop technology.

Identifying Major Issues of Tourism Analysis Using Big Data : Focused on Mobile and Credit Card Data

2018 ◽  
Vol 30 (3) ◽  
pp. 3-22
Author(s):  
Won-Seop Shim ◽  
Seung-Mook Choi ◽  
Chang-Sup Shim
Keyword(s):  
Big Data ◽  
Credit Card ◽  
Credit Card Data

Navigating the Ethics of Big Data in Public Health

2019 ◽  
pp. 353-367
Author(s):  
Effy Vayena ◽  
Lawrence Madoff
Keyword(s):  
Public Health ◽  
Big Data ◽  
Credit Card ◽  
Health Sector ◽  
Public Health Research ◽  
Ethical Challenges ◽  
The Public ◽  
Health Community ◽  
Digital Disease Detection ◽  
The Public Sphere

“Big data,” which encompasses massive amounts of information from both within the health sector (such as electronic health records) and outside the health sector (social media, search queries, cell phone metadata, credit card expenditures), is increasingly envisioned as a rich source to inform public health research and practice. This chapter examines the enormous range of sources, the highly varied nature of these data, and the differing motivations for their collection, which together challenge the public health community in ethically mining and exploiting big data. Ethical challenges revolve around the blurring of three previously clearer boundaries: between personal health data and nonhealth data; between the private and the public sphere in the online world; and, finally, between the powers and responsibilities of state and nonstate actors in relation to big data. Considerations include the implications for privacy, control and sharing of data, fair distribution of benefits and burdens, civic empowerment, accountability, and digital disease detection.

Procurement Analytics: Enabling Data Architectures in the Age of Big Data

2020 ◽  
Author(s):  
Michael Goul ◽  
T. S. Raghu ◽  
Ziru Li
Keyword(s):  
Big Data ◽  
Academic Research ◽  
Research Literature ◽  
Structured Interviews ◽  
Data Space ◽  
Prescriptive Analytics ◽  
Data Architecture ◽  
The Future ◽  
High Level ◽  
Area Data

As procurement organizations increasingly move from a cost-and-efficiency emphasis to a profit-and-growth emphasis, flexible data architecture will become an integral part of a procurement analytics strategy. It is therefore imperative for procurement leaders to understand and address digitization trends in supply chains and to develop strategies to create robust data architecture and analytics strategies for the future. This chapter assesses and examines the ways companies can organize their procurement data architectures in the big data space to mitigate current limitations and to lay foundations for the discovery of new insights. It sets out to understand and define the levels of maturity in procurement organizations as they pertain to the capture, curation, exploitation, and management of procurement data. The chapter then develops a framework for articulating the value proposition of moving between maturity levels and examines what the future entails for companies with mature data architectures. In addition to surveying the practitioner and academic research literature on procurement data analytics, the chapter presents detailed and structured interviews with over fifteen procurement experts from companies around the globe. The chapter finds several important and useful strategies that have helped procurement organizations design strategic roadmaps for the development of robust data architectures. It then further identifies four archetype procurement area data architecture contexts. In addition, this chapter details exemplary high-level mature data architecture for each archetype and examines the critical assumptions underlying each one. Data architectures built for the future need a design approach that supports both descriptive and real-time, prescriptive analytics.

Construction of a multi-source heterogeneous hybrid platform for big data

2021 ◽  
pp. 1-10
Author(s):  
Ying Wang ◽  
Yiding Liu ◽  
Minna Xia
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Forest Fire ◽  
Original Data ◽  
Big Data Analysis ◽  
Multiple Sources ◽  
Data Types ◽  
Fire Monitoring ◽  
Data Platform

Big data is featured by multiple sources and heterogeneity. Based on the big data platform of Hadoop and spark, a hybrid analysis on forest fire is built in this study. This platform combines the big data analysis and processing technology, and learns from the research results of different technical fields, such as forest fire monitoring. In this system, HDFS of Hadoop is used to store all kinds of data, spark module is used to provide various big data analysis methods, and visualization tools are used to realize the visualization of analysis results, such as Echarts, ArcGIS and unity3d. Finally, an experiment for forest fire point detection is designed so as to corroborate the feasibility and effectiveness, and provide some meaningful guidance for the follow-up research and the establishment of forest fire monitoring and visualized early warning big data platform. However, there are two shortcomings in this experiment: more data types should be selected. At the same time, if the original data can be converted to XML format, the compatibility is better. It is expected that the above problems can be solved in the follow-up research.

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