Big Data, Small Credit: The Digital Revolution and Its Impact on Emerging Market Consumers

This research report sheds light on a new cadre of technology companies who are disrupting the credit scoring business in emerging markets. Using non-financial data -- such as social media activity and mobile phone usage patterns -- complex algorithms and big data analytics are forever changing the economics of how we identify, score, and underwrite credit to consumers who have been invisible to lenders until now

Challenges of Big Data Analysis

Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article give overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasis on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions

Dependency Grammar Induction with Neural Lexicalization and Big Training Data

We study the impact of big models (in terms of the degree of lexicalization) and big data (in terms of the training corpus size) on dependency grammar induction. We experimented with L-DMV, a lexicalized version of Dependency Model with Valence and L-NDMV, our lexicalized extension of the Neural Dependency Model with Valence. We find that L-DMV only benefits from very small degrees of lexicalization and moderate sizes of training corpora. L-NDMV can benefit from big training data and lexicalization of greater degrees, especially when enhanced with good model initialization, and it achieves a result that is competitive with the current state-of-the-art.Comment: EMNLP 201

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Exploring human capital: discrimination factors and group-specific performance in the football industry

The aim of the study is to investigate whether discrimination factors exist within professional football clubs, concerning the management of their human capital, by analysing the correlation between the footballers’ wages and their performance. An analysis was conducted to show that discrimination, based both on nationality and race, can affect the strategies adopted by football club managers and in the professional footballer labour market, where players are considered to be the human capital of football enterprises. The research framework consists of an analysis of the existing literature on discrimination in sports and of a quantitative analysis based on an exploratory approach, where the wage differences among Italian Serie A league footballers are compared to the performance of each group of players (organised by race or nationality). The results of the analysis of data for all Italian Serie A clubs show that discrimination (in pay) exists against Italian and white players. In contrast, when small and big clubs are considered separately, the findings relating to small clubs highlight that foreign and black players face such discrimination. The results suggest that managers of professional football clubs apply a discrimination strategy. In addition, the results provide practical implications on the types of discrimination errors that are committed by the management of big and small football clubs. Big clubs tend to overrate the contributions of foreign and/or black players compared to those of Italian and white players, while small clubs tend to overrate the contributions of Italian and white players compared to those of foreign and black players. To reduce discrimination, clubs have to correlate how much players are paid with their performance. Further research is recommended to identify the impact of wage inequality on the football labour market and on professional team management

On the Sampling Size for Inverse Sampling

In the Big Data era, sampling remains a central theme. This paper investigates the characteristics of inverse sampling on two different datasets (real and simulated) to determine when big data become too small for inverse sampling to be used and to examine the impact of the sampling rate of the subsamples. We find that the method, using the appropriate subsample size for both the mean and proportion parameters, performs well with a smaller dataset than big data through the simulation study and real-data application. Different settings related to the selection bias severity are considered during the simulation study and real application

HOW BIG DATA WILL BE AN ADDED VALUE TO SCM?

It is the era of digital information technology where almost everything is going smart. Thus, organizations move towards digitalization that cause the emergent of Big Data. Analyzing big data is the big challenge today. Being smart puts the world under a big challenge to adapt, change, and upgrade systems to analyze the Big Data using the high technology. Challenges are growing with the market and appears in different forms. Many of these challenges can be hard or difficult to handle on your own if you are a small to medium size business without the help of supply chain management system. The main objective of this paper is to contribute and examine these research questions: What are the Big Data Analytical tools used in SCM? In addition to the Impact of Analyzing Big Data on Supply Chain Management? The methodology used was a systematic review over the existing literature including Big Data, supply chain, SCM, and the impact of BD analytical tools on SCM. Data collected and unsystematically interpreted and the findings summarized in a subjective way that describes and discusses the literature from a contextual or theoretical point of view. Big data can tremendously affect the supply chain units and can add values to the overall supply chain operations by improving the processes to be more effective and efficient based on the analysis results. Big data analytics become a core differentiation factor for any organization that acquire it in the last few years

Job Mobility and Skill Transferability. Some Evidences from Denmark and a Large Italian Region

This paper investigates the effect of job mobility and tenure on wage dynamics. In this respect, theory assesses that high job mobility and low tenure are associated to lower wage drop when workers experience a job change. We test this theory first comparing two labour market (i.e. Denmark and a large Italian region, Veneto) characterized by different job mobility and tenure, as a consequence of different level of EPL. Secondly, we perform a within Veneto analysis, comparing the different effects when workers are employed in small rather than big firms. Data drawn from the VWH (Veneto Workers History) and IDA (for Denmark) registered data, from 1987 to 2001, are used. In Denmark job mobility has a positive effect on wage increases, while built up on firm-specific human capital has a negative effect. In Veneto, instead, it appears that long tenure are more rewarding. Some evidences of positive impact of moving from job to job when the barriers are lower come from the analysis of the differences between small and big firms in Veneto.Information sale, Cheap talk, Conflicts of interest, Information Acquisition, Firewalls, Market efficiency