Statistical Data Analysis in the Era of Big Data

Abstract

Big data is on everyone's lips and often raises emotions. On the one hand, the notion is a basis for much technological optimism, mostly directed towards new business models, or simplifications and optimizations in professional and private life. On the other hand, it is a basis for dystopic perspectives, which are targeted, e.g., at profiling of the individual and their privacy space, overarching optimization in daily life and intransparency of decision making. In this article, after a short historical prolog, it is discussed what distinguishes big data from traditional data analysis. The underlying mathematical methods are introduced and scientific successes are reported. Additionally, the risks and limits-especially regarding the derivation of causal relationships-of data analysis are discussed.

The Importance of Consumer Insights for Precision Marketing in the Era of Big Data

“…Generally, big data refers to a collection of data that cannot be sensed, acquired, managed, processed, and serviced within a certain period of time by conventional machines and hardware and software tools. Network big data refers to the big data generated by interacting and integrating the three worlds of people, machines, and things in cyberspace and available on the internet [8]. Applying data to life and production can effectively help people or enterprises to make more accurate judgments on information to take appropriate actions.…”

Analyzing the Influencing Factors of Economic Fluctuations in the Era of Big Data

“…Among them, fiscal policy and monetary policy are the two most important policy tools for the government to regulate and control economic operation. Fiscal policy is included, and a macroeconomic policy status index, including monetary policy and fiscal policy, is established to measure the tightness of macroeconomic policy [31]. e monetary policy status index is expressed by the weighted average of percentage point changes of interest rate and exchange rate, and its formula is as follows:…”

Análisis bibliométrico de la producción científica sobre la aplicación de las ciencias de datos para la toma de decisiones en análisis de riesgos, 2015-2020

“…En el proceso descrito en la figura 4, podemos ver la manera en que la Big data forma parte de la primera etapa; pues dentro de sus características, encontramos lo comúnmente denominado "Las 5 V's": volumen, velocidad, variedad, veracidad y valor (Lengauer, 2020); dichas características buscan la extracción de datos que son válidos para nuestro problema o contexto, esto se logra usando tecnologías como bodegas de datos, que permiten reunir información proveniente de diversos orígenes, información estructurada ya existente en bases de datos por lo general modelos relacionales, semi estructurada como archivos XML e incluso información no estructurada como archivos de texto o en formato PDF, para obtener la mayor y mejor cantidad de datos posible. Luego de esto se pasa al proceso de transformación que consiste en limpiar y convertir los datos para estandarizarlos, eliminar datos erróneos o vacíos, datos no coherentes, esto con el fin de tratarlos de la misma manera, para esto se usan sistemas de software especializados, o algunas librerías de procesamiento y preprocesamiento de datos, finalmente los datos se cargan en la bodega de datos o data Waterhouse para su subsiguiente análisis (Mukherjee y Kar, 2017).…”