The Role of Big Data Analytics on Innovation: A Study from The Telecom Industry

Telecom companies face a fierce competition from innovation based start-up companies, particularly those that are using internet networks to offer communication services through the voice and video over internet protocol (VoIP & PVoIP) technologies. More than 10 years have passed from the time the internet and VoIP were widely used, but still, telecom companies are having a great deal of business success through offering a wide spectrum of services, products, deals, and packages to consumers using both B2C and B2B models. The future landscape of how telecom companies will evolve in the market is still not clear, particularly with the increase of aggressive competition from companies that are technology-innovative and starting to deliver new forms of ubiquitous communication technology and services. Understanding why and how telecom companies innovate in the market is very crucial in order to predict the future of this business sector. In this paper, we argue that telecom companies are utilising their capabilities that have a significantly important role in fostering innovation, namely information technology (IT) capability and knowledge management (KM) capability. IT capabilities have changed dramatically in the last few years with the introduction of intelligent systems, big data analytics, the Internet of Things and the wide use of mobile apps and sensors. It is not clear how these technologies play a role in telecom companies’ innovation and it is not clear whether IT impacts innovation directly or if KM capability has a mediation role in utilising technology to support innovation. This paper is a position paper to establish grounds for understanding how telecom companies innovate, and in particular how IT and KM capabilities influence innovation. We outline the methodology of this investigation as a qualitative study with stakeholders from multiple telecom companies and we expect at the end of the study to be able to offer a holistic view on the way these companies innovate in regard to their products and services. We aim at providing a cross case studies comparison towards a prediction of the future of the telecom business sector

The spectrum of big data analytics

Big data analytics is playing a pivotal role in big data, artificial intelligence, management, governance, and society with the dramatic development of big data, analytics, artificial intelligence. However, what is the spectrum of big data analytics and how to develop the spectrum are still a fundamental issue in the academic community. This article addresses these issues by presenting a big data derived small data approach. It then uses the proposed approach to analyze the top 150 profiles of Google Scholar, including big data analytics as one research field and proposes a spectrum of big data analytics. The spectrum of big data analytics mainly includes data mining, machine learning, data science and systems, artificial intelligence, distributed computing and systems, and cloud computing, taking into account degree of importance. The proposed approach and findings will generalize to other researchers and practitioners of big data analytics, machine learning, artificial intelligence, and data science. © 2019 International Association for Computer Information Systems

Aporte de los sistemas de inteligencia de negocios a los sistemas de información organizacionales para la toma de decisiones

En la actualidad, en todos los estratos sociales, los datos, la información y el conocimiento se han convertido en uno de los recursos más valiosos para la toma de decisiones. Ante una inquietud o consulta de cualquier tipo, se puede acceder rápidamente a grandes volúmenes de datos. Sin embargo, el almacenamiento, procesamiento y su posterior análisis, representan uno de los problemas más críticos debido al gran volumen de datos. En las organizaciones, esto representa un desafío, ya que tienen que lidiar diariamente con grandes cantidades de datos que a menudo se generan en las operaciones del día a día. Dichos datos deben ser procesados y convertidos en información, la cual se utilizará para tomar decisiones sobre estrategias a seguir, inversiones a realizar, entre otras acciones. Si no se recolectan los datos adecuados o más relevantes, la información generada no será precisa, los resultados probablemente serán erróneos y, en consecuencia, cualquier decisión tomada no será la mejor ni la más adecuada. Ante esta problemática planteada, algunas ciencias interdisciplinarias, como Sistemas de Información (IS), Inteligencia de Negocios (BI), Minería de Datos (DM), Big Data (BD), Analítica de Negocios (BA) e Ingeniería del Conocimiento (KE), han fusionado sus saberes y esfuerzos de procesamiento para dar apoyo a la toma de decisiones en las actuales organizaciones; que presentan algunas características tales como: almacenar y gestionar grandes cantidades de datos, adecuarse rápidamente al mercado, tomar decisiones de forma casi inmediata, etc. Por lo tanto, atendiendo a las necesidades por las cuales transitan actualmente las organizaciones, y observando la debilidad en la actual currícula académica, para apoyar al medio local, es que la presente contribución propone determinar cómo los Sistemas de Inteligencia de Negocios (BIS) aportan a los Sistemas de Información Organizacionales (OIS), para la toma de decisiones.Eje: Ingeniería de Software.Red de Universidades con Carreras en Informátic

Aplicación de una estrategia y de técnicas de inteligencia y analítica de negocio a los sistemas de información del Ministerio de Salud de la provincia de San Juan

En la actualidad, en todos los niveles organizacionales, los datos, la información y el conocimiento se han convertido en uno de los recursos más valiosos para la toma de decisiones. Ante una inquietud o consulta de cualquier tipo, se debería acceder rápidamente a grandes volúmenes de datos. Sin embargo, su almacenamiento, procesamiento y posterior análisis, representan uno de los problemas más críticos a resolver. El Ministerio de Salud de la provincia de San Juan, debido al gran volumen de datos que maneja y dado que los mismos se encuentran dispersos en distintos sistemas, ha comenzado a desplegar un sistema de salud integral. En este sistema confluirán todos los actores y sistemas de la salud provincial, tanto públicos como privados. Se espera que en el corto plazo un gran volumen de información, referida a la salud de los sanjuaninos, esté digitalizada. Es decir, que se cuente con un cúmulo importante de valiosa información, accesible a pacientes, profesionales y centros asistenciales. Pero, además, que estos sistemas de información contribuyan, principalmente, a la elaboración de políticas públicas del sector. Sin embargo, aun contando con estos sistemas de información integrados y digitalizados si no se dispone de recursos humanos y tecnológicos que permitan transformar esos datos en información y ésta en conocimiento, difícilmente se pueden hacer inferencias, pronosticar y en ciertos casos prescribir acciones a seguir. Por lo tanto, la presente investigación consistirá en definir una estrategia a seguir que, aplicando una determinada técnica y herramienta tecnológica de BI y BA, permita la visualización y análisis de los datos de los sistemas de información del Ministerio de Salud de la provincia de San Juan. Posteriormente, adquirir patrones de comportamiento, generar predicciones y posibles escenarios de acción (prescripciones).Red de Universidades con Carreras en Informátic

An Evaluation Framework for Business Intelligence Visualization

Nowadays, data visualization is becoming an essential part of data analysis. Business Intelligence Visualization (BIV) is a powerful tool that helps modern business flows faster and smoother than ever before. However, studies on BIV evaluation are severely lacking; most evaluation studies for BIV is guided by general principles of usability, which have limited aspects covered for customers? needs. The purpose of this research is to develop a framework that evaluates BIV, including decision-making experience. First, we did a literature review for good understanding of research progress on related fields, and established a conceptual framework. Second, we performed a user study that implemented this framework with a set of questionnaires to demonstrate how our framework can be used in real business. Our result proved that this framework can catch differences among different designs of BIV from the users? standpoints. This can help design BIV and promote better decision-makings on business affairs

The Role of Big Data Analytics in Innovation: A Study from The Telecom Industry

Organisations are looking for new definitions and guidelines for innovation direction due to the changing nature of technology, user behaviour, competition and market trends. Data sources, types and analysis mechanisms have changed dramatically in the last few years, and there are pieces of evidence that these are influencing the level of innovation in a firm. We found that it is very important to explore how telecom companies capture, analyse and make innovation insights from big data. Our review shows a clear scarcity of research on this topic. The study aims to use qualitative methods of both interviews and documents review in three telecom companies in Jordan, with an opportunity to extend the study to different regions and countries. The understanding of how big data and its analysis are carried out by companies will support our effort in building more systematic procedures and guidelines for companies who wish to utilise big data for different types of innovation with different levels of maturity indicators

SAT-hadoop-processor: a distributed remote sensing big data processing software for earth observation applications

Nowadays, several environmental applications take advantage of remote sensing techniques. A considerable volume of this remote sensing data occurs in near real-time. Such data are diverse and are provided with high velocity and variety, their pre-processing requires large computing capacities, and a fast execution time is critical. This paper proposes a new distributed software for remote sensing data pre-processing and ingestion using cloud computing technology, specifically OpenStack. The developed software discarded 86% of the unneeded daily files and removed around 20% of the erroneous and inaccurate datasets. The parallel processing optimized the total execution time by 90%. Finally, the software efficiently processed and integrated data into the Hadoop storage system, notably the HDFS, HBase, and Hive.This research was funded by Erasmus+ KA 107 program, and the UPC funded the APC. This work has received funding from the Spanish Government under contracts PID2019-106774RBC21, PCI2019-111851-2 (LeadingEdge CHIST-ERA), PCI2019-111850-2 (DiPET CHIST-ERA).Peer ReviewedPostprint (published version

Optimización de la logística de distribución utilizando técnicas de la inteligencia artificial

La logística en Argentina está influida por el decisivo peso de la producción primaria en la organización del sistema, por una orientación prevalente hacia las exportaciones globales y las dirigidas al mercado regional sudamericano, y por una matriz de transporte dominada por el transporte automotor de cargas y la concentración portuaria. La logística remite a flujos de materiales y de información; a lugares de manipulación, depósito y transformación de las mercancías; a redes y nodos de circulación; y a tiempos de movimiento y no movimiento que responden a aspectos materiales (las infraestructuras, los transportes y las cargas) y también a aspectos funcionales (los servicios, las normativas y regulaciones). En suma, la logística implica un uso del territorio en el tiempo, una convergencia espacio-temporal, una organización y sincronización de flujos a través de estrategias sobre los nodos y las redes. Esta aproximación resalta las limitaciones de miradas parciales y destaca la necesidad de considerar una perspectiva integral que incluya, además, los aspectos organizacionales y de coordinación. La consideración de infraestructuras de circulación; servicios de transporte; infraestructuras de comunicaciones; servicios de almacenamiento y agregado de valor; normativas y regulaciones y costos operativos, entre otros, da cuenta del desafío que involucra una coordinación amplia de políticas estatales de diferente tipo y a cargo de estructuras administrativas diversas. El despliegue de la logística como uno de los “usos del territorio”, abre la posibilidad de acentuar el enfoque territorial. En esta línea de investigación se aborda la optimización de la logística de distribución de cargas y paquetería. Para tal fin se propone el desarrollo de software logístico, que incorpore herramientas basadas en inteligencia artificial, como soporte para la toma de decisiones a nivel gerencial y asistido por herramientas que permitan evaluar la incidencia de la matriz de costos y buscar el equilibrio del sistema. Se considera que el abordaje de la optimización de la logística de distribución centrada en la provincia de La Pampa tendría impacto en la matriz productiva y de servicios de toda la región.Red de Universidades con Carreras en Informátic

Technology Selection for Big Data and Analytical Applications

The term Big Data has become pervasive in recent years, as smart phones, televisions, washing machines, refrigerators, smart meters, diverse sensors, eyeglasses, and even clothes connect to the Internet. However, their generated data is essentially worthless without appropriate data analytics that utilizes information retrieval, statistics, as well as various other techniques. As Big Data is commonly too big for a single person or institution to investigate, appropriate tools are being used that go way beyond a traditional data warehouse and that have been developed in recent years. Unfortunately, there is no single solution but a large variety of different tools, each of which with distinct functionalities, properties and characteristics. Especially small and medium-sized companies have a hard time to keep track, as this requires time, skills, money, and specific knowledge that, in combination, result in high entrance barriers for Big Data utilization. This paper aims to reduce these barriers by explaining and structuring different classes of technologies and the basic criteria for proper technology selection. It proposes a framework that guides especially small and mid-sized companies through a suitable selection process that can serve as a basis for further advances

Analytics as a Service: Cloud Computing and the Trans-formation of Business Analytics Business Models and Ecosystems

Due to the growth of data volumes, volatility and variety, business analytics (BA) become an essential driver of today’s business strategies. However, BA is mainly adopted by large enterprises because it may require a complex and costly infrastructure. As many companies strive to make better use of their data and to adopt data-driven management paradigms, cloud computing has been discussed as a costeffective approach to BA implementation challenges. To date, there has been little attention on the emerging class of analytical cloud services, “Analytics as a service” (AaaS). This article aims at demarcating AaaS as a cloud offering through an explorative research approach based on multiple case studies. Based on the analysis of 28 AaaS offerings, we derive a classification scheme for AaaS business model configurations and derive five business model archetypes. We discuss cloud computing’s implications on the business analytics ecosystem where partner networks play an important role at all levels. By clarifying the definition and characteristics of AaaS business models, our study contributes to the ‘Theory for Analyzing’ that lays the groundwork for future research