Leadership for Big Data and Business Intelligence

2015 ◽  
pp. 371-378
Author(s):  
Richard T. Herschel
Keyword(s):  
Big Data ◽  
Business Intelligence

Applying a Business Intelligence System in a Big Data Context: Production Companies

2020 ◽  
pp. 268-274
Author(s):  
Jesús Silva ◽  
Mercedes Gaitán ◽  
Noel Varela ◽  
Doyreg Maldonado Pérez ◽  
Omar Bonerge Pineda Lezama
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Intelligence System ◽  
Data Context

scholarly journals DATA SOURCES FOR BUSINESS INTELLIGENCE

2021 ◽  
Author(s):  
Naveen Kunnathuvalappil Hariharan
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Personal Data ◽  
Data Sources ◽  
Enterprise Information System ◽  
Enterprise Information ◽  
External Data ◽  
Operational Systems ◽  
Internal Sources ◽  
Key Questions

As organizations' desire for data grows, so does their search for data sources that are both usable and reliable.Businesses can obtain and collect big data in a variety of locations, both inside and outside their own walls.This study aims to investigate the various data sources for business intelligence. For business intelligence,there are three types of data: internal data, external data, and personal data. Internal data is mostly kept indatabases, which serve as the backbone of an enterprise information system and are known as transactionalsystems or operational systems. This information, however, is not always sufficient. If the company wants toanswer market and industry questions or better understand future customers, the analytics team may need to look beyond the company's own data sources. Organizations must have access to a variety of data sources in order to answer the key questions that guide their initiatives. Internal sources, external public sources, andcollaboration with a big data expert could all be beneficial. Companies who are able to extract relevant datafrom their mountain of data acquire new perspectives on their business, allowing them to become morecompetitive

scholarly journals Atualização da doutrina de gerenciamento de crises: Incidentes policiais e centros de consciência situacional C5I na quarta revolução industrial

2020 ◽  
Vol 13 (1) ◽  
pp. 49-59
Author(s):  
Paulo Augusto Aguilar
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Business Intelligence

O presente artigo tem como objetivo abordar a atualização de gerenciamento de crises. Trata-se de um tema bastante importante para a atividade policial, pois visa a expandir a possibilidade das polícias brasileiras, seja militar, civil ou federal, de investigar delitos diversos, relacionados à segurança pública, ao fornecer conceitos de Big Data, Data Mining, Data Storytelling e Business Intelligence como forma de gerar melhor consciência situacional e imagem operacional comum de incidentes de todos os tipos e tamanhos, tudo isso com a flexibilidade de aplicativos disponíveis em smartphones, em tempo real, agilizando a capacidade de resposta e de adaptação do Estado diante de cenários VUCA, utilizado para descrever cenários caracterizados por volatilidade (volatility), incerteza (uncertainty), complexidade (complexity) e ambiguidade (ambiguity).

THE RELATIONSHIP OF SOCIOTECHNICAL AND TECHNOLOGICAL ASPECTS OF THE INTRODUCTION AND APPLICATION OF BUSINESS INTELLIGENCE SYSTEMS

2021 ◽  
Vol 5 (12) ◽  
pp. 30-35
Author(s):  
Edward N. Ozhiganov ◽  
◽  
Alexander A. Chursin ◽  
Alexey D. Linkov ◽  
◽  
...  
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
New Technologies ◽  
Business Analytics ◽  
Scientific Publications ◽  
Business Decisions ◽  
Business Decision ◽  
Business Intelligence Systems ◽  
Demand Fluctuations ◽  
New Research

This article describes a relation between sociotechnical and technological factors involved in launching and implementing Business Intelligence systems. Advanced BI systems include business analytics, data mining, data visualization, data tools and infrastructure, and advanced IT solutions to support business decisions based on big data. Various industries and businesses handle large amounts of data to adapt to changing markets and demand fluctuations, push new technologies, and repair ineffective strategies, etc. With an upsurge in data sizes, more and more new research papers are published today to describe BI implemen-tation, use and results. However, today most studies and scientific publications focus on Business Intelligence technological challenges, while sociotechnical aspects – that is processes involved in business decision mak-ing based on big data – are studied in much rarer cases.

Big Data Predictive and Prescriptive Analytics

2014 ◽  
pp. 370-391 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Action Plan ◽  
Big Data Analytics ◽  
Clear Understanding ◽  
Future Planning ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Recent Trends

The Analytics tools are capable of suggesting the most favourable future planning by analyzing “Why” and “How” blended with What, Who, Where, and When. Descriptive, Predictive, and Prescriptive analytics are the analytics currently in use. Clear understanding of these three analytics will enable an organization to chalk out the most suitable action plan taking various probable outcomes into account. Currently, corporate are flooded with structured, semi-structured, unstructured, and hybrid data. Hence, the existing Business Intelligence (BI) practices are not sufficient to harness potentials of this sea of data. This change in requirements has made the cloud-based “Analytics as a Service (AaaS)” the ultimate choice. In this chapter, the recent trends in Predictive, Prescriptive, Big Data analytics, and some AaaS solutions are discussed.

Knowledge Management and Business Analytics

2019 ◽  
pp. 1-42
Author(s):  
Nirali Nikhilkumar Honest ◽  
Atul Patel
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Business Intelligence ◽  
Business Performance ◽  
Virtual Organization ◽  
Data Driven ◽  
Business Analytics ◽  
Broad Category ◽  
Business Operations

Knowledge management (KM) is a systematic way of managing the organization's assets for creating valuable knowledge that can be used across the organization to achieve the organization's success. A broad category of technologies that allows for gathering, storing, accessing, and analyzing data to help business users make better decisions, business intelligence (BI) allows analyzing business performance through data-driven insight. Business analytics applies different methods to gain insight about the business operations and make better fact-based decisions. Big data is data with a huge size. In the chapter, the authors have tried to emphasize the significance of knowledge management, business intelligence, business analytics, and big data to justify the role of them in the existence and development of an organization and handling big data for a virtual organization.

Big Data, Knowledge, and Business Intelligence

2018 ◽  
pp. 943-950
Author(s):  
G. Scott Erickson ◽  
Helen N. Rothberg
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Competitive Advantage ◽  
Intellectual Capital ◽  
Business Intelligence ◽  
Competitive Intelligence ◽  
Business Analytics ◽  
The Past ◽  
The People ◽  
Tools And Techniques

Knowledge management (KM), intellectual capital (IC), and competitive intelligence are distinct yet related fields that have endured and grown over the past two decades. KM and IC have always differentiated between the terms and concepts of data, information, knowledge, and wisdom/intelligence, suggesting value only comes from the more developed end of the range (knowledge and intelligence). But the advent of big data/business analytics has created new interest in the potential of data and information, by themselves, to create competitive advantage. This new attention provides opportunities for some exchange with more established theory. Big data gives direction for reinvigorating the more mature fields, providing new sources of inputs and new potential for analysis and use. Alternatively, big data/business analytics applications will undoubtedly run into common questions from KM/IC on appropriate tools and techniques for different environments, the best methods for handling the people issues of system adoption and use, and data/intelligence security.

Sign in / Sign up

Export Citation Format

Share Document