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

Big Data Architecture Components

2018 ◽  
pp. 10-31
Keyword(s):  
Big Data ◽  
Missing Values ◽  
Noise Removal ◽  
Data Sources ◽  
Data Types ◽  
Layer Analysis ◽  
External Sources ◽  
Internal Sources ◽  
High Level

The previous chapter overviewed big data including its types, sources, analytic techniques, and applications. This chapter briefly discusses the architecture components dealing with the huge volume of data. The complexity of big data types defines a logical architecture with layers and high-level components to obtain a big data solution that includes data sources with the relation to atomic patterns. The dimensions of the approach include volume, variety, velocity, veracity, and governance. The diverse layers of the architecture are big data sources, data massaging and store layer, analysis layer, and consumption layer. Big data sources are data collected from various sources to perform analytics by data scientists. Data can be from internal and external sources. Internal sources comprise transactional data, device sensors, business documents, internal files, etc. External sources can be from social network profiles, geographical data, data stores, etc. Data massage is the process of extracting data by preprocessing like removal of missing values, dimensionality reduction, and noise removal to attain a useful format to be stored. Analysis layer is to provide insight with preferred analytics techniques and tools. The analytics methods, issues to be considered, requirements, and tools are widely mentioned. Consumption layer being the result of business insight can be outsourced to sources like retail marketing, public sector, financial body, and media. Finally, a case study of architectural drivers is applied on a retail industry application and its challenges and usecases are discussed.

scholarly journals Big Data’s Disruptive Effect on Job Profiles: Management Accountants’ Case Study

2021 ◽  
Vol 14 (8) ◽  
pp. 376
Author(s):  
Adriana Tiron-Tudor ◽  
Delia Deliu
Keyword(s):  
Big Data ◽  
Data Sources ◽  
Disruptive Effect ◽  
Positive Effects ◽  
External Data ◽  
Management Accountants ◽  
Flexible Framework ◽  
Potential Risks ◽  
The Impact ◽  
Job Profile

The abundance of new innovative data sources creates opportunities and challenges for all professions and professionals working with information. One of these professionals is the management accountant (MA). Although their tasks have expanded over time and especially recently, MAs have not fully employed all the available internal and external data sources to describe, diagnose, visualize, predict and prescribe possible solutions that enable smart decisions with positive effects on businesses. Thus, the paper investigates the impact of Big Data, including Data Analytics, on MA’s job profile. Through a review of the most recent academic and professional publications, the paper contributes to the debate surrounding the redefinition of the role of MAs in organizations in a novel informational perspective of Abbott’s theory. The results could serve as a research agenda and incentive for further studies, as well as provide MAs with a guide on the topic of the enlargement of their role(s), respectively, the augmentation of their tasks and responsibilities regarding the analysis of Big Data. Furthermore, the research may provide both a rich and flexible framework to help practitioners in their analysis of potential risks, opportunities and challenges when handling Big Data, and a lens for professional accounting associations and bodies by helping them to prioritize the holding and seizing of jurisdictions as an imperative part of safety and security.

scholarly journals A Scalable Business Intelligence Decision-Making System in the Era of Big Data

2019 ◽  
Vol 8 (12) ◽  
pp. 2034-2042
Keyword(s):  
Big Data ◽  
Data Warehouse ◽  
Data Storage ◽  
Business Intelligence ◽  
Unstructured Data ◽  
Data Sources ◽  
Second Step ◽  
Decision Making System ◽  
Set Up

Transformation presents the second step in the ETL process that is responsible for extracting, transforming and loading data into a data warehouse. The role of transformation is to set up several operations to clean, to format and to unify types and data coming from multiple and different data sources. The goal is to get data to conform to the schema of the data warehouse to avoid any ambiguity problems during the data storage and analytical operations. Transforming data coming from structured, semi-structured and unstructured data sources need two levels of treatments: the first one is transformation schema to schema to get a unified schema for all selected data sources and the second treatment is transformation data to data to unify all types and data gathered. To ensure the setting up of these steps we propose in this paper a process switch from one database schema to another as a part of transformation schema to schema, and a meta-model based on MDA approach to describe the main operations of transformation data to data. The results of our transformations propose a data loading in one of the four schemas of NoSQL to best meet the constraints and requirements of Big Data.

The Fundamentals of Business Intelligence

2016 ◽  
Vol 6 (2) ◽  
pp. 12-25 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Business Intelligence ◽  
Recent Literature ◽  
Easy Access ◽  
Enterprise Information System ◽  
Enterprise Information ◽  
Acceptance Model ◽  
Insight Into ◽  
The Ideal

This article analyzes the recent literature in the search for the fundamentals of business intelligence (BI). The literature review covers the overview of BI; BI and technology acceptance model (TAM); BI, Big Data, and social media; the elements of BI; the characteristics of BI; enterprise information system (EIS) and cloud computing; the importance of BI; and the implementation of BI. BI involves creating any type of data visualization that provides insight into a business for the purpose of making a decision or taking an action.BI can assist organizations by facilitating better decisions in all facets of operations. The ideal BI system gives the organizations easy access to the information and the ability to analyze and share this information with other business enterprises. The findings present valuable insights and further understanding of the way in which BI perspectives should be emphasized.

RDSP: A Railway Decision Support Platform for Integrating and Bridging Existed Legacy Systems

2013 ◽  
Author(s):  
I-Chang Chen ◽  
Shu-Keng Hsu ◽  
Teh-Juan Wu ◽  
Li-Hsien Yen ◽  
Yusin Lee ◽  
...  
Keyword(s):  
Information System ◽  
Decision Support ◽  
Traffic Control ◽  
Legacy Systems ◽  
System Operation ◽  
Enterprise Information System ◽  
Enterprise Information ◽  
External Data ◽  
Historical Factors ◽  
Railway System

Railway system operation is a very complicated task and must be supported by the coordination of several systems including engineering, transportation, locomotive maintenance and management, ticket system and passengers service, etc. Ideally, a modern railway enterprise information system should be an integrated, consisted, and site-opened database to support railway system operation. However, multiple isolated applications instead of one integrated enterprise information system are often formed due to historical factors such as independent departments, budgetary constraint and application requirements diversification. Take Taiwan Railways Administration (TRA) as an example, four major departments have their own support systems and databases that store critical planning and operational data. Those systems are isolated and can hardly communicate with each other. As a result, most cross-systems information analyses or data reference for decision making are done manually, which significantly affect the organizational efficiency of TRA. To address the aforementioned issues, a railway decision support platform (RDSP) for integrating the legacy systems (databases) is proposed in this paper. RDSP is designed to be a railway enterprise information system that supports the critical functions of data warehouse and decision support. Furthermore, RDSP is built by integrating the existing legacy systems rather than by building it from scratch. A data bridging system (HDBS) including four modules are design and implemented, input module for connecting the external data sources, output module for exporting integrated report or dumping data by predefined criteria for other systems, configuration module with a web-based user interface for setting up the periodic operations of data input or output tasks, and DB connection module for connecting external databases. Various types of railway system data are designed in RDSP schema and collected, including facilities, timetable, train services records, tickets, centralized traffic control (CTC) system records, and automatic train protection (ATP) system records. RDSP provides a system framework to integrate many isolated island-style databases that currently exist in TRA, and can form a cross-enterprise database that serves as the primary and only data platform. To demonstrate the efficacy of the RDSP, a spatiotemporal ticket-selling analysis report, a train delay cause analysis report, and a timetable planning software (TrainWorld) are designed on top of it. In the future, RDSP will play a major supportive role in infrastructure maintenance, operations, decision support, and planning.

Enterprise Information System and Data Mining

2010 ◽  
Vol 1 (3) ◽  
pp. 34-41 ◽  
Author(s):  
Kenneth D. Lawrence ◽  
Dinesh R. Pai ◽  
Ronald Klimberg ◽  
Sheila M. Lawrence
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Information Systems ◽  
Business Intelligence ◽  
Large Data ◽  
Customer Relationship ◽  
Enterprise Information System ◽  
Enterprise Information ◽  
Competitive Edge ◽  
Organizational Functions

The advent of information technology and the consequent proliferation of information systems have lead to generation of vast amounts of data, both within the organization and across its supply chain. Enterprise information systems (EIS) have added to organizational complexity, and at the same time, created opportunities for enhancing its competitive advantage by utilizing this data for business intelligence purposes. Various data mining tools have been used to gain a competitive edge through these large data bases. In this paper, the authors discuss EIS-aided business intelligence and data mining as applicable to organizational functions, such as supply chain management (SCM), marketing, and customer relationship management (CRM) in the context of EIS.

scholarly journals ANNOTATING DATA WITH MULTIDIMENSIONAL PROPERTIES

2019 ◽  
pp. 249-257
Author(s):  
Yassine Laadidi ◽  
Mohamed Bahaj
Keyword(s):  
Semantic Web ◽  
Business Intelligence ◽  
Data Sources ◽  
Multidimensional Data ◽  
Web Technologies ◽  
External Data ◽  
Description Framework ◽  
Resource Description ◽  
Different Sources ◽  
The Web

The evolution of web technologies and the data we are manipulating announce profound changes on Business Intelligence (BI) systems and open up important researches and innovations particularly in multidimensional data modeling and data integration. The emergence of the semantic Web highlights the need of including external data sources in the BI system. The semantic web came with Resource Description Framework (RDF) model to describe data over the Web by annotating resources with semantics and properties and consequently establishing reasoning mechanisms. However, integrating and/or analyzing information from Wide World Sources still a very challenging process because of their “unpredictability” and heterogeneity. Consequently, the transition to an open BI/SW system is required to handle automatic alteration on structures and enabling discovery of multidimensional entities over multiple Web sources. In this paper, we introduce our prospective approach and architecture for including external data sources in an open BI/SW system and we provide an automatic method aimed to define multidimensional entities and properties over different sources for data acquisition and data analysis requests.

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