Business Open Big Data Analytics to Support Innovative Leadership Decision in Canada

This paper summarizes how social media and other technologies continue to proliferate; the shifting economic landscape will precipitate more adaptive approaches for managers attempting to understand the multidimensional virtual aspects of communication with the artificial intelligence aspect. Also, we discover the different existing support of big data analytics to make the rational business decision. The methodology is the systematization literature sources within this context and approaches for underlining approach to open big data analytics and support innovative leadership decisions in Canada

Combining Big Data And Traditional Business Intelligence – A Framework For A Hybrid Data-Driven Decision Support System

Since the emergence of big data, traditional business intelligence systems have been unable to meet most of the information demands in many data-driven organisations. Nowadays, big data analytics is perceived to be the solution to the challenges related to information processing of big data and decision-making of most data-driven organisations. Irrespective of the promised benefits of big data, organisations find it difficult to prove and realise the value of the investment required to develop and maintain big data analytics. The reality of big data is more complex than many organisations’ perceptions of big data. Most organisations have failed to implement big data analytics successfully, and some organisations that have implemented these systems are struggling to attain the average promised value of big data. Organisations have realised that it is impractical to migrate the entire traditional business intelligence (BI) system into big data analytics and there is a need to integrate these two types of systems. Therefore, the purpose of this study was to investigate a framework for creating a hybrid data-driven decision support system that combines components from traditional business intelligence and big data analytics systems. The study employed an interpretive qualitative research methodology to investigate research participants' understanding of the concepts related to big data, a data-driven organisation, business intelligence, and other data analytics perceptions. Semi-structured interviews were held to collect research data and thematic data analysis was used to understand the research participants’ feedback information based on their background knowledge and experiences. The application of the organisational information processing theory (OIPT) and the fit viability model (FVM) guided the interpretation of the study outcomes and the development of the proposed framework. The findings of the study suggested that data-driven organisations collect data from different data sources and process these data to transform them into information with the goal of using the information as a base of all their business decisions. Executive and senior management roles in the adoption of a data-driven decision-making culture are key to the success of the organisation. BI and big data analytics are tools and software systems that are used to assist a data-driven organisation in transforming data into information and knowledge. The suggested challenges that organisations experience when they are trying to integrate BI and big data analytics were used to guide the development of the framework that can be used to create a hybrid data-driven decision support system. The framework is divided into these elements: business motivation, information requirements, supporting mechanisms, data attributes, supporting processes and hybrid data-driven decision support system architecture. The proposed framework is created to assist data-driven organisations in assessing the components of both business intelligence and big data analytics systems and make a case-by-case decision on which components can be used to satisfy the specific data requirements of an organisation. Therefore, the study contributes to enhancing the existing literature position of the attempt to integrate business intelligence and big data analytics systems.Dissertation (MIT (Information Systems))--University of Pretoria, 2021.InformaticsMIT (Information Systems)Unrestricte

Revisiting Ralph Sprague’s Framework for Developing Decision Support Systems

Ralph H. Sprague Jr. was a leader in the MIS field and helped develop the conceptual foundation for decision support systems (DSS). In this paper, I pay homage to Sprague and his DSS contributions. I take a personal perspective based on my years of working with Sprague. I explore the history of DSS and its evolution. I also present and discuss Sprague’s DSS development framework with its dialog, data, and models (DDM) paradigm and characteristics. At its core, the development framework remains valid in today’s world of business intelligence and big data analytics. I present and discuss a contemporary reference architecture for business intelligence and analytics (BI/A) in the context of Sprague’s DSS development framework. The practice of decision support continues to evolve and can be described by a maturity model with DSS, enterprise data warehousing, real-time data warehousing, big data analytics, and the emerging cognitive as successive generations. I use a DSS perspective to describe and provide examples of what the forthcoming cognitive generation will bring

Data Warehouse performance comparing Relational Database Management Systems and the Hadoop-based NoSQL Database system

One of the biggest problems that many companies face nowadays is dealing with the huge volumes of data that they generate daily. In the data-driven world all data needs to be stored, organized and analyzed to get the required information that will help the administration to make the right decision to support the next step of the company. Big Data and Business Intelligence have become very popular terms in the business field, where Big Data highlights the tools that are used to manage the huge volume of data. One of the Big Data tools is the Data Warehouse, which is used to manipulate the massive amount of data, while the Business Intelligence (BI) focuses on how we can analyze information from the huge volumes of data that support companies in decision making In this thesis, we will compare the implementation of the DW concepts using the Relational Database Management Systems (RDBMS), specifically, SQL Server DB over the Hadoop system, and then analyze the resource (CPU and RAM) consumption. I prove that using the Hadoop system speeds up the process of manipulating these huge volumes of data with very low cost, based on the nature of the Hadoop system that is efficient in processing all kinds of structured, semi-structured, unstructured or raw data with minimum cost and high efficiency in manipulating and storing massive amounts of data

Model of Big Data Failure: Review of Information System Failure

In the new age of information technology, big data has grown to be the prominent phenomena. As information technology evolves, organizations have begun to adopt big data and apply it as a tool throughout their decision-making processes. Research on big data has grown in the past years however mainly from a technical stance and there is a void in business related cases. This thesis fills the gap in the research by addressing big data challenges and failure cases. The Technology-Organization-Environment framework was applied to carry out a literature review on trends in Business Intelligence and Knowledge management information system failures. A review of extant literature was carried out using a collection of leading information system journals. Academic papers and articles on big data, Business Intelligence, Decision Support Systems, and Knowledge Management systems were studied from both failure and success aspects in order to build a model for big data failure. I continue and delineate the contribution of the Information System failure literature as it is the principal dynamics behind technology-organization-environment framework. The gathered literature was then categorised and a failure model was developed from the identified critical failure points. The failure constructs were further categorized, defined, and tabulated into a contextual diagram. The developed model and table were designed to act as comprehensive starting point and as general guidance for academics, CIOs or other system stakeholders to facilitate decision-making in big data adoption process by measuring the effect of technological, organizational, and environmental variables with perceived benefits, dissatisfaction and discontinued use.siirretty Doriast

Intelligent Technologies Supporting the Management of a Smart City. Qualitative Approach

Intelligent technologies such as Business Intelligence systems, big data, artificial intelligence including machine learning and cognitive technologies play crucial role in the process of a smart city management. The aim of the paper is to indicate the role of intelligent solutions in the management of a contemporary city, particularly focusing on the support of decision making process. The research methodology is based on a qualitative approach where six case studies were conducted in the selected big cities in Poland in 2021 year. The respondents belonged to the group of mainly managers of IT departments in the cities. The case study analyses showed that implemented intelligent solutions in the process of a smart city management positively and significantly affect efficacy, efficiency, quality, and acceleration of the decision-making process and also support the creation of a particular city development strategy. The paper puts also an emphasis on the review of AI applications within the concept of smart city in a big worldwide metropolies

Next Generation Data Warehouse Design with Big data for Big Analytics and Better Insights

Traditionally organizations invested more in decision support systems. With the evolution of business intelligence tools many organizations were able to get analytical reports based on OLAP systems. Now with the frequently changing trends in customer behaviour and customer markets there is a huge necessity for enterprises to get analytical reports beyond OLAP system based analysis. There is huge innovation in the area of hardware and software which helps enterprises to gain advantage of all available formats of data and help enterprise to get business insights based on that data. Big data is one of the key factors to be focused which can help to get real time analytics on all available formats of data. This document presents the overview of the next generation data warehouse architecture based on Big data for better business insights

BigDimETL with NoSQL Database

In the last decade, we have witnessed an explosion of data volume available on the Web. This is due to the rapid technological advances with the availability of smart devices and social networks such as Twitter, Facebook, Instagram, etc. Hence, the concept of Big Data was created to face this constant increase. In this context, many domains should take in consideration this growth of data, especially, the Business Intelligence (BI) domain. Where, it is full of important knowledge that is crucial for effective decision making. However, new problems and challenges have appeared for the Decision Support System that must be addressed. Accordingly, the purpose of this paper is to adapt Extract-Transform-Load (ETL) processes with Big Data technologies, in order to support decision-making and knowledge discovery. In this paper, we propose a new approach called Big Dimensional ETL (BigDimETL) dealing with ETL development process and taking into account the Multidimensional structure. In addition, in order to accelerate data handling we used the MapReduce paradigm and Hbase as a distributed storage mechanism that provides data warehousing capabilities. Experimental results show that our ETL operation adaptation can perform well especially with Join operation