scholarly journals Implementation of Business Intelligence for Sales Data Management Using Interactive Dashboard Visualization in XYZ Stores

2020 ◽  
Author(s):  
Ricky Akbar ◽  
Meza Silvana ◽  
Mohammad Hafiz Hersyah ◽  
Miftahul Jannah
Keyword(s):  
Data Management ◽  
Business Intelligence ◽  
Sales Data

scholarly journals The influence of data management, organisation culture and organisation motivation on the likeliness of Business Intelligence adoption

2021 ◽  
Author(s):  
◽  
Ria van den Berg
Keyword(s):  
New Zealand ◽  
Technology Adoption ◽  
Data Management ◽  
Business Intelligence ◽  
Business Environment ◽  
Study Objective ◽  
Organisational Factors ◽  
Organisation Culture ◽  
Management Organisation ◽  
Research Studies

<p>Business Intelligence has become a powerful business tool that describes the business environment, the organisation, its situation in terms of markets, customers, competitors and its financial situation. The objective of BI is to increase the overall performance of the organisation through an informed decision making process. This research study objective is to identify the organisational factors that will increase the likeliness of BI adoption by small-to-medium enterprises (SME’s) in New Zealand. Existing research studies however, focus predominantly on the challenges and benefits of BI technologies adoption. Importantly this study do not define BI as purely a technology but defines it as methods, processes and technology that work together to gain intelligent insight from business information. The organisational factors identified that formed the hypotheses of the research model included data management, organisation culture and organisation motivation. These factors were identified through factor analysis that included technology adoption models and existing research studies specifically related to SME BI and technology adoption. The outcome of the research has identified that only organisation motivation in the context of competitiveness and perception of BI’s value and benefits can significantly influence the likeliness of BI adoption. New Zealand SME’s form the backbone of the country’s economy and also operate in extreme competitive niche markets. The adoption of BI practice and the use of information as a strategic resource will enable SME’s to be more innovative and competitive.</p>

ISSUES AND CHALLENGES IN BUSINESS INTELLIGENCE CASE STUDIES

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Azizah Abdul Rahman ◽  
Nooradilla Abu Hasan ◽  
Norminshah A. Lahad
Keyword(s):  
Decision Making ◽  
Data Management ◽  
Case Studies ◽  
Business Intelligence ◽  
Qualitative Method ◽  
User Acceptance ◽  
Three Dimensions ◽  
Implementation Project ◽  
Organization Process ◽  
The Impact

Business Intelligence (BI) has embarked on decision-making setting. This has influenced many organizations from different industries that are located in diverse regions to implement BI. Critical Successful Factors (CSF) becomes the guideline for the implementer to adopt BI successfully. However, lack of BI knowledge and weak consideration of BI CSF led to the failure of BI implementation project. Several issues and challenges have been identified during the BI implementation. In addition, other researchers rarely discussed this subject. Thus, the objective of this paper is to recognize the issues and challenges on BI implementation. Through qualitative method, BI practices systematically described the purpose of BI execution on selected organizations, industries and regions. It has given the path towards the issues and challenges of BI implementation. The identified issues and challenges are defining the business goal, data management, limited funding, training and user acceptance as well as the lack of expertise issues. The findings categorized the issues and challenges into three dimensions of CSF for BI implementation, which are Organization, Process and Technology dimension. Limitation in this study requires future researchers to study in details of these issues and challenges including the solutions and the impact of BI implementation.

scholarly journals IOT Based Analyzation of Press Machine in Automation Industry

2020 ◽  
pp. 602-606
Author(s):  
Jagdish Patel ◽  
Komal Murtadak ◽  
Sayali Deore ◽  
Vaishnavi Thorat
Keyword(s):  
Process Control ◽  
Data Management ◽  
Continuous Improvement ◽  
Business Intelligence ◽  
Data Analytics ◽  
Process Industry ◽  
Valuable Resource ◽  
Modern Economy ◽  
Efficient Data ◽  
Asset Data

They say that companies that do not understand the importance of Analyzation are less likely to survive in the modern economy. Your data is your most valuable asset. Data management is important because the data your organization create is a very valuable resource. The last thing you want to do is spend time and resources collecting data and business intelligence, only to lose or misplace that information. In that case, you would then have to spend time and resources again to get that same business intelligence you already had. However, only well prepared and analyzed data leads to process knowledge and finally, to process control and continuous improvement. Thus, a robust and efficient data analytics strategy is one of the most valuable concepts for the process industry.

Principled Reference Data Management for Business Intelligence

2014 ◽  
pp. 219-235
Author(s):  
Ivan Milman ◽  
Martin Oberhofer ◽  
Sushain Pandit ◽  
Yinle Zhou
Keyword(s):  
Data Management ◽  
Business Intelligence ◽  
Information Integration ◽  
Call Center ◽  
Business Processes ◽  
Reference Data ◽  
Critical Role ◽  
Business Success ◽  
Quality Of Data ◽  
Operational Excellence

Most large enterprises requiring operational business processes (e.g., call center, human resources, order fulfillments, billing, etc.) utilize anywhere from a few hundred to several thousand instances of legacy, upgraded, cloud-based, and/or acquired information management applications. Due to this vastly heterogeneous information landscape, Business Intelligence (BI) systems (e.g., enterprise data warehouses) receive unconsolidated data from a wide-range of data sources with no overarching governance procedures to ensure quality, consistency, or appropriateness. Although different applications deal with their own flavor of data (e.g., master data, metadata, unstructured and structured data, etc.), reference data (residing in code tables) is found invariably in all of them. Given the critical role that BI plays in ensuring business success, the fact that BI relies heavily on the quality of data to ensure that the intelligence being provided is trustworthy and the prevalence of reference data in the information integration landscape, a principled approach towards management, stewardship, and governance of reference data becomes necessary to ensure quality and operational excellence across BI systems. In this chapter, the authors discuss this approach in the domain of typical reference data management concepts and features, leading to a comprehensive solution architecture for BI integration.

scholarly journals Real-Time Analysis of Online Sources for Supporting Business Intelligence Illustrated with Bitcoin Investments and IoT Smart-Meter Sensors in Smart Cities

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1101 ◽  
Author(s):  
Iván García-Magariño ◽  
Moustafa M. Nasralla ◽  
Shah Nazir
Keyword(s):  
Data Management ◽  
Real Time ◽  
Business Intelligence ◽  
Smart Cities ◽  
Online Information ◽  
Smart Meter ◽  
Time Data ◽  
Real Time Analysis ◽  
Real Time Data ◽  
Historic Data

Real-time data management analytics involve capturing data in real-time and, at the same time, processing data in a light way to provide an effective real-time support. Real-time data management analytics are key for supporting decisions of business intelligence. The proposed approach covers all these phases by (a) monitoring online information from websites with Selenium-based software and incrementally conforming a database, and (b) incrementally updating summarized information to support real-time decisions. We have illustrated this approach for the investor–company field with the particular fields of Bitcoin cryptocurrency and Internet-of-Things (IoT) smart-meter sensors in smart cities. The results of 40 simulations on historic data showed that one of the proposed investor strategies achieved 7.96% of profits on average in less than two weeks. However, these simulations and other simulations of up to 69 days showed that the benefits were highly variable in these two sets of simulations (respective standard deviations were 24.6% and 19.2%).

The End of the Job Title

2011 ◽  
pp. 143-158
Author(s):  
Georg Juelke
Keyword(s):  
Expert System ◽  
Data Management ◽  
Information Extraction ◽  
Business Intelligence ◽  
Data Driven ◽  
Market Conditions ◽  
It Systems ◽  
Job Title ◽  
Integrate Data

The staffing industry, despite being a global, multi-billion dollar business, has not yet widely exploited the use of business intelligence to make companies more competitive. Staffing companies are far removed from developing enterprise wide analytics and their analytical capabilities are either impaired or localized in their approach. Business intelligence commonly used in many other industries to optimize processes, reduce costs, or develop new services is dormant in staffing. This chapter analyses some of the root causes that impair the industry’s ability to develop analytics. While some originate in specific market conditions that are reflected in the design of IT systems, it is the absence of a common nomenclature to classify job categories that prevents consistent data management and the ability to integrate data across divisions and geographies. The chapter introduces the application of information extraction and expert system to generate artificial job classifications that could replace existing ones, which are largely based on conventional semantic notions. Under the assumption that companies in the staffing industry can deploy shared and common job classifications across their IT systems this chapter presents a range of service improvements, new services and data driven insights that are presently unrealized.

scholarly journals The influence of data management, organisation culture and organisation motivation on the likeliness of Business Intelligence adoption

2021 ◽  
Author(s):  
◽  
Ria van den Berg
Keyword(s):  
New Zealand ◽  
Technology Adoption ◽  
Data Management ◽  
Business Intelligence ◽  
Business Environment ◽  
Study Objective ◽  
Organisational Factors ◽  
Organisation Culture ◽  
Management Organisation ◽  
Research Studies

<p>Business Intelligence has become a powerful business tool that describes the business environment, the organisation, its situation in terms of markets, customers, competitors and its financial situation. The objective of BI is to increase the overall performance of the organisation through an informed decision making process. This research study objective is to identify the organisational factors that will increase the likeliness of BI adoption by small-to-medium enterprises (SME’s) in New Zealand. Existing research studies however, focus predominantly on the challenges and benefits of BI technologies adoption. Importantly this study do not define BI as purely a technology but defines it as methods, processes and technology that work together to gain intelligent insight from business information. The organisational factors identified that formed the hypotheses of the research model included data management, organisation culture and organisation motivation. These factors were identified through factor analysis that included technology adoption models and existing research studies specifically related to SME BI and technology adoption. The outcome of the research has identified that only organisation motivation in the context of competitiveness and perception of BI’s value and benefits can significantly influence the likeliness of BI adoption. New Zealand SME’s form the backbone of the country’s economy and also operate in extreme competitive niche markets. The adoption of BI practice and the use of information as a strategic resource will enable SME’s to be more innovative and competitive.</p>

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