Data Management, Data Analytics, and Business Intelligence Can Assist in Process Management and Process Improvement Efforts

2018 ◽  
Author(s):  
Neha Kumari
Keyword(s):  
Data Management ◽  
Process Improvement ◽  
Business Intelligence ◽  
Data Analytics ◽  
Process Management

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.

FDM: Fuzzy-Optimized Data Management Technique for Improving Big Data Analytics

2021 ◽  
Vol 29 (1) ◽  
pp. 177-185
Author(s):  
Gunasekaran Manogaran ◽  
P. Mohamed Shakeel ◽  
S. Baskar ◽  
Ching-Hsien Hsu ◽  
Seifedine Nimer Kadry ◽  
...  
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Management Technique

Lessons Learned from Implemented Internal and External Digitisation Processes at the Croatian Agency for Medicinal Products and Medical Devices

2018 ◽  
Vol 1 (1) ◽  
pp. 197-212
Author(s):  
Arian Rajh ◽  
Keyword(s):  
Process Improvement ◽  
Medical Devices ◽  
Business Process Management ◽  
Process Management ◽  
Lessons Learned ◽  
Medicinal Products ◽  
Annual Cycles ◽  
Internal Process ◽  
Capability Maturity ◽  
Modelling Methods

The goal of this work is to present and explain the differences between internal and external digitisation process instances in the Croatian Agency for Medicinal Products and Medical Devices. The study is to share lessons learned from Agency's digitisation practice, discuss the applicability of these process instances, and demonstrate ways of evaluating digitisation and accompanying capabilities. The Agency started with digitisation in 2013, and the program ran for four annual cycles. The Agency also established its internal process in 2016. For establishing it, the authors have used business process management methods - interviews, analysis and modelling. For constant process improvement, the authors use capability/maturity modelling methods, focused on the quality component, particular issues and preservation of results.

Service Process Improvement Based on Business Process Management

2021 ◽  
Vol 22 (5) ◽  
pp. 1117-1128
Author(s):  
Jia-Xing Wang Jia-Xing Wang ◽  
Si-Bin Gao Jia-Xing Wang ◽  
Cong-Er Yuan Si-Bin Gao ◽  
Da-Peng Tan Cong-Er Yuan ◽  
Jing Fan Da-Peng Tan
Keyword(s):  
Process Improvement ◽  
Business Process ◽  
Business Process Management ◽  
Process Management ◽  
Service Process

scholarly journals A survey and analysis of big data management based on computational methodologies

2020 ◽  
Vol 6 (20) ◽  
pp. 48-53
Author(s):  
Shweta Kumari
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Enterprise ◽  
Enormous Amount ◽  
Current Scenario ◽  
Data Points ◽  
The Impact ◽  
Day By Day

n a business enterprise there is an enormous amount of data generated or processed daily through different data points. It is increasing day by day. It is tough to handle it through traditional applications like excel or any other tools. So, big data analytics and environment may be helpful in the current scenario and the situation discussed above. This paper discussed the big data management ways with the impact of computational methodologies. It also covers the applicability domains and areas. It explores the computational methods applicability scenario and their conceptual design based on the previous literature. Machine learning, artificial intelligence and data mining techniques have been discussed for the same environment based on the related study.

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