A Comparative Analysis on Various Business Intelligence (BI), Data Science and Data Analytics Tools

2021 ◽  
Author(s):  
Koustubh Sharma ◽  
Aditya Shetty ◽  
Arnish Jain ◽  
Ritesh Kumar Dhanare
Keyword(s):  
Comparative Analysis ◽  
Business Intelligence ◽  
Data Analytics ◽  
Data Science

The Nature of Intelligent Analytics

2021 ◽  
pp. 1-21
Author(s):  
Zhaohao Sun ◽  
Andrew Stranieri
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Research And Development ◽  
Business Intelligence ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Novel Approach ◽  
Advanced Analytics

Intelligent analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores the nature of intelligent analytics. More specifically, this chapter identifies the foundations, cores, and applications of intelligent big data analytics based on the investigation into the state-of-the-art scholars' publications and market analysis of advanced analytics. Then it presents a workflow-based approach to big data analytics and technological foundations for intelligent big data analytics through examining intelligent big data analytics as an integration of AI and big data analytics. The chapter also presents a novel approach to extend intelligent big data analytics to intelligent analytics. The proposed approach in this chapter might facilitate research and development of intelligent analytics, big data analytics, business analytics, business intelligence, AI, and data science.

scholarly journals Business intelligence addressing service quality for big data analytics in public sector

2019 ◽  
Vol 16 (1) ◽  
pp. 491
Author(s):  
Sadesh Manikam ◽  
Shamsul Sahibudin ◽  
Vinothini Kasinathan
Keyword(s):  
Big Data ◽  
Service Quality ◽  
Business Intelligence ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Cost Savings ◽  
Maturity Model ◽  
Data Governance ◽  
Public Sectors

<span>With the inauguration of Big Data Analytics initiative nationally, many nations have participated and paved way for BDA ecosystem. The initiative is a catalyst to further encourage economic growth in Public Sectors. Some of the common key deliverables identified are increasing productivity involving information communications technology, cost savings, shared benefits, and encourage innovation. The objectives can be further elaborated by driving big data analytics demands in various public sectors agency, adopting big data analytics framework supporting the building of big data industry. This has encouraged talents and startup companies inspiring their capabilities by developing various technology platform, collaborate and innovate amongst public and private sectors, and further strengthen data governance by creating policy and procedures. With the establishment of big data analytics framework, performance measurement can be enforced effortlessly using the principles of business intelligence maturity model and the technological stack comes with it. Various data sources can be used to benchmark service quality using advanced analytics and data science techniques.</span>

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

2020 ◽  
Vol 13 ◽  
Author(s):  
Ritu Khandelwal ◽  
Hemlata Goyal ◽  
Rajveer Singh Shekhawat
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Data Science ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Future Trends ◽  
Data Set ◽  
Learning Stage ◽  
Learning Techniques ◽  
Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

scholarly journals New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
César de Oliveira Ferreira Silva ◽  
Mariana Matulovic ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Data Science ◽  
Final Decision ◽  
Aquifer Recharge ◽  
Groundwater Governance ◽  
Ethical Attitude ◽  
New Information ◽  
Modeling Experiment ◽  
Using Data

Abstract Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract

Using of data science in healthcare

2021 ◽  
pp. 149-156
Author(s):  
Ihor Ponomarenko ◽  
Oleksandra Lubkovska
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Business Intelligence ◽  
Data Science ◽  
Large Data ◽  
Science Methods ◽  
Medical Field ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

The subject of the research is the approach to the possibility of using data science methods in the field of health care for integrated data processing and analysis in order to optimize economic and specialized processes The purpose of writing this article is to address issues related to the specifics of the use of Data Science methods in the field of health care on the basis of comprehensive information obtained from various sources. Methodology. The research methodology is system-structural and comparative analyzes (to study the application of BI-systems in the process of working with large data sets); monograph (the study of various software solutions in the market of business intelligence); economic analysis (when assessing the possibility of using business intelligence systems to strengthen the competitive position of companies). The scientific novelty the main sources of data on key processes in the medical field. Examples of innovative methods of collecting information in the field of health care, which are becoming widespread in the context of digitalization, are presented. The main sources of data in the field of health care used in Data Science are revealed. The specifics of the application of machine learning methods in the field of health care in the conditions of increasing competition between market participants and increasing demand for relevant products from the population are presented. Conclusions. The intensification of the integration of Data Science in the medical field is due to the increase of digitized data (statistics, textual informa- tion, visualizations, etc.). Through the use of machine learning methods, doctors and other health professionals have new opportunities to improve the efficiency of the health care system as a whole. Key words: Data science, efficiency, information, machine learning, medicine, Python, healthcare.

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