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'Without data, you're just another person with an opinion'

EXPERT REVIEW OF PHARMACOECONOMICS & OUTCOMES RESEARCH 2020年第2期20卷 147-154页

作者： Kolasa, Katarzyna Goettsch, Wim Petrova, Guenka Berler, Alexander Akad Leona Kozminskiego Hlth Econ & Healthcare Ma Dept Hlth Econ & Healthcare Management Warsaw Poland Univ Utrecht WHO Collaborating Ctr Pharmaceut Policy Div Pharmacoepidemiol & Clin Pharmacol Utrecht Netherlands Med Univ Sofia Fac Pharm Dept Social Pharm & Pharmacoecon Sofia Bulgaria Gnomon Dept Hlth Econ & Healthcare Management Athens Greece

Introduction: Given the recent impressive digital transformation worldwide, the importance of data has reached a new dimension. It is, therefore, provocative to ask whether data can save healthcare systems from bankruptcy. Areas covered: We reviewed published examples in the search for the evidence on how the growing amount of data could change the way we used to assess the value of healthcare technologies, ensuring a more holistic approach in the decision-making process while reducing the waste in the healthcare. Expert opinion: The growing amount of data will continue to provide a multitude of valuable insights that can save healthcare systems from bankruptcy. Electronic medical records, IoT, wearables, and mobile applications generate constant data streams that can be utilized endlessly thanks to methodological advancements such as SNA, unsupervised and supervised machine learning, and natural language programming. However, interoperability across these multiple data sources still pose a challenge for the future development of data-driven healthcare. Already today however, decision makers can utilize Big Data to develop conditional coverage schemes for very expensive and complicated health technologies suitable for personalized healthcare. More advanced payers may utilize even data analytics even further and develop AI-based pricing schemes.

关键词： Big data predictive modeling descriptive analytics prescriptive analytics predictive analytics

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Open Data Visual analytics to Support Decisions on Physical Investments

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Procedia Computer Science 2019年 161卷 797-804页

作者： Meditya Wasesa M. Mashuri Putri Handayani Utomo S. Putro School of Business and Management Bandung Institute of Technology Jl. Ganesha 10 Bandung Indonesia Indonesian Center for Logistics and Value Chains Jl. Sarijadi Baru III no: 15 Bandung Indonesia Water Science and Management Masters Programme Utrecht University Utrecht the Netherlands

Before making decisions about the location of physical investments (e.g. factories, warehouses, stores, etc.), investors have to conduct thorough profiling on the prospective locations of the physical facilities. For this profiling purpose, investors consider a number of important aspects such as the location’s local resources, market size, economic growth, regional minimum wage (salary) standard, land acquisition cost, human development index, and gross domestic product (GDP). While conducting manual research on those decision variables can cost extensive time and efforts, in this article, we present an open data visual analytics which will help investors in making decisions on the prospective location of physical investments. Investors can use the web-based application to easily gather information and do a quick comparison among prospective investment locations in terms of the selected decision variables.

关键词： Open Data Visual analytics descriptive analytics Spatiotemporal Data Physical Investment

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Business analytics and AGI in corporate management systems

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Procedia Computer Science 2018年 145卷 533-544页

作者： Artem A. Sukhobokov SAP DBS CIS Kosmodemyanskaya nab. 52/4 115054 Moscow Russian Federation Bauman Moscow State Technical University ul. Baumanskaya 2-ya 5 Moscow 105005 Russian Federation

The article proposes a classification of tools and stages of business analytics development, extending previously known examples of such classifications. As the highest point of business analytics tools development, AGI is considered. It is substantiated that corporate management systems are the most likely environment in which the first copies of AGI will be created. The process of corporate management systems evolution is considered in conjunction with the time shift between the development of ideas and the practical use of the corresponding class of systems. A forecast of time frames of AGI introduction in these systems is made.

关键词： descriptive analytics Predictive analytics Predicative analytics Implicative analytics Prescriptive analytics Executive analytics Reflexive analytics AGI

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Mining on Senior Citizens Data: Empirical Evidence in the Philippines Locale "Basis for Social Services Improvement and Decision Making"

Mining on Senior Citizens Data: Empirical Evidence in the Ph...

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2022 International Conference on Automatic Control and Intelligent Systems

作者： Castro, Eltimar T., Jr. Romano, Nephi P. Father Saturnino Urios Univ Data Sci & Analyt Ctr Butuan City Philippines

ISBN: (纸本)9781665495813

Senior citizens are among the most vulnerable members of society. Each country is concerned about how to improve their level of life and secure their social support and services. The Philippines faced with the problem of improving social services for its expanding population of elderly. Several agencies are tasked with the responsibility of providing improved social services that promote the welfare of the city's senior citizens. To achieve highest degree of satisfaction to all elderly, rigorous steps must be taken to identify process gaps and build a system that aid in the process's acceleration. Data mining technique help to generate insights and visualization, thus facilitating government organizations that oversee health and social services. The results can aid for policy and decision making of the National and Local Government Agency. The results showed that there are increasing number of senior citizens profiled with an estimated average of 3,338 per year started last 2017. The target numbers of elderlies to be profiled is around 25,000 in 2022, these figures are aligned to Philippines Statistic Authority (PSA). The findings revealed that 25 percent of profiled elderlies are not registered in PhilHealth, and 85 percent of them are not pensioner thus, the results are implying that there are elderlies entitled to receive benefits as mandated by Law. Overall, the assessment showed that there are areas in social services that needs immediate actions to deliver quality social services for senior citizens in future. Furthermore, researcher recommends more dataset to be analyze and system integration for realtime monitoring and immediate actions.

关键词： Senior Citizens Data Mining analytics descriptive analytics Trends

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Prevalence of HCV in Shopian: Using Data Visualization (Data Mining) 3rd

Prevalence of HCV in Shopian: Using Data Visualization (Data...

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3rd International Conference on Intelligent Computing and Communication (ICICC)

作者： Ahmad, Zubair Seethlani, Jitendra Gul, Sheikh Babar Sri Satya Sai Univ Technol & Med Sci Dept Comp Sci Sehore 466001 MP India Mini Secretariat Chief Med Off Block B Shopian 192303 J&K India

ISBN: (纸本)9789811510847;9789811510830

Data mining is an important subfield of Data Science and is also called knowledge discovery within a database. In this paper, we actually use data mining visualization techniques to analyze the huge database of District Shopian and assume prevalence rate of HCV in Specific Area (Vehil) of District Shopian (J&K). Health Department of District Shopian uses different types of registers to maintain the data of patients. During survey, we go through the registers to collect data of symptomatic HCV patients analyze it by maintaining the disease trends. Data mining analytical tools were being used to detect prevalence of HCV infection distribution and also identify the reasons that contributed to such widespread of HCV in the area of Vehil

关键词： descriptive analytics Predictive analytics Data visualization techniques hepatic C CASE STUDY

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Baca Beverage Distributors: surviving the great attrition

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CASE Journal 2024年第3期20卷 604-633页

作者： Anderson, Joe Joshi, Mahendra Williams, Susan K. The W. A. Franke College of Business Northern Arizona University

Theoretical basis: This compact case provides a relatively large data set that students explore using visualization and a Tableau dynamic dashboard that they create. Students were asked to describe what the data set contained in relation to employee attrition experience of Baca Beverage Distributors (BBD). The application and managerial questions are set in human resources and a company that is facing high attrition during the pandemic. Research methodology: BBD shared their data and problem scenario for this compact case. The protagonist, Morgan Matthews, was the authors’ contact and provided significant clarification and guidance about the data. Both the company and the protagonist have been disguised. Some of the job positions have been rephrased. All names of employees, supervisors and managers have been replaced with codes. Case overview/synopsis: During the 2020–2022 pandemic years, BBD experienced, like many companies, a higher than usual employee turnover rate and Morgan Matthews, Director of People, was concerned. Not only was it time-consuming, expensive and disruptive but the company had prided itself on being a good place to work. Were they hiring the right people, people that fit the company culture and people that fit the positions for which they were hired? The company had been using the Predictive Index [1] when on-boarding employees. In addition, there were results from self-reviews and manager reviews that could be used. Morgan wondered if data visualization and visual analytics would be useful in describing their employees and whether it would reveal any opportunities to improve the turnover rate. Before seeking a solution for the high turnover, it was important to step back and learn what the data said about who was leaving and the reasons they gave for leaving. Complexity academic level: This compact case can be used in courses that include visualization using Tableau and dashboards. As it is a compact case, it requires less preparation time fro

关键词： Dashboards Data storytelling descriptive analytics Dynamic dashboard Employee attrition Human resource management Tableau Visualization

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Market Intelligence for Agricultural Commodities Using Forecasting and Deep Learning Techniques 1

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7th International Conference on Big Data analytics (BDA)

作者： Shrivastava, Swapnil Pal, Supriya N. Walia, Ranjeet Ctr Dev Adv Comp CDAC 1 Old Madras Rd Bangalore 560038 Karnataka India Natl Inst Technol Hamirpur 177005 Himachal Prades India

ISBN: (数字)9783030371883

ISBN: (纸本)9783030371883;9783030371876

About two third of the Indian population continues to live in villages and depends on agriculture as main source of livelihood. The agrarian distress that took decades to build up, surfaced in the form of decline in the proportion of rural population and increase in the number of farmer's suicide cases. There is a strong hypothesis that in addition to production techniques, the widespread availability of Market Intelligence would bring substantial improvement in financial condition of farmers. Hence there is a need for comprehensive system, which would fetch, interlink, transform and analyze relevant data from various ministries/departments/organizations spread across the country to generate precise, appropriate and timely Market Intelligence. We took a step in this direction by design and implementation of Market Intelligence System Proof of Concept (PoC) using available datasets for few agricultural commodities. This PoC takes daily market price and weather data as input, transforms it into information and generates actionable intelligence by applying forecasting and deep learning techniques. The system provides trend analysis, short term as well as long term commodity price prediction and market selection as insights for farmers. The Auto Regressive Integrated Moving Average (ARIMA) forecasting technique and Recurrent Neural Network (RNN) deep learning techniques are applied for short term and long term agricultural commodity price prediction respectively. The study results demonstrate intended utility of forecasting and deep learning techniques for generating Market Intelligence System. The paper concludes with benefits of comprehensive Market Intelligence system, challenges and future work.

关键词： descriptive analytics Predictive analytics Forecasting Market Intelligence Deep learning

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Defining Data Science and Data Scientist

Defining Data Science and Data Scientist

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作者： Dedge Parks, Dana M University of South Florida

学位级别：D.B.A.

The world's data sets are growing exponentially every day due to the large number of devices generating data residue across the multitude of global data centers. What to do with the massive data stores, how to manage them and defining who are performing these tasks has not been adequately defined and agreed upon by academics and practitioners. Data science is a cross disciplinary, amalgam of skills, techniques and tools which allow business organizations to identify trends and build assumptions which lead to key decisions. It is in an evolutionary state as new technologies with capabilities are still being developed snd deployed. The data science tasks and the data scientist skills needed in order to be successful with the analytics across the data stores are defined in this document. The research conducted across twenty-two academic articles, one book, eleven interviews and seventy-eight surveys are combined to articulate the convergence on the terms data science. In addition, the research identified that there are five key skill categories (themes) which have fifty-five competencies that are used globally by data scientists to successfully perform the art and science activities of data science.@pqdt@break@Unspecified portions of statistics, technology programming, development of models and calculations are combined to determine outcomes which lead global organizations to make strategic decisions every day.@pqdt@break@This research is intended to provide a constructive summary about the topics data science and data scientist in order to spark the dialogue for us to formally finalize the definitions and ultimately change the world by establishing set guidelines on how data science is performed and measured.

关键词： data analyst data driven decisions business analysis descriptive analytics

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Smart Cities for Sustainability An Analytical Perspective

Smart Cities for Sustainability An Analytical Perspective

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4th World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4)

作者： Mundada, Monica Mukkamala, Raghava Rao Copenhagen Business Sch Ctr Business Data Analyt Dept Digitalizat Copenhagen Denmark Kristiania Univ Coll Dept Technol Oslo Norway

ISBN: (纸本)9781728168234

Smart cities is an trending concept with an focus of development in urban areas from all edges. The primary aim of this research work, is concentrated for literature review on Smart Economy, Smart Environment, Smart Government, Smart Living, Smart Mobility, Smart People integrated with technology research themes with a key focus on data analytics approach towards sustainable development. Our conclusion reveals that a lot of research in smart cities is focused on prescriptive analytics, which indicates that smart cities initiatives aligned on taking the data analytics perspective for sustainable development in urban areas.

关键词： Smart cities Sustainable Development Predictive analytics descriptive analytics Prescriptive analytics

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Data analytics on Price Prediction of Travelling Package using Regression Models

Data Analytics on Price Prediction of Travelling Package usi...

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International Conference on Artificial Intelligence and Data Sciences (AiDAS)

作者： Ezzatul Akmal Kamaru Zaman Nur Athirah Azirah Binti Rahmat Azlin Ahmad Nur Huda Nabihan Md Shahri Mohd Najib Ismail Advanced Analytics Engineering Centre Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Shah Alam Selangor Malaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Shah Alam Selangor Malaysia School of Computing Asia Pacific University Technology Park Malaysia Bukit Jalil Kuala Lumpur Malaysia

ISBN: (纸本)9781728130422

Travel agencies set new prices on travel packages based on their experiences by analyzing the trend on holiday and festive season. However, they find it hard to set and predict exact travel packages with minimum prices to be offered for the upcoming years. Prices keep changing due to other reasons rather than the holiday and festive season. This research paper applied data analytics which is divided into two parts, 1) descriptive analytics to facilitate the agencies to have better insights of the data and 2) predictive analytics for price forecasting. Visualization is a part of descriptive analytics where dispersion and correlation of data are produced to gain insight of data. Meanwhile, in the predictive analytics part, Linear Regression and Multiple Linear Regression models are applied to predict the price of travel packages. Different parameter settings are applied to optimize the score of R-square. Hence, the final result of 0.9346 R-square is achieved by applying Multiple Linear Regression with all variables are taken into consideration.

关键词： Data analytics descriptive analytics Linear regression Multiple linear regression Predictive analytics R-square Travel packages Linear Regression Holidays Prices travel data association Regression (Psychology) packages AGENCIES parameter setting Data analysis multiple regression model

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