Big Data and Official Statistics

2019 ◽  
Vol 26 (12) ◽  
pp. 5-14
Author(s):  
S. Upadhyaya
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Industrial Revolution ◽  
Data Transformation ◽  
Small Sample ◽  
Theory And Practice ◽  
Deep Penetration ◽  
Process Data ◽  
Official Statistics ◽  
The Government

Big data is a component of the Fourth Industrial Revolution. The deep penetration of digital technology has turned data into an essential component of the production process. Data are automatically generated by machines during the course of operation and during interactions with humans. This paper describes the concept and composition of big data. Most of the big data are unstructured and include text, audio-video files, images, emails, log files, etc. Statisticians are more interested in structured data presented in a pre-defined database model. Big data offer new sources and opportunities that cannot be discounted. However, the use of big data requires proper assessment in terms of quality dimensions such as accuracy, comparability and methodological soundness. Against the backdrop of arguments regarding big data, some users view big data as a replacement of official statistics. Such a conclusion is premature for at least two reasons: first, only a small part of big data can be used for decision-making. Second, theory and practice prove that a small sample based on scientific methods can yield much more reliable and accurate estimates than the results obtained from the processing of large amounts of unstructured data. The paper assesses the possibility of using big data for Sustainable Development Goals (SDG) monitoring, which is a nationally owned process, and NSOs are accountable for the SDG data they report. If the data are derived from a big data source, irrespective of the level of technical sophistication used in data transformation, the reliability of such data might be questioned by the national institutions. The paper concludes that the reliability of data obtained from big data sources hinges on the quality of tools and methods applied to data transformation. Statisticians can play an important role in alerting society, decision-making bodies of the government and businesses about the reliability of information derived from the different sources.

Big Data in the philippines: How do we actually use them?

2021 ◽  
pp. 1-30
Author(s):  
Lisa Grace S. Bersales ◽  
Josefina V. Almeda ◽  
Sabrina O. Romasoc ◽  
Marie Nadeen R. Martinez ◽  
Dannela Jann B. Galias
Keyword(s):  
Big Data ◽  
High Speed ◽  
The Philippines ◽  
Complex Data ◽  
Official Statistics ◽  
Group Discussions ◽  
The Public ◽  
The Government ◽  
The Many ◽  
Current Utilization

With the advancement of technology, digitalization, and the internet of things, large amounts of complex data are being produced daily. This vast quantity of various data produced at high speed is referred to as Big Data. The utilization of Big Data is being implemented with success in the private sector, yet the public sector seems to be falling behind despite the many potentials Big Data has already presented. In this regard, this paper explores ways in which the government can recognize the use of Big Data for official statistics. It begins by gathering and presenting Big Data-related initiatives and projects across the globe for various types and sources of Big Data implemented. Further, this paper discusses the opportunities, challenges, and risks associated with using Big Data, particularly in official statistics. This paper also aims to assess the current utilization of Big Data in the country through focus group discussions and key informant interviews. Based on desk review, discussions, and interviews, the paper then concludes with a proposed framework that provides ways in which Big Data may be utilized by the government to augment official statistics.

scholarly journals KURIKULUM PENDIDIKAN VOKASI PADA ERA REVOLUSI INDUSTRI 4.0

2019 ◽  
Vol 20 (1) ◽  
pp. 82
Author(s):  
Unung Vera Wardina ◽  
Nizwardi Jalinus ◽  
Lise Asnur
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Blended Learning ◽  
Vocational Education ◽  
Industrial Revolution ◽  
Technology Literacy ◽  
Business Practices ◽  
Education Curriculum ◽  
The Government ◽  
Good Learning

Vocational education purpose is to produce ready-to-work graduates who have the relevant skills for current job employment. Entering the industrial revolution era 4.0 there were massive changes in various industries and workers' ability needs. This article intends to examine the implications of the industrial revolution 4.0 era for vocational education curriculum. Based on the study of various sources and business practices, it is necessary to develop vocational education curriculum that are in accordance with the era of industrial revolution 4.0 and relevant to answering the needs of new skills, such as the ability to create and manage coding, big data, and artificial intelligence. The vocational curriculum needs to apply blended learning, which integrates face-to-face and online learning, so as to more effectively build graduates' abilities and skills. The curriculum also needs to contain mastery of 4.0 competencies such as data literacy, technology literacy and human literacy. In order for the vocational education curriculum to have a broad impact, the government, educational institutions, industries must work together to revitalize the approach and content of the vocational education curriculum. Teachers must also be able to implement good learning to produce optimal graduate performance. Pendidikan vokasi merupakan pendidikan yang menghasilkan lulusan siap kerja yang memiliki keterampilan sesuai kebutuhan dunia kerja. Memasuki era revolusi indusri 4.0 terjadi perubahan yang masif pada perbagai industri dan kebutuhan kemampuan pekerja. Artikel ini bermaksud mengkaji implikasi era revolusi industri 4.0 bagi kurikulum pendidikan vokasi. Berdasarkan kajian berbagai sumber dan praktek bisnis, diperlukan pengembangan kurikulum pendidikan vokasi yang sesuai dengan era revolusi industri 4.0 dan relevan menjawab kebutuhan keterampilan baru, seperti kemampuan membuat dan mengelola coding, big data, dan artificial intelligence. Kurikulum vokasi perlu menerapkan pembelajaran blended learning, yang mengintegrasikan pembelajaran tatap muka dan online, supaya lebih efektif membangun kemampuan dan ketrampilan lulusan. Kurikulum juga perlu memuat penguasaan kompetensi 4.0 seperti literasi data, literasi teknologi dan literasi manusia. Agar kurikulum pendidikan vokasi menghasilkan dampak yang luas, pemerintah, lembaga pendidikan, industri harus bersinergi untuk merevitalisasi pendekatan dan isi kurikulum pendidikan vokasi. Pengajar juga harus dapat menyelenggarakan pembelajaran yang baik untuk menghasilkan kinerja optimal lulusan.

Research on the Application of Big Data in the Environmental Decision-Making of the Government

2014 ◽  
Vol 685 ◽  
pp. 524-527
Author(s):  
Yan Ju Zhu
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Video Compression ◽  
Computer Processing ◽  
Environmental Data ◽  
Environmental Decision Making ◽  
Management Platform ◽  
Data Platform ◽  
The Government ◽  
Environmental Decision

The article mainly researches on the application of big data in the environment decision-making of the government. Through the integration of the technology of Internet, video compression, computer processing, we pose the model of the government environmental data platform. The platform includes the environmental data acquisition platform, the environmental decision-making platform and the environmental management platform.

scholarly journals The dynamics of traffic congestion: a specific look into malaysian scenario and the plausible solutions to eradicate it using machine learning

2019 ◽  
Vol 15 (2) ◽  
pp. 1086
Author(s):  
M. Ali ◽  
T. K. Sheng ◽  
K. M. Yusof ◽  
M. R. Suhaili ◽  
N. E. Ghazali ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Traffic Congestion ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Active Role ◽  
Machine Learning Algorithms ◽  
Asia Pacific ◽  
The Government

Transportation has been considered as the backbone of the economy for the past many years. Unfortunately, since few years due to the uncontrolled urbanization and inadequate planning, countries are facing problem of congestion. The congestion is hindering the economic growth and also causing environmental issues. This has caused serious concerns among the major economies of the world, especially in Asia-Pacific region. Many countries are playing an active role in eradicating this problem and some have been quite successful so far. Malaysia, being a major ASEAN economy is also tackling with this huge problem. The authorities are committed to solve the issue. In this regard, solving the issue leveraging the use of big data analytics has become crucial. The authorities can form a complete robust framework based on big data analytics and decision making process to solve the issue effectively. The work focuses and observes the traffic data samples and analyzes the accuracy of machine learning algorithms, which helps in decision making. Yet, here is a lot to be done if the government needs to solve the problem effectively. Supposedly, a comprehensive big data transport framework leveraging machine learning, is one way to solve the issue.

scholarly journals Big Data-Driven Macroeconomic Forecasting Model and Psychological Decision Behavior Analysis for Industry 4.0

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jie Liu
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Behavior Analysis ◽  
Industry 4.0 ◽  
Value Added ◽  
Complete Information ◽  
Data Driven ◽  
Macroeconomic Forecasting ◽  
The Government ◽  
Decision Making Behavior

With the advent of Industry 4.0, economic development has become a rapid information age. The content of macroeconomic forecast is very extensive, and the existence of big data technology can provide the government with multilevel, diversified, and complete information and comprehensively process, integrate, summarize, and classify these pieces of information. This paper forecasts the CPI value in the next 12 months according to the CPI in China in the recent 20 years. Compared with the traditional forecasting methods, the forecasting results have higher accuracy and timeliness. At the same time, the trend of growth rate of industrial value-added is analyzed, and the experiments on MAE and RMSE show that the method proposed in this paper has obvious advantages. It also analyzes the disadvantages of traditional psychological decision-making behavior analysis, introduces the development status and advantages of big data-driven psychological decision-making behavior analysis, and opens up new research ideas for psychological decision-making analysis.

scholarly journals Implementasi Big Data Analytical Untuk Perguruan Tinggi Menggunakan Machine Learning

2021 ◽  
Vol 2 (1) ◽  
pp. 77-88
Author(s):  
Rakhmat Purnomo ◽  
Wowon Priatna ◽  
Tri Dharma Putra
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Student Performance ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Process Data ◽  
Grouping Students

The dynamics of higher education are changing and emphasize the need to adapt quickly. Higher education is under the supervision of accreditation agencies, governments and other stakeholders to seek new ways to improve and monitor student success and other institutional policies. Many agencies fail to make efficient use of the large amounts of available data. With the use of big data analytics in higher education, it can be obtained more insight into students, academics, and the process in higher education so that it supports predictive analysis and improves decision making. The purpose of this research is to implement big data analytical to increase the decision making of the competent party. This research begins with the identification of process data based on analytical learning, academic and process in the campus environment. The data used in this study is a public dataset from UCI machine learning, from the 33 available varibales, 4 varibales are used to measure student performance. Big data analysis in this study uses spark apace as a library to operate pyspark so that python can process big data analysis. The data already in the master slave is grouped using k-mean clustering to get the best performing student group. The results of this study succeeded in grouping students into 5 clusters, cluster 1 including the best student performance and cluster 5 including the lowest student performance

scholarly journals Historical Evolution of Audit Theory and Practice

2020 ◽  
Vol 16 (1) ◽  
pp. 2252-2259
Author(s):  
A. O Olagunju ◽  
S. A. Owolabi
Keyword(s):  
Decision Making ◽  
Industrial Revolution ◽  
New Technology ◽  
Theory And Practice ◽  
Historical Evolution ◽  
Business World ◽  
Adoption And Implementation ◽  
Technology Age ◽  
Effective Decision Making ◽  
The Way

The separation of ownership and control due to industrial revolution and expansionary system of businesses has brought the need for checks and balances by the owners of the businesses. Decision making requires information that is exhaustive, consistent, reliable, and credible and such there is need for cross-examination of records for effective decision making. Starting from fraud detection to attesting to credibility of financial statements are auditing practices. As every field of study has its root, thus this paper examined the historical evolution of audit theory and practice from ancient civilization till present age and focusing on the way forward as regards the future of audit. A desk research was conducted and from the review, it was discovered that lots of transitions have occurred in audit theories and practices over time as business world turns digitalized, thus leading to past audit practices becoming outdated and auditing evolution has reached a critical juncture whereby auditors may not have choice than to adjust to the new technology age system. It is imperative that accountants and auditors ultimately lead the way in adoption and implementation of technology-enhanced auditing.

scholarly journals KURIKULUM PENDIDIKAN VOKASI PADA ERA REVOLUSI INDUSTRI 4.0

2019 ◽  
Vol 20 (1) ◽  
pp. 82-90
Author(s):  
Unung Vera Wardina ◽  
Nizwardi Jalinus ◽  
Lise Asnur
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Blended Learning ◽  
Vocational Education ◽  
Industrial Revolution ◽  
Technology Literacy ◽  
Business Practices ◽  
Education Curriculum ◽  
The Government ◽  
Good Learning

Vocational education purpose is to produce ready-to-work graduates who have the relevant skills for current job employment. Entering the industrial revolution era 4.0 there were massive changes in various industries and workers' ability needs. This article intends to examine the implications of the industrial revolution 4.0 era for vocational education curriculum. Based on the study of various sources and business practices, it is necessary to develop vocational education curriculum that are in accordance with the era of industrial revolution 4.0 and relevant to answering the needs of new skills, such as the ability to create and manage coding, big data, and artificial intelligence. The vocational curriculum needs to apply blended learning, which integrates face-to-face and online learning, so as to more effectively build graduates' abilities and skills. The curriculum also needs to contain mastery of 4.0 competencies such as data literacy, technology literacy and human literacy. In order for the vocational education curriculum to have a broad impact, the government, educational institutions, industries must work together to revitalize the approach and content of the vocational education curriculum. Teachers must also be able to implement good learning to produce optimal graduate performance. Pendidikan vokasi merupakan pendidikan yang menghasilkan lulusan siap kerja yang memiliki keterampilan sesuai kebutuhan dunia kerja. Memasuki era revolusi indusri 4.0 terjadi perubahan yang masif pada perbagai industri dan kebutuhan kemampuan pekerja. Artikel ini bermaksud mengkaji implikasi era revolusi industri 4.0 bagi kurikulum pendidikan vokasi. Berdasarkan kajian berbagai sumber dan praktek bisnis, diperlukan pengembangan kurikulum pendidikan vokasi yang sesuai dengan era revolusi industri 4.0 dan relevan menjawab kebutuhan keterampilan baru, seperti kemampuan membuat dan mengelola coding, big data, dan artificial intelligence. Kurikulum vokasi perlu menerapkan pembelajaran blended learning, yang mengintegrasikan pembelajaran tatap muka dan online, supaya lebih efektif membangun kemampuan dan ketrampilan lulusan. Kurikulum juga perlu memuat penguasaan kompetensi 4.0 seperti literasi data, literasi teknologi dan literasi manusia. Agar kurikulum pendidikan vokasi menghasilkan dampak yang luas, pemerintah, lembaga pendidikan, industri harus bersinergi untuk merevitalisasi pendekatan dan isi kurikulum pendidikan vokasi. Pengajar juga harus dapat menyelenggarakan pembelajaran yang baik untuk menghasilkan kinerja optimal lulusan.

scholarly journals PROBLEMS OF APPLICATION OF DECISION-MAKING MODELS IN THE REGIONAL ECONOMY UNDER CONDITIONS OF PARTIALLY RELIABLE INFORMATION

2021 ◽  
Vol 10 (06) ◽  
pp. 70-79
Author(s):  
Vidadi Akhundov Vidadi Akhundov
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Content Analysis ◽  
Data Analysis ◽  
Industry 4.0 ◽  
Value Added ◽  
High Tech ◽  
Clustering Method ◽  
Process Data ◽  
The World

In this study, attention is drawn to the under-explored area of strategic content analysis and the development of strategic vision for managers, with the supporting role of interpreting visualized big data to apply appropriate knowledge management strategies in regional companies. The study suggests improved models that can be used to process data and apply solutions to Big Data. The paper proposes a model of business processes in the region in the context of information clusters, which become the object of analysis in the conditions of active accumulation of big data about the external and internal environment. Research has shown that traditional econometric and data collection techniques cannot be directly applied to Big Data analysis due to computational volatility or computational complexity. The paper provides a brief description of the essence of the methods of associative and causal data analysis and the problems that complicate its application in Big Data. The scheme of accelerated search for a set of causal relationships is described. The use of semantically structured models, cause-effect models and the K-clustering method for decision making in big data is practical and ensures the adequacy of the results. The article explains the stages of applying these models in practice. In the course of the study, content analysis was carried out using the main methods of processing structured data on the example of the countries of the world using synthetic indicators showing the trends of Industry 4.0. When assessing Industry 4.0 technologies by region, the diversity of country grouping attributes should be considered. Therefore, during the analysis, the countries of the world were compared in two groups. The first group - the results for developed countries are presented in tabular form. For the second group, the results are presented in an explanatory form. In the process of assessing industrial 4.0 technologies, statistical indicators were used: "The share of medium and high-tech activities", "Competitiveness indicators", "Results in the field of knowledge and technology", "The share of medium and high-tech production in the total value added in the manufacturing industry", “Industrial Competitiveness Index (CIP score)”. As a result, the rating of the countries was determined based on the analysis of these indicators. . The reasons for the difficulties of calculations when processing Big Data are given in the concluding part of the article. Keywords: K - clustering method, causal links, data point, Euclidean distance

‘Big data’ patentometrics for R&D decision-making

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Charu Verma ◽  
Pradeep Kumar Suri
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Performance Management ◽  
Relevant Information ◽  
Research Strategy ◽  
Global Market ◽  
Content Type ◽  
Country Level ◽  
Free Data ◽  
The Government

Purpose The purpose of this paper is to highlight the use of big data through patentometric insights for R&D decision-making. Design/methodology/approach This study assesses the inventive activity through ‘big data’ patents, registered by inventors worldwide, using WIPO Patentscope database. The objective is to use the insights from patentometrics for R&D decision-making. The data from WIPO PatentScope (https://patentscope.wipo.int/search/en/search.jsf) was searched for current patent scenario in area of ‘big data’. The data was further organized and cleaned using the Google ‘OpenRefine’. Data was pre-processed to remove all null values. Cleaned data was analyzed using programming language ‘R’, MS Excel (charts and Pivot tables) and free data visualization tool called ‘Tableau Public’, to get insights for R&D decision-making. Findings The key insights included trends (patents with years of publication), top technologies trending the current space, top organizations leading in these technologies and the top inventors who are publishing patents in these technologies through leading organizations were drawn. Details in Section 5 in the paper. Research limitations/implications Global patent data is multi-lingual and spreads across a set of multiple databases. Domain experts may be required to assess, identify and extract the relevant information for analysis and visualization of multi-lingual distributed data sets. Government organizations generally have multi-dimensional goals that may be more toward societal benefits. On the other hand, the commercial companies are more focused on profit. Therefore, the performance management process has to be really effective because it is critical for getting value in the government sector. Practical implications Insights from patent analytics serve as the important input to R&D managers as well as policymakers to assess the global needs to plan the national orientation according to the global market. This will help further for R&D projects prioritization, planning, budget allocations, human capital planning and other gamut of R&D management and decision-making. Social implications Facilitation for R&D institutions (government as well as private) to formulate the research strategy for the domains or research areas to delve into. R&D decisions will be completely data-driven making them more accurate, reliable, valid and informed. These insights are very relevant for policymakers as well to facilitate the need assessment to determine the National priorities, make improvements in meeting societal country-level challenges during the resource allocation at top and subsequently at all other levels. Originality/value Data analytics of global patents in “big data” till 2019 to get insights to facilitate R&D decision-making.

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