Data Analytics for Transmission and Distribution
1
Citation
38
Reference
10
Related Paper
Citation Trend
Abstract:
Abstract With more sources of monitoring coming on‐line, manual analysis of the raw data becomes increasingly infeasible. Data analytics can provide the toolset for automated decision support for utility engineers, helping to unlock the potential of network data. This chapter will focus on when and how data analytics can support transmission and distribution engineers in various job functions. Some particular applications are considered first, highlighting what role analytics can play and how this benefits the utility. Then, the enabling technologies for data analytics are discussed, exploring the links with fields such as data science and Big Data. Finally, the chapter concludes with various case studies of transmission and distribution data analytics in practice, and draws out some key design and deployment challenges overcome in each case.Keywords:
Data Analysis
Software analytics
Business analytics
This article describes how in today's hyper-competitive environment, business leaders around the world are using analytics technologies to create business values, and to gain a better understanding of their customer's needs and wants. However, traditional business analytics are undergoing major changes. The Internet revolution, cloud computing, and the evolution to self-service analytics have all contributed to the changing dimensions of business intelligence. To compete effectively in a digitally driven world, business leaders must understand and address the critical shifts taking place in the field of analytics, and how these shifts impact their overall strategy. The key objective of this article is to propose a conceptual model for successful implementation of Embedded Analytics in organizations. This article also covers some of the potential benefits of analytics, explores the changing dimensions of analytics, and provides a guide to some of the opportunities that are available for using embedded analytics in business. Furthermore, this study reviews key attributes of a successful modern analytics platform and illustrates how to overcome some of the key challenges of incorporating embedded analytics into an analytic strategy in business. Finally, this article highlights successful implementation of analytics solutions in manufacturing and service industry.
Business analytics
Business Intelligence
Web analytics
Software analytics
Cite
Citations (4)
Software analytics
Business analytics
Data Analysis
IBM
Predictive Analytics
Empirical Research
Web analytics
Business Intelligence
Cite
Citations (1)
This paper reviews privacy-preserving techniques in data analytics. Data analytics is a concept of processing raw data and analysing the output for various beneficial reasons. There are four types of data analytics stated in this paper, those are descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Data itself contains unstructured, semi-structured and structured data. We compare the privacy-preserving techniques in data analytics to see whether the techniques are suitable or unsuitable to be applied in data analytics.
Data Analysis
Software analytics
Business Intelligence
Business analytics
Web analytics
Predictive Analytics
Cite
Citations (3)
Analytics helps organizations make better decisions. However, organizations need to know the analytics tools available to support decision making. This study ranked tools used by organizations in learning and human resources. Respondents reported using many tools for analytics, including statistical tools, analytics software, and database-management systems. The most common tool used is Microsoft Excel. About seven times more respondents reported using Microsoft Excel for descriptive analytics than the next most common tool.
Software analytics
Business analytics
Microsoft excel
Business Intelligence
Predictive Analytics
Cite
Citations (14)
Business analytics and big data are being discussed everywhere right now. The objective of this paper is to provide a research and teaching introduction to business analytics. It begins by providing a quick overview of the three types of analytics. To assist the future analytics professionals, we identify various sectors of the analytics industry and provide a classification of different types of industry participants. Then it includes a brief description of some current research projects under way in our team. We also note some research opportunities in Big Data analytics. The paper also concludes with a discussion of teaching opportunities in analytics.
Business analytics
Software analytics
Business Intelligence
Cultural Analytics
Data Analysis
Web analytics
Cite
Citations (22)
The increasing use of data-driven decision making and big data is leading organizations to invest in analytics software and services. However, little is known about the type of analytics capabilities within IT that are required and whether there is a common progression or development model of analytics capabilities. Also unknown is how the level of analytics capabilities and other factors influence a firm's decision to invest in analytics. The purpose of this research is to explore the relationships between levels of distinct analytics capabilities and to understand how they and other factors influence the analytics investment decision. The findings suggest that there is a distinct progression in the development of analytics capabilities, and that firm size is associated with increased capability. The results suggest that firms more likely to invest in analytics have higher current levels of specific analytics capabilities, are larger, and are located in less-competitive industries.
Software analytics
Business analytics
Data Analysis
Business Intelligence
Cite
Citations (17)
Data Analysis
Software analytics
Predictive Analytics
Business analytics
Cite
Citations (0)
Table of contents:
Foreword by Dr Suresh Divakar
Preface
Acknowledgements
Introduction to Business Analytics
Data Analytics for Business
Data Exploration in Business Analytics
Mapping Chart for Analytics Outcomes
Technology Infrastructure for Business Analytics
Analytical Methods for Parametric and Non-parametric Data
Analytical Methods for Complex Data
Data Mining Methods in Business Analytics
Interpreting the Statistical Outcomes
Documenting the Processes
Building the Storyboard of Outcomes
Appendices
Index
Business analytics
Business Intelligence
Storyboard
Software analytics
Data Analysis
Cultural Analytics
Cite
Citations (0)
For any leading organizations poised to play in the digitized data economy where consumers and users are empowered, selective, and highly informed, analytics is key. Analytics enables organizations of all sizes to meet and exceed customers’ expectations by becoming data-driven to play in the new digital economy, where customers and market deep knowledge are front and center. This chapter showcases few layers of new information that are being generated and helping organizations to create business value. The emergence of big data has triggered the overall traction around advanced business analytics. In the analytics age, companies can now address business questions that were previously ignored or omitted because there was no software robust enough to process and analyze the data. The evolution of analytics can be summarized in three major eras: analytics before big data, analytics in the big data era, and post-big data analytics era.
Business analytics
Software analytics
Data Analysis
Cultural Analytics
Business Intelligence
Web analytics
Cite
Citations (0)