HELIN Data Analytics Task Force Final Report

The main task undertaken by the HELIN Data Analytics Task Force was to conduct a proof-of-concept usability test of HELIN OneSearch, which is the Consortium’s brand name for the Encore Duet discovery service. After the initial meeting in November 2014, the Task Force met 6 times in 2015 to plan and execute a prototype test. Staff members from EBSCO Information Services’ User Research group acted as usability test advisers and coordinators and attended all meetings, either onsite or via WebEx. Task Force members collaborated to come up with specific scenarios and personas which would best emphasize patron likes, dislikes and general understanding of OneSearch. Using a small sample of volunteer student test subjects from 3 different HELIN institutions, testing took place in mid-April. The results were analyzed by EBSCO and presented at the final meeting of the Task Force on April 28. Based on this limited testing, general findings were as follows: Students who don’t receive prior information instruction are generally not aware of OneSearch. Students who do know about OneSearch do not necessarily understand the difference between OneSearch and the HELIN Catalog. Most students still continue to do their research by searching database lists, LibGuides, the Journal A to Z list, and the HELIN catalog (although not necessarily in that order). When features and operation of OneSearch are explained to students, they recognize its usefulness (especially facets, which many referred to as “filters”). Lack of clarity on how to get directly to full text items causes frustration. A larger and more comprehensive usability test would be needed to draw out more specific conclusions. Secondary tasks undertaken by the Task Force included trials and reviews of 5 data analysis tools, as well as a review of EBSCO User Research, which is quantitative data on the use of OneSearch available directly from EBSCO. The remainder of this document is a detailed account of the proceedings of the HELIN Data Analytics Task Force

What Types of Predictive Analytics are Being Used in Talent Management Organizations?

[Excerpt] Talent management organizations are increasingly deriving insights from data to make better decisions. Their use of data analytics is advancing from descriptive to predictive and prescriptive analytics. Descriptive analytics is the most basic form, providing the hindsight view of what happened and laying the foundation for turning data into information. More advanced uses are predictive (advanced forecasts and the ability to model future results) and prescriptive (“the top-tier of analytics that leverage machine learning techniques … to both interpret data and recommend actions”) analytics (1). Appendix A illustrates these differences. This report summarizes our most relevant findings about how both academic researchers and HR practitioners are successfully using data analytics to inform decision-making in workforce issues, with a focus on executive assessment and selection

Using Google Analytics Data to Expand Discovery and Use of Digital Archival Content

This article presents opportunities for the use of Google Analytics, a popular and freely available web analytics tool, to inform decision making for digital archivists managing online digital archives content. Emphasis is placed on the analysis of Google Analytics data to increase the visibility and discoverability of content. The article describes the use of Google Analytics to support fruitful digital outreach programs, to guide metadata creation for enhancing access, and to measure user demand to aid selection for digitization. Valuable reports, features, and tools in Google Analytics are identified and the use of these tools to gather meaningful data is explained

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions