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

Autoencoders for strategic decision support

In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are available. We introduce and extend the use of autoencoders to provide strategically relevant granular feedback. A first experiment indicates that experts are inconsistent in their decision making, highlighting the need for strategic decision support. Furthermore, using two large industry-provided human resources datasets, the proposed solution is evaluated in terms of ranking accuracy, synergy with human experts, and dimension-level feedback. This three-point scheme is validated using (a) synthetic data, (b) the perspective of data quality, (c) blind expert validation, and (d) transparent expert evaluation. Our study confirms several principal weaknesses of human decision-making and stresses the importance of synergy between a model and humans. Moreover, unsupervised learning and in particular the autoencoder are shown to be valuable tools for strategic decision-making

Exploring Cloud Adoption Possibilities for the Manufacturing Sector: A Role of Third-Party Service Providers

As the manufacturing sector strides towards digitalization under the influence of Industry 4.0, cloud services have emerged as the new norm, driving change and innovation in this rapidly transforming landscape. This study investigates the possibilities of cloud adoption in the manufacturing sector by developing a conceptual model to identify suitable cloud-based solutions and explores the role of third-party service providers in aiding manufacturers throughout their cloud adoption journey. The research methods consist of a comprehensive literature review of the manufacturing industry, digital transformation, cloud computing, etc., followed by qualitative analyses of industrial benchmarks case studies and an investigation into an application of the developed model to a hypothetical food manufacturing company as an example. This study indicates that cloud adoption can yield substantial benefits in the manufacturing sector, including operational efficiency, cost reduction, and innovation, etc. The study concludes that the developed conceptual model provides a practical framework to identify the most suitable cloud-based solutions during the cloud adoption process in the manufacturing context. In addition, third-party service providers like Capgemini are capable of not only filling the technical gaps but also consulting strategic directions and innovations for their client organizations, hence playing a vital role in driving the industrial digital transformation process. With an extensive mapping of their capabilities, a set of recommendations intended to assist Capgemini in enhancing capabilities and improving competitive performance in the market has been offered

Is operational research in UK universities fit-for-purpose for the growing field of analytics?

Over the last decade considerable interest has been generated into the use of analytical methods in organisations. Along with this, many have reported a significant gap between organisational demand for analytical-trained staff, and the number of potential recruits qualified for such roles. This interest is of high relevance to the operational research discipline, both in terms of raising the profile of the field, as well as in the teaching and training of graduates to fill these roles. However, what is less clear, is the extent to which operational research teaching in universities, or indeed teaching on the various courses labelled as analytics , are offering a curriculum that can prepare graduates for these roles. It is within this space that this research is positioned, specifically seeking to analyse the suitability of current provisions, limited to master s education in UK universities, and to make recommendations on how curricula may be developed. To do so, a mixed methods research design, in the pragmatic tradition, is presented. This includes a variety of research instruments. Firstly, a computational literature review is presented on analytics, assessing (amongst other things) the amount of research into analytics from a range of disciplines. Secondly, a historical analysis is performed of the literature regarding elements that can be seen as the pre-cursor of analytics, such as management information systems, decision support systems and business intelligence. Thirdly, an analysis of job adverts is included, utilising an online topic model and correlations analyses. Fourthly, online materials from UK universities concerning relevant degrees are analysed using a bagged support vector classifier and a bespoke module analysis algorithm. Finally, interviews with both potential employers of graduates, and also academics involved in analytics courses, are presented. The results of these separate analyses are synthesised and contrasted. The outcome of this is an assessment of the current state of the market, some reflections on the role operational research make have, and a framework for the development of analytics curricula. The principal contribution of this work is practical; providing tangible recommendations on curricula design and development, as well as to the operational research community in general in respect to how it may react to the growth of analytics. Additional contributions are made in respect to methodology, with a novel, mixed-method approach employed, and to theory, with insights as to the nature of how trends develop in both the jobs market and in academia. It is hoped that the insights here, may be of value to course designers seeking to react to similar trends in a wide range of disciplines and fields

Harnessing Collaborative Technologies: Helping Funders Work Together Better

This report was produced through a joint research project of the Monitor Institute and the Foundation Center. The research included an extensive literature review on collaboration in philanthropy, detailed analysis of trends from a recent Foundation Center survey of the largest U.S. foundations, interviews with 37 leading philanthropy professionals and technology experts, and a review of over 170 online tools.The report is a story about how new tools are changing the way funders collaborate. It includes three primary sections: an introduction to emerging technologies and the changing context for philanthropic collaboration; an overview of collaborative needs and tools; and recommendations for improving the collaborative technology landscapeA "Key Findings" executive summary serves as a companion piece to this full report

2018 SDSU Data Science Symposium Program

Table of Contents: Letter from SDSU PresidentLetter from SDSU Department of Mathematics and Statistics Dept. HeadSponsorsGeneral InformationKeynote SpeakersInvited SpeakersSunday ScheduleWorkshop InformationMonday ScheduleAbstracts| Invited SpeakersAbstracts | Oral PresentationsPoster PresentationCommittee and Volunteer

Data Science Roles and the Types of Data Science Programs

A growing field, data science (and, by extension, analytics) integrates concepts across a range of domains, such as computer science, information systems, and statistics. While the number of data science programs continues to increase, few discussions have examined how we should define this emerging educational field. With this in mind, during the 23rd Americas Conference on Information Systems (AMCIS’17), a panel discussion explored emerging questions regarding data science and analytics education. This paper reports on that panel discussion, which focused on questions such as what a data science degree is and what a data science program’s learning objectives are. The panel also debated if there should be different types of data science-related programs (such as an applied data science program or a business analytics program) and, if so, should there be a common core across the different variations of programs. Information system educators who can gain a better understanding of current trends in data science/analytics education and other information system researchers who are interested in how data science/analytics might impact the broader field of information systems and management education should find interest in this report

Interpretability of machine learning solutions in public healthcare : the CRISP-ML approach

Public healthcare has a history of cautious adoption for artificial intelligence (AI) systems. The rapid growth of data collection and linking capabilities combined with the increasing diversity of the data-driven AI techniques, including machine learning (ML), has brought both ubiquitous opportunities for data analytics projects and increased demands for the regulation and accountability of the outcomes of these projects. As a result, the area of interpretability and explainability of ML is gaining significant research momentum. While there has been some progress in the development of ML methods, the methodological side has shown limited progress. This limits the practicality of using ML in the health domain: the issues with explaining the outcomes of ML algorithms to medical practitioners and policy makers in public health has been a recognized obstacle to the broader adoption of data science approaches in this domain. This study builds on the earlier work which introduced CRISP-ML, a methodology that determines the interpretability level required by stakeholders for a successful real-world solution and then helps in achieving it. CRISP-ML was built on the strengths of CRISP-DM, addressing the gaps in handling interpretability. Its application in the Public Healthcare sector follows its successful deployment in a number of recent real-world projects across several industries and fields, including credit risk, insurance, utilities, and sport. This study elaborates on the CRISP-ML methodology on the determination, measurement, and achievement of the necessary level of interpretability of ML solutions in the Public Healthcare sector. It demonstrates how CRISP-ML addressed the problems with data diversity, the unstructured nature of data, and relatively low linkage between diverse data sets in the healthcare domain. The characteristics of the case study, used in the study, are typical for healthcare data, and CRISP-ML managed to deliver on these issues, ensuring the required level of interpretability of the ML solutions discussed in the project. The approach used ensured that interpretability requirements were met, taking into account public healthcare specifics, regulatory requirements, project stakeholders, project objectives, and data characteristics. The study concludes with the three main directions for the development of the presented cross-industry standard process

Global Innovations in Measurement and Evaluation

We researched the latest developments in theory and practice in measurement and evaluation. And we found that new thinking, techniques, and technology are influencing and improving practice. This report highlights 8 developments that we think have the greatest potential to improve evaluation and programme design, and the careful collection and use of data. In it, we seek to inform and inspire—to celebrate what is possible, and encourage wider application of these ideas