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To understand how the most successful organizations use big data and analytics innovate, researchers studied 341 respondents’ usage of big data and analytics tools for innovation.

Researchers asked about innovation goals, barriers to innovation, metrics used to measure innovation outcomes, treatment and types of innovation projects and the role of big data and analytics in innovation processes.

Three distinct groups emerged: Leaders, Strivers and Strugglers. Leaders are markedly different as a group: they innovate using big data and analytics within a structured approach, and they focus in particular on collaboration.

Respondents were from the 2014 IBM Innovation Survey. We conducted cluster analysis with 81 variables. The three cluster solution was determined deploying latent class analysis (LCA), a family of techniques based around clustering and data reduction for segmentation projects. It uses a number of underlying statistical models to capture differences between observed data or stimuli in the form of discrete (unordered) population segments; group segments; ordered factors (segments with an underlying numeric order); continuous factors; or mixtures of the above.

Leaders don’t just embrace analytics and actionable insights; they take them to the next level, integrating analytics and insights with innovation. Leaders follow three basic strategies that center on data, skills and tools and culture: promote excellent data quality and accessibility; make analytics and innovation a part of every role; build a quantitative innovation culture.

The research found that leaders leverage big data and analytics more effectively over a wider range of organizational processes and functions. They are significantly better at leveraging big data and analytics throughout the innovation process – from conceiving new ideas to creating new business models and developing new products and services.

The purpose of this paper is to provide an overview of the development of an institution wide approach to learning analytics at the University of Wollongong (UOW) and the inclusion of library data drawn from the Library Cube.

The Student Support and Education Analytics team at UOW is tasked with creating policy, frameworks and infrastructure for the systematic capture, mapping and analysis of data from the across the university. The initial data set includes: log file data from Moodle sites, Library Cube, student administration data, tutorials and student support service usage data. Using the learning analytics data warehouse UOW is developing new models for analysis and visualisation with a focus on the provision of near real-time data to academic staff and students to optimise learning opportunities.

The distinct advantage of the learning analytics model is that the selected data sets are updated weekly, enabling near real-time monitoring and intervention where required. Inclusion of library data with the other often disparate data sets from across the university has enabled development of a comprehensive platform for learning analytics. Future work will include the development of predictive models using the rapidly growing learning analytics data warehouse.

Data warehousing infrastructure, the systematic capture and exporting of relevant library data sets are requisite for the consideration of library data in learning analytics.

What was not anticipated five years ago when the Value Cube was first realised, was the development of learning analytic services at UOW. The Cube afforded University of Wollongong Library considerable advantage: the framework for data harvesting and analysis was established, ready for inclusion within learning analytics data sets and subsequent reporting to faculty.

This viewpoint study aims to make the case that the field of knowledge management (KM) must respond to the significant changes that big data/analytics is bringing to operationalizing the production of organizational data and information.

This study expresses the opinions of the guest editors of “Does Big Data Mean Big Knowledge? Knowledge Management Perspectives on Big Data and Analytics”.

A Big Data/Analytics-Knowledge Management (BDA-KM) model is proposed that illustrates the centrality of knowledge as the guiding principle in the use of big data/analytics in organizations.

This is an opinion piece, and the proposed model still needs to be empirically verified.

It is suggested that academics and practitioners in KM must be capable of controlling the application of big data/analytics, and calls for further research investigating how KM can conceptually and operationally use and integrate big data/analytics to foster organizational knowledge for better decision-making and organizational value creation.

The BDA-KM model is one of the early models placing knowledge as the primary consideration in the successful organizational use of big data/analytics.

Although published research is limited to big data, some research focuses on the challenges that companies face in implementing big data projects. Specifically, in the field of information systems, researchers realize that the success of Big Data projects is not only the result of data and analytics tools and processes, but also includes broader aspects. To address this issue, people have come up with a perception of big data analytics capabilities, often defined as the ability of businesses to take advantage of data management, infrastructure, and talent to turn business into competencies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

The relationship between analytics and organizational performance has been the subject of the extant research. Prior studies have highlighted the direct influence of analytics on organizational performance. For example, big data analytics capabilities are significantly correlated with market performance and operational performance. The mechanisms through which analytics affect organizations were also examined from various perspectives.

The paper provides strategic insights and practical thinking that have influenced some of the world’s leading organizations.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

The purpose of this paper is to describe the need for academic libraries to demonstrate and increase their impact of student learning and success. It highlights the data problems present in existing library value correlation research and suggests a pathway to surmounting existing data obstacles. The paper advocates the integration of libraries into institutional learning analytics systems to gain access to more granular student learning and success data. It also suggests using library-infused learning analytics data to discover and act upon new linkages that may reveal library value in an institutional context.

The paper describes a pattern pervasive in existing academic library value correlation research and identifies major data obstacles to future research in this vein. The paper advocates learning analytics as one route to access more usable and revealing data. It also acknowledges several challenges to the suggested approach.

This paper describes learning analytics as it may apply to and support correlation research on academic library value. While this paper advocates exploring the integration of library data and institutional data via learning analytics initiatives, it also describes four challenges to this approach including librarian concerns related to the use of individual level data, the tension between claims of correlation and causation in library value research, the need to develop interoperability standards for library data and organizational readiness and learning analytics maturity issues.

This paper outlines a path forward for academic library value research that may otherwise be stymied by existing data difficulties.

In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.

We propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.

The literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.

Based on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.

Local governments face increasingly complex challenges related to their internal operations as well as the provision of public services. However, research on how they embrace evidence-based approaches such as data analytics practices, which could help them face some of those challenges, is still scarce. This study aims to contribute to existing knowledge by examining the data analytics practices in Kansas City, Missouri (KCMO), a city that has become prominent for engaging in data analytics use through the Bloomberg’s What Works Cities (WWC) initiative with the purpose of improving efficiency and enhancing response to local constituents.

This research conducted semistructured interviews with public servants who had data analytics experience at KCMO. Analysis looked for common and emerging patterns across transcripts. A conceptual framework based on related studies is built and used as the theoretical basis to assess the evidence observed in the case.

Findings suggest that data analytics practices are sponsored by organizational leadership, but fostered by data stewards who engage other stakeholders and incorporate data resources in their analytical initiatives as they tackle important questions. Those stewards collaborate to nurture inclusive networks that leverage knowledge from previous experiences to orient current analytical endeavors.

This study explores the experience of a single city, so it does not account for successes and failures of similar local governments that were also part of Bloomberg's WWC. Furthermore, the fact that selected interviewees were involved in data analytics at least to some extent increases the likelihood that their experience with data analytics is relatively more positive than the experience of other local government employees.

Results suggest that data analytics benefits from leadership support and steering initiatives such as WWC, but also from leveraging stakeholder knowledge through collaborative networks to have access to data and organizational resources. The interplay of data analytics sponsored activities and organizational knowledge could be used as means of assessing local governments’ existing data analytics capability.

This study suggests that data analytics practices in local governments that are implementing a smart city agenda are knowledge-driven and developed incrementally through inclusive networks that leverage stakeholder knowledge and data resources. The incrementality identified suggests that data analytics initiatives should not be considered a “blank slate” practice, but an endeavor driven and sustained by data stewards who leverage stakeholder knowledge and data resources through collaborative networks.

The study objective was to understand what components of organizational culture and capability combined with analytic skillsets are needed to allow organizations to exploit real-time analytic technologies to create supply chain performance improvements.

The authors relied on information processing theory to support a hypothesized model, which is empirically tested using an ordinary least squares equation model, and survey data from a sample of 208 supply chain executives across multiple industries.

The authors found strong support for the concept that real-time analytics will require specialized analytical skills for the managers who use them in their daily work, as well as an analytics-focused organizational culture that promotes data visibility and fact-based decision-making.

Based on the study model, the authors found that a cultural bias to embrace analytics and a strong background in statistical fluency can produce decision-makers who can make sense of a sea of data, and derive significant supply chain performance improvements.

The research was initiated through five workshops and presentations with supply chain executives leading real-time analytics initiatives within their organizations, which were then mapped onto survey items and tested. The authors complement our findings with direct observations from managers that lend unique insights into the field.

Previous research assumes that technologies 4.0, particularly big data, may be highly relevant for organizations to increase human resources (HR) communication strategies, but the research provides little or no evidence on whether and how these tools are applied in employees and labor market relations. This study intends to offer a first insight on the adoption of data-driven HR/talent management approach, contributing to the ongoing debate on the Industry 4.0. This study aims to investigate the use of 4.0 technologies in HR and talent management functions, focusing also on the adoption of big data analytics for internal and recruitment communication.

The analysis of the literature enables to define the research questions and an exploratory web survey was carried out through a structured questionnaire. The analysis unit of the empirical survey includes the communication and marketing managers of 90 organizations in Italy, examined in the Mediobanca Report on the “Main Italian Companies.”

Findings highlight a lack of the use of 4.0 technologies and big data analytics in employee and labor market relations and reveal some sectoral differences in the adoption of 4.0 technologies. Moreover, the study points out that the development of HR analytics is hampered by short-term perspective, data quality problems and the lack of analytics skills.

Due to the exploratory research design and the circumscribed sample from a single country (Italy), further cross-national evidence is needed. This study provides digital communication managers with useful insights to improve the data-driven HR/talent management approach, which is a strategic asset for ensuring a sustainable competitive advantage and optimizing business performance.

The study offers an overview about the use of big data analytics in internal and recruitment communications. Considering the alignment between Italian and European trends in the use of big data and in the adoption of HR analytics, the study can provide insights also for other European organization.

The purpose of this paper is to explore the key ingredients that people analytics teams require to contribute to organizational performance. As the information that is currently available is fragmented, it is difficult for organizations to understand what it takes to execute people analytics successfully.

To identify the key ingredients, a narrative literature review was conducted using both traditional people analytics and broader business intelligence literature. The findings were summarized in the People Analytics Effectiveness Wheel.

The People Analytics Effectiveness Wheel identifies four categories of ingredients that a people analytics team requires to be effective. These are enabling resources, products, stakeholder management and governance structure. Under each category, multiple sub-themes are discussed, such as data and infrastructure; senior management support; and knowledge, skills, abilities and other characteristics (KSAOs) (enablers).

Many organizations are still trying to set up their people analytics teams, and many others are struggling to improve decision-making by using people analytics. For these companies, this paper provides a comprehensive overview of the current literature and describes what it takes to contribute to organizational performance using people analytics.

This paper is designed to provide organizations and researchers with a comprehensive understanding of what it takes to execute people analytics successfully. By using the People Analytics Effectiveness Wheel as a guideline, scholars are now better equipped to research the processes that are required for the ingredients to be truly effective.