Search

Today’s software makes it easier to track patterns, trends, and even granular details of how customers use products. This data is collected in systems that support marketing, sales, finance, and operations. But many companies aren’t sure how they can best support employees who are overseeing hundreds of unique relationships. They’re grappling with how to manage and use all their data to improve customer retention. And they’re wondering how they can assess the health of their customer relationships before it’s too late. One solution is the use of customer-health scores. In this article the authors introduce a model that any B2B company can use to measure and improve customer health. They identify three dimensions of customer health—customer-relationship quality, product usage, and value realization—and offer advice on how to measure and weigh relevant metrics. The authors then take an in-depth look at how BigCommerce, an e-commerce platform, developed and refined its customer-health model. They share five lessons the company learned through the process, including how to identify the relationship between health and churn, and that a flywheel effect occurs as health scoring matures. [ABSTRACT FROM AUTHOR]

This article focuses on mSAIL, a milligram-scale multi-modal sensor platform, for migration tracking of Monarch butterflies. The article discusses how the platform works including a focus on its ability to achieve small form factor integration, its energy autonomous operation, how it achieves wireless RF communication, and the data management capabilities of the system.

This article explores the topic of data mesh implementation in complex organizations. Topics include a detailed look at how data mesh architecture and governance work as well as the benefits of it. In addition, a look at necessities to optimize its implementation and use including the balance of human intuition and automatic efficiency when dealing with both type I and type II errors.

Purpose: This study aims to investigate the impact of the ethical management of data privacy on the overall reputation of businesses. Design/methodology/approach: A conceptual model was proposed and tested. Data were collected from 208 small and medium-sized enterprises (SMEs) in the textile industry in Valencia, Spain using a survey instrument. Partial least squares (PLS) allowed for the analysis of the data collected. Findings: The theoretical model explains 46.1% of the variation in the organisational reputation variable. The findings indicate that ethical data privacy has a beneficial effect on an organisation's reputation and eco-innovation. The findings also demonstrate how eco-innovation drives the development of new knowledge and green skills that, in turn, communicate to stakeholders a company's ethical commitment. These results should encourage SMEs to invest in data privacy in order to meet the needs of the SMEs' increasingly technology- and environment-sensitive stakeholders and to improve their reputation. Originality/value: This study provides the first empirical evidence that ethical data privacy management has a positive impact on the reputation of firms. Furthermore, the originality of the research derives from the analysis of the results from an environmental perspective. Indeed, this study shows that effective data privacy management can indirectly support organisational reputation through eco-innovation and green skills. [ABSTRACT FROM AUTHOR]

The article emphasizes the critical role of data in creating value for companies and gaining competitive advantages. Topics include the importance of data sharing for innovation and problem-solving, the barriers to effective data utilization across organizational boundaries, and the lessons learned from historical business failures due to neglecting digitalization and data exchange.

This article presents a discussion with textbook authors Alfred Spector, Peter Norvig, Chris Wiggins and Jeanette Wing with venture partner Ben Fried and software engineer Michael Tingley about data science. Topics include the potential problems when data science is not utilized properly, establishing and executing an analysis rubric, and the authors’ upcoming book, Data Science in Context.

This article presents the six principles of data-centric artificial intelligence (DCAI), a departure from the current model-centric approach which is only concerned with data in the preprocessing phase. The principles detail the philosophy of data quality and improvement throughout the entire artificial intelligence life cycle and adds the layer of human-centered data science creating a sociotechnical system that focus on both technological elements as well as social norms and biases.

This article discusses the recent areas of focus at the National Institute of Informatics (NII) in Japan. First, a look at its academic services including SINET6, a backbone network service across Japan and its data platform service, the NII Research Data Cloud. The article also details NII research in computer science with projects in innovative computer vision and engineerable artificial intelligence, as well as applied research projects including a COVID-19 pneumonia diagnosis tool.

This article addresses the development of the scientific service (librarians with an academic degree - M.A. or higher) at the University Library of the Ludwig-Maximilians-Universität in Munich. As part of an organizational development process, new and growing areas such as research data management or bibliometrics were adopted by the scientific service. That way the library is able to respond to new digital requirements and to raise the profile of its services. This process was accompanied by a refocusing of the tasks of the subject librarians, who continue to play a key role as mediators of library services. In order to improve cooperation between subject librarians and other units, new committees and formats were established that are designed for exchange and collaboration. [ABSTRACT FROM AUTHOR]

The article focuses on strategizing cloud-based data management, emphasizing scalability, cost-efficiency, and enhanced security as key benefits. Topics include defining data governance frameworks to ensure data accuracy and compliance, designing scalable data architectures for flexibility, and implementing robust data security measures such as encryption and access controls.

Copyright of Marketing Magazine (France) is the property of Netmedia Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article discusses the challenges faced by research scientists in managing and accessing scientific data. It highlights the need for a more integrated and science-aware approach to data management that prioritizes scientists. The article explores different approaches, such as scientific data management systems (SDMS) and data lakes, warehouses, and lakehouses, but notes that these solutions often overlook the needs of scientists. The article introduces Jarvis, a comprehensive and science-aware data management solution, which allows scientists to search, analyze, and visualize data with full scientific context. Jarvis automates data consolidation and provides tools for data retrieval, interaction, and analysis, including AI and ML capabilities. The article emphasizes the importance of a science-aware approach to data management in accelerating research efforts. [Extracted from the article]

This article discusses ideas around data use in the online data ecosystem. Topics include the limitations of current data use regulation, a framework for the various ways data circulates between users and platforms as well as the novel idea of data cooperatives that give solutions for both the outgoing and incoming data with an emphasis on data privacy.

The article offers an exploration of how to distinguish between real and fake news, using a case study on Donald Trump's presidential victory. Topics include understanding the key elements of a trustworthy news story, such as the date, sources, and byline, and why these details help verify the credibility of the information.

The article explores the process of data management, including data collection, organization, security, and preservation to facilitate business decisions and streamline tasks. It also discusses the distinctions between data management and data governance, the role of ETL (extract, transform, load) and ELT (extract, load, transform) processes, and various data management tools and platforms, such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform.

This article presents the Veracity Project in Aotearoa New Zealand with goals in investigating cultural and formal approaches to clarifying and investigating veracity in all forms of digital technologies. Topics include the goals of the Veracity Project, its cultural significance in Aotearoa New Zealand, and its proposed national data infrastructure involving the use of artificial intelligence-supported content with utilization of large language models and a claims systems where any component in the infrastructure can challenge the veracity of the content.

The article discusses the results of a survey conducted among members of the American Statistical Association (ASA) to investigate data management practices within the ASA community. The survey aimed to identify the needs for services and support in order to develop data management tools for researchers funded by the Public Health Service. The survey included questions about motivations for implementing data management procedures, involvement in data management tasks, recordkeeping practices, data organization, data maintenance, and adherence to data management procedures. The results provide valuable insights for self-assessment and improving data management practices. [Extracted from the article]

The article offers information on the interconnection between data management and AI/ML, emphasizing the importance of modern data management practices for successful AI implementation. Topics include the challenges faced by enterprises in adopting AI, the need for a cultural shift toward AI as a strategic imperative, and the significance of data governance and quality in AI readiness.

The article describes promising research directions in the database community, reporting on current themes and challenges. Particular focus is given to what has changed in the field since 2017, like technological breakthroughs in machine learning and artificial intelligence. Other topics discussed include data science, which is defined as the processes and systems that enable the extraction of knowledge or insights from data in various forms, dealing with the increasingly important social challenge of data governance, and the need to rethink architecture for cloud data services.

The article discusses the concept of responsible data management and the need for responsible design, use, and oversight of automated decision systems (ADSs). According to the article, legal and ethical aspects across data collection and use comprise responsible data management, as does responsible decision making.

The article focuses on leveraging open source solutions for robust data management and security. Topics include the role of open source in fostering community-driven innovation for evolving data management strategies, crafting cloud-era data management strategies with tools like Kubernetes and Apache Hadoop, and addressing challenges posed by new-age technologies such as AI and IoT with scalable solutions like TensorFlow and Apache Kafka.

The article discusses various aspects of the use of machine learning (ML) in the software engineering field, and it mentions ML system failures, the basic elements of software engineering, and efforts to construct dependable and safe systems using ML. Machine learning operations (MLOps) techniques and software engineers are mentioned, along with a call for organizations to utilize data pipelines to manage data and protect personally identifiable information.

Copyright of AutomotiveIT is the property of Media-Manufaktur GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article highlights the struggle of organizations in managing complex modern data landscapes and discusses how Aiven, an open-source data platform, simplifies cloud data management. It showcases how Aiven helps companies like Supermetrics and TV 2 Norway efficiently manage their data infrastructure across different clouds and regions.

The article focuses on the problems that data managers face while transitioning to hybrid and multi-cloud data architectures in the face of increasing demand for AI, machine learning, and real-time streaming projects. Topics include cloud migration considerations, the need for appropriate data management tools, and the benefits and drawbacks of hybrid and multi-cloud approaches, emphasizing the importance of collaboration, observability, and automation in addressing these complexities.

The article focuses on the imperative for data managers to design modern data architectures that cater to the digital economy's demands, covering aspects like cloud integration, real-time streaming, and the adoption of Artificial Intelligence (AI). It highlights the necessity of robust data governance, challenges such as ensuring data quality, overcoming silos, and the prevalent shift towards a "cloud-first" strategy, with acknowledgment of instances where on-premises solutions remain viable.

The integration of artificial intelligence (AI) into libraries marks an evolution towards “smart libraries,” tailored to current technological and educational needs. These institutions are leveraging AI to enhance user experience, offering everything from personalized recommendations to advanced search services and efficient data management. However, this advancement also poses challenges, such as data privacy and the reliability of AI-generated information. Libraries are leading AI literacy efforts, empowering their users to interact responsibly with this technology and preparing them for the digital job market of the future. In this context, libraries continue to be crucial as spaces for continuous learning and equitable access to knowledge in a society increasingly influenced by AI. [ABSTRACT FROM AUTHOR]

The article focuses on the critical role of effective data management and security as indispensable pillars for success in the contemporary business landscape. Topics discussed include the significance of data in the digital age, challenges faced in data management and security, and the game-changing benefits that arise from implementing robust data practices.

The article highlights the centrality of effective data management on Amazon Web Services (AWS), underscoring the critical role of backend storage and the benefits offered by AWS data management services. Topics discussed such as database migration to AWS, optimal sizing of database solutions for efficiency and cost-effectiveness, and the application of AWS Data Lab to accelerate data engineering activities.

The article discusses the challenges and solutions related to managing enterprise data technology issues. It emphasizes the increasing amount of data being generated and the need for organizations to tap into this data to stay competitive. The challenges highlighted include data complexity, unstructured data, infrastructure for the edge, building a viable infrastructure for AI, scaling AI initiatives, data governance, data quality, legacy databases, and data security. The article provides solutions for each challenge, such as investing in data management solutions, adopting new technologies like AI and machine learning, and prioritizing data quality and security. It also emphasizes the importance of developing a data-driven culture and having a strategic plan in place. [Extracted from the article]

The article "15 WAYS Excel to analyze data" written by Yolanda Cuesta, an Excel and Power Platform expert economist, offers Excel tools for managing and analyzing accounting data. These tools include text filters and color filters, sorting data, subtotals, conditional cell formatting, goal seek, pivot tables, charts, and sparklines, among others. These tools allow for the creation of tables, charts, predictions, statistics, and data models. The article also mentions the Excel spreadsheet add-in number 69, available in December 2023, which allows for processing and analyzing data to obtain meaningful information and create models. Additionally, Microsoft offers a free program called Power BI Desktop that includes these and other Business Intelligence (BI) tools. In summary, the article presents different ways to use Excel for data analysis in professional areas such as accounting, finance, and management, with the aim of improving productivity. [Extracted from the article]

The article presents a selection of research papers focusing on the theme of convergence, also known as eventual consistency, and specific computer science topics including systems, programming languages, human-computer interaction, and data management. Topics include conflict-free replicated data type (CRDT), consistency as logical monotonicity (CALM), and mergeable replicated data types (MRDTs).

GRAPHS ARE, BY nature, 'unifying abstractions' that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users and consumers of graph instances and graph workloads understand these abstractions, future problems will require new abstractions and systems. What needs to happen in the next decade for big graph processing to continue to succeed? [ABSTRACT FROM AUTHOR]

The article focuses on the need for enhancing personnel databases in the U.S. Army to include an "Installation of Assignment" data point, which would enable commanders to better understand and address the needs of soldiers within their geographic area. Topics discussed include the challenges posed by current personnel systems in accurately identifying soldiers' locations and the proposed solution of adding a new data point to improve data accuracy and support effective resource allocation.

The article discusses a recent industry event where CIOs explored data strategies, challenges, opportunities, and innovations in data management. The event focused on the importance of data-driven strategies for businesses in the digital age. Speakers highlighted the exponential growth of data and the challenges organizations face in managing diverse data types across various platforms. They also discussed ways organizations can utilize data to become data-driven, emphasizing the role of technology and digital transformation. The article includes insights from CIOs in various industries, who shared their perspectives on data strategies and the importance of a cultural shift towards data-driven decision-making. The event also featured discussions on data governance, security, and the future of hybrid cloud solutions. Overall, the article provides a comprehensive overview of the topics covered at the event and offers valuable insights for library patrons conducting research on data management and innovation. [Extracted from the article]

The article underscores the critical importance of data management in today's data-driven landscape, emphasizing its role in enabling smart decisions, innovation, compliance, and maintaining data quality for competitive advantage. Topics include data collection, storage, organization, security, governance, quality management, lifecycle management, architecture, integration, cleaning, backup, recovery, analytics, and reporting.

The rise of vernacular AI in India is set to revolutionize business communication and responsiveness. Indian startups are creating AI models specifically tailored to Indian languages and cultural biases. The government is also involved in the development of vernacular AI through initiatives like the India AI Programme and Bhashini, a central repository of organized Indic language data. However, funding and data management remain significant challenges for these startups. Despite competition from global AI entities, India's first-generation AI startups have laid the groundwork for the future of vernacular AI in the country. [Extracted from the article]

The article emphasize the growing importance of data in the field of parks and recreation. It highlights how data can be a valuable tool for managing, planning, and making informed decisions related to parks, recreational facilities, and programs. It introduces the concept of quantitative and qualitative data, discusses the significance of collecting and organizing data effectively.

Effective species management and conservation benefit from knowledge of species distribution and status. Surveys to obtain that information often involve replicate sampling, which increases survey effort and costs. We simultaneously modeled species distribution, abundance and spatial correlation, and compared the uncertainty in replicate abundance estimates of the endangered palila (Loxioides bailleui) using hierarchical generalized additive models with a soap film smoother that incorporated random effects for visit. Based on survey coverage and detections, we selected the 2017 point‐transect distance sampling survey on Mauna Kea, Hawai'i Island, for our modeling. Our modeling approach allowed us to account for imperfect detections, control the effects of boundary features, and generate visit‐specific density surface maps. We found that visit‐specific smooths were nearly identical, indicating that little information was gained from a subsequent visit, and that most of the estimator uncertainty was derived from within‐visit variability. Scaling back the palila survey to a single visit would halve the survey effort and logistical costs and increase efficiencies in data management and processing. Changing the sampling protocol warrants careful consideration and our findings may help management and regulatory agencies by maximizing efficiency and minimizing costs of surveying protocols, while providing guidelines on how to best collect information critical to species' conservation. [ABSTRACT FROM AUTHOR]

Purpose: Smart grid is an integration between traditional electricity grid and communication systems and networks. Providing reliable services and functions is a critical challenge for the success and diffusion of smart grids that needs to be addressed. The purpose of this study is to determine the critical criteria that affect smart grid reliability from the perspective of users and investigate the role big data plays in smart grid reliability. Design/methodology/approach: This study presents a model to investigate and identify criteria that influence smart grid reliability from the perspective of users. The model consists of 12 sub-criteria covering big data management, communication system and system characteristics aspects. Multi-criteria decision-making approach is applied to analyze data and prioritize the criteria using the fuzzy analytic hierarchy process based on the triangular fuzzy numbers. Data was collected from 16 experts in the fields of smart grid and Internet of things. Findings: The results show that the "Big Data Management" criterion has a significant impact on smart grid reliability followed by the "System Characteristics" criterion. The "Data Analytics" and the "Data Visualization" were ranked as the most influential sub-criteria on smart grid reliability. Moreover, sensitivity analysis has been applied to investigate the stability and robustness of results. The findings of this paper provide useful implications for academicians, engineers, policymakers and many other smart grid stakeholders. Originality/value: The users are not expected to actively participate in smart grid and its services without understanding their perceptions on smart grid reliability. Very few works have studied smart grid reliability from the perspective of users. This study attempts to fill this considerable gap in literature by proposing a fuzzy model to prioritize smart grid reliability criteria. [ABSTRACT FROM AUTHOR]

Research Data Management and basic services, such as persistent identifiers (PIDs) that permanently refer to research data (e.g., ORCID iDs for authorship), or terminology management, are central to a FAIR Research Data Management. Terminologies play an important role in semantic networks. This report describes the work by Base4NFDI, a joint initiative of the NFDI since March 2023 with institutions from scientific infrastructure provision and research centers in Germany together, such as DESY. An NFDI-wide base service portfolio with generic solutions is currently being established. The 26 NFDI consortia form the Base4NFDI network. [ABSTRACT FROM AUTHOR]

The article discusses database systems research at several universities in Arab countries in areas such as graph data management, prescriptive analytics, and data privacy.

The article announces the release of the PostgreSQL 17 open source database by PostgreSQL Global Development Group

The article emphasizes the need to modernize database management by integrating automation and platform engineering to streamline workflows. Topics include the outdated and siloed nature of current database management practices; the complexity of managing diverse databases across multiple environments, which overwhelms DBAs; and the benefits of automation, which can reduce manual tasks, improve efficiency, and enable developers to focus on innovation.

Copyright of AutomotiveIT is the property of Media-Manufaktur GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article details how to build an ontology in an organization that will allow for future artificial intelligence incorporation. The article discusses the author’s role in creating an ontology for the manufacturing company, Applied Materials, as well as five steps to building an ontology which include auditing data architecture, generating solutions for any problems uncovered in the audit, and pinpointing how people are using the information to set the ontology's organizing principles.

As data analytics has become an important application for modern data management systems, a new category of data management system has appeared recently: the scalable linear algebra system. We argue that a parallel or distributed database system is actually an excellent platform upon which to build such functionality. Most relational systems already have support for cost-based optimization--which is vital to scaling linear algebra computations--and it is well known how to make relational systems scalable. We show that by making just a few changes to a parallel/distributed relational database system, such a system can become a competitive platform for scalable linear algebra. Taken together, our results should at least raise the possibility that brand new systems designed from the ground up to support scalable linear algebra are not absolutely necessary, and that such systems could instead be built on top of existing relational technology. [ABSTRACT FROM AUTHOR]

The University of Montevideo will offer a new career in Data Engineering and Artificial Intelligence starting in 2024. This career focuses on the development of advanced technological solutions for data management and the implementation of artificial intelligence. It differentiates itself from other options in the market by combining data engineering and artificial intelligence in the same career with a practical approach adapted to current changes in society. Graduates will be able to work as data engineers, machine learning engineers, artificial intelligence consultants, among other related technical roles. The university also aims to develop a line of research in data engineering and artificial intelligence with an applied and practical focus, and will offer opportunities for practical learning and contact with the real application of these technologies in an international context. [Extracted from the article]

Copyright of China Pulp & Paper Industry is the property of China Pulp & Paper Industry Publishing House and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Institutional services for research data management (RDM) are manifold, ranging from consulting services to technical offerings such as backup and storage services to discipline-specific data analysis tools or documentation environments. In the collaborative project FDNext, funded by the German Research Foundation (DFG), Humboldt-Universität zu Berlin addressed this diversity and investigated how services from service providers such as computer and media centres or university libraries can be selected and evaluated. Over the course of the three-year project, a maturity model for RDM services was developed that provides a strategic assessment of the RDM service portfolio and identifies potential for further development. [ABSTRACT FROM AUTHOR]