AgriThority? 的动态

Ever wondered about the role and design of data quality tools in data ecosystems? ?? In our latest study, titled?“A Design Theory for Data Quality Tools in Data Ecosystems: Findings from Three Industry Cases”, we present insights gained from a multi-case study on data quality tools in three data ecosystem scenarios. ?? Read more in our article, which is available open-access in the Data & Knowledge Engineering journal: https://lnkd.in/eys-k2F4 ?? I'm grateful to my co-authors Tobias Guggenberger and Frederik M?ller for their help on this work. Looking forward to further discussions and collaborations in the research field! #DataQuality #DataEcosystems #DataEngineering

"Data is a precious thing and will last longer than the systems themselves" is attributed to Tim Berners-Lee, the inventor of the World Wide Web. This statement highlights the enduring value of data in the digital age. Here are some key points that this quote emphasizes :- ?? Enduring Value : Data remains valuable over time, providing lasting insights and historical context even as technology changes. ?? Tech Changes : Systems and platforms evolve or become obsolete, but data can be migrated and adapted to new technologies, ensuring its continued relevance. ?? Preservation : Effective data management practices like backups, archiving, and documentation ensure that data is preserved for future use, maintaining its integrity and accessibility. ?? Reusability : High-quality data can be repurposed across different contexts and applications, offering ongoing value and fostering innovation. ?? Asset : Data is increasingly recognized as a strategic asset that can drive decision-making, support research, and create competitive advantages for organizations. #DataIsPrecious #DataManagement #DigitalLegacy #DataPreservation #TechEvolution #DataReusability #DataAsset #BigData #DataScience #DataDriven #DataSecurity #DataStorage #DataArchiving #FutureOfData #DataIntegrity #DataValue

Have you ever wanted to share your CEO data? Great news, CEO has implemented Digital Object Identifiers. You can now publish your collected data using a DOI and share it with the world. Read more: https://lnkd.in/gRPEN343

Researchers should archive their data for two reasons: to ensure research integrity and to allow others to reuse the data. The minimum data to archive includes raw, processed, and analyzed data, along with documentation for understanding the data. #researchdata #dataarchiving #researchintegrity https://lnkd.in/eVGXTuVP

Why bother sampling? Small-scale portfolio projects tend to conceal the value of data sampling. However, in real-world scenarios, #datasampling has major implications for cost-effectiveness and resource management. In this short post, let's explore some of the major benefits of sampling in a production environment. First, what is sampling? Population Sampling is the process of creating a representative subset of a larger population (dataset). With these samples, instead of carrying out complex #analytics on large volumes of data, using #statistics, conclusions can be drawn about the much larger #dataset from a representative sample. Benefits of sampling - Analytical efficiency: Analyzing a large dataset can be resource-intensive. With sampling, analysts can arrive at valid conclusions about data in less time with less computational resources. This saves time and money for companies. - Overall cost-effectiveness: Beyond resources expended on #analytics, large datasets consume more resources during collection, storage, and processing. Sampling reduces these costs. - Feasibility: In some cases, obtaining complete population data might be impossible. Sampling provides a practical alternative for analysis. Overall sampling facilitates a more efficient and effective #dataanalytics approach where applicable. Please comment on what you think below. Let's start a conversation. Follow and connect with me for more interesting posts on the world of data.

Overview of the Open Footprint Data Model (OFP) ?? ?? (https://lnkd.in/eP37U_qH) Colin Chalmers of Goal 17 thanks for pointing out! Who is The Open Group? The Open Group is a global organization ?? that brings?together businesses ??, technology providers ??, and government entities ??? to create open, vendor-neutral IT standards. They work on various areas ??, and the Open Footprint Data Model is one of their initiatives focused on sustainability and environmental reporting ??. https://www.opengroup.org/ Let's dive into OFP, a framework designed to transform emissions reporting. Based on Version 1.0, Snapshot 1, here's what you need to know: Key Objectives of OFP Structural Harmonization: Create a single set of data definitions for streamlined emission data exchange and analysis ??. Standard Alignment: Foster consistency by mapping terminology to existing frameworks ??. API-Driven Accessibility: Promote seamless data access and integration through standard APIs under development ??. OFP Data Structures Master Data:?Core operational data ?? Reference Data:?Rarely changing external codes and standards ?? Work-Product-Component Data:?Emission-related measurements and observations ?? The Four Domains of OFP Organizational Structures & Assets:??? ??? Product Life Cycle:??? Emission Calculation & Recording:??? Reporting:??? ?? Why OFP is a Game-Changer Universal Language:??? Efficiency Gains:?? ?? Compliance Readiness:??? Data-Driven Decisions:??? THE OPEN GROUP - OPEN FOOTPRINT FORUM: https://lnkd.in/emDezDi7 #openfootprint #emissionsreporting #sustainability #datastandards #climateaction #supplychaintransparency #greenit #finops #greensoftware #softwaredevelopment HighTech Innovators

A study by STAUFEN. and AppliediT found six out of ten industrial companies in Germany, Austria, and Switzerland struggle with data exploitation due to a lack of skilled professionals. Over half also find it hard to implement data-driven insights. ?? Key Issues: - 58% cite lack of human resources. - 53% face unstructured data. - 43% lack analytical skills or platforms. ?? Needed Actions: - Foster a data-focused culture (53%). - Recruit more data experts (47%). - Improve analysis tools (42%). ?? Check out more about the study https://lnkd.in/gpqF5DS5 #DigitalTransformation #Industry40 #DataAnalysis #Innovation

According to 451 Research, a data professional's time is mainly devoted to data cleaning and organization rather than extracting insights or driving innovation. www.querysurge.com #DataValidation #DataQuality

Managing and Sharing Research Data — A Guide to Good Practice "Robust research data management techniques give researchers and data professionals the skills required to deal with the rapid, and uneven, developments in the data management environment. Research funders (...) are gradually implementing data management (and sharing) policies in order to maximize openness of data, transparency and accountability of research they support. Journal publishers increasingly require submission of the data upon which publications are based for peer review. Research funders and data users recognize the long-term value of well-prepared data. And institutions need good quality research information infrastructure to manage the ethical and security risks of their data assets. Researchers’ responsibilities towards their research data are set to change across all domains of scientific endeavour. ??Research funders are increasingly mandating open access to research data; ??governments internationally are demanding transparency in research; ??the economic climate is requiring much greater reuse of data; ??and fear of data loss calls for more robust information security practices. All these factors mean that researchers will need to: ??Improve, ??Enhance, and ??Professionalize their research data management skills to meet the challenge of producing the highest quality shareable and reusable research outputs in a responsible and efficient way." by Louise Corti et al. [ Excerpt from the preface ] ? Louise Corti, Veerle Van den Eynden, Libby Bishop & Matthew Woollard — 2014 #Research #Researching #Data #DataManagement #DataScience #Statistics

Enterprise data overload is increasingly leading to cost overruns and hindering the ability of organizations to leverage data for #growth, according to the latest research. One of the key factors contributing to this is the complexity of modern #data ecosystems. The use of a growing number of tools and data ingestion pathways is creating a tangled mess that makes it difficult to track costs and identify areas for #optimization.? The result is that nearly all organizations encountered data cost overruns at least a few times per year and over half experienced overages or unexpected spending spikes monthly, according to a Wakefield Research survey. Read more about the results of the survey and the impact of data ecosystems ballooning on organizations in this CIO Dive article -?? https://lnkd.in/dpfmEYmK #dataoptimization #dataROI #datagovernance #moderndatastack