Management Based on Data Analysis. Part One. Data Visualization Analysis

Data clustering is an important technique for data visualization and statistical data analysis. Using data clustering technique for geo-located data analysis and visualization has been shown to be useful in many practical domains. Although there are numerous studies for data clustering algorithms, the implementations and applications based on these algorithms are rare and inadequate, especially for web-based data visualization services. In this research, a new web-based interactive data clustering prototype is proposed for geo-located data visualization. The necessary web-based infrastructure for geo-located data analysis and visualization is designed and implemented at first. And then the new interactive data clustering prototype using multi-dimensional K-Means algorithm is implemented on the existing infrastructure. In order to assess whether the new prototype can contribute meaningfully to architectural design and urban studies, qualified users with urban study or architectural backgrounds are invited to use the prototype and then make their comments and evaluations. To make it clear how this new prototype might apply in real world settings, an application scenario in which the user might interact with the prototype is provided and analyzed.

Data visualization and data mining are transdisciplinary tools for predictive and descriptive analytics. This presentation shows the abundance of data visualization tools currently available that are applicable for multi-disciplinary data. Some examples of visualization are presented as applied for multi-disciplines. Results of applying the most commonly used data visualization tools of Tableau [1] and Power BI [2] are presented as preliminary outputs of a funded Seed Money Grant that applies data for transmissible diseases for humans and also plant pathology. Tableau Software, LLC is an American interactive data visualization software company focused on business intelligence and can be used across all disciplines. Tableau products query relational databases, online analytical processing cubes, cloud databases, and spreadsheets to generate graph-type data visualizations.[1] Power BI (Business Intelligence) is a suite of business analytics tools developed by Microsoft that is widely used across various industries for business reporting, data analysis, and decision-making. [2] Future directions of this research are discussed that include using other data mining and visualization methods such as image processing techniques for applications to data for plant pathology and human diseases.

Deep-learning-based visual data analytics for smart construction management

In the modern business landscape, data visualization has become an indispensable element of business intelligence (BI), particularly within the context of US companies. The aim of this research paper is to examine how data visualization technologies might improve business intelligence and decision making in American businesses by examining adaptation, advantages and disadvantages. This research paper explores the transformative contribution of data visualization tools to improving business intelligence capabilities and their significant influence on the processes of decision-making. The study begins by examining the adoption of various data visualization tools and platforms, such as Tableau, Power BI, and Elkview within US companies. It delves into how these tools facilitate the conversion of complex data sets into visually intuitive formats, allowing stakeholders at all levels to grasp trends, patterns, and anomalies with greater ease. By integrating these visualization tools into their BI systems, companies can achieve a more nuanced understanding of their operational landscapes and market dynamics. The paper further evaluates the impact of data visualization on decision-making processes. Through an analysis of case studies and empirical data, it highlights several key benefits, including improved accuracy in data interpretation, enhanced ability to identify actionable insights, and accelerated decision-making timelines. The paper discusses strategies to overcome these challenges, such as ensuring data integrity and investing in user training to maximize the efficacy of visualization tools. Overall, the findings underscore that while data visualization is not a panacea, it significantly augments the decision-making capabilities of US companies by making data more accessible and actionable. The integration of advanced visualization techniques into BI practices is increasingly seen as a strategic advantage, driving both operational efficiencies and competitive differentiation. As data continues to proliferate, the role of data visualization in shaping business strategies and outcomes will likely become even more pronounced, making it a critical area of focus for future research and development in the field of business intelligence.

Focus on software

Introduction: Human beings express their emotions through different types of language. The combination of verbal and non-verbal language makes it possible to broaden the capture of the senses and deepen the understanding of a certain social phenomenon. The use of software aims to support the systematization and optimization of the combined data analysis process; Goals: To map scientific production on the use of software for combined analysis of textual and visual data in qualitative research; Methods: Scope review carried out in four international databases (Scopus, EMBASE, MEDLINE and Web of Science), between September 2021 and January 2022. A semi-structured instrument was developed for data collection. The data processing and analysis process was supported by the Rayyan QCR application and the webQDA qualitative analysis software; Results: The final review sample consisted of 11 studies published between 2014 and 2022, mainly in the health area. The publications involved three types of qualitative analysis software: NVivo, Atlas.ti and MaxQda. Photography was the most used source to produce visual data, while focus and discussion groups stood out to produce textual data. Thematic analysis was used in most publications and the software essentially supported the data coding step in a combined way; Conclusions: The combined analysis of textual and visual data carried out with the support of software proved to be a methodological path still on the rise, given that the review identified only 11 studies published in the last eight years.

Business analytics involves using various technologies to analyze data. Data mining focuses on the automated search for knowledge, patterns, or regularities in data. As a business analyst, it is essential to recognize the type of analytical technique appropriate for solving a specific problem. Exploratory Data Analysis (EDA) describes data using statistical and visualization techniques to highlight important aspects of that data for further analysis. This involves examining a data set from many angles, describing it, and summarizing it without making assumptions about its content. Exploratory data analysis is an essential step before diving into statistical modeling or machine learning to ensure that the data is really what it claims to be and that there are no apparent errors. This type of analysis should be part of data science projects in every organization. Visual analytics is sometimes confused with data visualization. Visual analysis is not simply a matter of graphically representing data. Modern, interactive visual analytics makes combining data from multiple sources easy and performs in-depth data analysis directly in the visualization. Additionally, artificial intelligence and machine learning algorithms can offer recommendations for exploration. Ultimately, visual analytics helps transform massive data sets into business insights that can positively impact an organization. Considering the previous comment about visual data analysis, it should be added that the system has extensive capabilities to create graphical dashboards containing reports and charts. It is essential that in the case of desktops, in addition to the visualizations included in the system itself, it is possible to embed reports from third-party tools.

Drawing on qualitative methodologies that integrate verbal and non-verbal texts, this study investigated novice teachers’ attributions of their experiences of internship, as conveyed through a visual text. Novices were invited to design a visual text that represented their experience during internship, as part of a national call entitled ‘Novices in Poster.’ The posters submitted yielded the publication of 127 published posters, which constituted the data for our study. The visual data were analyzed using a multilayered approach to the analysis of visual texts, drawing on Lemke’s analysis of visual and verbal data as complementary channels for obtaining an integrative interpretation of the expressed experience. Findings indicate that novices expose critical stances in relation to activism, collegiality, and leverage, making public their unique potential to improve the educational system. The visual texts underscored novices’ strong social–professional stances towards teaching, positioning them as delegates and agents striving for professional justice.

Both interactive visualization and computational analysis methods are useful for data studies and an integration of both approaches is promising to successfully combine the benefits of both methodologies. In interactive data exploration and analysis workflows, we need successful means to quantitatively externalize results from data studies, amounting to a particular challenge for the usually qualitative visual data analysis. In this paper, we propose a hybrid approach in order to quantitatively externalize valuable findings from interactive visual data exploration and analysis, based on local linear regression models. The models are built on user-selected subsets of the data, and we provide a way of keeping track of these models and comparing them. As an additional benefit, we also provide the user with the numeric model coefficients. Once the models are available, they can be used in subsequent steps of the workflow. A model-based optimization can then be performed, for example, or more complex models can be reconstructed using an inversion of the local models. We study two datasets to exemplify the proposed approach, a meteorological data set for illustration purposes and a simulation ensemble from the automotive industry as an actual case study.

The study of resistomes using whole metagenomic sequencing enables high-throughput identification of resistance genes in complex microbial communities, such as the human microbiome. Over recent years, sophisticated and diverse pipelines have been established to facilitate raw data processing and annotation. Despite the progress, there are no easy-to-use tools for comprehensive visual, statistical and functional analysis of resistome data. Thus, exploration of the resulting large complex datasets remains a key bottleneck requiring robust computational resources and technical expertise, which creates a significant hurdle for advancements in the field. Here, we introduce ResistoXplorer, a user-friendly tool that integrates recent advancements in statistics and visualization, coupled with extensive functional annotations and phenotype collection, to enable high-throughput analysis of common outputs generated from metagenomic resistome studies. ResistoXplorer contains three modules—the ‘Antimicrobial Resistance Gene Table’ module offers various options for composition profiling, functional profiling and comparative analysis of resistome data; the ‘Integration’ module supports integrative exploratory analysis of resistome and microbiome abundance profiles derived from metagenomic samples; finally, the ‘Antimicrobial Resistance Gene List’ module enables users to intuitively explore the associations between antimicrobial resistance genes and the microbial hosts using network visual analytics to gain biological insights. ResistoXplorer is publicly available at http://www.resistoxplorer.no.

The curse of dimensionality, i.e., the fact that feature spaces of increasing dimensionality with finite sample sizes tend to be empty, has given incentive to a plethora of research activities in various disciplines and diverse application fields, e.g., statistics or neural networks. Three major application fields are multivariate data classification, data analysis and data visualization. In this contribution, methods for dimensionality reduction from three decades of interdisciplinary research are browsed and their applicability in the above application domains is briefly discussed. Complementing techniques for ensuing interactive data visualization, data navigation and visual exploratory data analysis are presented, which exploit the remarkable human perceptive and associative capabilities for interactive visual exploratory data analysis and systematic recognition system design. The main focus of this paper is on the comparison of feature selection and feature extraction techniques and the potential benefit of their combination. Further, the interesting implications of dimensionality reduction for VLSI design and related area and power consumption are pointed out.

High Impact Data Visualization withPower View, Power Map, and Power BIhelps youtake business intelligence delivery to a new level that is interactive, engaging, even fun, all while driving commercial success through sound decision-making. Learn to harness the power of Microsofts flagship, self-service business intelligence suite to deliver compelling and interactive insight with remarkable ease. Learn the essential techniques needed to enhance the look and feel of reports and dashboards so that you can seize your audiences attention and provide them with clear and accurate information. Also learn to integrate data from a variety of sources and create coherent data models displaying clear metrics and attributes. Power View is Microsoft's ground-breaking tool for ad-hoc data visualization and analysis. It's designed to produce elegant and visually arresting output. It's also built to enhance user experience through polished interactivity. Power Map is a similarly powerful mechanism for analyzing data across geographic and political units. Power Query lets you load, shape and streamline data from multiple sources. PowerPivot can extend and develop data into a dynamic model. Power BI allows you to share your findings with colleagues, and present your insights to clients. High Impact Data Visualization withPower View, Power Map, and Power BIhelps you master this suite of powerful tools from Microsoft. You'll learn to identify data sources, and to save time by preparing your underlying data correctly. You'll also learn to deliver your powerful visualizations and analyses through the cloud to PCs, tablets and smartphones. Simple techniques take raw data and convert it into information. Slicing and dicing metrics delivers interactive insight. Visually arresting output grabs and focuses attention on key indicators. What youll learnProduce designer output that will astound your bosses and peers. Drive business intelligence from Excel using BI in the Cloud. Gather source data from corporate and public sources. Integrate charts, maps, and tables to deliver visually stunning information. Discover new insights as you chop and tweak your data as never before. Adapt delivery to mobile devices. Outshine competing products and enhance existing skills. Who this book is for High Impact Data Visualization withPower View, Power Map, and Power BIis written for any Excel or SharePoint user. Business Intelligence developers, power users, IT managers, finance experts, and more can use this book to outshine the competition by producing high-impact business intelligence reporting on a variety of devices from a variety of sources.

Visual analysis has witnessed a growing acceptance as a method of scientific inquiry in the research community. It is used in qualitative and mixed research methods. Even so, visual data analysis is likely to produce biased results when used in analysing a large and noisy dataset. This can be evident when a data analyst is not able to holistically explore, all the values associated with the objects of interest in a dataset. Consequently, the data analyst may assess inconsistent data as consistent when contradiction associated with the data is not visualised. This work identifies incomplete analysis as a challenge in the visual data analysis of a large and noisy dataset. It considers Formal Concept Analysis (FCA) tools and techniques and prescribes the mining and visualisation of Incomplete or Inconsistent Data (IID) when dealing with a large and noisy dataset. It presents an automated approach for transforming IID from a noisy context whose objects are associated with mutually exclusive many-valued attributes, to a formal context.

Microsoft Excel has proved itself to be a powerful development platform, and the need for material on advanced Excel development has never been greater. In recent years, however, few books have afforded developers the instruction and depth required to create advanced Excel applications. Two titles in particular, Professional Excel Development by Bullen, Bovey, and Green and Excel Dashboard and Reports for Dummies by Michael Alexander, have given serious focus to these subjects; yet a dearth of quality material still remains. Where printed material has failed to fill this gap, thriving communities and blogs have spawned on the Internet over the last few years devoted to Excel development. Naturally, the last few years have also seen growing interest in data visualization, business intelligence, and data analytics.

Compliance with regulation has shifted to become a dynamic data-centric reporting and regulating financial services, healthcare and energy. The traditional audit and remediation approach which is manual and relies on the conventional compliance approaches can also lead to inefficiencies and a higher exposure to sanctions. This paper will attempt to explore the procedural integration of business intelligence (BI) systems, data visualization, and predictive analytics in the strategic compliance planning. The paper will be focused on evaluating how dashboards might assist in gauging the key risk indicators in close time, and predictive modelling may be implemented to test the modifications in regulations and prepare to them in the long-term, according to the concepts of Basel III, GDPR, and ESG disclosure requirements. The case evidence of the example of banking, healthcare case, and manufacturing indicates that compliance based on analytics is worthwhile and that implementation poses difficulties. The experience in Santander and Metro Bank brings into the limelight the cost of ineffective system integration and system governance. The next change stage, which will lead to efficiency and resilience, is implied by regulatory reporting using AI and data platforms based on standards and cloud-like compliance architecture. The analysis would establish BI-motivated compliance as not only a technical enhancement but as a leadership necessity, and the compatibility of the organization strategy with the evolving regulatory landscapes and the reduction of the vulnerability of the systems