A snail shell process model for knowledge discovery via data analytics

Misra

Administrative Sciences

et al. 2019

Social investment programs are designed to provide opportunities to the less privileged so that they can contribute to the socioeconomic development of society. Stakeholders in social safety net programs (SSNPs) target vulnerable groups, such as the urban poor, women, the unemployed, and the elderly, with initiatives that have a transformative impact. Inadequate policy awareness remains a challenge, resulting in low participation rates in SSNPs. To achieve all-inclusive development, deliberate policies and programs that target this population have to be initiated by government, corporate bodies, and public-minded individuals. Artificial intelligence (AI) techniques could play an important role in improving the managerial decision support and policy-making process of SSNPs and increasing the social resilience of urban populations. To enhance managerial decision-making in social investment programs, we used a Bayesian network to develop an intelligent decision support system called the Social Safety Net Expert System (SSNES). Using the SSNES, we provide an advisory system to stakeholders who make management decisions, which clearly demonstrates the efficacy of SSNPs and inclusive development.

Section: Discussionmentioning

confidence: 99%

Section: Applications Of Data Analyticsmentioning

confidence: 99%

An Intelligent Advisory System to Support Managerial Decisions for A Social Safety Net

Misra

Administrative Sciences

et al. 2019

“… The quick development of huge information condition forces new difficulties that conventional knowledge discovery and data mining process (KDDM) models are not enough fit to address. [6] We propose a snail shell process show for knowledge discovery via data analytics (KDDA) to address these difficulties. We assess the utility of the KDDA procedure display utilizing certifiable diagnostic contextual analyses at a worldwide multi-media organization.…”

Section: Review Of Literaturementioning

confidence: 99%

Implementation and Effective Utilization of Analytical Tools and Techniques in Knowledge Management

Shivakumar¹

2019

IJRTE

Knowledge management is a multidisciplinary approach to achieve organizational objectives by making best use of knowledge and information resources. Analytics is the process of systematic analysis of the past data/statistics and trying to predict the future trends or data with various tools and techniques. With the advancement of technology and increasing competition, companies are prone to make better use of the information and analytics to sustain their position in the marketplace. Software as a Service (SaaS) has become an important trend in organizations in addition to that of the usual Excel and Google sheets analytics. In this study, comparative analysis has been done between SPSS & Google Sheets Techniques and also Google data studio with Tableau, Power BI & Google Sheets for data visualization processes. Efficient dashboard was created using the different data visualization tools and compared with their pros and cons. A survey was conducted in the form of Questionnaire and the responses are obtained from people ranging from Interns to Managerial level and the factors that influence the Knowledge management processes in an efficient manner are obtained. Google apps script coding and basic excel techniques like VLOOKUP, HLOOKUP, Conditional formatting, Data Validation are being implemented as a part of automating the repetitive tasks.

International Journal of Production Research

“…The advances in sensor technology, the Internet, wireless communication, and inexpensive memory have all contributed to the explosion of data (Zhong et al, 2016b;Lee et al, 2014). The rapid growth of the data environment imposes new challenges that traditional knowledge discovery and data mining process models are not adequately suited to address (Li et al, 2016b).…”

Section: Element Of Exchangementioning

confidence: 99%

Data supply chain (DSC): research synthesis and future directions

Spanaki

Gürgüç

Adams

et al. 2017

In the digital economy, the volume, variety and availability of data produced in myriad forms from a diversity of sources has become an important resource for competitive advantage, innovation opportunity as well as source of new management challenges. Building on the theoretical and empirical foundations of the traditional manufacturing Supply Chain (SC), which describes the flow of physical artefacts as raw materials through to consumption, we propose the Data Supply Chain (DSC) along which data are the primary artefact flowing. The purpose of this paper is to outline the characteristics and bring conceptual distinctiveness to the context around DSC as well as to explore the associated and emergent management challenges and innovation opportunities. To achieve this, we adopt the systematic review methodology drawing on the operations management and supply chain literature and, in particular, taking a framework synthetic approach which allows us to build the DSC concept from the preexisting SC template. We conclude the paper by developing a set of propositions and outlining an agenda for future research that the DSC concept implies.