Jayanthi Ranjan

Real time business intelligence in supply chain analytics

Purpose – Rapid innovation and globalization have generated tremendous opportunities and choices in the marketplace for firms and customers. Competitive pressures have led to sourcing and manufacturing on a global scale resulting in a significant increase in products. The paper tries to identify the need for real time business intelligence (BI) in supply chain analytics.Design/methodology/approach – The paper provides argument and analysis of the advantages and hurdles in BI.Findings – The paper focuses on the necessity to revisit the traditional BI concept that integrates and consolidates information in an organization in order to support firms that are service oriented and seeking customer loyalty and retention. Enhancing effectiveness and efficiency of supply chain analytics using a BI approach is a critical component in a companys ability to achieve its competitive advantage.Originality/value – This paper furthers understanding of the issues surrounding the use of BI systems in supply chains.

Role of knowledge management and analytical CRM in business: data mining based framework

Purpose – The purpose of the paper is to provide a thorough analysis of the concepts of business intelligence (BI), knowledge management (KM) and analytical CRM (aCRM) and to establish a framework for integrating all the three to each other. The paper also seeks to establish a KM and aCRM based framework using data mining (DM) techniques, which helps in the enterprise decision‐making. The objective is to share how KM and aCRM can be integrated into this seamless analytics framework to sustain excellence in decision making using effective data mining techniques and to explore how working on such aCRM system can be effective for enabling organizations delivering complete solutions.Design/methodology/approach – This paper is based on focused and dedicated study of the literature present on the aCRM, KM and data mining techniques. The paper considered how to develop a strategy and operational framework that would build aCRM on the foundation of existing DM techniques and KM approach to meet the business chall...

Data mining techniques for better decisions in human resource management systems

This paper presents the role of data mining in Human Resource Management Systems (HRMS). A deep understanding of the knowledge hidden in Human Resource (HR) data is vital to a firms competitive position and organisational decision making. Analysing the patterns and relationships in HR data is quite rare. The HR data is usually treated to answer queries. Because HR data primarily concerns transactional processing getting data into the system, recording it for reporting purposes it is necessary for HRMS to become more concerned with the quantifiable data. We show how data mining discovers and extracts useful patterns from this large data set to find observable patterns in HR. The paper demonstrates the ability of data mining in improving the quality of the decision-making process in HRMS and gives propositions regarding whether data-mining capabilities should lead to increased performance to sustain competitive advantage.

Effective educational process: a data‐mining approach

Purpose – The purpose of this paper is to develop a holistic model for educational purposes using data‐mining techniques for exploring the effects of probable changes in processes related to admissions, course delivery and recruitments.Design/methodology/approach – The paper proposes a framework for an effective educational process using data‐mining techniques to uncover the hidden trends and patterns and making accuracy based predictions through a higher level of analytical sophistication in the process of counselling students.Findings – Data‐mining tools are used in academia for capitalizing on the advances of information technology. This process improves research and academic decision making through uncovering hidden trends and patterns that predict using a combination of explicit knowledge base, sophisticated analytical skills and academic domain knowledge.Originality/value – The paper presents a model using a data‐mining approach for academics.

A holistic framework for mCRM – data mining perspective

Purpose – The purpose of this paper is twofold. First, in order to understand mobile customer relationship management (mCRM) and data mining application in the mCRM, this paper aims to present a conceptualization of mCRM in respect of data mining. Second, the paper also aims to develop the empirically grounded framework of the mCRM from data mining perspective.Design/methodology/approach – The empirical paper is used to gain a conceptual view of mCRM. Semi‐structured interviews and contact methodology is used to form the main data source through which the major concerns and issues of mCRM are identified. This lead to holistic framework of mCRM. The paper followed the paradigm of natural science research on information technology by March and Smith and Hervner et al.Findings – The framework identified three critical issues that are categorized as customer care information center, data store and data access systems, and mobile services and technology. The paper on various existing literatures in mCRM strate...

Data mining in pharma sector: benefits

PURPOSE The amount of data getting generated in any sector at present is enormous. The information flow in the pharma industry is huge. Pharma firms are progressing into increased technology-enabled products and services. Data mining, which is knowledge discovery from large sets of data, helps pharma firms to discover patterns in improving the quality of drug discovery and delivery methods. The paper aims to present how data mining is useful in the pharma industry, how its techniques can yield good results in pharma sector, and to show how data mining can really enhance in making decisions using pharmaceutical data. DESIGN/METHODOLOGY/APPROACH This conceptual paper is written based on secondary study, research and observations from magazines, reports and notes. The author has listed the types of patterns that can be discovered using data mining in pharma data. FINDINGS The paper shows how data mining is useful in the pharma industry and how its techniques can yield good results in pharma sector. RESEARCH LIMITATIONS/IMPLICATIONS Although much work can be produced for discovering knowledge in pharma data using data mining, the paper is limited to conceptualizing the ideas and view points at this stage; future work may include applying data mining techniques to pharma data based on primary research using the available, famous significant data mining tools. ORIGINALITY/VALUE Research papers and conceptual papers related to data mining in Pharma industry are rare; this is the motivation for the paper.

Enhancing the teaching‐learning process: a knowledge management approach

Purpose – The purpose of this paper is to emphasize the need for knowledge management (KM) in the teaching‐learning process in technical educational institutions (TEIs) in India, and to assert the impact of information technology (IT) based KM intervention in the teaching‐learning process.Design/methodology/approach – The approach of the paper is basically conceptual and descriptive. The paper studies the role of KM in facilitating knowledge sharing among stakeholders in TEIs in India and elaborates on the need for knowledge management in the teaching‐learning process. A KM framework for enhancement of knowledge sharing by the use of shared intellectual repositories (IRs) is proposed for the enhancement of teaching and learning in technical educational institutions in India.Findings – The authors value the impact that KM can have in enhancing the quality of teaching and learning in TEIs, and underscore the need for credible research into the benefits and challenges that the implementation of IT‐based KM i...

Principles for successful aCRM in organizations

Purpose – The purpose of this paper is to propose the important principles for successful CRM analytics (aCRM) in organizations.Design/methodology/approach – The paper employs the approach of identifying the major critical business information needed for aCRM in organizations. The approach to identify the critical business is based on the cross‐pollination of information related to aCRM. Analyzing aCRM from the hybrid blends of business sources provides a clear understanding in a more realist dimension.Findings – The paper finds that knowing what to capture is fundamental to business alignment of aCRM in a typical business environment.Practical implications – The successful implementation of the principles of aCRM will help organizations to measure the effectiveness of their direct marketing activities.Originality/value – The chosen research strategy was to survey aCRM in organizations that are being incorporated worldwide and analyze their content, looking for similarities and complementarities in their ...

Impact of Information Technology in Academia

Purpose – The purpose of this paper is to study the provisions of information technology (IT) for development of academic resources and examines the effect of IT in academic institutions for sharing information.Design/methodology/approach – The paper examines the role of IT in sharing information in academic institutions and explores the IT effectiveness in an academic context that is used to improve efficiency and effectiveness of creation and sharing of information among people.Findings – The paper finds that the rapid pace of change in IT is increasingly influencing the creation, publication and dissemination of educational materials and sharing information. IT facilitates connections across disciplinary, institutional, geographical, and cultural boundaries. Taken together, the computational and communication capabilities in IT offer great promise for supporting continual improvements in academia. They also underscore the need for credible research into the practical benefits and limitations of teachin...

Big Data: Prospects and Challenges

We are living in an era of data deluge. Data that the human race has accumulated in the past one decade, far exceeds the data that was available to mankind during the preceding century. McKinsey & Co. foresees that the society is ‘on the cusp of a tremendous wave of innovation, productivity, and growth as well as new modes of competition and value capture—all driven by Big Data’.2 They also expect that different stakeholders such as consumers, companies and businesses are likely to exploit the potential of Big Data. Eric Siegel, founder of Predictive Analytics World, estimates that on an average day we accumulate 2.5 quintillion bytes of data.3 Another important character of the ‘datafication’, as Viktor Mayor–Schonborge and Kenneth Cukier call it, is that ‘Data can frequently be collected passively, without much effort or even awareness on the part of those being recorded. And because the cost of storage has fallen so much, it is easier to justify keeping data than discard it.’4 As the cost of storage has fallen and computing power has increased, the size of data that was challenging before, can be easily handled with a desktop computer now. Several estimates about the accumulation of data have challenged our earlier imagination. Data scientists are increasingly using data quantities in Peta and Zeta bytes. Businesses, governments and developmental organizations—all are foreseeing that Big Data is likely to create value in multiple