The function of competitive intelligence in South African insurance post-COVID-19 outbreak

Background: Competitive intelligence (CI) involves monitoring competitors and providing organizations with actionable and meaningful intelligence. Some studies have focused on the role of CI in other industries post-COVID-19 pandemic. Objectives: This article aims to examine the impact of COVID-19 on the South African insurance sector and how the integration of CI and related technologies can sustain the South African insurance sector post-COVID-19 epidemic. Method: Qualitative research with an exploratory-driven approach was used to examine the impact of the COVID-19 pandemic on the South African insurance sector. Qualitative secondary data analyses were conducted to measure insurance claims and death benefits paid during the COVID-19 pandemic. Results: The research findings showed that the COVID-19 pandemic significantly impacted the South African insurance industry, leading to a reassessment of pricing, products, and risk management. COVID-19 caused disparities in death benefits and claims between provinces; not everyone was insured. Despite challenges, South African insurers remained well-capitalised and attentive to policyholders. Integrating CI and analytical technologies could enhance the flexibility of prevention, risk management, and product design. Conclusion: COVID-19 requires digital transformation and CI for South African insurers' competitiveness. Integrating artificial intelligence (AI), big data (BD), and CI enhances value, efficiency, and risk assessments. Contribution: This study highlights the importance of integrating CI strategies and related technologies into South African insurance firms' operations to aid in their recovery from the COVID-19 crisis. It addresses a research gap and adds to academic knowledge in this area.

Retinitis pigmentosa in Usher syndrome in India: Electronic medical records driven big data analytics: Report III

Purpose: To describe the clinical presentation and demographic distribution of retinitis pigmentosa (RP) in patients with Usher syndrome (USH). Methods: This is a cross?sectional observational hospital?based study including patients presenting between March 2012 and October 2020. In total, 401 patients with a clinical diagnosis of USH and RP in at least one eye were included as cases. The data were retrieved from the electronic medical record database. For better analysis, all 401 patients were reclassified into three subtypes (type 1, type 2, and type 3) based on the USH criteria. Results: In total, there were 401 patients with USH and RP, with a hospital?based prevalence rate of 0.02% or 2/10,000 population. Further, 353/401 patients were subclassified, with 121 patients in type 1, 146 patients in type 2, and 86 patients in the type 3 USH group. The median age at presentation was 27 years (IQR: 17.5–38) years. There were 246 (61.35%) males and 155 (38.65%) females. Males were more commonly affected in all three subtypes. Defective night vision was the predominant presenting feature in all types of USH (type 1: 43 (35.54%), type 2: 68 (46.58%), and type 3: 40 (46.51%) followed by defective peripheral vision. Patients with type 2 USH had more eyes with severe visual impairment. Conclusion: RP in USH is commonly bilateral and predominantly affects males in all subtypes. Patients with USH and RP will have more affection of peripheral vision than central vision. The key message of our study is early visual and hearing rehabilitation in USH patients with prompt referral to otolaryngologists from ophthalmologists and vice versa.

Retinitis pigmentosa in Laurence-Moon-Bardet-Biedl syndrome in India: Electronic medical records driven big data analytics: Report II

Purpose: To describe the clinical presentation and demographic distribution of retinitis pigmentosa (RP) in Laurence–Moon–Bardet–Biedl (LMBB) syndrome patients. Methods: This is a cross?sectional observational hospital?based study wherein 244 patients with RP in LMBB syndrome presenting to our hospital network between March 2012 and October 2020 were included. An electronic medical record database was used for data retrieval. Results: There were 244 patients in total, with a hospital?based prevalence rate of 0.010% or 1000/100,000 population. The mean and median age of patients was 15.22 ± 7.56 and 14 (IQR: 10–18.5) years, respectively, with the majority being in the age group of 11–20 years (133/244 patients; 54.50%). Males were more commonly affected (164 patients; 67.21%), and the majority (182 patients; 74.59%) were students. All 244 patients (100%) complained of defective central vision at presentation. More than one?fourth of the patients had severe visual impairment to blindness at presentation. Prominent retinal feature at presentation was diffuse or widespread retinal pigment epithelial degeneration in all patients. Conclusion: Patients with RP in LMBB syndrome present mainly in the first to second decade of life with severe visual acuity impairment to blindness early in life. It is important to rule out LMBB syndrome in early?onset RP with central visual acuity impairment. On the contrary, all patients diagnosed or suspected with LMBB syndrome systemic features at physician clinic should also be referred for ophthalmic evaluation, low vision assessment, rehabilitation, and vice versa

The Role of Big Data Analytics in Digital Health for COVID-19 Prevention and Control in Asia

@#Big data analytics (BDA) in digital health is critical for gaining the knowledge needed to make decisions, with Asia at the forefront of utilising this technology for the Coronavirus disease 2019 (COVID-19). This review aims to study how BDA was incorporated into digital health in managing the COVID-19 pandemic in six selected Asian countries, discuss its advantages and barriers and recommend measures to improve its adoption. A narrative review was conducted. Online databases were searched to identify all relevant literature on the roles of BDA in digital health for COVID-19 preventive and control measures. The findings showed that these countries had used BDA for contact tracing, quarantine compliance, outbreak prediction, supply rationing, movement control, information update, and symptom monitoring. Compared to conventional approaches, BDA in digital health plays a more efficient role in preventing and controlling COVID-19. It may inspire other countries to adopt this technology in managing the pandemic.

Robotic Process Automation Extended with Artificial Intelligence Techniques in Power Distribution Utilities

Abstract Robotic Process Automation (RPA) is one of the several important techniques currently available for companies in search of performance improvement. The step forward in RPA is its association with Artificial Intelligence for more skilled robots. This scenario is not different in Power Distribution Utilities, in which a multitude of complex processes must be executed over different data sources. Making such situation even more complex, these processes are frequently regulated and subject to audit by external bodies. However, an old question remains: what should be robotized and what should be done by humans? This paper aims at partially answering the question in the context of data analysis tasks used for making decisions in complex processes. The research development is conducted based on an Artificial Intelligence methodology incorporated into one software robot (RPA) which acquires data automatically, treats and analyzes these data, helping the human professional take decisions in the process. It is applied to a real case process that is important for validating the research. Four approaches are tested in the data analysis, but only two are really used. The robot analyzes a series of information from an energy consumption meter. The detection of possible behavior deviations in the meter data is made by comparison with its data series. The robot is capable of prioritizing the detected occurrences in the energy consumption data, indicating to the human operator the most critical situations that require attention. The association of Artificial Intelligence and RPA is viable and can really apport important benefits to the company and teams, valuing human work and bringing more efficiency to the processes.

Estudio comparativo de técnicas de analítica del aprendizaje para predecir el rendimiento académico de los estudiantes de educación superior / Comparative study of learning analytics techniques to predict student academic performance in higher education

Resumen La deserción escolar involucra diversos factores, entre ellos, el compromiso del estudiante, a través del cual se puede predecir su éxito en la escuela. Ese compromiso tiene varios componentes, tales como conductual, emocional y cognitivo. La motivación y el compromiso están fuertemente relacionadas, ya que la primera es un precursor del compromiso. El objetivo de este estudio fue comparar la eficacia de la regresión lineal contra dos técnicas de minería de datos para predecir el rendimiento académico de los estudiantes en la educación superior. Se hizo un estudio transversal explicativo en el que se encuestó a 222 estudiantes universitarios de una institución pública de la Ciudad de México. Se realizó un análisis de regresión lineal jerárquico (RL) y de técnicas de analítica del aprendizaje, como redes neuronales (RN) y máquinas de vector soporte (SVM). Para evaluar la exactitud de las técnicas de analítica del aprendizaje se realizó un análisis de varianza (ANOVA). Se compararon las técnicas de analítica del aprendizaje y de regresión lineal usando la validación cruzada. Los resultados mostraron que el compromiso conductual y la autoeficacia tuvieron efectos positivos en el desempeño del estudiante, mientras que la pasividad mostró un efecto negativo. Asimismo, las técnicas de RL y de SVM pronosticaron igualmente el desempeño académico de los estudiantes. La RL tuvo la ventaja de producir un modelo simple y de fácil interpretación. Por el contrario, la técnica de SVM generó un modelo más complejo, aunque, si el modelo tuviese como objetivo el pronóstico del desempeño, la técnica SVM sería la más adecuada, ya que no requiere la verificación de ningún supuesto estadístico.

Abstract The issue of school dropout involves factors such as students' engagement that can predict his or her success in school. It has been shown that student engagement has three components: behavioral, emotional and cognitive. Motivation and engagement are strongly related since the former is a precursor of engagement. The aim of this study was to compare the efficiency of linear regression against two data mining techniques to predict the students' academic performance in higher education. A descriptive cross-sectional study was carried out with 222 students from a public higher education institution in Mexico city. An analysis of hiererchical linear regression (LR) and learning analytics techniques such as neural networks (NN) and support vector machine (SVM) was conducted. To assess the accuracy of the learning analytics techniques, an analysis of variance (ANOVA) was carried out. The techniques were compared using cross validation. The results showed that behavioral engagement and self-efficacy had positive effects on student achievements, while passivity showed a negative effect. Likewise, the LR and SVM techniques had the same performance on predicting students' achievements. The LR has the advantage of producing a simple and easy model. On the contrary, the SVM technique generates a more complex model. Although, if the model were aimed to forecast the performance, the SVM technique would be the most appropriate, since it does not require to verify any statistical assumption.

Big data and the eyeSmart electronic medical record system - An 8-year experience from a three-tier eye care network in India

Purpose: To assess the demographic details and distribution of ocular disorders in patients presenting to a three-tier eye care network in India using electronic medical record (EMR) systems across an 8-year period using big data analytics. Methods: An 8-year retrospective review of all the patients who presented across the three-tier eye care network of L.V. Prasad Eye Institute was performed from August 2010 to August 2018. Data were retrieved using an in-house eyeSmart EMR system. The demographic details and clinical presentation and ocular disease profile of all the patients were analyzed in detail. Results: In an 8-year period, a total of 2,270,584 patients were captured on the EMR system with 4,730,221 consultations. More than half of the patients presented at tertiary centers (n = 1,174,643, 51.73%), a quarter at the secondary centers (n = 564,251, 24.85%) followed by the vision centers (n = 531,690, 23.42%). The ratio of males and females was 1.18:1. Most common states of presentation were Andhra Pradesh (n = 1,103,733, 48.61%) and Telangana (n = 661,969, 29.15%). In total, 3,721,051 ocular diagnosis instances were documented in the patients. Most common ocular disorders were related to cornea and anterior segment (n = 1,347,754, 36.22%) followed by refractive error (n = 1,133,078, 30.45%). Conclusion: This study depicts the demographic details and distribution of various ocular disorders in a very large cohort of patients. There is a need to adopt digitization in geographies that cater to large populations to enable insightful research. The implementation of EMR systems enables structured data for research purposes and the development of real-time analytics for the same.

Decoding the microbiome for the development of translational applications: Overview, challenges and pitfalls

Recent studies have highlighted the potential of ‘translational’ microbiome research in addressing real-world challengespertaining to human health, nutrition and disease. Additionally, outcomes of microbiome research have also positivelyimpacted various aspects pertaining to agricultural productivity, fuel or energy requirements, and stability/preservation ofvarious ecological habitats. Microbiome data is multi-dimensional with various types of data comprising nucleic andprotein sequences, metabolites as well as various metadata related to host and or environment. This poses a major challengefor computational analysis and interpretation of data to reach meaningful, reproducible (and replicable) biological conclusions. In this review, we first describe various aspects of microbiomes that make them an attractive tool/target fordeveloping various translational applications. The challenge of deciphering signatures from an information-rich resourcelike the microbiome is also discussed. Subsequently, we present three case-studies that exemplify the potential of microbiome-based solutions in solving real-world problems. The final part of the review attempts to familiarize readers with theimportance of a robust study design and the diligence required during every stage of analysis for achieving solutions withpotential translational value.

Recursos humanos em saúde: do processo intuitivo ao People Analytics / Human resource in health: from the intuitive process to People Analytics / Recursos humanos en salud: del processo intuitivo a People Analytics

A análise de pessoas é uma ferramenta útil na investigação comportamental e na indicação de estratégias que potencializam a eficiência de colaboradores na realização dos seus encargos. Nessa perspectiva, o presente trabalho teve como objetivo explorar o potencial de análise da ferramenta de dados Big Data a partir do People Analytics para a área da saúde. Em vista disso, foram analisados os perfis dos profissionais da área de enfermagem a fim de examinar os benefícios do dispositivo para a gestão de recursos humanos em saúde e, assim, fomentar sua implementação.

People analysis is a useful tool in behavioral research and in the indication of strategies that enhance the efficiency of the participants to carry out their tasks. In this perspective, this paper's objective is to explore the potential of analysis of the data tool Big Data from the People Analytics for the health area. Therefore, the nursing professionals profiles were analyzed to examine the benefits of the device for the management of human resources in health and then, to promote its implementation.

El análisis de personas es una herramienta útil en la investigación comportamental y en la indicación de estrategias que potencializan la eficiencia de colaboradores en la realización de sus encargos. En esa perspectiva, el presente trabajo tuvo como objetivo explorar el potencial de análisis de la herramienta de datos Big Data a partir del People Analytics para el área de la salud. Por lo tanto, fueron analizados los perfiles de los profesionales del área de enfermería para examinar los beneficios del dispositivo para la gestión de recursos humanos en salud y así fomentar su implementación.

La analítica del aprendizaje en educación médica: ¿punto de inflexión? / Learning analytics in medical education: turning point?

Resumen La analítica del aprendizaje es una disciplina novedosa que tiene un enorme potencial para mejorar la calidad de la educación médica y la evaluación del aprendizaje. Se define como: “la medición, recopilación, análisis y reporte de datos sobre los alumnos y sus contextos, con el propósito de entender y optimizar el aprendizaje y los entornos en que ocurre”. En las últimas décadas, la aparición de grandes volúmenes de datos (big data), acompañada de una rápida evolución en la minería de datos educativos, la aparición de tecnologías sofisticadas para analizar y visualizar datos de cualquier tipo, así como la disponibilidad de dispositivos móviles con conectividad permanente, mayor velocidad de procesamiento y capacidad de recuperación de información, han generado un contexto que favorece el uso de la analítica del aprendizaje en la medicina clínica y la educación médica. En este artículo se describe la historia reciente del concepto de analítica del aprendizaje, sus ventajas y desventajas en educación superior, así como sus aplicaciones en la enseñanza de las ciencias de la salud y la evaluación educativa. Es necesario que la comunidad de educadores médicos conozca la analítica del aprendizaje, para ser capaces de integrarla en su contexto eficaz y oportunamente.

Abstract Learning analytics is an innovative discipline that has an enormous potential to improve the quality of medical education and learning assessment. It is defined as: “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs”. In recent decades, the appearance of large volumes of data (big data), accompanied by a quick evolution of educational data mining techniques, the emergence of sophisticated technologies to analyze and visualize any type of data, as well as the availability of permanently-connected mobile electronic devices, higher processing speed and capacity of information retrieval, have generated a context that favors the use of learning analytics in clinical medicine and medical education. In this paper, the recent history of the concept of learning analytics is described, as well as its advantages and disadvantages in higher education, and its applications in the teaching of health sciences and educational assessment. It is necessary for the community of medical educators to be acquainted with learning analytics, in order to be able to integrate it to our context in an efficacious and timely manner.

Amlodipine alters hemorheological parameters: Increased efficacy at the cost of edema?

Background Despite several decades of use of calcium channel blockers, the side effect of edema persists as a class effect, and its mechanism is unresolved. Amlodipine has effects on hemorheology (HR), and its hemodilutory property may partly contribute to its antihypertensive action. This aspect is not well studied, and the literature is sparse in this regard. Objective This experiment was planned to determine effect of a single-dose administration of amlodipine on HR parameters in normal human volunteers. Methods and results Amlodipine (5 mg) or S (-) amlodipine (2.5 mg) was administered to 27 normal human volunteers. Whole-blood viscosity (WBV) at different shear rates, plasma viscosity (PV), red cell rigidity (RCR), red cell aggregation (RCA), hematocrit (Hct), plasma hemoglobin, along with plasma drug concentration were determined at time intervals, t = 0, 4, 8, 12, and 24 h. Statistically significant reductions were observed at tmax = 4 h in WBV at shear rates of 0.512 s–1 (p < 0.005), WBV at shear rates of 5.26 s–1 (p < 0.01), PV (p < 0.05), and Hct (p < 0.01). At t = 8 h, as drug concentration reduced, some of the changes persisted and later slowly decreased with the decreasing drug concentration till t = 24 h. Red blood cell–related parameters such as RCA and RCR remained unaltered. WBV values at all shear rates, when corrected for Hct = 0.45, did not show deviation from their original values at any time. Conclusions Amlodipine causes a reduction in Hct and blood viscosity, along with hemodilution. These effects persist as long as the drug remains in plasma. Edema resulting from chronic dosing may be explained by the aforementioned effects. It is possible that antihypertensive action of the drug may be due to a combination of vasodilatation and an improvement in the HR properties.

Predicting the Number of People for Meals of an Institutional Foodservice by Applying Machine Learning Methods: S City Hall Case

Predicting the number of meals in a foodservice organization is an important decision-making process that is essential for successful food production, such as reducing the amount of residue, preventing menu quality deterioration, and preventing rising costs. Compared to other demand forecasts, the menu of dietary personnel includes diverse menus, and various dietary supplements include a range of side dishes. In addition to the menus, diverse subjects for prediction are very difficult problems. Therefore, the purpose of this study was to establish a method for predicting the number of meals including predictive modeling and considering various factors in addition to menus which are actually used in the field. For this purpose, 63 variables in eight categories such as the daily available number of people for the meals, the number of people in the time series, daily menu details, weekdays or seasons, days before or after holidays, weather and temperature, holidays or year-end, and events were identified as decision variables. An ensemble model using six prediction models was then constructed to predict the number of meals. As a result, the prediction error rate was reduced from 10%~11% to approximately 6~7%, which was expected to reduce the residual amount by approximately 40%.

Opinion: Strategy of Semi-Automatically Annotating a Full-Text Corpus of Genomics & Informatics

There is a communal need for an annotated corpus consisting of the full texts of biomedical journal articles. In response to community needs, a prototype version of the full-text corpus of Genomics & Informatics, called GNI version 1.0, has recently been published, with 499 annotated full-text articles available as a corpus resource. However, GNI needs to be updated, as the texts were shallow-parsed and annotated with several existing parsers. I list issues associated with upgrading annotations and give an opinion on the methodology for developing the next version of the GNI corpus, based on a semi-automatic strategy for more linguistically rich corpus annotation.

Moving anesthesiology educational resources to the point of care: experience with a pediatric anesthesia mobile app / 대한마취과학회지

BACKGROUND: Educators in all disciplines recognize the need to update tools for the modern learner. Mobile applications (apps) may be useful, but real-time data is needed to demonstrate the patterns of utilization and engagement amongst learners. METHODS: We examined the use of an anesthesia app by two groups of learners (residents and anesthesiologist assistant students [AAs]) during a pediatric anesthesiology rotation. The app calculates age and weight-based information for clinical decision support and contains didactic materials for self-directed learning. The app transmitted detailed usage information to our research team. RESULTS: Over a 12-month period, 39 participants consented; 30 completed primary study procedures (18 residents, 12 AAs). AAs used the app more frequently than residents (P = 0.025) but spent less time in the app (P < 0.001). The median duration of app usage was 2.3 minutes. During the course of the rotation, usage of the app decreased over time. ‘Succinylcholine' was the most accessed drug, while ‘orientation' was the most accessed teaching module. Ten (33%) believed that the use of apps was perceived to be distracting by operating room staff and surgeons. CONCLUSIONS: Real-time in-app analytics helped elucidate the actual usage of this educational resource and will guide future decisions regarding development and educational content. Further research is required to determine learners' preferred choice of device, user experience, and content in the full range of clinical and nonclinical purposes.

GNI Corpus Version 1.0: Annotated Full-Text Corpus of Genomics & Informatics to Support Biomedical Information Extraction

Genomics & Informatics (NLM title abbreviation: Genomics Inform) is the official journal of the Korea Genome Organization. Text corpus for this journal annotated with various levels of linguistic information would be a valuable resource as the process of information extraction requires syntactic, semantic, and higher levels of natural language processing. In this study, we publish our new corpus called GNI Corpus version 1.0, extracted and annotated from full texts of Genomics & Informatics, with NLTK (Natural Language ToolKit)-based text mining script. The preliminary version of the corpus could be used as a training and testing set of a system that serves a variety of functions for future biomedical text mining.

On predicting epileptic seizures from intracranial electroencephalography

This study investigates the sensitivity and specificity of predicting epileptic seizures from intracranial electroencephalography (iEEG). A monitoring system is studied to generate an alarm upon detecting a precursor of an epileptic seizure. The iEEG traces of ten patients suffering from medically intractable epilepsy were used to build a prediction model. From the iEEG recording of each patient, power spectral densities were calculated and classified using support vector machines. The prediction results varied across patients. For seven patients, seizures were predicted with 100% sensitivity without any false alarms. One patient showed good sensitivity but lower specificity, and the other two patients showed lower sensitivity and specificity. Predictive analytics based on the spectral feature of iEEG performs well for some patients but not all. This result highlights the need for patient-specific prediction models and algorithms.

Analysis of Adverse Drug Reaction Reports using Text Mining

BACKGROUND: As personalized healthcare industry has attracted much attention, big data analysis of healthcare data is essential. Lots of healthcare data such as product labeling, biomedical literature and social media data are unstructured, extracting meaningful information from the unstructured text data are becoming important. In particular, text mining for adverse drug reactions (ADRs) reports is able to provide signal information to predict and detect adverse drug reactions. There has been no study on text analysis of expert opinion on Korea Adverse Event Reporting System (KAERS) databases in Korea. METHODS: Expert opinion text of KAERS database provided by Korea Institute of Drug Safety & Risk Management (KIDS-KD) are analyzed. To understand the whole text, word frequency analysis are performed, and to look for important keywords from the text TF-IDF weight analysis are performed. Also, related keywords with the important keywords are presented by calculating correlation coefficient. RESULTS: Among total 90,522 reports, 120 insulin ADR report and 858 tramadol ADR report were analyzed. The ADRs such as dizziness, headache, vomiting, dyspepsia, and shock were ranked in order in the insulin data, while the ADR symptoms such as vomiting, 어지러움, dizziness, dyspepsia and constipation were ranked in order in the tramadol data as the most frequently used keywords. CONCLUSION: Using text mining of the expert opinion in KIDS-KD, frequently mentioned ADRs and medications are easily recovered. Text mining in ADRs research is able to play an important role in detecting signal information and prediction of ADRs.

Los desafíos del marketing en la era del big data

ste escrito plantea cuatro desafíos del marketing a los que se enfrentan las empresas en la era digital teniendo en cuenta el enfoque estratégico, tecnológico y táctico; su objetivo es ayudar a que las organizaciones, en especial los departamentos de marketing, puedan tomar mejores decisiones implementando la analítica de datos. En la primera parte del artículo se presenta la definición y evolución del marketing desde la revolución industrial, con su enfoque en el producto y la producción en masa, hasta el marketing actual, que está centrado en el consumidor y las necesidades del cliente buscando una diferenciación y personalización tanto de productos como de servicios basándose en los avances tecnológicos y los diversos accesos a la información. En la segunda parte se especifica qué es Big Data, los volúmenes de datos, los tipos de datos y sus fuentes; igualmente, se puntualiza qué es la analítica de datos (data analytics). El tercer aporte esboza la descripción del marketing en nuestros días y cómo los servicios de la Web 2.0 (redes sociales, RSS, tecnologías rápidas de mensajería, vídeos, mensajería instantánea, wikis, blogs, etc.) y sus bases teóricas ayudan a la captación, fidelización y posicionamiento de marca. Por último, se presentan los cuatro desafíos para las empresas en la actual era digital: el desafío de las 6V (volumen, velocidad, variedad, veracidad, valor y visualización); los retos estratégicos, tecnológicos y operativos en las organizaciones; las tendencias del marketing y la medición del ROMI (return on marketing investment).

his paper highlights four marketing challenges that companies face in the digital age, considering the strategic, technological and tactical approach; its aim is helping organizations, especially marketing departments, to make better decisions implementing analytical data. The first part of the article presents the definition and evolution of marketing from the industrial revolution, with its focus on the product and mass production, to the current consumer-focused marketing and customer needs, which look for differentiation and customization of both products and services based on technological advances and the various accesses to information. The second part specifies what Big Data, data volumes, data types and sources are; and it also points out what analytical data (data analytics) is. The third part outlines the description of marketing today, and how the services of Web 2.0 (social networks, RSS, fast messaging technologies, videos, instant messaging, wikis, blogs, etc.) and their theoretical underpinnings help client acquisition and loyalty, and branding. Finally, the article expounds the four challenges for companies in the current digital age: 6V challenge (volume, velocity, variety, veracity, value and visualization); the strategic, technological and operational challenges in organizations; marketing trends; and measuring ROMI (return on marketing investment).

Impression of Financial Measures in HR Analytics.

In today‟s competitive world, business is all about investments and revenues. Every function of business can be calculated in numbers, then why should HR be left behind. The paper explains how financial measures help the HR mangers to justify every investment made to human resource project. The gap has been filled with the help of HR analytics which transform the raw HR data into insightful information with help of financial measures. HR analytics designed with financial measures helps the Senior HR management to quantify the value of human resources which justifies the investments backing up with the hard reliable evidence. Financial measures along with HR analytics help the HR management to produce net benefits gained from the initiative. This paper gives the overview on how financial measures aids the HR managers to justify the investments made on the human resource projects.

Interactive Visualization for Patient-to-Patient Comparison

A visual analysis approach and the developed supporting technology provide a comprehensive solution for analyzing large and complex integrated genomic and biomedical data. This paper presents a methodology that is implemented as an interactive visual analysis technology for extracting knowledge from complex genetic and clinical data and then visualizing it in a meaningful and interpretable way. By synergizing the domain knowledge into development and analysis processes, we have developed a comprehensive tool that supports a seamless patient-to-patient analysis, from an overview of the patient population in the similarity space to the detailed views of genes. The system consists of multiple components enabling the complete analysis process, including data mining, interactive visualization, analytical views, and gene comparison. We demonstrate our approach with medical scientists on a case study of childhood cancer patients on how they use the tool to confirm existing hypotheses and to discover new scientific insights.