Value co-creation and potential benefits through big data analytics: Health Benefit Analysis

Big data analytics in healthcare context is often studied from a technical point of view. In the field of strategic management, researchers have indicated a research gap in how big data analytics create business value. This study examines how big data and advanced analytics generate potential benefits and business value for the healthcare service provider, and value for the individual patients and population health. In addition, the effects of advanced analytics to the value co-creation practices and actors in healthcare ecosystem are studied. The theoretical framework used for the purpose is the big data analytics-enabled transformation model which is adapted to answer the research questions. The study is conducted as a single case study. The studied case is the Health Benefit Analysis (HBA) tool. The empirical data is collected in eight semi-structured interviews with participants of the tool development project. Using the HBA tool reveals several paths-to-value chains. The most evident path shows how using advanced analytics affects the personalized care practice by enabling a more interactive service process between the health professionals and patients. It denotes a business scope redefinition as patients are now being interpreted as essential actors in the value co-creation of their own health outcomes. The benefits that arise from the advanced analytics are of several dimensions; operational, managerial, strategic, and organizational. Using the HBA tool generates strategic business value for the healthcare service provider as a differentiator that contributes to gaining competitive advantage compared to other service providers not using this innovation. Value emerges for the individual patient as improved patient experience and better health outcomes. Population health gains most value from the reduced health inequalities. The evolving value co-creation practices set requirements for the healthcare ecosystem actors as they need to conform to new practices with patients and other professionals from other sectors and levels of the ecosystem. The healthcare work and service culture need to develop and adapt to new tools, related processes, and a more diversified professional base, including health analysts and other new professionals. To conclude, it can be claimed that advanced analytics of healthcare big data contributes to the shift to value-based healthcare.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

BIG DATA-DRIVEN STOCHASTIC BUSINESS PLANNING AND CORPORATE VALUATION

The research question of this paper is concerned with the investigation of the links between Internet of Things and related big data as input parameters for stochastic estimates in business planning and corporate evaluation analytics. Financial forecasts and company appraisals represent a core corporate ownership and control issue, impacting on stakeholder remuneration, information asymmetries, and other aspects. Optimal business planning and related corporate evaluations derive from an equilibrated mix of top-down and bottom-up approaches. While the former follows a traditional dirigistic methodology where companies set up their strategic goals, the latter are grass-rooted with big data-driven timely evidence. Real options can be embedded in big data-driven forecasting to make expected cash flows more flexible and resilient, improving Value for Money of the investment and reducing its risk profile. More accurate and timely big data-driven predictions reduce uncertainties and information asymmetries, making risk management easier and decreasing the cost of capital. Whereas stochastic modeling is traditionally used for budgeting and business planning, this probabilistic process is seldom nurtured by big data that can refresh forecasts in real time, improving their predictive ability. Combination of big data and stochastic estimates for corporate appraisal and governance issues represents a methodological innovation that goes beyond the traditional literature and practice

Big data analytics and innovation in e-commerce: current insights and future directions

Big data analytics (BDA), as a new innovation tool, played an important role in helping businesses to survive and thrive during great crises and mega disruptions like COVID-19 by transitioning to and scaling e-commerce. Accordingly, the main purpose of the current research was to have a meaningful comprehensive overview of BDA and innovation in e-commerce research published in journals indexed by the Scopus database. In order to describe, explore, and analyze the evolution of publication (co-citation, co-authorship, bibliographical coupling, etc.), the bibliometric method has been utilized to analyze 541 documents from the international Scopus database by using different programs such as VOSviewer and Rstudio. The results of this paper show that many researchers in the e-commerce area focused on and applied data analytical solutions to fight the COVID-19 disease and establish preventive actions against it in various innovative manners. In addition, BDA and innovation in e-commerce is an interdisciplinary research field that could be explored from different perspectives and approaches, such as technology, business, commerce, finance, sociology, and economics. Moreover, the research findings are considered an invitation to those data analysts and innovators to contribute more to the body of the literature through high-impact industry-oriented research which can improve the adoption process of big data analytics and innovation in organizations. Finally, this study proposes future research agenda and guidelines suggested to be explored further

Big Data Major Security Issues: Challenges and Defense Strategies

Big data has unlocked the door to significant advances in a wide range of scientific fields, and it has emerged as a highly attractive subject both in the world of academia and in business as a result. It has also made significant contributions to innovation, productivity gains, and competitiveness enhancements. However, there are many difficulties associated with data collecting, storage, usage, analysis, privacy, and trust that must be addressed at this time. In addition, inaccurate or misleading big data may lead to an incorrect or invalid interpretation of findings, which can negatively impact the consumers\u27 experiences. This article examines the challenges related to implementing big data security and some important solutions for addressing these problems. So, a total of 12 papers have been extracted and analyzed to add to the corpus of literature by concentrating on several critical issues in the big data analytics sector as well as shedding light on how these challenges influence many domains such as healthcare, education, and business intelligence, among others. While studies have proven that big data poses issues, their approaches to overcoming these obstacles vary. The most frequently mentioned challenges were data, process, privacy, and management. To address these issues, this paper included previously discovered solutions

Hadoop Distributed File System (HDFS) and Various Facts Related to Big Data

The term big data, particularly when utilized by vendors, may allude to the innovation that an association requires to deal with the a lot of data and storage facilities. The term bigdata is accepted to have started with Web search organizations who expected to query big appropriated distributed aggregations of loosely-structured data. Bigdata is high-volume, high- velocity and high- variety data resources that request practical, inventive types of data handling for upgraded knowledge and decision making. Hadoop, used to process unstructured and semistructuredbigdata, utilizes the map-reduce worldview to find every applicable datum at that point select just the data straightforwardly noting the query. NoSQL, MongoDB, and TerraStore process organized bigdata. NoSQL data is described by being fundamentally accessible, delicate state (variable), and in the long run predictable. MongoDB and TerraStore are both NoSQL-related items utilized for report arranged applications.The approach of the period of bigdata presents openings and difficulties for organizations. Already inaccessible types of data would now be able to be spared, recovered, and prepared. Be that as it may, changes to equipment, programming, and data preparing systems are important to utilize this new worldview. Bigdata presents opportunities and difficulties for organizations. Data analytics will displace the utilization of just organized queries of relational database management system. Advantages of large data use to business officials incorporate upgraded data sharing through straightforwardness, improved execution however investigation, expanded market division, increased decision support through advanced analytics, and more prominent capacity to enhance items, services and business models. Business owners need to pursue inclines in bigdata cautiously to make the decision that fits their businesses

Technical Research Priorities for Big Data

To drive innovation and competitiveness, organisations need to foster the development and broad adoption of data technologies, value-adding use cases and sustainable business models. Enabling an effective data ecosystem requires overcoming several technical challenges associated with the cost and complexity of management, processing, analysis and utilisation of data. This chapter details a community-driven initiative to identify and characterise the key technical research priorities for research and development in data technologies. The chapter examines the systemic and structured methodology used to gather inputs from over 200 stakeholder organisations. The result of the process identified five key technical research priorities in the areas of data management, data processing, data analytics, data visualisation and user interactions, and data protection, together with 28 sub-level challenges. The process also highlighted the important role of data standardisation, data engineering and DevOps for Big Data

A tool for knowledge-oriented physics-based motion planning and simulation

The book covers a variety of topics in Information and Communications Technology (ICT) and their impact on innovation and business. The authors discuss various innovations, business and industrial motivations, and impact on humans and the interplay between those factors in terms of finance, demand, and competition. Topics discussed include the convergence of Machine to Machine (M2M), Internet of Things (IoT), Social, and Big Data. They also discuss AI and its integration into technologies from machine learning, predictive analytics, security software, to intelligent agents, and many more. Contributions come from academics and professionals around the world. Covers the most recent practices in ICT related topics pertaining to technological growth, innovation, and business; Presents a survey on the most recent technological areas revolutionizing how humans communicate and interact; Features four sections: IoT, Wireless Ad Hoc & Sensor Networks, Fog Computing, and Big Data Analytics.(Chapter) The recent advancements in robotic systems set new challenges for robotic simulation software, particularly for planning. It requires the realistic behavior of the robots and the objects in the simulation environment by incorporating their dynamics. Furthermore, it requires the capability of reasoning about the action effects. To cope with these challenges, this study proposes an open-source simulation tool for knowledge-oriented physics-based motion planning by extending The Kautham Project, a C++ based open-source simulation tool for motion planning. The proposed simulation tool provides a flexible way to incorporate the physics, knowledge and reasoning in planning process. Moreover, it provides ROS-based interface to handle the manipulation actions (such as push/pull) and an easy way to communicate with the real robotsPeer ReviewedPostprint (author's final draft

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft