Big data for big pharma

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

Massive amounts of building operational data are collected and stored in modern buildings, which provide rich information for in-depth investigation and assessment of actual building operational performance. However, the current utilization of big building operational data is far from being effective due to the gaps between building engineering and advanced big data analytics. Data mining is a promising technology for extracting previously unknown yet potentially useful insights from big data. This paper aims to explore the potential application of advanced data mining techniques for effective utilization of big building operational data. A case study of mining the operational data of an educational building for performance improvement is presented. Decision tree, clustering analysis, and association rule mining are adopted to analyze the operational data. The results show that useful knowledge can be extracted for identifying typical building operation patterns, detecting operation deficiencies, and spotting energy conservation opportunities. Practical application:The current utilization of big building operational data in the building industry is rather limited due to the lack in experience of using advanced big data analytics. This study presents a data mining-based method for analyzing massive building operational data. The case study results validate the efficiency and effectiveness of the method proposed. It can help building professionals to discover valuable insights into building operation patterns and thereby developing strategies for improving building energy efficiency. The method can be fully realized using the open-source software R, which provides great flexibilities in its integration with building automation systems.

Big Data Focus on Physicians: Start-up Profiles Physicians’ Pharma Friendliness

Organizational Ambidexterity, Performance and Knowledge Management: Empirical Evidence from the Energy and Pharmaceutical Sectors

This chapter explores how an ambidextrous organizations’ agility is affected by the BDA capable BPMS, a specific kind of BDA capable IS (Big Data Analytics capable Business Process Management Systems). Specifically, the BDA capable BPMS functionalities and its positive effects on organizational dynamism and reactiveness towards challenges are studied. A theoretical assessment of the possibility of BDA capable BPMS in improving organizational agility, with a specific focus on the ambidextrous organizations, has been explained. A theoretical framework explaining some traits of the phenomenon has also been explained. The results highlight how BDA capable BPMS may increase the agility of an ambidextrous organization and by the implementation of such BPMS, ambidextrous organizations may be able to better achieve market capitalization and operational adjustment agility.

Chapter 19 - An extensive discussion on utilization of data security and big data models for resolving healthcare problems

Business Process Management (BPM) is the factual criteria for business process modeling. Although it is widely used in various fields, when required When using business processes to express big data business processes, the existing business process specifications do not support it. Big data business can only be simulated as multiple manual tasks before execution, which is not friendly to non-big data technology practitioners, and will The non-standardized business process and poor reusability lead to a reduction in the efficiency of business completion. On the other hand, while supporting big data technology brings flexibility to BPM, it consumes a lot of process engine resources when executing big data business processes, and the execution efficiency is low, which brings greater burden to the process engine. Aiming at the above deficiencies, this paper carries out the following work: (1) Extend the existing business process modeling, and define a new category of " Big Data Service Task" by extending the activity elements in BPMN2.0 (2) Propose a compatible big data technology The task scheduling strategy in the business process includes a single big data task scheduling decision and an intensive big data task scheduling decision.

Purpose. The aim of the article is to identify the key features of the implementation of digital solutions in the business process management system of modern enterprises. Methodology of research. The following research methods were used to achieve the set goal: methods of system analysis (to analyse problems during the planning of the implementation of digital solutions in the business process management system of modern enterprises); methods of scientific abstraction, deduction and induction (for formulating a scientific hypothesis of work, laws and principles), analysis and synthesis – to analyse the state of development of the business process management system of modern enterprises; abstract and logical (for making theoretical generalizations, substantiating research methodology, formulating conclusions). The tabular method clearly reflects the current state of the investigated problem. Findings. It has been established that the transition to a digital economy is based on technologies that define the Fourth Industrial Revolution, which consists of digitization and includes the saturation of the physical world with electronic and digital devices. The study also found that the process of digital transformation of business processes consists of elements such as customer interaction, operational business processes and enterprise models. It is determined that the implementation of digital solutions in each of these parts of the model opens up new opportunities for the development of enterprises. It is substantiated that the introduction of digital solutions in the management of business processes of enterprises has a positive impact on efficiency, flexibility and adaptability to changes in the external environment. It is noted that in order to maintain competitiveness, it is important not only to implement technology, but also to change the corporate culture and adapt employees to new tools. The study also highlights the most common digital solutions that are often implemented by modern enterprises: artificial intelligence, big data, blockchain, Internet of Things and cloud computing, which provide transparency, security and integration in business process management. Originality. A modern systematic overview of the most common digital solutions used to manage business processes at the enterprise has been further developed. Practical value. The results of the study can be useful for business managers and business process management specialists, as they provide a clear idea of the advantages and peculiarities of using digital solutions in the practice of enterprises. Key words: artificial intelligence, blockchain, big data, cloud technologies, fourth industrial revolution, digitalization.

The Business Process Management & Enterprise Architecture (BPMEA) track aims at gathering researchers and practitioners around the broad topics of business processes and enterprise architecture with a special interest in modeling. These disciplines are quickly evolving and intertwining with each other, and are often referred to with the broad term of business modeling. While several researches are addressing these aspects, there is still a strong need of exploring new paths of improvement and consolidation, especially in light of the recent trends in the business, which put a lot of emphasis on large-scale system engineering, through modeling techniques that are applied at global scale, and data analysis approaches that combine cloud-based big data analytics with enterprise and system models. Topics of relevance for the BPMEA track include enterprise and systems architecture and modeling, multilevel models tracing and alignment, models transformation, IT & business alignment (both in terms of modeling and goals) and big data analysis applied to enterprise-wide data, tackling both technical (languages, systems, patterns, tools) and social (collaboration, human-in-the-loop) issues.

PurposeThe purpose of this paper is to improve understanding of the integration between big data (BD) and risk management (RM) in business processes (BPs), with special reference to corporate real estate (CRE).Design/methodology/approachThis conceptual study follows, methodologically, the structuring inter-textual coherence process – specifically, the synthesised coherence tactical approach. It draws heavily on theoretical evidence published, mainly, in the corporate finance and the business management literature.FindingsA new conceptual framework is presented for CRE to proactively develop insights into the potential benefits of using BD as a business strategy/instrument. The approach was found to strengthen decision-making processes and encourage better RM – with significant consequences, in particular, for business process management (BPM). Specifically, by recognising the potential uses of BD, it is also possible to redefine the processes with advantages in terms of RM.Originality/valueThis study contributes to the literature in the fields of real estate, RM, BPM and digital transformation. To the best knowledge of authors, although the literature has examined the concepts of BD, RM and BP, no prior studies have comprehensively examined these three elements and their conjoint contribution to CRE. In particular, the study highlights how the automation of data-intensive activities and the analysis of such data (in both structured and unstructured forms), as a means of supporting decision making, can lead to better efficiency in RM and optimisation of processes.

Purpose: This study aims to investigate the impact of Big Data Analytics Capabilities (BDAC) on Indonesian Firm Performance (FPER), addressing the lack of empirical research in this area despite Indonesia's significant Big Data expenditure growth. The study also proposes a conceptual model as a framework for developing BDAC. Method: This is a quantitative study conducted in Indonesia using purposive and snowball sampling techniques to collect data from 51 Indonesian companies. The research model is based on the IT Capability framework, Sociomaterialism Theory, and incorporates Business Process Agility (BPA) as a mediating variable, as suggested by previous research. Findings: The results demonstrate a significant positive impact of BDAC on FPER. Furthermore, the study confirms that BPA significantly mediates the relationship between BDAC and FPER, highlighting the crucial role of agile business processes in leveraging the benefits of Big Data Analytics for improved firm performance. Practical Implications: This research provides valuable insights for Indonesian companies by: 1) demonstrating the significant positive impact of investing in BDAC on firm performance; 2) highlighting the importance of developing agile business processes to maximize the return on Big Data Analytics investments; and 3) offering a conceptual framework for developing and enhancing BDAC within Indonesian organizations. Originality: This study contributes to the existing literature by: 1) providing empirical evidence on the impact of BDAC on FPER specifically within the Indonesian context; 2) proposing a novel conceptual model for developing BDAC based on a combination of IT Capability framework, Sociomaterialism Theory, and the mediating role of BPA; and 3) addressing the gap in research on the relationship between BDAC and FPER in the Indonesian market. Paper Type: This research can be classified as an empirical study within the field of Information Systems, specifically focusing on Big Data Analytics, firm performance, and business process management. Keywords: Big Data, Firm Performance, Business Process Agility

The impact of big data although being very insubstantial in its inception however has gradually originated to make significant contributions to variety of spheres of life. Development of smart cities, big data in e-business, big data in health care sector, big data in environmental sciences and implementation of Big Data in other areas has indisputably revealed to critics what can be achieved by Big Data. Big Data undeniably is a paradigm shift in the perception of ways of understanding and studying the world. Big data can be utilized to analyze fine grained data for public and private use. Big data utilization in education sector is relatively less but it has started growing gradually. As a matter of fact as Big Data can be effectively utilized to improve education delivery, it can equally be utilized to provide decision support to higher management to attain excellence in the field of education. This research paper is a mere effort to explore various possibilities of Big Data utilization in educational establishments and their implications.

The word Big Data is commonly used and it is not new today. Large, medium and small companies are starting to use Big Data to obtain their customers insight in order to serve them in a better way. The use of Big Data has become quite a crucial way for businesses to compete with their competitors. Also not only companies gain from the value of Big Data, it is also the customer’s hugely benefit from its usage. In association with Big Data’s real time information, which is one of the most heavily used application of personal and location data. As there is a significant growth in the use of smart phones and the use of GPS services from the phones and other devices, the use of smart traffic routing will definitely grow and in turn it will hugely benefit the customers. Big Data is not a single packaged technology, it is in general a platform consists of usage of various components to achieve a common goal. There are plenty of components available in the market for the businesses to customise their Big Data platform. The utilization of Big Data is becoming more and more essential to businesses and it is even more important for them to adopt the right Big Data platform to accomplish their goals. The main aim of this study is to propose a framework for building a Big Data platform for publishing industry. The proposed framework was validated in an UK based news publishing organisation to find out the suitability and adoptability of the framework for their Big Data platform.

The development of modern organizations is determined by the generation of a system state in which they will leverage the advancement of modern information and communication technologies (ICT). Artificial Intelligence (AI) stands out as a game changer in this regard. Concurrently, organizations striving for market dominance tend towards ambidexterity, i.e. balancing activities related to exploitation (genotypic operations) and the exploration of innovative areas generating added value. Therefore, a research challenge arises, aimed primarily at increasing the knowledge of ambidexterity and its various forms of achievement through the perspective on leveraging AI potential in the Business Process Management (BPM) context. This paper presents the results of a study conducted among 340 large and medium-sized service enterprises in Poland. The main objective of this research is to identify the characteristics of service enterprises associated with the maturity level of AI implementation, including examining the importance of the ambidexterity approach. The study indicates the potential of implementing the ambidexterity concept balancing exploitative and explorative BPM in association with the level of AI implementation. Research methods include literature review, survey, and statistical analyses (correlation analysis, classification tree). The enterprises surveyed varied greatly in the level of AI implementation maturity. Among the enterprise characteristics available in the study, ambidexterity and its forms turned out to be the most important factors associated with AI implementation maturity. This research contributes to the advancement of management theory by providing an integrative view of the role of artificial intelligence in achieving ambidexterity in organization.

Transportation big data is not limited to the transportation sector, but is a useful resource that will bring innovation to all aspects of our lives in the future, and various R&D for its utilization is currently in progress. However, the current level of utilization of transportation big data is very limited under the existing legal system. In this study, we will investigate the meaning and problems of the use of big data in the transportation sector, and investigate the improvement tasks to expand the use of big data. Research Design, Data and Methodology: The paper used a qualitative research methodology through the literature review. In this study, first, the definition and creation process of big data were studied. Second, the significance and problems of applying big data in the transportation sector were studied. Finally, the current status of research in the transportation sector using big data was investigated, and the tasks to be improved in the process from collecting transportation big data to analysis were reviewed. Results: Big data means creating new value by fusing data collected from different purposes. In the case of using big data, the transportation sector can establish more accurate and detailed transportation policies in basic data investigation, identification of phenomena, and prediction. In order to expand the use of big data, it is important to consider who owns it, what it was collected for, what the format of the collected data is, and what should be done to use it. Conclusion: Big data is a derivative thing, but it is becoming important enough to determine the success or failure of a country depending on how it is used. However, problems such as data errors or invasion of privacy that may occur when using big data are expected. This is not just a problem in the transportation sector. When using big data, there are many problems to be solved, such as data ownership, Big Brother problems, and the implementation of smart mobility. If the advent of the big data era is taken for granted, the task from now on is how to solve these problems and share their values.