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Background: There is an increasing interest in big data. However, challenges shape and affect the gathering, retrieval, use and management of big data in many organisations. Some of the challenges are linked to a lack of architecture that is specific to big data. Attempts have been made from both business and academic fronts, yet the challenges persist. The challenges are attributed to a lack of an understanding of the factors that influence the design of architecture for big data in an organisation. Objectives: The study aims to propose big data architecture for enterprises. Method: We employed the qualitative method, using document analysis to gather data. Activity theory (AT) was employed in the analysis of the data. Results: From the analysis, governance, interactions, relationships and allocative were found to be the factors that influence the design of big data architecture. An interpretation was conducted following the inductive reasoning approach to gain a deeper insight of how the factors manifest themselves. Conclusion: Big data architecture is proposed. The architecture is intended to address some of the challenges encountered in gathering, retrieving, using or managing big data in organisations. Contribution: This study advances our understanding of the complex interplay of factors influencing the architecture of big data. Applying AT, the study fortifies our understanding of complex interactions between humans and big data including the architecture design.

Background: The distinction between small data and big data is increasingly muted and has caused challenges and confusion in many quarters. Objective: The objective of the study is to gain a deeper understanding of the confounded confusion that exists between small data and big data. Firstly, to develop a taxonomy that distinguishes between small data and big data. Secondly, it seeks to extract the value from the concepts, which can be of fundamental importance to an organisation. Methods: This study follows the interpretive approach and employs qualitative methods, based on which 57 related materials were gathered, covering big data and small data, and analysed. Results: The study reveals the factors that differentiate the concepts, which are of a technical front, business logic and data processing. Conclusion: This study addresses the challenges which are increasingly of prohibitive ramifications for both academic and business domains. By removing the confusion, the classifications of small data and big data including associated attributes will be better understood. This increases their business use towards enhancement and competitive advantage. Contribution: The article distinguishes between small data and big data, which has been missing, from both academic and business perspectives, since the emergence of the latter. The differentiation between small data and big data provides a guide to organisations in developing strategic frameworks and operational plans.

Background: Despite the increasing interest and investment in big data analytics (BDA), many organisations find the implementation and use of the tools challenging. This is attributed to the cumbersome nature of some of the tools. Objectives: From both business and academic domains, this study sets out to provide a model that enables, supports, and makes the selection and use of BDA tools easier. Method: The qualitative methods from the perspective of an interpretive approach were employed in the study. The actor-network theory (ANT) was applied as a lens to underpin the phenomenon being studied and gain a deeper understanding of why things happen in the way that they confusedly do, in the selection and subsequent use of BDA tools. Results: The research revealed that five factors, organisational requirements, top-down versus bottom-up approach, the role of stakeholders, the usefulness of BDA, and organisational structure, primarily influence the selection and use of BDA tools in organisations. Conclusion: Empirically, the factors bring fresh perspectives to support the decision in appropriately managing BDA deployment for organisational purposes. Contribution: The main contribution of this study lies in the use of the decisions support model, to practically and theoretically provide a guide for managers in the organisation, in selecting BDA for decision support purposes. From an academic perspective, the study contributes to the advancement in the use of ANT for analysis in information system (IS) research.

Background: In many developing countries including South Africa, there are challenges in understanding how the different networks of patients, diagnoses, and medical personnel are formed, as well as the types of big data that are generated. The challenges include the relationship and interaction that exist between the big data within the various networks. Some of the challenges manifest into different factors such as inaccuracy of data, inconsistency, incompleteness of data, and lack of cohesion. The trajectory of the challenge is the inability to select the most appropriate analytics tools for big data analysis. Objectives: The objective of the study was to propose a solution that can be used to address the challenges in selecting analytics tools, to enhance big data usefulness for the improvement of healthcare services, particularly in developing countries. Method: Literature of within 10 years of publication in the areas of big data, big data analytics and healthcare were gathered and used as data. The analysis of the data followed the hermeneutics approach within the interpretivist paradigm. Results: From the analysis, factors that influence big data and analytics tools’ usefulness were found, based on which a solution (framework) is proposed. The solution is intended to contribute to the works of information systems and technologies (IS/IT) personnel, health practitioners and academics. Conclusion: The relationships that exit between actors, and the actors’ interactions in the process of providing medical services contribute to the sources of big data. Therefore, it is necessary to holistically analyse healthcare big data from both technical and non-technical perspectives.

Background: Even though it is well documented in the academic domain, including various English dictionaries, that data and information are not the same thing, with the latter being a refinement of the former, many people within organisations and academic environments continue to loosely and interchangeably use these terms. This is a challenge that extends to the enterprise architecture discipline, which has an impact on how the domains of data and information architectures are defined, developed and implemented in many organisations. Objectives: The challenge leads to misunderstanding of both technical and business requirements, as well as confusion about the differentiation between data and information architectures. This challenge affects the accomplishment of either data or information in an organisation. This is caused by the fact that data is referred to as information, meaning it has been refined, which is incorrect, and therefore affects requirements. Additionally, this challenge influences the organisational structure and the development of employees’ career. Thus, the objective of this study was to examine the implication of practice in the loose and interchangeable use of the terms, ‘data’ and ‘information’, within an organisation. Method: Based on this objective, the interpretivist stance was employed in the study. Qualitative data was gathered and the hermeneutics approach was employed in the analysis. Results: From the analysis, a solution is proposed to halt further misconstruction of the architectures, and avoid the confusing challenges it has for organisations. Conclusion: The study reveals what we need to know about the confounded confusion between data and information architectures. The differentiation between the two concepts can be of importance to professionals in the field of information systems and technologies as well as academics.

Background: Over the years, big data analytics has been statically carried out in a programmed way, which does not allow for translation of data sets from a subjective perspective. This approach affects an understanding of why and how data sets manifest themselves into various forms in the way that they do. This has a negative impact on the accuracy, redundancy and usefulness of data sets, which in turn affects the value of operations and the competitive effectiveness of an organisation. Also, the current single approach lacks a detailed examination of data sets, which big data deserve in order to improve purposefulness and usefulness. Objective: The purpose of this study was to propose a multilevel approach to big data analysis. This includes examining how a sociotechnical theory, the actor network theory (ANT), can be complementarily used with analytic tools for big data analysis. Method: In the study, the qualitative methods were employed from the interpretivist approach perspective. Results: From the findings, a framework that offers big data analytics at two levels, micro- (strategic) and macro- (operational) levels, was developed. Based on the framework, a model was developed, which can be used to guide the analysis of heterogeneous data sets that exist within networks. Conclusion: The multilevel approach ensures a fully detailed analysis, which is intended to increase accuracy, reduce redundancy and put the manipulation and manifestation of data sets into perspectives for improved organisations’ competitiveness.

Background: In the last decade, social media users across the world have crossed 1 billion, making it one of the fastest growing sources of big data. Also, people needing healthcare continue to increase in every society. Through accessibility, communication and interaction between health practitioners and patients, this type of ever-growing, social media subscriber–based platform can be of significant use in improving healthcare delivery to society. However, users encounter serious challenges in their attempts to make use of social media and big data for health-related services. The challenges are primarily caused by factors such as integration, complexity, security and privacy. The challenges are mainly owing to the sensitive nature of the healthcare environment, as a result of personalisation and privacy of information. Objectives: The objectives of the study were to examine and gain a better understanding of the complexities that are associated with the use of social media and healthcare big data, through influencing factors, and to develop a framework that can be used to improve health-related services to the patients. Methods: The interpretivist approach was employed, within which qualitative data were collected. This included documents and existing literature in the areas of social media and healthcare big data. To have a good spread of both previous and current state of events within the phenomena being studied, literature published between 2006 and 2016 were gathered. The data were interpretively analysed. Results: Based on the analysis of the data, factors of influence were found, which were used to develop a model. The model illustrates how the factors of influence can enable and at the same time constrain the use of social media for healthcare services. The factors were interpreted from which a framework was developed. The framework is intended to guide integration of social media with healthcare big data through which service delivery to patients can be improved. Conclusion: This study can be used to guide integration of social media with healthcare big data by health facilities in the communities. The study contributes to healthcare workers’ awareness on how social media can possibly be used to improve the services that they provide to the needy. Also, the study will benefit information systems and technologies and academic domains, particularly from the health services’ perspective.

Background: Personal information about individuals is stored by organisations including government agencies. The information is intended to be kept confidential and strictly used for its primary and legitimate purposes. However, that has not always been the case in many South African government agencies and departments. In recent years, personal information about individuals and groups has been illegally leaked for other motives, in which some were detrimental. Even though there exists a legislation, Protection of Personal Information (POPI) Act, which prohibits such malpractices, illegally leaked information has however, not stopped or reduced. In addition to the adoption of the POPI Act, a more stringent approach is therefore needed in order to improve sanity in the use and management of personal information. Otherwise, the detriment that such malpractices cause too many citizens can only be on the increase. Objectives: The objectives of this study were in twofold: (1) to examine and understand the activities that happen with personal information leaks, which includes why and how information is leaked; and (2) to develop a conceptual framework, which includes identification of the factors that influence information leaks and breaches in an environment. Method: Qualitative research methods were followed in achieving the objectives of the study. Within the qualitative methods, documents including existing literature were gathered. The activity theory was employed as lens to guide the analysis. Result: From the analysis, four critical factors were found to be of influence in information leaks and breaches in organisations. The factors include: (1) information and its value, (2) the roles of society and its compliance to information protection, (3) government and its laws relating to information protection and (4) the need for standardisation of information usage and management within a community. Based on the factors, a conceptual framework was developed. Conclusion: This study can be used to guide implementation of information protection acts in any environment. It empirically contributes to societal awareness on how and why personal information is leaked and breached. Also, it will benefit academic domain, particularly in the use of activity theory.

Background: There is always emphasis on information technology (IT) projects because of their significance in organisations. Thus, efforts and resources are reciprocally committed to ensure the successes. Still, failure of IT projects in many organisations remains high and affects competitiveness. As recourse for remedy, different techniques and approaches have been employed. However, little or no progress has been made in increasing the success rate of IT projects in many organisations. Objectives: The objective of this study was to examine the factors that influence and impact IT projects, improvisation and how improvisation manifests. Method: The study was carried out using a single case study approach. Qualitative data were collected and duality of structure from the perspective of structuration theory was used as lens to guide the analysis. Results: Findings from this study reveal how reproduction of actions manifests from non-technical factors, such as cultural value, organisational structure, power relationship, human capacity, know-how and change management. These factors help to gain a more constructive and better understanding of how IT projects improvisation is influenced or impacted by non-technical factors in organisations. Conclusion: The study is intended to benefit both practitioners and academics. Some of the benefits will be gained from fresh perspectives on the complexities of IT projects improvisation, which are often caused by various seen and unforeseen non-technical factors. This includes how actions from relationship, know-how about facilities and communicative scheme are produced and reproduced.

Over the last two decades competition amongst organisations including financial institutions has increased tremendously. The value of information is critical to competition in different organisations. In addition, the management of cost of delivery and cohesiveness of information flow and use in the organisations continue a challenge to information technology (IT). In an attempt to address these challenges, many organisations sought various solutions, including enterprise information architecture (EIA). The EIA is intended to address the needs of the organisation for competitive advantage.This research article focused on the role of principles in the development and implementation of EIA. The article aimed to investigate how EIA could be best leveraged, exploited, or otherwise used to provide business value. The research brings about a fresh perspective and new methodological principles required in architecting the enterprise information.