Graeme Shanks’s research while affiliated with University of Melbourne and other places

Despite the substantial body of evidence detailing the multifaceted use of data within organizations, the conceptualizations of data and their value propositions remain disjointed and require updating. Information Systems scholars contend that the traditional ways of conceiving data now appear inadequate in framing this ever-evolving data-driven phenomenon. In this context, we argue for a reassessment of the fundamental assumptions about data in the field. This paper offers a comprehensive literature review, through which we conceptualize the role of data into four distinguishable types: data as a tool, as a commodity, as a practice, and as algorithmic intelligence. Each type possesses a set of identifiable characteristics, usage, and unique pathways of value creation. Together these elements form a typology, which provides an explanation for the intricate and complex nature of data use in organizations and the diverse sources of their value.

Despite the substantial body of evidence detailing the multifaceted use of data within organizations, the conceptualizations of data and their value propositions remain disjoint and require updating. Information Systems scholars contend that the traditional ways of conceiving data now appear inadequate in framing this ever-evolving data-driven phenomenon. In this context, we argue for a reassessment of the fundamental assumptions about data in the field. This paper offers a comprehensive literature review, through which we conceptualize the role of data into four distinguishable types: data as a tool, as a commodity, as a practice, and as algorithmic intelligence. Each type possesses a set of identifiable characteristics, usage, and unique pathways of value creation. Together these elements form a typology, which is aimed to provide an explanation for the intricate and complex nature of data use in organizations and the diverse sources of their value. Key words: data use, data artifacts, data value, analytics and AI value, IT artifacts.

To realize value from their wealth of digital data, organizations are investing in data-driven organizational initiatives—efforts in which they must draw expertise in data, algorithms, and visualization together with knowledge and skills in business domains such as marketing and human resources. However, they face the challenge of crossing the knowledge divide between analytics groups and business groups. Exploring relationships between the two groups in 37 data-driven organizational initiatives, we develop a configuration-based model that explains analytics and businessdomain knowledge integration through the lens of synergy. Our configurational analyses revealed five configurations of relationships between the two, which bring about two distinct change outcomes: “dedicated data groups” and “multidisciplinary teams” lead to the emergence of new datadriven ways to work, and “analytics institutionalization,” “analytics resource optimization,” and “networked communities” produce convergence, through the sharing of data-driven ways to work. Each configuration displays a distinct element of the core processes identified (“developing group connectedness,” “exchanging analytics and business domain knowledge,” and “incentivizing organizational data use”) and yields either an emergence or convergence of data-driven ways of working. The findings demonstrate how data-driven organizational initiatives can benefit from a pervasive form of organizing that entwines analytics groups and business groups such that their members’ tools, mindsets, and behaviors are merged to profoundly change ways of working. Together, these findings and the configurational methodology used provide a nuanced picture of how organizations integrate the requisite specialist knowledge across domains to realize value from data.

Enterprise architecture (EA) is an important IS management capability that has attracted much practitioner interest and has many claimed benefits. However, the mechanisms underpinning EA benefit realisation are not fully understood and have only relatively recently begun to receive research attention. Based on twenty-two expert interviews and two in-depth case studies, this theory building paper develops a new EA Benefit Mechanisms Model (the EABMM). It argues that EA benefits depend on the quality of EA service provision and are realised through three key mechanisms. First, EA helps improve IS decision-making by creating a transparent and structured decision process, by providing objective information to inform decisions, and by educating decision makers on good IS investment practices. Second, EA services help improve IS project delivery by improving project coordination through contextual awareness and by accelerating projects through guiding standards. Finally, EA services help an organisation to improve its IS platform by increasing platform alignment with business needs, platform flexibility, IS resource utilisation, and IS resource complementarity. Each of these three benefit mechanisms ultimately contributes to organisational benefits from EA, including higher return on IT investment, increased organisational agility, and competitive differentiation. The paper also highlights a conundrum in demonstrating value from EA.

Enterprise architecture (EA) is a collection of artifacts that describe an organization from an integrated business and IT perspective intended to improve business and IT alignment. EA artifacts can be very diverse in nature and have different use cases in disparate organizational activities. Previous studies have identified numerous benefits and challenges of establishing EA practice. However, most existing studies discuss the benefits and problems of EA practice in general without relating them to any particular activities constituting EA practice. In order to address this gap, this study analyzes the benefits and blockers associated with specific EA-related activities and respective artifacts. Based on 18 interviews with practicing architects, we identify eight consistent activity areas constituting EA practice. Each of these activity areas essentially represents a separate “story” in the context of EA practice and implies certain activities supported by some EA artifacts leading to specific benefits often impeded by some blockers. These eight activity areas provide a more detailed understanding of EA practice than the one offered by the current EA literature. Moreover, our findings indicate that EA practice should not be viewed as some homogeneous organizational activity and that EA should not be conceptualized simply as a unified blueprint for information systems. We also argue for the need to rethink the very terms “enterprise architecture” and “EA practice”, which appear to be oversimplified and unsuitable for analyzing EA practice in depth. This study has significant implications for both research and practice.

Context: Enterprise architecture (EA) is a collection of artifacts describing various aspects of an organization from an integrated business and IT perspective. EA practice is an organizational activity that implies using EA artifacts for facilitating decision-making and improving business and IT alignment. EA practice involves numerous participants ranging from C-level executives to project teams and effective engagement between these stakeholders and architects is critically important for success. Moreover, many practical problems with EA practice can be also attributed to insufficient engagement between architects and other EA stakeholders. However, the notion of engagement received only limited attention in the EA literature and the problem of establishing engagement has not been intentionally studied. Objective: This paper intends to explore in detail the problem of achieving effective engagement between architects and other EA stakeholders in an organization, identify the main inhibitors of engagement and present a theoretical model explaining the problem of establishing engagement in practice. Method: This paper is based on a single in-depth revelatory case study including nine interviews with different participants of EA practice (e.g. architects and other EA stakeholders) and documentation analysis. It leverages the grounded theory method to construct a conceptual model explaining the problem of engagement in the studied organization. Results: This paper identifies 28 direct and indirect inhibitors of engagement and unifies them into a holistic conceptual model addressing the problem of achieving engagement that covers the factors undermining both strategic and initiative-based engagement between architects and other EA stakeholders. Conclusions: This paper focuses on the notion of engagement and offers arguably the first available theoretical model that explains how typical engagement problems between architects and other stakeholders inhibit the realization of value from EA practice. However, the developed model has a number of limitations and we call for further empirical research on engagement problems in EA practice and coping strategies for addressing these problems.

Big data analytics is a fast-evolving phenomenon shaped by interactions among individuals, organizations, and society. However, its ethical implications for these stakeholders remain empirically underexplored and not well understood. We present empirical findings from a Delphi study that identified, defined, and examined the key concepts that underlie ethical issues in big data analytics. We then analyze those concepts using stakeholder theory and discourse ethics and suggest ways to balance interactions between individuals, organizations, and society in order to promote the ethical use of big data analytics. Our findings inform practitioners and policymakers concerned with ethically using big data analytics and provide a basis for future research.

How can we use synergy to explain the value created by business analytics systems? In this article, we conceptualize and operationalize two important aspects of synergy: namely, the synergistic relationship and the synergistic outcome. We explore the enablers and mechanisms that are involved in a synergistic relationship between business analytics systems and customer relationship management systems and define it as the ability of systems to work together, span their boundaries and complement each other. Synergistic outcomes are the new business analytics–enabled customer relationship management systems that emerge from the synergistic relationship between business analytics systems and customer relationship management systems. Taking a whole system perspective, business analytics–enabled customer relationship management systems comprise the components and the emergent properties that arise from their interaction (e.g. the ability to cross-sell and up-sell based on advanced computational methods), in which the emergent properties are new because they do not exist in the individual components. We develop a research model that uses Synergistic Relationship and Synergistic Outcomes to explain the business value created by business analytics systems and customer relationship management systems, and we test this model using a survey of 201 managers in Australia and the United States. We find that the synergistic relationship plays a significant role in the creation of business analytics–enabled customer relationship management systems and subsequently business value. Business analytics–enabled customer relationship management systems—comprising business analytics systems, customer relationship management systems and their emergent properties—contribute to transactional, informational and strategic value. This goes beyond the value created by the business analytics and customer relationship management systems individually, as measured through statistical interaction.

Research on the organizational impact of business analytics (BA) systems has mainly focused on the identification of analytics resources and capabilities in isolation and linking them directly or indirectly to business value. In contrast, achieving value from data assets in real organizational settings requires data teams to form connections with other organizational (non-data) teams so that the teams can work together and utilize complementary and shared resources. This paper focuses on the embeddedness of data and non-data teams within an organizational network, and it examines how the two teams connect and collectively influence organizational performance. For that, we built an agent-based computational model in which data and non-data teams solve organizational problems under dynamic organizational structures and team-level learning. In our model, data and non-data teams (i) simultaneously search for satisfying solutions over a complex space (i.e. an NK landscape), (ii) are initially connected to each other through a given network configuration, (iii) are endowed with learning capabilities (through a reinforcement learning algorithm), and (iv) update their links to other agents (i.e. create new connections or disconnect existing ones) according to their learning capabilities. Results reveal conditions under which performance differences are obtained, considering variations in the number of agents, space complexity, agents' screening capabilities and reinforcement learning.