Towards a Capability Model for Big Data Analytics

Big data analytics is becoming a veritable source of competitive advantageas it helps companies to better understand their business environment and to create or improve their products and services accordingly. However, big data analytics also poses challenges to organizations with respect to establishing the required capabilities. Building upon a design science research approach and the Work System Theory as a kernel theory, we identify several capabilities necessary to leverage the potential of big data analytics. To achieve this goal, we conducted 16 interviews with experts from an IT-strategy consulting firm. We furthermore organize the identified capabilities into a coherent model. The resulting capability model consists of eight capability groups that contain 34 capabilities. It provides a basis to systematically develop the necessary capabilities for the adoption und strategic usage of big data analytics

The Data Product Canvas - A Visual Collaborative Tool for Designing Data-Driven Business Models

The availability of data sources and advances in analytics and artificial intelligence offers the opportunity for organizations to develop new data-driven products, services and business models. Though, this process is challenging for traditional organizations, as it requires knowledge and collaboration from several disciplines such as data science, domain experts, or business perspective. Furthermore, it is challenging to craft a meaningful value proposition based on data; whereas existing research can provide little guidance. To overcome those challenges, we conducted a Design Science Research project to derive requirements from literature and a case study, develop a collaborative visual tool and evaluate it through several workshops with traditional organizations. This paper presents the Data Product Canvas, a tool connecting data sources with the user challenges and wishes through several intermediate steps. Thus, this paper contributes to the scientific body of knowledge on developing data-driven business models, products and services

Open Data Capability Architecture - An Interpretive Structural Modeling Approach

Despite of increasing availability of open data as a vital organizational resource, large numbers of start-ups and organizations fail when it comes to utilizing open data effectively. This shortcoming is attributable to the poor understanding of what types of capabilities are required to successfully conduct data related activities. At the same time, research on open data capabilities and how they relate to one another remains sparse. Guided by extant literature, interviews of these organizations, and drawn from Interpretive Structural Modeling (ISM) approach which are pair comparison methods to evolve hierarchical relationships among a set of elements to convert unclear and unstructured mental models of systems into well-articulated models that act as base for conceptualization and theory building, this study explores open data capabilities and the relationships and the structure of the dependencies among these areas. Findings from this study reveal hitherto unknown knowledge regarding how the capability areas relate one another in these organizations. From the practical standpoint, the resulting architecture has the potential to transform capability management practices in open data organizations towards greater competitiveness through more flexibility and increased value generation. From the research point of you, this paper motivates theory development in this discipline

Qualitative Structural Model for Capabilities in Open Data Organizations

Open data is increasingly becoming an essential asset for many organizations. However, large numbers of organizations fall short when it comes to utilizing open data effectively to fully leverage the potential of it. There are ample evidences that this shortcoming is attributable to the poor understanding of what types of capabilities are required to successfully conduct data related activities. At the same time, research on open data capabilities and how they relate to one another remains sparse. Based on the theoretical foundation constructed from the integration of Capability-based Theory and Dynamic Capability Theory and, extant literature and interviews of leadership of open data organizations, we attempt to address this knowledge gap by investigating open data capabilities and relationships between them. Findings help validate the two theories in the open data organizations and reveal unknown knowledge about open data capability areas and how they affect one another

How to Structure a Company-wide Adoption of Big Data Analytics

Driven by increasing amounts of data and by emerging technologies to store and analyze them, companies adopt Big Data Analytics (BDA) to improve their innovativeness and decision-making. However, adopting BDA across the company in the sense of an insight-driven organization (IDO) is challenging, since it influences the entire company and requires an organizational change. Despite mature knowledge, approaches that provide concrete methods for structuring the company-wide adoption of BDA to fully exploit the benefits of BDA and to reduce the risk of its failure are still missing. Following action design research, we developed and evaluated a method for structuring the company-wide adoption of BDA in a concerted research effort at a German bank. Based on knowledge of BDA and the road mapping approach, the method structures the adoption along the BDA capabilities. We illustrate how companies can define a target state, identify gaps, and derive a BDA roadmap

Formative Evaluation of Data-Driven Business Models – The Data Insight Generator

New technological developments such as Big Data or, the Internet of Things lead to exponentially increasing amounts of data created and stored by organizations. As a consequence, new data-driven business models (DDBMs) appear. These business models have special characteristics which need to be included in the business model development process. Thus, different methods and tools have emerged to support the development of DDBMs. One of these is the Data Insight Generator (DIG) which seeks to combine the key resource and value proposition of a DDBM. This paper comprises the application of the thinking-aloud method for a formative evaluation of the DIG. The contribution of this paper is twofold. First, the usability of the DIG is tested and implications for further development are derived. Second, the paper provides empirically-based insights into development of DDBM that facilitate the future development of such business models

ADA-CMM: A Capability Maturity Model for Advanced Data Analytics

Despite the rising importance of advanced data analytics, there is limited guidance on how organizations should leverage it. The benefits that an organization can gain through advanced data analytics depends on the organization’s ability to gain and use relevant capabilities. This study introduces a capability maturity model (ADA-CMM) for advanced data analytics to help organizations assess their current state of capabilities for managing advanced data analytics. We used the Delphi method to develop the maturity model and performed a survey to evaluate its validity. The results confirm that the maturity level of the advanced data analytics capabilities of organizations is positively related to the business value that they can capture from their use, which in turn found positively related to organizational performance. ADA-CMM can be used by organizations as a self-assessment tool and to create a roadmap for improving their relevant capabilities

Unravelling the relationship between a firm’s big data analytics capability and the realization of a competitive advantage: an IT business value approach

Big Data Analytics (BDA) has the potential to transform business models, firms and the competitive landscape. Though, creating value from BDA investments seems challenging as many technical and managerial challenges are involved. Due to its complexity, the value generated from big data depends on how well a firm’s Big Data Analytics Capability (BDAC) is developed. Drawing on the Resource Based View (RBV), the IT business value approach and the BDAC literature, we study the relationship between a firm’s BDAC and the realization of a competitive advantage. We used survey data from multiple respondents per firm (i.e. IT managers and Business managers) in 112 Belgian and Dutch firms. Using PLS-SEM, we found a direct relationship between a firm’s BDAC and the perceived realization of a competitive advantage. We also found a partial mediation of this relationship via the performance of the firm’s operation management process

Investigating the Role of Enterprise Architecture in Big Data Analytics Implementation: A Case Study in a Large Public Sector Organization

Big Data Analytics (BDA) offers capabilities that can support a wide range of business areas across an organization. Organizations are increasingly turning to Enterprise Architecture (EA) to manage BDA implementation complexities. Through a case study in a large public sector organization, how EA supports various stages of BDA implementation is examined. The findings show that EA can address BDA challenges through 18 specific roles, which are categorised into four domains: Strategy (6 roles), Technology (4 roles), Collaboration (3 roles) and Governance (5 roles). While EA appears to have the most prominent role in strategy planning process, our study also identifies factors that can lead to the ineffectiveness of EA roles, such as frequent changes in business strategy. This study offers important implications to research and practice in EA and BDA implementation