Data Science as an Innovation Challenge: From Big Data to Value Proposition

Kayser, Victoria; Nehrke, Bastian; Zubovic, Damir

doi:10.22215/timreview/1143

Cited by 42 publications

(40 citation statements)

References 13 publications

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“…In recent years, research has also started to examine problem finding for creating solutions with digital technologies. As a result, several frameworks were proposed that focus on digital technologies in general [e.g., 6,8,23]. For instance, most closely related to our study, Vanauer et al (2015) [58] propose an ideation framework for Big Data solutions.…”

Section: Figure 1 Problem-solving Process [4]mentioning

confidence: 83%

“…To achieve research rigor, the coding process and initial framework were validated in subsequent discussions between five IS researchers and three student assistants [17]. Additional data sources regarding problem finding for digital technology contexts and the use of AI in organizations [i.e., 2,6,8,9,16,23,48,57,58] were also considered to compare results with existing knowledge. Based on this data and investigator triangulation [17], the final framework was formed.…”

Section: Qualitative Research Methodologymentioning

confidence: 99%

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To Use or Not to Use Artificial Intelligence? A Framework for the Ideation and Evaluation of Problems to Be Solved with Artificial Intelligence

Sturm¹,

Fecho²,

Buxmann³

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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The recent advent of artificial intelligence (AI) solutions that surpass humans' problem-solving capabilities has uncovered AIs' great potential to act as new type of problem solvers. Despite decades of analysis, research on organizational problem solving has commonly assumed that the problem solver is essentially human. Yet, it remains unclear how existing knowledge on human problem solving translates to a context with problem-solving machines. To take a first step to better understand this novel context, we conducted a qualitative study with 24 experts to explore the process of problem finding that forms the essential first step in problem-solving activities and aims at uncovering reasonable problems to be solved. With our study, we synthesize emerged procedural artifacts and key factors to propose a framework for problem finding in AI solver contexts. Our findings enable future research on human-machine problem solving and offer practitioners helpful guidance on identifying and managing reasonable AI initiatives. 2.1. The Process of Problem Solving Problem solving, the act of uncovering problems and searching for effective solutions [20, 53, 54, 56], is

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Section: Figure 1 Problem-solving Process [4]mentioning

confidence: 83%

Section: Qualitative Research Methodologymentioning

confidence: 99%

To Use or Not to Use Artificial Intelligence? A Framework for the Ideation and Evaluation of Problems to Be Solved with Artificial Intelligence

Sturm¹,

Fecho²,

Buxmann³

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…There is a lack of a comprehensive framework in this respect as well as a lack of examples on how to build and use such a framework in organizations. Moreover, as stated in [21], the existing frameworks are mainly technically focused. There is no maturity framework that addresses both big data adoption based on the temporal dimension and its implications for an organization's sustainable development issues.…”

Section: Introductionmentioning

confidence: 99%

A Conceptual Framework for Assessing an Organization’s Readiness to Adopt Big Data

Olszak

Mach‐Król

2018

Sustainability

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The main aim of this paper is to provide a theoretically and empirically grounded discussion on big data and to propose a conceptual framework for big data based on a temporal dimension. This study adopts two research methods. The first research method is a critical assessment of the literature that aims to identify the concept of big data in organizations. This method is composed of a search for source materials, the selection of the source materials, and their analysis and synthesis. It has been used to develop a conceptual framework for assessing an organization’s readiness to adopt big data. The purpose of the second research method is to provide an initial verification of the developed framework. This verification consisted of conducting qualitative research with the use of an in-depth interview in 15 selected organizations. The main contribution of this study is the Temporal Big Data Maturity Model (TBDMM) framework, which can help to measure the current state of an organization’s big data assets, and to plan their future development with respect to sustainability issues. The proposed framework has been built over a time dimension as a fundamental internal structure with the goal of providing a complete means for assessing an organization’s readiness to process the temporal data and knowledge that can be found in modern information sources. The proposed framework distinguishes five maturity levels: atemporal, pre-temporal, partly temporal, predominantly temporal, and temporal, which are used to evaluate data/knowledge, information technology (IT) solutions, functionalities offered by IT solutions, and the sustainable development context.

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“…The starting point is, of course, big data -the composite of all individual information generated on the internet -which comes in all forms. It can be structured in the form of numeric data or just contain unstructured data such as text, images, or videos (Kayser, Nehrke, & Zubovic, 2018). But what makes big data highly valuable is the growth of other fourth IR technologies that rely on big data to create advantages, including IoT, machine learning (where computers learn from existing data), RPA and AI, and allow for data mining and use at several levels.…”

Section: Mapping Data-driven Innovationmentioning

confidence: 99%

Industrial Policy 4.0: Promoting Transformation in the Digital Economy

Sampath

2018

SSRN Journal

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Data Science as an Innovation Challenge: From Big Data to Value Proposition

Cited by 42 publications

References 13 publications

To Use or Not to Use Artificial Intelligence? A Framework for the Ideation and Evaluation of Problems to Be Solved with Artificial Intelligence

To Use or Not to Use Artificial Intelligence? A Framework for the Ideation and Evaluation of Problems to Be Solved with Artificial Intelligence

A Conceptual Framework for Assessing an Organization’s Readiness to Adopt Big Data

Industrial Policy 4.0: Promoting Transformation in the Digital Economy

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