13 Organizations' Attempts to Become Data-Driven

Berndtsson, Mikael; Lennerholt, Christian; Svahn, Thomas; Larsson, Peter

doi:10.4018/ijbir.2020010101

Cited by 12 publications

(13 citation statements)

References 20 publications

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“…Barriers to implementing advanced analytics have previously been reported in the literature (Berndtsson, Lennerholt, Svahn, and Larsson 2020; Halper and Stodder 2017; LaValle et al 2011; NewVantagePartners 2019). In this section, we draw upon our own experiences and related literature for presenting a sample of barriers that can appear in a data‐driven pilot project.…”

Section: Barriersmentioning

confidence: 99%

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Scaling‐Up Data‐Driven Pilot Projects

Berndtsson

Ericsson²,

Svahn³

2020

AI Magazine

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Conducting pilot projects are a common approach among organizations to test and evaluate new technology. A pilot project is often conducted to remove uncertainties from a large-scale project and should be limited in time and scope. Nowadays, several organizations are testing and evaluating artificial intelligence techniques and more advanced forms of analytics via pilot projects. Unfortunately, many organizations are experiencing problems in scaling-up the findings from pilot projects to the rest of the organization. Hence, results from pilot projects become siloed with limited business value. In this article, we present an overview of barriers for conducting and scaling-up data-driven pilot projects. Lack of senior management support is a frequently mentioned top barrier in the literature. In response to this, we present our recommendations on what type of activities can be performed, to increase the chances of getting a positive response from senior management regarding scaling-up the usage of artificial intelligence and advanced analytics within an organization.

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Section: Barriersmentioning

confidence: 99%

“…Do not fall for the pitfall and only develop technical strategies. This is a shortcut that, in the long run, will require that you also develop strategies for how the business side should use and adopt a data‐driven approach to decision‐making (Berndtsson et al 2020).…”

Section: Checklist For Future Data‐driven Pilot Projectsmentioning

confidence: 99%

Scaling‐Up Data‐Driven Pilot Projects

Berndtsson

Ericsson²,

Svahn³

2020

AI Magazine

Self Cite

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“…The lack of data competencies often yields a greater risk of committing errors in data entry as well as losing valuable information (Alharthi et al, 2017). In organizations striving to be data-driven, employees often find it difficult to acquire the necessary skills to adapt (Berndtsson et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Effective subunits in ineffective systems: Simultaneous tight and loose coupling in higher education data use

et al. 2023

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Higher education institutions are pressured to use data for university rankings, accreditation, decision making, and technical support of students and faculty. However, these organizations also experience barriers and resistance to such data-driven aspirations. Rather than simply focusing on the whole organization, this research attends to variations in organizational subunits and asks what processes help and hinder data use. It pays attention to the puzzle of how data use variation can lead to effective subunits just as data fragmentation contributes to ineffective systems. Although universities may create centralized coordinating bodies, the structural and cultural bases of loosely coupled university subunits may upset these efforts. Using the case of a university attempting to centralize its data systems, we employ in-depth interviews of senior and mid-level administrators to understand why technical changes happen within specific subunits even as only ceremonial changes happen for the larger organization. This suggests new insights for interrogating how loose and tight coupling can co-exist in the same organization.

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“…Mikael Berndtsson in two recent publications details the nontechnical factors that should be considered to enable AI adoption. In [29], the need for nontechnical strategy for scaling analytics in organisations by proposing an initial plan is presented, whereas in [30] information gathered from 13 organisations confirmed the value of nontechnical strategies and suggested the application of change management techniques [3]. In these papers, the nontechnical aspects are addressed through techniques from business related research anticipating that AI has similar requirements with other Information Systems (IS) as is also stated in [31].…”

Section: B Ai In Contextmentioning

confidence: 99%

AdaptiveSystems: An Integrated Framework for Adaptive Systems Design and Development Using MPS JetBrains Domain-Specific Modeling Environment

2021

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This paper contains the design and development of an adaptive systems (AdaptiveSystems Domain-Specific Language -DSL) framework to assist language developers and data scientists in their attempt to apply Artificial Intelligence (AI) algorithms in several application domains. Big-data processing and AI algorithms are at the heart of autonomics research groups among industry and academia. Major advances in the field have traditionally focused on algorithmic research and increasing the performance of the developed algorithms. However, it has been recently recognized by the AI community that the applicability of these algorithms and their consideration in context is of paramount importance for their adoption. Current approaches to address AI in context lie in two areas: adaptive systems research that mainly focuses on implementing adaptivity mechanisms (technical perspective) and AI in context research that focuses on business aspects (business perspective). There is currently no approach that combines all aspects required from business considerations to appropriate level of abstraction. In this paper, we attempt to address the problem of designing adaptive systems and therefore providing AI in context by utilising DSL technology. We propose a new DSL (AdaptiveSystems) and a methodology to apply this to the creation of a DSL for specific application domains such as AdaptiveVLE (Adaptive Virtual Learning Environment) DSL. The language developer will be able to instantiate the AdaptiveSystems DSL to any application domain by using the guidelines in this paper with an integrated path from design to implementation. The domain expert will then be able to use the developed DSL (e.g. AdaptiveVLE DSL) to design and develop their application. Future work will include extension and experimentation of the applicability of this work to more application domains within British Telecom (BT) and other areas such as health care, finance, etc.

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13 Organizations' Attempts to Become Data-Driven

Cited by 12 publications

References 20 publications

Scaling‐Up Data‐Driven Pilot Projects

Scaling‐Up Data‐Driven Pilot Projects

Effective subunits in ineffective systems: Simultaneous tight and loose coupling in higher education data use

AdaptiveSystems: An Integrated Framework for Adaptive Systems Design and Development Using MPS JetBrains Domain-Specific Modeling Environment

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