Management of analytics-as-a-service - results from an action design research project

Dremel, Christian; Stoeckli, Emanuel; Wulf, Jochen

doi:10.1080/2573234x.2020.1740616

Cited by 5 publications

(4 citation statements)

References 42 publications

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“…Low-barrier access to computational infrastructure allows for large-scale, complex computing ensuring successful data processing and analysis to improve or innovate business activities (Hashem et al, 2015 ; Lismont et al, 2019 ; Naous et al, 2017 ). These activities may range from improving decision-making processes to driving product or service innovation (Davenport & Harris, 2017 ; Lehrer et al, 2018 ), yet require a mature business understanding as well as technological and analytical readiness from AaaS customers (Dremel et al, 2020 ). The repertoire of analysis tools available for this purpose is usually divided into descriptive, diagnostic, predictive, and prescriptive analysis methods (Mishra et al, 2017 ).…”

Section: Related Workmentioning

confidence: 99%

Creating customer value from data: foundations and archetypes of analytics-based services

2021

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The digital transformation offers new opportunities for organizations to expand their existing service portfolio in order to achieve competitive advantages. A popular way to create new customer value is the offer of analytics-based services (ABS)—services that apply analytical methods to data to empower customers to make better decisions and to solve complex problems. However, research still lacks to provide a profound conceptualization of this novel service type. Similarly, actionable insights on how to purposefully establish ABS in the market to enrich the service portfolio remain scarce. We perform a cluster analysis of 105 ABS and triangulate it with a revelatory case study to identify four generic ABS archetypes and to unveil their specific service objectives and characteristics. We also isolate essential factors that shape decision-making regarding the choice of adequate archetypes and subsequent transitions between them. The detailed characterization of different ABS types contributes to a more profound theorizing process on ABS as well as provides a systematization for strategic opportunities to enrich service portfolios in practice.

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Section: Related Workmentioning

confidence: 99%

Creating customer value from data: foundations and archetypes of analytics-based services

2021

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“…Digitalization in business is challenged by old ways hindering new, and not understanding that it is not about technology [1], but about changing the way things are done. One of today's success factors involves digital business analytics, the ability of generating businessrelevant information from data to improve decisionmaking, business processes, products, services, and related activities [2]. This new ability is also linked to discussion on digital transformation [3], big data analytics [2], and industry 4.0 [4].…”

Section: A Backgroundmentioning

confidence: 99%

“…One of today's success factors involves digital business analytics, the ability of generating businessrelevant information from data to improve decisionmaking, business processes, products, services, and related activities [2]. This new ability is also linked to discussion on digital transformation [3], big data analytics [2], and industry 4.0 [4]. It is recognized how data and their exploitation can support the decision-making but are experienced challenging by companies [5].…”

Section: A Backgroundmentioning

confidence: 99%

“…Product, service, and portfolio management are examples of areas that can benefit of true digital analytics [8] [11]. However, similarly markets [3] [8], supply chain [2] [7], processes [2], and other areas of business focus can benefit of the effective digital analytics. Further, digitalization can change the business by applying technology to solve problems and create value.…”

Section: A Backgroundmentioning

confidence: 99%

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Digitizing Company Analytics – Digitalization Concept for Valuable Insights

Härkönen

Mustonen

Koskinen

et al. 2020

2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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Digitalizing business analytics by generating relevant information from data to gain valuable insights and to enhance decision-making is a key ability for companies. We describe a potential concept for digitizing company analytics to enable data-driven approach by providing a path from information needs to visualizing the analysis results, including the consideration of analysis logic, relevant data, and necessary data model. Company business processes, IT applications, and data assets provide the foundations to build upon. The developed concept may enable practitioners to consider possible applications and the needed, already existing technologies.

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A Method to Classify Data Quality for Decision Making Under Uncertainty

Simard

Rönnqvist

LeBel

et al. 2023

J. Data and Information Quality

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Every decision-making process is subject to a certain degree of uncertainty. In sectors where the outcomes of the operations planned are uncertain and difficult to control such as in forestry, data describing the available resources can have a large impact on productivity. When planning activities, it is often assumed that such data are accurate, which causes a need for more replanning efforts. Data verification is kept to a minimum even though using erroneous information increases the level of uncertainty. In this context, it is relevant to develop a process to evaluate whether the data used for planning decisions are appropriate, so as to ensure the decision validity and provide information for better understanding and actions. However, the level of data quality alone can sometimes be difficult to interpret and needs to be put into perspective. This paper proposes an extension to most data quality assessment techniques by comparing data to past quality levels. A classification method is proposed to evaluate the level of data quality in order to support decision making. Such classification provides insights into the level of uncertainty associated with the data. The method developed is then exploited using a theoretical case based on the literature and a practical case based on the forest sector. An example of how classified data quality can improve decisions in a transportation problem is finally shown.

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Management of analytics-as-a-service - results from an action design research project

Cited by 5 publications

References 42 publications

Creating customer value from data: foundations and archetypes of analytics-based services

Creating customer value from data: foundations and archetypes of analytics-based services

Digitizing Company Analytics – Digitalization Concept for Valuable Insights

A Method to Classify Data Quality for Decision Making Under Uncertainty

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