A Lean Design Thinking Methodology (LDTM) for Machine Learning and Modern Data Projects

Ahmed, Bakhtiyar; Dannhauser, Thomas; Philip, Nada

doi:10.1109/ceec.2018.8674234

Cited by 23 publications

(16 citation statements)

References 2 publications

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“…Beyond this, there has been no identified research relating to how teams select their DS-PMM. The lack of research in this area is consistent with Ahmed et al's [5] observation, in that Ahmed's research did not identify any other research that was specifically focused on evaluating a DS-PMM.…”

Section: Research On Selecting a Ds-pmmsupporting

confidence: 68%

“…In fact, it was recently noted that minimal research was available on the effectiveness and impact of the different possible methodologies that data science teams use. It was also noted that no research was identified that focused specifically on evaluating a methodology/framework that supports the design and implementation process of data science projects [5].…”

Section: Introductionmentioning

confidence: 99%

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Factors that Influence the Selection of a Data Science Process Management Methodology: An Exploratory Study

Saltz¹,

Hotz²

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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This paper explores the factors that impact the adoption of a process methodology for managing and coordinating data science projects. Specifically, by conducting semi-structured interviews from data scientists and managers across 14 organizations, eight factors were identified that influence the adoption of a data science project management methodology. Two were technical factors (Exploratory Data Analysis, Data Collection and Cleaning). Three were organizational factors (Receptiveness to Methodology, Team Size, Knowledge and Experience), and three were environmental factors (Business Requirements Clarity, Documentation Requirements, Release Cadence Expectations). The research presented in this paper extends recognized factors for IT process adoption by bringing together influential factors that apply to data science. Teams can use the developed process adoption model to make a more informed decision when selecting their data science project management process methodology.

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Section: Research On Selecting a Ds-pmmsupporting

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Factors that Influence the Selection of a Data Science Process Management Methodology: An Exploratory Study

Saltz¹,

Hotz²

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…To our knowledge, there is no formal standard for methodology in the data science projects (see Saltz and Shamshurin, 2016 ). Through the years, the CRISP-DM methodology (Shearer, 2000 ) created in the late 1990s has become a de-facto standard, as evidenced from a range of works (see, e.g., Huang et al, 2014 ; Niño et al, 2015 ; Fahmy et al, 2017 ; Pradeep and Kallimani, 2017 ; Abasova et al, 2018 ; Ahmed et al, 2018 ). An important factor of its success is the fact that it is industry, tool, and application agnostic (Mariscal et al, 2010 ).…”

Section: Challenges and Opportunities In Creating Methodologies Which Consistently Embed Interpretabilitymentioning

confidence: 99%

“…An important factor of its success is the fact that it is industry, tool, and application agnostic (Mariscal et al, 2010 ). However, the research community has emphasized that, since its creation, CRISP-DM had not been updated to reflect the evolution of the data science process needs (Mariscal et al, 2010 ; Ahmed et al, 2018 ). While various extensions and refined versions of the methodology, including IBM's Analytics Solutions Unified Method for Data Mining (ASUM-DM) and Microsoft's Team Data Science Process (TDSP), were proposed to compensate the weaknesses of CRISP-DM, at this stage, none of them has become the standard.…”

Section: Challenges and Opportunities In Creating Methodologies Which Consistently Embed Interpretabilitymentioning

confidence: 99%

Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach

Kolyshkina¹,

Simoff

2021

Front. Big Data

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Public healthcare has a history of cautious adoption for artificial intelligence (AI) systems. The rapid growth of data collection and linking capabilities combined with the increasing diversity of the data-driven AI techniques, including machine learning (ML), has brought both ubiquitous opportunities for data analytics projects and increased demands for the regulation and accountability of the outcomes of these projects. As a result, the area of interpretability and explainability of ML is gaining significant research momentum. While there has been some progress in the development of ML methods, the methodological side has shown limited progress. This limits the practicality of using ML in the health domain: the issues with explaining the outcomes of ML algorithms to medical practitioners and policy makers in public health has been a recognized obstacle to the broader adoption of data science approaches in this domain. This study builds on the earlier work which introduced CRISP-ML, a methodology that determines the interpretability level required by stakeholders for a successful real-world solution and then helps in achieving it. CRISP-ML was built on the strengths of CRISP-DM, addressing the gaps in handling interpretability. Its application in the Public Healthcare sector follows its successful deployment in a number of recent real-world projects across several industries and fields, including credit risk, insurance, utilities, and sport. This study elaborates on the CRISP-ML methodology on the determination, measurement, and achievement of the necessary level of interpretability of ML solutions in the Public Healthcare sector. It demonstrates how CRISP-ML addressed the problems with data diversity, the unstructured nature of data, and relatively low linkage between diverse data sets in the healthcare domain. The characteristics of the case study, used in the study, are typical for healthcare data, and CRISP-ML managed to deliver on these issues, ensuring the required level of interpretability of the ML solutions discussed in the project. The approach used ensured that interpretability requirements were met, taking into account public healthcare specifics, regulatory requirements, project stakeholders, project objectives, and data characteristics. The study concludes with the three main directions for the development of the presented cross-industry standard process.

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“…This issue can be resolved by using DT's ability to employ a variety of tools suited for this purpose, such as tools for identifying needs, tools for generating ideas, tools for testing ideas, and so on (Elsbach & Stigliani, 2018). Since several such handover points exist during the project execution phase, adopting a hybrid process such as Lean Design Thinking Methodology (LDTM) (Ahmed et al, 2019) would help reduce these filters by ensuring that all the relevant stakeholders working on a project are exposed to the unfiltered voice of customers.…”

Section: Implications For Practicementioning

confidence: 99%

Barriers and Opportunities of Implementing Design Thinking in Product Development Process of a Business to Business Company

et al. 2021

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Customer centricity is described as placing value creation for customers at the core of business decisions and organizational practices and is progressively regarded as a foundation of sustainable competitive advantage by companies. Hence in recent years, there is a shift from companies being product-centric to them adapting customer-centric practices as a practice to create balanced and sustainable businesses. Although there are several methods and processes that can help companies become customer-centric; Design Thinking (DT) is championed by many practitioners and academics alike as being effective in introducing customer-centricity in organizations. Despite being a highly researched topic in the last decade, the bulk of the research is focused on success stories or one-off cases of using design thinking in Business to Customer (B2C) environments. This paper is based on a qualitative study performed at a high-tech Swedish electronics company and focuses on highlighting the barriers and opportunities of adapting DT in Business to Business (B2B) companies with established product development processes. The barriers we identified can help companies to address the impediments and will make the DT implementation easier for companies

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A Lean Design Thinking Methodology (LDTM) for Machine Learning and Modern Data Projects

Cited by 23 publications

References 2 publications

Factors that Influence the Selection of a Data Science Process Management Methodology: An Exploratory Study

Factors that Influence the Selection of a Data Science Process Management Methodology: An Exploratory Study

Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach

Barriers and Opportunities of Implementing Design Thinking in Product Development Process of a Business to Business Company

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