Barriers and opportunities of implementing design thinking in product development process of a business to business company

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 companie

Design Thinking for Journalism in the AI age: Towards an Innovation Process for Responsible AI Applications

Artificial Intelligence(AI) technologies offer opportunities for news organizations to become more efficient. At the same time, the adoption of AI in journalism raises concerns, including whether such efficiency-driven AI applications will endanger the democratic function of news organizations. In this paper, we present a Design Thinking (DT) process that draws on co-creation with journalists, aims to balance efficiency and quality standards, and prototypes a responsible AI application for journalism. We conceptualized the DT Process based on interdisciplinary literature and tested it in a project with journalism students from a leading German journalism school

Achieving Lean Data Science Agility Via Data Driven Scrum

This paper first explores the concept of a lean project and defines four principles team should follow to achieve lean data science. It then describes a new team process framework, which we call Data Driven Scrum (DDS), which enables lean data science project agility. DDS is similar to Scrum but key differences include that DDS defines capability-based iterations (as compared to Scrum time-based sprints), DDS increases the focus in observing and analyzing the output of each iteration (experiment), and that DDS defines process improvement meetings (e.g. retrospectives iteration reviews) to be held on a frequency the team deems appropriate (as compared to Scrum which defines these meetings to be at the end of each iteration). The paper also reports on a pilot study of an organization that adopted the DDS framework

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

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 are applicable within a data science context. Teams can use the developed process adoption model to make a more informed decision when selecting their data science project management process methodology

Interpretability of machine learning solutions in public healthcare : the CRISP-ML approach

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

Inflection vs. Continuation: A Discussion of Data Centralization in Startups

This paper delves into the pursuit to introduce data centralization methods to small-scale firms and startups. This is done by teaching the founders skills and methods in Excel while tailoring the outputs to their specific industry and customer segments. Throughout this consulting-like process, I recorded each firm’s ability to understand my methods and the relative likelihood for adoption post-consulting. This study has found that the likelihood for adoption lies not in the complexity of the model, but the stage of the startup. By understanding this key difference, this paper aims to provide a blueprint for firms to follow when implementing data-centric practices, but giving specific recommendations based on two key life cycle points: inflection and continuation. The significance of these two terms will be discussed in the body of this thesis

The Application of Design Thinking on Evaluating a User Self-Service Data Analytics/Science Platform

This thesis is aimed at utilising design thinking and the first half of the double diamond framework to i) set-up a research and select the appropriate participants, ii) gather requirements and define user personas from those eligible participants, and then iii) define the framework for evaluating a user self-service data analytics/science platform. Derived from the author’s own experiences, both as a Business Analyst (BA) and Citizen Data Scientist, with no-, low-, and code-based data analytics and science platforms are being implemented for enabling user self-service analytics – for users who are completely new to the space of data analysis and science as well as those who are experienced analysts and data scientists across a variety of industries and global regions – and there has been a need to outline an enablement process for this space. Through this research, the current state of the marketplace is researched, analysed, and evaluated alongside user research carried out on the feasibility and applicability of a UI- and UX-centric framework for ensuring human-centred design. A literature review showcases the benefits of human-centred design for humans when it comes to usability and techniques for such an application in various other fields. The key aspects of this research are to understand the users’ capabilities, needs, and wants, then categorise those users into personas, analyse and segment the requirements, create functional and non-functional requirements for platform capabilities, and then, ultimately, provide an evaluation framework for any organisation and/or individual looking for a user self-service data analytics/science platform by carrying out a pilot research study on ten (10) participants

Redesign in the textile industry: Proposal of a methodology for the insertion of circular thinking in product development processes

Despite the growing attention toward negative environmental impacts generated by the textile industry, companies face challenges in achieving sustainable and circular economy (CE) transition. The literature has so far lacked a systematic effort to analyze how textile companies can insert CE elements in their new product development process (NPD), especially regarding the proposition of methodologies that can better assist the companies in this regard. This study aims to identify good green innovation and CE practices in NPD adopted by textile companies and propose a methodology from Design Thinking (DT) to insert circular thinking in NPD. To that end, we conducted the research in two steps: (i) narrative bibliographic review and (ii) field research. The bibliographic review was conducted in the “Web of Science”, “Scopus”, and “Scielo” databases. The field research was executed with four textile companies. Our results show that companies tend to consider socio-environmental aspects at different stages of the development of new products. However, there is opportunities for improvement, especially through the use of ideas from DT. The proposed methodology is composed of two main cycles: the design cycle (DT stages) and the consumption cycle (subsequent stages). It encompasses the five main stages of the DT and the three macro phases of NPD of the textile industry. The ideas coming from the DT, especially creativity, focus on the user and stakeholder integration, assist in the development of innovative and circular solutions. The methodology presents how companies can work on reuse, recycling, and manufacturing issues, so that CE occurs. In the end, we evaluated, together with experts, the applicability of the proposed use of ideas of DT in practical cases. The research advances the discussions on NPD in the textile sector, especially on its potential to contribute to the transition to CE. It explores how DT assists in inserting circular thinking into the NPD and presents alternatives for companies to develop circular products and insert green innovations in their NPD