Determinants for successful deployment of clinical prediction models : a design science research in the Dutch healthcare sector

Whereas the promises of (predictive) analytics in healthcare are clear and extensively reported, the executive practicalities are not. Mapping the factors that have a hand in the implementation and continuation (i.e. deployment) of such projects improves the execution of prediction models and hence improves diagnostic and prognostic healthcare for patients. This research takes a design science approach to create an artifact aimed at successful deployment of clinical prediction models (CPMs). Through a literature review, various factors that play a role in the deployment of CPMs are categorized. Interviews with an extensive expert panel lead to the development of the CRISP-DM Deployment Extension for CPMs. Next to opinions on the importance of each factor, new in-sights are collected on related topics. A case study at a Dutch hospital allows for the testing of the artifact. A gap analysis is conducted, leading to a practical advice in terms of successful deployment. The research concludes with a proposed deployment strategy and a list of eight recommendations that can be considered the determinants for successful deployment of clinical prediction models

Demystifying Data Science Projects: A Look on the People and Process of Data Science Today

Peer reviewe

Remote sensing in the analysis between forest cover and COVID-19 cases in Colombia

This article explores the relationship between forest cover and coronavirus disease 2019 (COVID-19) cases in Colombia using remote sensing techniques and data analysis. The study focuses on the CORINE land cover methodology's five main land cover categories: artificial territory, agricultural territories, forests and semi-natural areas, humid areas, and water surfaces. The research methodology involves several phases of the unified method of analytical solutions for data mining (ASUM-DM). Data on COVID-19 cases and forest cover are collected from the Colombian National Institute of Health and Advanced Land Observation Satellite (ALOS PALSAR), respectively. Land cover data is processed using QGIS software. The results indicate an inverse relationship between forest cover and COVID-19 cases, as evidenced by Pearson's index ρ of -0.439 (p-value <0.012). In addition, a negative correlation is observed between case density and forests and semi-natural areas, one of the land cover categories. The findings of this study suggest that higher forest cover is associated with lower numbers of COVID-19 cases in Colombia. The results could potentially inform government organizations and policymakers in implementing strategies and policies for forest conservation and the inclusion of green areas in densely populated urban areas

Improving the Impact of Big Data Analytics Projects with Benefits Dependency Networks

Big data analytics is regarded as the next frontier in creating digital opportunities for businesses. Analytics projects rarely deliver the intended benefits for the organisation that invest in these data analytics, and currently, no widely accepted design method for analytics projects exists. To address this, we report from an action research project in an organisation highly involved with big data analytics and how benefits materialize from these projects through the practices of tailored and focused benefits management. We argue for using the benefits dependency network for orchestrating commitment to benefits. Benefits dependency networks create linkages between analytics technology, organisational change activities, stakeholders’ interests and to-be benefits of a project. With this study, we contribute with: (1) a tailored technique for benefits dependency networks, (2) focus on benefits into established project development practices for big data analytics (3) facilitation as a key capability in developing a benefits dependency network

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

FIN-DM: finantsteenuste andmekaeve protsessi mudel

Andmekaeve hõlmab reeglite kogumit, protsesse ja algoritme, mis võimaldavad ettevõtetel iga päev kogutud andmetest rakendatavaid teadmisi ammutades suurendada tulusid, vähendada kulusid, optimeerida tooteid ja kliendisuhteid ning saavutada teisi eesmärke. Andmekaeves ja -analüütikas on vaja hästi määratletud metoodikat ja protsesse. Saadaval on mitu andmekaeve ja -analüütika standardset protsessimudelit. Kõige märkimisväärsem ja laialdaselt kasutusele võetud standardmudel on CRISP-DM. Tegu on tegevusalast sõltumatu protsessimudeliga, mida kohandatakse sageli sektorite erinõuetega. CRISP-DMi tegevusalast lähtuvaid kohandusi on pakutud mitmes valdkonnas, kaasa arvatud meditsiini-, haridus-, tööstus-, tarkvaraarendus- ja logistikavaldkonnas. Seni pole aga mudelit kohandatud finantsteenuste sektoris, millel on omad valdkonnapõhised erinõuded. Doktoritöös käsitletakse seda lünka finantsteenuste sektoripõhise andmekaeveprotsessi (FIN-DM) kavandamise, arendamise ja hindamise kaudu. Samuti uuritakse, kuidas kasutatakse andmekaeve standardprotsesse eri tegevussektorites ja finantsteenustes. Uurimise käigus tuvastati mitu tavapärase raamistiku kohandamise stsenaariumit. Lisaks ilmnes, et need meetodid ei keskendu piisavalt sellele, kuidas muuta andmekaevemudelid tarkvaratoodeteks, mida saab integreerida organisatsioonide IT-arhitektuuri ja äriprotsessi. Peamised finantsteenuste valdkonnas tuvastatud kohandamisstsenaariumid olid seotud andmekaeve tehnoloogiakesksete (skaleeritavus), ärikesksete (tegutsemisvõime) ja inimkesksete (diskrimineeriva mõju leevendus) aspektidega. Seejärel korraldati tegelikus finantsteenuste organisatsioonis juhtumiuuring, mis paljastas 18 tajutavat puudujääki CRISP- DMi protsessis. Uuringu andmete ja tulemuste abil esitatakse doktoritöös finantsvaldkonnale kohandatud CRISP-DM nimega FIN-DM ehk finantssektori andmekaeve protsess (Financial Industry Process for Data Mining). FIN-DM laiendab CRISP-DMi nii, et see toetab privaatsust säilitavat andmekaevet, ohjab tehisintellekti eetilisi ohte, täidab riskijuhtimisnõudeid ja hõlmab kvaliteedi tagamist kui osa andmekaeve elutsüklisData mining is a set of rules, processes, and algorithms that allow companies to increase revenues, reduce costs, optimize products and customer relationships, and achieve other business goals, by extracting actionable insights from the data they collect on a day-to-day basis. Data mining and analytics projects require well-defined methodology and processes. Several standard process models for conducting data mining and analytics projects are available. Among them, the most notable and widely adopted standard model is CRISP-DM. It is industry-agnostic and often is adapted to meet sector-specific requirements. Industry- specific adaptations of CRISP-DM have been proposed across several domains, including healthcare, education, industrial and software engineering, logistics, etc. However, until now, there is no existing adaptation of CRISP-DM for the financial services industry, which has its own set of domain-specific requirements. This PhD Thesis addresses this gap by designing, developing, and evaluating a sector-specific data mining process for financial services (FIN-DM). The PhD thesis investigates how standard data mining processes are used across various industry sectors and in financial services. The examination identified number of adaptations scenarios of traditional frameworks. It also suggested that these approaches do not pay sufficient attention to turning data mining models into software products integrated into the organizations' IT architectures and business processes. In the financial services domain, the main discovered adaptation scenarios concerned technology-centric aspects (scalability), business-centric aspects (actionability), and human-centric aspects (mitigating discriminatory effects) of data mining. Next, an examination by means of a case study in the actual financial services organization revealed 18 perceived gaps in the CRISP-DM process. Using the data and results from these studies, the PhD thesis outlines an adaptation of CRISP-DM for the financial sector, named the Financial Industry Process for Data Mining (FIN-DM). FIN-DM extends CRISP-DM to support privacy-compliant data mining, to tackle AI ethics risks, to fulfill risk management requirements, and to embed quality assurance as part of the data mining life-cyclehttps://www.ester.ee/record=b547227

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

Improving the impact of Big Data Analytics Projects with Benefits Dependency Networks

Big data analytics is the next frontier in creating digital opportunities for businesses. However, analytics projects rarely deliver the intended benefits for the organizations that invest in these. To address this challenge, we report from an action research study on improving benefits realization in Vestas, an organization highly involved with big data analytics. Here, we introduce the benefits dependency network, a map of the relationships between analytics technology, organizational change activities, stakeholders’ interests, and the potential benefits of a big data analytics project. Through four action research iterations involving three projects in Vestas, we developed and evaluated a method for benefits dependency networks for big data analytics. In this study, we present lessons on: (1) the usefulness of the method in big data analytics projects, (2) how it can be embedded into existing project methodologies, and (3) how facilitation is needed in connecting the domains supporting benefits realization needs. We conclude the paper by discussing our lessons’ contributions to the extant research on big data analytics and benefits realization management

Managing and Composing Teams in Data Science: An Empirical Study

Data science projects have become commonplace over the last decade. During this time, the practices of running such projects, together with the tools used to run them, have evolved considerably. Furthermore, there are various studies on data science workflows and data science project teams. However, studies looking into both workflows and teams are still scarce and comprehensive works to build a holistic view do not exist. This study bases on a prior case study on roles and processes in data science. The goal here is to create a deeper understanding of data science projects and development processes. We conducted a survey targeted at experts working in the field of data science (n=50) to understand data science projects’ team structure, roles in the teams, utilized project management practices and the challenges in data science work. Results show little difference between big data projects and other data science. The found differences, however, give pointers for future research on how agile data science projects are, and how important is the role of supporting project management personnel. The current study is work in progress and attempts to spark discussion and new research directions.acceptedVersionPeer reviewe