Conference Paper
To read the full-text of this research, you can request a copy directly from the authors.

Abstract

It is widely recognized by public and private organizations, that the biggest challenge faced in light of the data revolution is finding people with the required set of skills to transform data into actionable insight. The growing interest on the role of the data scientist and the relating data analytics skills has seen an increasing amount of research on the importance of data analytics skills in the contemporary working environment. Yet, there is still limited understanding on the importance of data analytic skills, and even more, there is limited research on the discrepancies between the skills that are needed in the market and what graduates possess. To this end, this research uses a mixed-methods approach combining quantitative survey data from 113 IT executives, and qualitative interview data from 27 big data project managers to explore the significance, discrepancies, and aspects of data analytic skills. Our results show that data analytic skills significantly contribute firm performance, particularly for firms that are data-oriented. In addition, we find that the need for skills greatly exceeds those that graduates possess. Lastly, our analysis suggests that the data skills of the data scientist span multiple subject areas which are further discussed.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... This is because personnel and management capabilities cannot be easily imitated and are, therefore, an important source of competitive advantage [6,9,14]. To establish necessary internal knowledge, firms adopting BA need to provide opportunities for their employees to develop the required skill sets for BA through formal trainings or establish recruitment programs to acquire qualified personnel with comprehensive skills or knowledge in data science practices [124]. ...
... In this context, recent studies have emphasized that finding qualified personnel with the required set of skills constitutes a major challenge for companies given the high skills gaps [6,124]. McKinsey has forecasted that there will be a shortage of 140,000 to 190,000 people with analytical skills as well as a shortfall of 1.5 million managers and analysts for BDA and decision-making roles [2]. ...
... McKinsey has forecasted that there will be a shortage of 140,000 to 190,000 people with analytical skills as well as a shortfall of 1.5 million managers and analysts for BDA and decision-making roles [2]. This skills gap has emerged because of the difference between the rising demand from organizations seeking personnel with data science skills and the actual skills that are possessed by graduates and professionals in industry [124,125]. For policy-makers, this issue has important implications for their future work. ...
Article
The main purpose of this study is to examine the factors that are critical to create business value from business analytics (BA). Therefore, we conduct a meta-analysis of 125 firm-level studies spanning ten years of research from across 26 countries. We found evidence that the social factors of BA, such as human resources, management capabilities, and organizational culture show a greater impact on business value, whereas technical aspects play a minor role in enhancing firm performance. Through these findings, we contribute to the ongoing debate concerning BA business value by synthesizing and validating the findings of the body of knowledge. Download-Link: https://authors.elsevier.com/a/1fQnn1M7749Bin
... Data scientists require the following competencies: 1) strong statistical analysis, computing, and datamanipulation skills (Davenport & Patil, 2012;Debortoli, Müller, & vom Brocke, 2014;Mikalef, Giannakos, Pappas, & Krogstie, 2018a;Power, 2016); 2) research skills (Mikalef et al., 2018a;Power, 2016), which includes the ability to explore and test cause-and-effect explanations; 3) curiosity (Davenport & Patil, 2012;Vidgen et al., 2017); 4) a deep understanding of the enquiry context (i.e., the business domain) (Davenport & Harris, 2007;Debortoli et al., 2014;Waller & Fawcett, 2013); and 5) strong communication and interpersonal skills both for liaising with the business domain experts (Viaene, 2013) and for conveying BA insights convincingly to business stakeholders (Kim et al., 2016;Patil, 2011). Due to the nature of data scientists' work, many of these competencies overlap with the competencies that doctoral researchers (Kim et al., 2016), statisticians, and econometricians require. ...
... Data scientists require the following competencies: 1) strong statistical analysis, computing, and datamanipulation skills (Davenport & Patil, 2012;Debortoli, Müller, & vom Brocke, 2014;Mikalef, Giannakos, Pappas, & Krogstie, 2018a;Power, 2016); 2) research skills (Mikalef et al., 2018a;Power, 2016), which includes the ability to explore and test cause-and-effect explanations; 3) curiosity (Davenport & Patil, 2012;Vidgen et al., 2017); 4) a deep understanding of the enquiry context (i.e., the business domain) (Davenport & Harris, 2007;Debortoli et al., 2014;Waller & Fawcett, 2013); and 5) strong communication and interpersonal skills both for liaising with the business domain experts (Viaene, 2013) and for conveying BA insights convincingly to business stakeholders (Kim et al., 2016;Patil, 2011). Due to the nature of data scientists' work, many of these competencies overlap with the competencies that doctoral researchers (Kim et al., 2016), statisticians, and econometricians require. ...
... The third literature stream examines the nascent data science profession. Many studies in this stream focus on data science practice (Davenport & Patil, 2012;Granville, 2014;Patil, 2011;Viaene, 2013), though some also examine data science education (Asamoah, Sharda, Hassan Zadeh, & Kalgotra, 2017;Mikalef et al., 2018a). These books and papers provide valuable insights on data scientists' work (and particularly the competencies they require). ...
... These demands then create the need for systemic change in education focusing on preparing new specialists (data-related roles) and training for new skills (Coccoli, Maresca, & Stanganelli, 2017;Demchenko et al., 2014;Demchenko et al., 2017). According to the findings of the recent study of Mikalef, Giannakos, Pappas, and Krogstie (2018), there is a gap between the skills needed and the ones taught in academic curricula. ...
... Therefore, there is no consensus framework on how these skills should be classified and what the required level is for different stakeholders. The first group of authors are oriented toward emphasis on mastering hard skills, arguing that the real value lies in applying them on these data using different analytics techniques and tools (Demchenko et al., 2014;Li & Ni, 2015;Mikalef et al., 2018;Williamson, 2016;Zadeh et al., 2018). Lists of core areas required for common body of knowledge were introduced by Demchenko et al. (2014) or Mikalef et al. (2018). ...
... The first group of authors are oriented toward emphasis on mastering hard skills, arguing that the real value lies in applying them on these data using different analytics techniques and tools (Demchenko et al., 2014;Li & Ni, 2015;Mikalef et al., 2018;Williamson, 2016;Zadeh et al., 2018). Lists of core areas required for common body of knowledge were introduced by Demchenko et al. (2014) or Mikalef et al. (2018). Among other, they include an emphasis on data management and challenges, security, anonymity, privacy and ethics of data, computing models, visualization and presentation of results, etc. Grillenberger and Romeike (2014) emphasised broader aspects of data management, e.g. ...
Article
Full-text available
The concept of openness and information sharing (linking) together with increasing amounts of data available significantly affect the current educational system. Institutions as well as other stakeholders are facing challenges how to successfully deal with them and potentially profit from them. In this regard, this paper explores opportunities of big and open linked data analytics in the educational process intended to develop the new set of skills. A comprehensive literature review resulted in a framework of relevant skills, namely soft, hard, and data analytics skills. Their importance was evaluated using a Delphi method. In order to determine the relationships between involved stakeholders, their roles and requirements, a stakeholder theory is utilized. It resulted in the identification of current and emerging roles of stakeholders in the data analytics ecosystem. A structural classification of stakeholders’ influences and impacts then represents a necessary background for establishing strategies for the development of the right skills needed to gain the value from these data. This paper provides a comprehensive view on big and open linked data analytics in the educational context, defines and interlinks data-related with current roles as well as the skills required to perform data analytics.
... These barriers appear during the different diffusion stages and are manifested as various types of organizational inertia. Some of these forms are discernible at the early adoption phase, while others appear at the decision-making stage, in which managers for a combination of reasons tend not to adopt the insight that is generated by big data analytics, but rather follow their instinct [88]. ...
... When it comes down to the IT department, educational seminars and incremental projects seem to be the way to limit negative psychology barriers. This allows employees to be educated in the necessary skills that are needed for the big data analytics transition, rather than being left alone to navigate how to do so [50,88]. Several such online educational tools have been developed and have proven to be successful in training employees and providing them with a step-by-step approach to develop their big data analytics competencies [113,114]. ...
Article
Full-text available
Although big data analytics have been claimed to revolutionize the way firms operate and do business, there is a striking lack of knowledge about how organizations should adopt and routinize such technologies to support their strategic objectives. The aim of this research is to explore how different inertial forces during deployments of big data analytics hinder the emergence of dynamic capabilities. To do so, we follow a multiple-case study design approach of 27 European firms and examine the different forms of inertia that materialize during big data analytics diffusion. The findings contribute to the growing body of knowledge on how big data analytics can be leveraged effectively to enable and strengthen a firm's dynamic capabilities. By disaggregating dynamic capabilities into the underlying capabilities of sensing, seizing and transforming, findings indicate that different combinations of organizational inertia including economic, political, socio-cognitive, negative psychology, and socio-technical hamper the formation of each type of capability.
... Data science and analytics (DSA) projects are usually multidisciplinary in their nature and therefore require combined expertise from multiple fields, such as solid domain knowledge about the problem of interest, analytical modeling skills and experiences with the acquisition and preparation of data assets generated by different IT systems (Mikalef et al. 2018;Zschech 2018). For this reason, there have been several initiatives to support the execution of data-driven projects in a step-wise manner, such as CRISP-DM (cross-industry standard process for data mining), by providing instructions for all relevant tasks from data pre-processing to analytical method selection and evaluation with the purpose to give guidance and structure the overall implementation process (Kurgan & Musilek 2006). ...
... Usually this step is carried out by well qualified data scientists, who bring the necessary skillset to combine both contexts, i.e., the methodical skills along a typical data lifecycle and the required business understanding to comprehend underlying domain characteristics and the desired output towards economic goals (Debortoli et al. 2014;Schumann et al. 2016). In practice, however, such fully equipped data scientists are often still a rare species (Mikalef et al. 2018;Zschech et al. 2018) and thus, the mapping task often remains to be carried out by multiple stakeholders in terms of modeling experts and domain experts, making the DSA process an iterative and time-consuming endeavor. ...
Conference Paper
Full-text available
The task of mapping a domain-specific problem space to an adequate set of data mining (DM) methods is a crucial step in data science projects. While there have been several efforts for automated method selection in general, only few approaches consider the particularities of problem contexts expressed in domain-specific language. Therefore, we propose the concept of a text-based recommender system (TBRS) which takes problem descriptions articulated in domain language as inputs and then recommends the best suitable class of DM methods. Following a design science research methodology, the current focus is on the initial steps of motivating the problem and conducting a requirements analysis. In particular, we outline the problem setting using an exemplary scenario from industrial practice and derive requirements towards an adequate solution artifact. Subsequently, we discuss potential TBRS methods with regard to requirement fulfillment while organizing both methods and requirements in a structured framework. Finally, we conclude the paper, discuss limitations and draw an outlook.
... Il n'est en effet plus rare d'avoir à traiter des gigaoctets de données pour une simple tâche d'apprentissage automatique [21] qui, une fois enrichis, nécessitent en retour au moins autant d'espace de stockage pour être correctement sauvegardés et utilisés pour répondre à diverses problématiques métier. Troisièmement, et pour finir, la manne de personnes disposant des connaissances et savoir-Introduction en français faire techniques nécessaires pour rassembler, traiter et extraire les informations de ces masses de données est faible [46]. En effet, les data scientists et autres spécialistes de la donnée sont des ingénieurs / chercheurs de haut niveau dont la formation exige de 5 à 8 années à temps plein, ce qui restreint d'autant le nombre potentiel de candidats à l'embauche. ...
... One can quickly have to process gigabytes of data for a simple machine learning task or even terabytes of data [21] that require at least as much storage to be kept for other purposes such as business or research. Thirdly, there are few people who have the knowledge that is necessary to gather, process and extract information of this massively available data [46]. Indeed, experts like data scientists are high-level engineers / researchers for which education takes 5 to 7 years to complete, yielding a very tense employment market in this specific domain. ...
Thesis
The so called data era we have entered in is accompanied by an explosion of data, both in variety and quantity. Public transportation is a data-intensive field, and related information systems are often supported by old technologies that struggle to keep up as the amount of data continually increases. This poses two problems. First, the massive data generated by the transportation network must be qualified and enriched with external data sources in order to be used for decision making. Second, in order to limit the number of tools and the complexity of maintenance, it is desirable to integrate data governance with decision support tools to allow non-expert operators to manipulate this data. Through four contributions leading to the proposal of a technical framework that integrates the past, present and future into a traditional information system containing a priori models, this thesis argues that the integration of various highly qualified datasets from the real world into a single spatio-temporal model provides a qualitative, efficient and low-cost mean of analysis, prediction and strategic decision support for bus networks while depreciating the use of data management systems in a non integrated multi-tool data management systems ?
... Data Scientist's competences are transdisciplinary in nature, creating value via synergy of multiple knowledge areas (Lotrecchiano and Misra (2018);) Such specialists rely heavily on the scientific methodology for systematically approaching problems and need research experience. Mikalef at al.(2018) note that data skills are perhaps the most sought-after resource in companies that have BD, as the skills captured by the scientists' profile allow companies to ask the right questions and convert data into practical insights. They conclude that software, infrastructure and data are insufficient to provide value if personal skills and knowledge are not available to instil knowledge from data. ...
... Providing human capital with high analytical skills for working with BD allows for more efficient and effective solutions and undoubtedly increases the overall performance of organizations. Despite the great potential of new technologies, tools and applications for analysis, the biggest problems faced by practitioners in using these technologies are finding employees with the necessary skills (LaValle et al., 2011;Mikalef et al. 2018) Nowadays we speak of the existence of a new digital divide between those who meet the indicators demanded by the business sector and those who do not meet the full range of the requirements. Although many universities and academies offer training courses and master programs for the training of Data Scientists, there is a shortage of these specialists on the labor market today. ...
Chapter
With the advent of big data, the search for respective data experts has become more intensive. This study aims to discuss data scientist skills and some topical issues that are related to data specialist profiles. A complex competence model has been deployed, dividing the skills into three groups: hard, soft, and analytical skills. The primary focus is on analytical thinking as one of the key competences of the successful data scientist taking into account the trans-discipline nature of data science. The chapter considers a new digital divide between the society and this small group of people that make sense out of the vast data and help the organization in informed decision making. As data science training needs to be business-oriented, the curricula of the Master's degree in Data Science is compared with the required knowledge and skills for recruitment.
... However, research has proven that the occupational group of data scientists appears to be very heterogeneous in the context of required skills and tasks (Davenport, 2020; and, therefore, has to be considered in more detail. In this context, research has already defined job profiles (Costa & Santos, 2017) and educational curricula (Richards & Marrone, 2014), or collected information from experts (Mikalef et al., 2018; to identify a data scientist's required skills and occupational roles. ...
... Employees who are able to efficiently handle and create knowledge out of data have reached particular relevance through the increased availability, compilation, and storage of huge amounts of data provided by the digital transformation of businesses, leading to a great demand for these employees (Davenport 2020;. Though, it has been challenging to pinpoint tasks and responsibilities of these so-called data scientists: researchers have explored job profiles (Costa & Santos 2017), educational curricula (Richards & Marrone 2014), or gathered key insights from experts (Mikalef et al. 2018; to identify a data scientist's required skills and knowledge. ...
Thesis
Based on recent developments caused by the big data revolution, data science has massively increased its importance for businesses. Within the marketing context, various types of customer data have become available in enormous amounts and need to be processed as efficiently as possible for creating valuable knowledge. Therefore, data scientists’ performance has become crucial for marketing departments to achieve competitive advantages in the modern highly digitalized economy. Within the raising field of data science, machine learning has become an outstanding trend since these approaches are able to automatically solve numerous classification and prediction problems with enormous performance. Thus, machine learning is seen as a key technology which will radically transform business practice in the future. Even though machine learning has already been applied to various marketing tasks, research is still at an early stage requiring further investigations of how marketing can successfully benefit from machine learning applications. Besides these data-driven opportunities provided by digitalization, technostress has evolved into an enormous downside of digitalized workplaces, leading to a significant decrease in employees’ performance. However, existing research lacks to provide evidence about different coping strategies and their potential to support employees in overcoming technostress. Furthermore, research currently fails to consider technostress regarding both highly digitalized occupational groups like data scientists and respective workplace environments for providing a deeper understanding of how employees suffer from stress caused by the use of digital technologies. Due to these recent challenges for data scientists, this cumulative thesis provides useful insights and new opportunities by focusing on machine learning and technostress issues as two aspects which promise major potentials for enhancing data scientists’ performance in today’s marketing contexts. Five research papers are included for effectively tackling both fields of research: three papers deliver both methodological and empirical findings for extending machine learning in marketing research by examining model architectures as well as applying machine learning to recent marketing problems. In addition, two research papers contribute to research by providing knowledge about technostress issues of data scientists as a heterogeneous and highly digitalized occupational group as well as examining different coping strategies for effectively overcoming stress due to the use of digital technologies. Beyond that, the findings deliver practical implications for marketing managers who aim to improve the performance of data scientists in a contemporary marketing environment.
... Such practice-driven articles highlight the importance that big data analytics have on a growing number of organizations, and the requirement of individuals to be Information Systems and e-Business Management DOI: 10.1007/s10257-018-0377-z knowledgeable about the use of big data analytics. In fact, several recent studies have delved on the multiplicity of necessary skills that are looked for in industry (De Mauro, Greco, Grimaldi, & Nobili, 2016;Mikalef, Giannakos, Pappas, & Krogstie, 2018). Such studies show that there are large gaps in the skills that graduates possess and those that are needed in industry. ...
Article
Full-text available
The digitalization process and its outcomes in the 21st century accelerate transformation and the creation of sustainable societies. Our decisions, actions and even existence in the digital world generate data, which offer tremendous opportunities for revising current business methods and practices, thus there is a critical need for novel theories embracing big data analytics ecosystems. Building upon the rapidly developing research on digital technologies and the strengths that information systems (IS) discipline brings in the area, we conceptualize big data and business analytics ecosystems and propose a model that portraits how big data and business analytics ecosystems can pave the way towards digital transformation and sustainable societies, that is the Digital Transformation and Sustainability (DTS) model. This editorial discusses that in order to reach digital transformation and the creation of sustainable societies, first, none of the actors in the society can be seen in isolation, instead we need to improve our understanding of their interactions and interrelations that lead to knowledge, innovation, and value creation. Second, we gain deeper insight on which capabilities need to be developed to harness the potential of big data analytics. Our suggestions in this paper, coupled with the five research contributions included in the special issue, seek to offer a broader foundation for paving the way towards digital transformation and sustainable societies.
... The e-recruitment has made a new competitive environment for organisations to rethink and adapt technology to increase effectiveness and efficiency of recruitment practice. Combined with various challenges such as the increased importance of human capital, the ubiquity of technologies, the increased level of qualifications with new generations, strong economic situations, and less unemployment rates [8,16,46], recruitment has been marked as a top priority [31,43,47]. Research has offered evidence about the rationale behind this increased interest in e-recruitment. ...
Article
Full-text available
Internet-led labour market has become so competitive it is forcing many organisations from different sectors to embrace e-recruitment. However, realising the value of the e-recruitment from a Requirements Engineering (RE) analysis perspective is challenging. This research was motivated by the results of a failed e-recruitment project conducted in military domain which was used as a case study. After reviewing the various challenges faced in that project through a number of related research domains, this research focused on two major problems: (1) the difficulty of scoping, representing, and systematically transforming recruitment problem knowledge towards e-recruitment solution specification; and (2) the difficulty of documenting e-recruitment best practices for reuse purposes in an enterprise recruitment environment. In this paper, a Problem-Oriented Conceptual Model (POCM) with a complementary Ontology for Recruitment Problem Definition (Onto-RPD) is proposed to contextualise the various recruitment problem viewpoints from an enterprise perspective, and to elaborate those problem viewpoints towards a comprehensive recruitment problem definition. POCM and Onto-RPD are developed incrementally using action-research conducted on three real case studies: (1) Secureland Army Enlistment; (2) British Army Regular Enlistment; and (3) UK Undergraduate Universities and Colleges Admissions Service (UCAS). They are later evaluated in a focus group study against a set of criteria. The study shows that POCM and Onto-RPD provide a strong foundation for representing and understanding the e-recruitment problems from different perspectives.
... Evolution of computer and communication technologies (CCT) and their wide application reach the point when data accumulated and stored on databases in almost every entity represents significant asset, but to gain its value are needed specific competences and technologies. Mikalef, et al. [1] note that data skills are perhaps the most sought-after resource in companies that have big data, as the skills captured by the scientists' profile allow companies to ask the right questions and convert data into practical insights. They conclude that software, infrastructure, and data are insufficient to provide any value if personal skills and knowledge are not available to implement them. ...
Conference Paper
Full-text available
Contemporary business is challenged by the phenomenon of Big Data. Utilization of opportunities provided by recent advancement of Information Technology reached the point when all data describing what happened in the business are recorded. The phrase "We are drowning in information but starved for knowledge" is the nowadays reality for the majority of business entity. The term Data Science appeared to mark competences needed to explore Big Data in a way to understand better the cause-and-effect that drives the processes and behavior intermediated by computer and communication technologies. The paper objectives are to open a discussion regarding the risks' management within projects aimed to create the infrastructure allowing the business to benefit from accumulated data-Data Science Business Infrastructure (DSBI). DSBI is composed first of all by professionals with diverse expertise, computer technologies, specialized software, and organizational measures directed to facilitate data collection, data quality, data analytics, and proper use of findings. The main objectives are to presents our vision on developing training programs directed to build competences needed for Data Scientists, especially in the area of identifying, assessing, evaluating, and managing risks in Big Data processing.
... This raises the role of human skills in data processing. This idea is particularly important in the context of Big Data, where skills are not only needed in exploitation of technology, such as software and infrastructure, but mainly in generation of insights leading to organizational decisions [7,10]. ...
... Such technical skills are important when it comes to understanding what data is of value, and how different data types can be cleansed, processed, and analysed to derive insights that are actionable (Costa & Santos, 2017). Nevertheless, while technical skills are important, one of the most critical aspects of data science is the ability of data-analytic thinking and strategic planning based on data-driven insight (Mikalef et al., 2018a;Prescott, 2014). Several studies underscore the importance of top management in driving big data initiatives and identifying areas where analytics can be utilised (Park et al., 2017;Vidgen et al., 2017). ...
Article
Full-text available
The potential of big data analytics in enabling improvements in business processes has urged researchers and practitioners to understand if, and under what combination of conditions, such novel technologies can support the enactment and management of business processes. While there is much discussion around how big data analytics can impact a firm's incremental and radical process innovation capabilities, we still know very little about what big data analytics resources firms must invest in to drive such outcomes. To explore this topic, we ground this study on a theory-driven conceptualisation of big data analytics based on the resource-based view (RBV) of the firm. Based on this conceptualisation, we examine the fit between the big data analytics resources that underpin the notion, and their interplay with organisational contextual factors in driving a firm's incremental and radical process innovation capabilities. Survey data from 202 chief information officers and IT managers working in Norwegian firms are analysed by means of fuzzy set qualitative comparative analysis (fsQCA). Results show that under different combinations of contextual factors the significance of big data analytics resources varies, with specific configurations leading to high levels of incremental and radical process innovation capabilities.
... Next, the results showed that communication and collaboration skills are critical in the digital workplace, which means involvement with other employees or external stakehold-ers in terms of teamwork and empathy, coordination skills, and the emotional intelligence to work with culturally diverse teams (van Laar et al. 2020). Further, employees must have data analytic skills, become familiar with different analysis methods, and hence gain the know-how to work with data (e.g., in the same manner as a beginning data scientist) (Mikalef et al. 2018). ...
Article
Full-text available
Businesses are confronted with digital challenges and require skilled employees to work effectively in the digital workplace. Drawing on the theoretical background of digital workplace transformation and the conceptual learning framework, we conducted a qualitative study. With the help of a cross-case analysis of nine multinational corporations, we provide a skillset for leaders on how to train the workforce in the digital workplace. The insights showed that an entrepreneurial mindset, digital responsible thinking, digital literacy, transformative skills, personal development skills, communication skills, community management skills, data analytic skills, and web development skills are critical in the digital workplace. These findings contribute to the literature by offering an exploratory understanding of essential skills for the digital workplace. Furthermore, we provide a theoretical foundation for future empirical investigations of cognitive and metacognitive, social-emotional, and practical skills. The study also offers practical implications for businesses and leaders on how to upskill the workforce and what kind of employees to recruit in the future workplace.
... The growing application of Machine Learning in a wide range of fields has led to the design of platforms and frameworks facilitating the production of readily actionable models. Even if statistical and coding expertise are still required for data science (Mikalef et al., 2018), such automatized systems are intended to non-experts in classical situations. ...
Conference Paper
Automated Machine Learning aims at preparing effective Machine Learning models with little or no data science expertise. Tedious tasks like preprocessing, algorithm selection and hyper-parameters optimization are then automatized: end-users just have to apply and deploy the model that best suits the real world problem. In this paper, we experiment Automated Machine Learning to leverage open data sources for predicting potential next wind farms location in Luxembourg, France, Belgium and Germany.
... More than 40% of organizations are currently challenged to attract and retain skilled data scientists, while by 2020, the U.S alone will need more than 190,000 skilled data analysts (Mikalef, Giannakos, Pappas, & Krogstie, 2018). The general IT problem is that there is a lack of knowledge regarding data governance principles for analyzing big data. ...
Preprint
Full-text available
Many data scientists are struggling to adopt effective data governance practices as they transition from traditional data analysis to big data analytics. Data governance of big data requires new strategies to deal with the volume, variety, and velocity attributes of big data. The purpose of this qualitative multiple case study was to explore big data governance strategies employed by data scientists to provide a holistic perspective of those data for making decisions. The participants were 10 data scientists employed in multiple mid-market companies in the greater Salt Lake City, Utah area who have strategies to govern big data. This study’s data collection included semi-structured in-depth individual interviews ( n = 10) and analysis of process documentation relating to big data governance in those organizations ( n = 4). Through thematic analysis, 4 major themes emerged from the study: ensuring business centricity, striving for simplicity, establishing data source protocols, and designing for security. The strategies outlined in this study can lead to positive social change by proactively addressing the ethical use of personally identifiable information in big data. By implementing strategies relating to the segregation of duties, encryption of data, and personal information, data scientists can mitigate contemporary concerns relating to the use of private information in big data analytics.
... While analytics tasks have become more complex with the extensive data growth in recent years, the skills gap has continued to widen (Cao, 2018, p. 130). Finding qualified and skilled analytics personnel is still a major challenge for firms (Grover et al., 2018;Mikalef et al., 2018); hence, augmented analytics provides firms with the possibility to develop self-learning and self-optimizing models with less involvement from human analysts (Davenport and Harris, 2017;LaPlante, 2019;Prat, 2019). ...
Conference Paper
Full-text available
Recently, augmented analytics has increasingly gained attention as one of the more advanced, novel approaches for handling big data. Based on machine learning and natural language processing, augmented analytics benefits from recent advancements in the artificial intelligence field to automate the analytics cycle. Despite the various benefits that augmented analytics offers for business and society, research on this topic is scarce to date. Based on the IT business value model, we examine the role of technological and social resources as well as the main use cases of augmented analytics. Therefore, we combine quantitative text mining with qualitative content analysis for an exploratory study of 350 academic and practical publications as well as 49 datasets of companies offering augmented analytics software and services. The findings contribute to the body of knowledge by enhancing the understanding of the augmented analytics concept, uncovering prevalent research gaps, and highlighting future research directions. Keywords:
... Many chemical engineers often struggle with modern datarelated tasks if their computing skills are limited to classical methods, such as manual data manipulation in spreadsheets, or simple univariate visualization of time series ( Beck et al., 2016 ). Likewise, several authors argued that the computational needs of practicing engineers have now expanded beyond simple engineering calculations, and mastery of advanced data manipulation skills is essential in the workplace ( Kandel et al., 2012;Mikalef et al., 2018;Piccione, 2019 ). Top-performing engineers are highly productive, motivated and skilled in integrating messy, disparate data sources to uncover engineering insights, and these engineers are functionally working as data scientists with deep engineering domain expertise ( Parker, 2019 ). ...
Article
Full-text available
This paper describes the use of self-service analytics on time series process data for the troubleshooting and optimization of refinery operations in the context of data visualization principles and best practices. Refining-relevant examples are used to demonstrate how end-users can access real-time and historical process data and apply the following analytics operations across several refining functions, including (1) incident troubleshooting – identifying periods of interest and methods available to investigate related plant data, patterns, events and disturbances leading up to the incident, and (2) data cleansing – filtering sensor data to remove outliers and bad quality data, splicing and aligning data streams for more accurate analysis and to improve the confidence in the outputs of subsequent analysis, such as the outputs of multivariate, regression-based system identification. The paper also provides examples of how ad hoc analyses can be scaled up to plantwide analytics and evolve into routine, automated tasks. The importance of analytic provenance and collaboration in sharing new insights from data is also discussed. To address the human factors associated with self-service analytics innovation, the paper concludes with lessons learnt, observations and adaptations compared to the traditional “business-as-usual approaches, best practices for data governance, and the implications for engineers that operate in a safety-critical environment.
... One important point however is that such mobilization of resources does not only concern technical infrastructure and data, as human-related resources are equally as important. It is therefore critical that organizations make plans about how to secure employees with the necessary skills to drive digital transformation or to develop educational programs in order to re-train their current personnel [35]. ...
Chapter
Full-text available
The past years of researching digital transformation and the accumulated experience of practitioners in deploying projects of novel digital technologies have allowed us to gain much valuable insight about the process. From this assembly of knowledge, there is a lot we can learn about how to conduct future research, as well as a depth of knowledge regarding best practices that can aid practitioners. In this chapter, we provide some key input on how research and practice can approach digital transformation and discuss some ideas that are likely to be central in the near future. We draw on some streams of literature which have yet to be fully integrated in the current discourse of digital transformation research and provide some practical guidelines that can aid practitioners at different levels. We conclude with a brief overview of some key technologies which are likely to be in the spotlight of attention in the upcoming years and discuss their implications for research and practice.
... Employees who are able to efficiently handle and create knowledge out of data have reached particular relevance through the increased availability, compilation, and storage of large amounts of data provided by the digital transformation of businesses, leading to a great demand for these employees (Davenport, 2020;Ismail & Abidin, 2016;Mauro et al., 2018). Though, it has been challenging to pinpoint tasks and responsibilities of these so-called data scientists: researchers have explored job profiles (Costa & Santos, 2017), educational curricula (Richards & Marrone, 2014), or gathered key insights from experts (Mikalef et al., 2018;Stanton & Stanton, 2016) to identify a data scientist's required skills and knowledge. ...
Article
Data scientists represent a heterogeneous occupational group that has reached high relevance due to the widespread availability of quantitative data generated in the rapid progress of digital transformation. These employees play a crucial role in gaining competitive advantages for companies out of such big data. In this context, employees who frequently analyse data often occupy different job titles and, therefore, are difficult to detect. At the same time, a psychological downside of digitalization, which is called tech-nostress, has risen. However, these issues caused by the use of information and communication technologies are rarely examined in the context of specific occupational groups and workplace attributes. Considering these challenges, this article extends current tech-nostress research by focusing on technostress within the specific job class of data scientists. We classify different types of data scientists' workplaces through performing latent 1 Derra et al.: Examining Technostress at Different Types of Data Scientists' Workplaces Published by AIS Electronic Library (AISeL), © Scandinavian Journal of Information Systems, 2022 34(1), 71-118 Derra et al.: Examining Technostress at Different Types of Data Scientists' Workplaces 72 class analysis using several workplace attributes within a sample of n=486 German data scientists. Subsequently, we reveal considerable distinctions between these classes regarding the intensity of technostress creators, strains due to ICT use, and job performance. We discuss our empirical findings and deliver theoretical contributions as well as practical implications for both employees and employers and starting points for future research.
... Providing human capital with high analytical skills for working with big data allows for more efficient and effective solutions and undoubtedly increases the overall performance of organizations. Despite the great potential of new technologies, tools and applications for analysis, the biggest problems faced by practitioners in using these technologies are finding employees with the necessary skills [5], [6]. ...
Conference Paper
Full-text available
Over the last few years, there has been an unprecedented growth in the interest of organizations in big data and analysis. The demand for certified data science and big data specialists is becoming more and more intense. For business executives, it is clear that providing human capital with better analytical skills to handle big data will allow for more efficient and effective solutions and will increase overall company productivity. This is the main factor that guides the manager in defining the Skills and Knowledge needed in a good data specialist. Recent researches has shown that there is a clear mismatch between the needs of the industry for data science specialists and the knowledge and skills offered by higher education institutions. It turns out that most of the universities do not prepare the specialists for the needs of the business. Based on a profile created by the data specialist, this article defines the set of skills that today's graduates do not possess, but should have.
Article
Full-text available
Several authors have illustrated the benefits of data in organizations. For realizing benefits, we see two major challenges for organizations. First, there are necessary investments, which have to be justified. Second, adequate data analytics competencies can be seen as enablers for realizing benefits. We aim to support organizations by showing relevant competencies and achievable business values. We present theoretical propositions and a research agenda on how to move the field of data analytics competencies forward.
Article
The interactive learning is a continuous process, which is full of a large number of learning interaction activities. The data generated between learners and learning interaction activities can reflect the online learning behaviors. Through the correlation analysis among learning interaction activities, this paper discusses the potential association rules, defines the data structures, mines the frequent item sets, and designs appropriate algorithms, then recommend learning decision makings based on association rules. The research methods and conclusions can provide feasible educational decision makings for the realization of personalization, probability prediction and decision feedback, which will improve the interactive learning environment, the algorithms, methods and modes designed in this paper are useful supplements for learning analytics.
Chapter
Data-Science-Vorhaben scheitern häufiger als andere Projekte. Viele Unternehmen schrecken daher noch davor zurück, komplexe datengetriebene Fragestellungen zu adressieren. Die Gründe für das Scheitern in der Neuartigkeit und Komplexität dieser Projekte zu suchen, fasst zu kurz. Eine Umfrage unter 85 Wissensarbeiterinnen und Wissensarbeitern aus Unternehmen verschiedener Größen zeigt neben Problemen, für die bereits etablierte Lösungsansätze existieren, auch besondere Herausforderungen der Disziplin Data Science auf. Zu nennen sind vor allem fehlende Data-Science-Kompetenzen bei den relevanten Gruppen im Unternehmen sowie eine falsche Herangehensweise an Data-Science-Vorhaben. Die Erkenntnisse aus dieser Untersuchung können von Unternehmen und Forschenden genutzt werden, um das Misserfolgsrisiko durch ein geeignetes Projektvorgehen zu reduzieren.
Thesis
Full-text available
The rise of new digital technologies is making firms conduct a variety of initiatives to explore their benefits. The improvements can provide businesses with competitive advantages that can lead to many potential benefits. However, in practice, the process of digital transformation is accompanied by many obstacles like regulation in the initiation phase and insufficient digital skills, culture and mindset issues to change, and lack of vision in the execution phase. Therefore, success with digital transformation requires companies to work in a variety of components continually. Successful implementation of digital transformation goes beyond technology and requires creating a transformative vision, plugging the skills gap, engaging the organization, creating a nimble and agile organization structure, and developing a digital strategy.
Conference Paper
Full-text available
To uncover the key mechanisms of how value is created through big data analytics (BDA), our main research objective is to integrate prior empirical findings on the relationship between BDA capabilities and firm performance. We conducted meta-analytic structural equation modeling based on 271 correlations and 33,281 observations collected from 63 individual studies. The findings confirm that creating business value from BDA is a complex and dynamic process affected by various value creation mechanisms. Aside from direct relationships between BDA capabilities and firm performance, we highlight the mediating role of operational performance in the value transmission to market and financial performance. Our study contributes to the rising debate on the business value of BDA by providing an integrated and novel picture of the value-adding pathways emanating from BDA capabilities. This informs future information systems research on theory building and assists practitioners in effectively formulating their objectives of BDA initiatives.
Chapter
Several studies indicate that there are no enough people in the market with data science skills and even those graduates in ICT from universities do not possess skills required by employers. Thus, researchers have suggested the urgency for universities to review their curricular as the world is heading towards data era. The aim of this research was to analyze the current skill-gaps needs from stakeholders and opportunities to establish data science postgraduate programme that reflects the current technological trends and market demands at the University of Dar es Salaam (UDSM). A questionnaire was administered to 85 identified organizations to solicit information on the needs for data scientists and existing skill gaps. A total of 61 filled questionnaires response were received out of the 85 that were administered to selected organizations indicating a turn out rate of over 70%. Overall the analyzed data articulated a compelling evidence for the local industry growing need for data scientist. The survey that was conducted was followed up by conduct of various workshops and meetings to solicit inputs from different experts and stakeholders on different versions of the developed curriculum. Finally, a new programme in MSc in data Science was approved and established from April 2018 at UDSM. Despite its late approval and without formal advertisement on the public media, the programme attracted a large number of applicants for 2018/19 academic year, compared to other several postgraduate programmes in ICT offered at UDSM.
Article
Developing analytics capability has become one of the main priorities in organizations today. Despite the increasing use of analytics, the necessary conditions to obtain the expected benefits from such investment still need to be examined. Relying on information processing theory (OIPT), this study sheds some light on the requirements for properly utilizing analytics to receive the potential benefits in supply chain firms. Specifically, we study the role of supply chain process integration in developing analytics capability, and we further examine the role of analytics capability and employees’ analytics skills in improving firm performance. Survey data collected from 240 supply chain top- and middle-level managers show that supply chain process integration enhances firms’ analytics capability. However, analytics capability alone is not sufficient in improving firm performance; it must be complemented with employees’ analytics skills. These findings extend the current literature on supply chain analytics and provide guidance and insights to supply chain managers for their analytics capability investments.
Article
Full-text available
With big data growing rapidly in importance over the past few years, academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. To date, emphasis has been on the technical aspects of big data, with limited attention paid to the organizational changes they entail and how they should be leveraged strategically. As with any novel technology, it is important to understand the mechanisms and processes through which big data can add business value to companies, and to have a clear picture of the different elements and their interdependencies. To this end, the present paper aims to provide a systematic literature review that can help to explain the mechanisms through which big data analytics (BDA) lead to competitive performance gains. The research framework is grounded on past empirical work on IT business value research, and builds on the resource-based view and dynamic capabilities view of the firm. By identifying the main areas of focus for BDA and explaining the mechanisms through which they should be leveraged, this paper attempts to add to literature on how big data should be examined as a source of competitive advantage. To this end, we identify gaps in the extant literature and propose six future research themes.
Conference Paper
Full-text available
Big data has managed in a very short time to dominate the interest of researchers and managers, vastly changing the way information is generated and used in decision making. Nevertheless, there has been disproportionate focus on the technical aspects of this emerging technology, and limited attention on other relevant organizational elements. Past research in IT business value has demonstrated that investments alone do not generate business value; rather, firms need to develop idiosyncratic and difficult to imitate capabilities. Drawing on the resource based view and dynamic capabilities view of the firm, this study examines the resources that are necessary to develop a big data analytics capability, identifies the organizational capabilities they enable, and determines factors that moderate or condition the value of a big data analytics capability. Employing a multiple case study approach on six international firms, we develop a deeper understanding of the importance of big data analytics resources and the mechanisms through which they leveraged towards the strengthening of organizational capabilities.
Conference Paper
Full-text available
Purpose – This paper promises to shed light on the heterogeneous nature of the skills required to ‘win’ with Big Data by analysing a large amount of job posts published online. More specifically we: 1) identify the most important ‘job families’ related to Big Data; 2) recognize homogeneous groups of skills (skillsets) that are most sought after by companies; 3) characterize each job family with the appropriate level of competence required within each Big Data skillset. Design/methodology/approach – We implement a semi-automated, fully reproducible, analytical methodology that is able to cope with the significant amount of job posts obtained by scraping some of the most popular job search online portals. Job families are identified through the expert evaluation of the most important keywords appearing in job posts’ titles. Skillsets are instead obtained by using Latent Dirichlet Allocation (LDA), an unsupervised machine learning algorithm used for text classification. Finally, we characterize the job families through a measure of the relative importance of each skillset. Originality/value – This study represents one of the first attempts to classify jobs in families and describe them in terms of skill requirements by means of a large-scale, semi-automated job post analysis, based on machine learning algorithms. To do so, we propose an original combination of various analytical techniques, which are widely established in previous scientific works. The characterization of job families through text mining and topic modelling techniques is innovative and can be reapplied to similar future studies focusing on any other professional field. Practical implications – This paper brings clarity to the multifaceted nature of Big Data competency requirements and job role types. Our results can concretely help business leaders and HR managers create clearer strategies for the procurement of the right skills needed to leverage Big Data at best. In addition, the structured classification of job families and skillsets will help establish a common language to be used within the job market, through which supply and demand can more effectively meet.
Chapter
Full-text available
Although our capabilities to store and process data have been increasing exponentially since the 1960s, suddenly many organizations realize that survival is not possible without exploiting available data intelligently. Out of the blue, “Big Data” has become a topic in board-level discussions. The abundance of data will change many jobs across all industries. Moreover, also scientific research is becoming more data-driven. Therefore, we reflect on the emerging data science discipline. Just like computer science emerged as a new discipline from mathematics when computers became abundantly available, we now see the birth of data science as a new discipline driven by the torrents of data available today. We believe that the data scientist will be the engineer of the future. Therefore, Eindhoven University of Technology (TU/e) established the Data Science Center Eindhoven (DSC/e). This article discusses the data science discipline and motivates its importance.
Article
Full-text available
Current trends suggest that academia may be behind the curve in delivering effective Business Intelligence programs and course offerings to students. In December 2009 and 2010, the AIS Special Interest Group on Decision Support, Knowledge and Data Management Systems (SIGDSS) and the Teradata University Network (TUN) cosponsored the Business Intelligence Congresses and conducted surveys to improve the understanding of the state of BI in academia. This panel report describes the key findings and best practices that were identified. The article also serves as a "call to action" for universities regarding the need to close a widening gap between the BI skills of university graduates in Information Systems and other fields and BI market needs. The IS field is well positioned to be the leader in creating the next generation BI workforce. To do so, it is important for IS to begin moving on this opportunity now. We believe the necessary first step is for BI and IS leaders to advance the BI curriculum.
Article
Full-text available
Business intelligence (BI), “big data”, and analytics solutions are being deployed in an increasing number of organizations, yet recent predictions point to severe shortages in the number of graduates prepared to work in the area. New model curriculum is needed that can properly introduce BI and analytics topics into existing curriculum. That curriculum needs to incorporate current big data developments even as new dedicated analytics programs are becoming more prominent throughout the world. This paper contributes to the BI field by providing the first BI model curriculum guidelines. It focuses on adding appropriate elective courses to existing curriculum in order to foster the development of BI skills, knowledge, and experience for undergraduate majors, master of science in business information systems degree students, and MBAs. New curricula must achieve a delicate balance between a topic’s level of coverage that is appropriate to students’ level of expertise and background, and it must reflect industry workforce needs. Our approach to model curriculum development for business intelligence courses follows the structure of Krathwohl’s (2002) revised taxonomy, and we incorporated multi-level feedback from faculty and industry experts. Overall, this was a long-term effort that resulted in model curriculum guidelines.
Conference Paper
Full-text available
This paper presents current results and ongoing work to develop effective educational courses on the Big Data (BD) and Data Intensive Technologies (DIT) that is been done at the University of Amsterdam in cooperation with KPMG and by the Laureate Online Education (online partner of the University of Liverpool). The paper introduces the main Big Data concepts: multicomponent Big Data definition and Big Data Architecture Framework that provide the basis for defining the course structure and Common Body of Knowledge for Data Science and Big Data technology domains. The paper presents details on approach, learning model, and course content for two courses at the Laureate Online Education/University of Liverpool and at the University of Amsterdam. The paper provides also background information about existing initiatives and activities related to information exchange and coordination on developing educational materials and programs on Big Data, Data Science, and Research Data Management.
Article
Full-text available
While data science, predictive analytics, and big data have been frequently used buzzwords, rigorous academic investigations into these areas are just emerging. In this forward thinking article, we discuss the results of a recent large-scale survey on these topics among supply chain management (SCM) professionals, complemented with our experiences in developing, implementing, and administering one of the first master's degree programs in predictive analytics. As such, we effectively provide an assessment of the current state of the field via a large-scale survey, and offer insight into its future potential via the discussion of how a research university is training next-generation data scientists. Specifically, we report on the current use of predictive analytics in SCM and the underlying motivations, as well as perceived benefits and barriers. In addition, we highlight skills desired for successful data scientists, and provide illustrations of how predictive analytics can be implemented in the curriculum. Relying on one of the largest data sets of predictive analytics users in SCM collected to date and our experiences with one of the first master's degree programs in predictive analytics, it is our intent to provide a timely assessment of the field, illustrate its future potential, and motivate additional research and pedagogical advancements in this domain.
Article
Full-text available
In recent years, the rapid development of Internet, Internet of Things, and Cloud Computing have led to the explosive growth of data in almost every industry and business area. Big data has rapidly developed into a hot topic that attracts extensive attention from academia, industry, and governments around the world. In this position paper, we first briefly introduce the concept of big data, including its definition, features, and value. We then identify from different perspectives the significance and opportunities that big data brings to us. Next, we present representative big data initiatives all over the world. We describe the grand challenges (namely, data complexity, computational complexity, and system complexity), as well as possible solutions to address these challenges. Finally, we conclude the paper by presenting several suggestions on carrying out big data projects.
Article
Full-text available
While many studies on big data analytics describe the data deluge and potential applications for such analytics, the required skill set for dealing with big data has not yet been studied empirically. The difference between big data (BD) and traditional business intelligence (BI) is also heavily discussed among practitioners and scholars. We conduct a latent semantic analysis (LSA) on job advertisements harvested from the online employment platform monster.com to extract information about the knowledge and skill requirements for BD and BI professionals. By analyzing and interpreting the statistical results of the LSA, we develop a competency taxonomy for big data and business intelligence. Our major findings are that (1) business knowledge is as important as technical skills for working successfully on BI and BD initiatives; (2) BI competency is characterized by skills related to commercial products of large software vendors, whereas BD jobs ask for strong software development and statistical skills; (3) the demand for BI competencies is still far bigger than the demand for BD competencies; and (4) BD initiatives are currently much more human-capital-intensive than BI projects are. Our findings can guide individual professionals, organizations, and academic institutions in assessing and advancing their BD and BI competencies.
Article
Full-text available
In recent years, chief information officers have begun to report exponential increases in the amounts of raw data captured and retained across the organization. Managing extreme amounts of data can be complex and challenging at a time when information is increasingly viewed as a strategic resource. Since the dominant focus of the information technology (IT) governance literature has been on how firms govern physical IT artifacts (hardware, software, networks), the goal of this study is to extend the theory of IT governance by uncovering the structures and practices used to govern information artifacts. Through detailed interviews with 37 executives in 30 organizations across 17 industries, we discover a range of structural, procedural, and relational practices used to govern information within a nomological net that includes the antecedents of these practices and their effects on firm performance. While some antecedents enable the speedy adoption of information governance, others can delay or limit the adoption of information governance practices. Once adopted, however, information governance can help to boost firm performance. By incorporating these results into an extended theory of IT governance, we note how information governance practices can unlock value from the ever-expanding mountains of data currently held within organizations.
Article
Full-text available
Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data science programs, and publications are touting data science as a hot -- even "sexy" -- career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this paper we argue that there are good reasons why it has been hard to pin down exactly what data science is. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of Data Science precisely right now is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii) we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this paper we present a perspective that addresses all these things. We close by offering as examples a partial list of fundamental principles underlying data science.
Article
Full-text available
A major issue facing managers of information systems organizations is the increasing pressure to demonstrate the business value of the firm's investment in information technology. The working relationship between the IS department and other diverse organizational groups can have a major contribution to increasing IS performance. This paper explores the concept of shared knowledge between IS groups and their line customers as a contributor to IS performance. Shared knowledge is achieved through the mechanisms of mutual trust and influence between these groups. The relationship of mutual trust, influence, and shared knowledge with IS performance is tested empirically using path analysis in a study of 86 IS departments. The results of this study show that shared knowledge mediates the relationship between IS performance and trust and influence and that increasing levels of shared knowledge between IS and line groups leads to increased IS performance. Recommendations are given for ways managers can develop mutual trust and influence between these diverse groups and therefore achieve higher levels of shared knowledge and IS performance.
Article
Full-text available
The resource-based view of the firm attributes superior financial performance to organizational resources and capabilities. This paper develops the concept of IT as an organizational capability and empirically examines the association between IT capability and firm performance. Firm specific IT resources are classified as IT infrastructure, human IT resources, and IT-enabled intangibles. A matched-sample comparison group methodology and publicly available ratings are used to assess IT capability and firm performance. Results indicate that firms with high IT capability tend to outperform a control sample of firms on a variety of profit and cost-based performance measures.
Article
Full-text available
Over 10 years ago, the issue of whether IS researchers were rigorously validating their quantitative, positivist instruments was raised (Straub 1989). In the years that have passed since that time, the profession has undergone many changes. Novel technologies and manage- ment trends have come and gone. New profes- sional societies have been formed and grown in prominence and new demands have been placed on the field's research and teaching obligations. But the issue of rigor in IS research has persisted throughout all such changes. Without solid valida- tion of the instruments that are used to gather data upon which findings and interpretations are based, the very scientific basis of positivist, quan- titative research is threatened.
Article
Full-text available
ABSTRACT The establishment,of strong alignment,between,Information Technology,(IT) and organizational objectives has consistently been,reported as one of the key,concerns,of information systems managers. This paper presents findings from a study which investigated the influence of several factors on the social dimension,of alignment within ten business units in the Canadian life insurance industry. The social dimension of alignment refers to the state in which business and Information Technology,(IT) executives understand and are committed,to the business and IT mission, objectives, and plans.
Article
Full-text available
We provide a review of the alignment literature in IT, addressing questions such as: What have we learned? What is disputed? Who are contributors to the debate? The article is intended to be useful to faculty and graduate students considering conducting research on alignment, instructors preparing lectures, and practitioners seeking to assess the 'state-of-play'. It is both informational and provocative. Challenges to the value of alignment research, divergent views, and new perspectives on alignment are presented. It is hoped that the article will spark helpful conversation on the merits of continued investigation of IT alignment.
Chapter
Infusing data literacy into a curriculum is an unrealized opportunity for higher education to truly make an impact on the current generation as they prepare to move into the workforce. This chapter describes the design and structure of a new, unique undergraduate elective course introduced into the curriculum of a large, public University in the Northeastern United States. The design of the course is designed to inspire an “evidence-based” mindset, encouraging students to identify and use data relevant to them in their field of study and the larger world around them. The chapter includes the course goals mapped to specific learning objectives, examples of exercises and assignments, a reading list, and a course syllabus. Instructors and institutions interested in bringing data science concepts to a broad audience can use this course as a foundation to build their own curriculum in this area.
Article
The activities in our current world are mainly supported by data-driven web applications, making extensive use of databases and data services. Such phenomenon led to the rise of Data Scientists as professionals of major relevance, which extract value from data and create state-of-the-art data artifacts that generate even more increased value. During the last years, the term Data Scientist attracted significant attention. Consequently, it is relevant to understand its origin, knowledge base and skills set, in order to adequately describe its profile and distinguish it from others like Business Analyst. This work proposes a conceptual model for the professional profile of a Data Scientist and evaluates the representativeness of this profile in two commonly recognized competences/skills frameworks in the field of Information and Communications Technology (ICT), namely in the European e-Competence (e-CF) framework and the Skills Framework for the Information Age (SFIA). The results indicate that a significant part of the knowledge base and skills set of Data Scientists are related with ICT competences/skills, including programming, machine learning and databases. The Data Scientist professional profile has an adequate representativeness in these two frameworks, but it is mainly seen as a multi-disciplinary profile, combining contributes from different areas, such as computer science, statistics and mathematics.
Article
The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model.
Article
A central question for researchers and practitioners is whether and how IT (information technology) can help build a competitive advantage in uncertain environments. To address this question, the present study seeks to empirically explore the relationship between IT-enabled dynamic capabilities and competitive performance. By drawing upon recent thinking in the strategy and IT management literatures, this paper argues that the impact of IT-enabled dynamic capabilities on competitive performance is mediated by organizational agility. Using survey data from 274 international firms and by applying structural equation modelling (SEM), outcomes suggest that IT-enabled dynamic capabilities facilitate two types of agility, market capitalizing and operational adjustment agility, which in sequence enhance competitive performance. The confluence of environmental factors is examined by fuzzy-set qualitative comparative analysis (fsQCA). The results of fsQCA reinforce and refine findings of the PLS analysis concerning the limits and conditions to which IT-enabled dynamic capabilities add value.
Article
The era of big data has begun such that organizations in all industries have been heavily investing in big data initiatives. We know from prior studies that investments alone do not generate competitive advantage; instead, firms need to create capabilities that rival firms find hard to match. Drawing on the resource-based theory of the firm and recent work in big data, this study (1) identifies various resources that in combination build a big data analytics (BDA) capability, (2) creates an instrument to measure BDA capability of the firm, and (3) tests the relationship between BDA capability and firm performance. Results empirically validate the proposed theoretical framework of this study and provide evidence that BDA capability leads to superior firm performance.
Article
Data science is a new academic trans-discipline that builds on 60 years of research about supporting decision-making in organisations. It is an important and potentially significant concept and practice. Contemplating the need for data scientists encourages academics and managers to examine issues of decision-maker rationality, data and data analysis needs, analytical tools, job skills and academic preparation. This article explores data science and the data professionals who will use new data streams and analytics to support decision-making. It also examines the dimensions that are changing in the data stream and the skills needed by data scientists to analyse the new data streams. Organisations need data scientists, but academics need to understand the new data science jobs to prepare more people to support decision-making.
Article
Purpose - Strategic alignment is a theory-based state that is considered as crucial for organizations in order to realize performance gains from Information Technology (IT) investments and deployments. Within the domain of purchasing and supply chain management there has been a growing interest on how purchasing strategy can be effectively aligned with IT and what conditions fac ilitate this state. This study investigates complex causal relationships of contingency elements that are key in enabling the ‘fit’ between purchasing strategy and IT. Design/methodology/approach - We employ a configuration theory approach and propose that purchasing alignment is dependent upon patterns of multiple contingencies. In adherence with contingency theory, we group these elements as relating to strategic orientation, organizational factors, and purchasing decisions. On a sample of 172 international companies we then apply the novel methodology of fuzzy set qualitative comparative analysis (fsQCA). Findings - We empirically demonstrate that depending on the strategic orientation that a company follows, there are alternative combinations of elements that lead to high purchasing alignment. For companies following an operational excellence strategic orientation, a high contract binding scheme or a small firm size facilitates purchasing alignment. Enabling elements for product leadership companies include a decentralized purchasing structure, a broad supplier base, and a large firm size. Purchasing alignment for customer intimacy companies is supported by a centralized purchasing structure, loose contract binding, and a large supplier base. Practical Implications - The findings of this study suggest that practitioners aiming to attain a state of purchasing alignment should consider a number of contingency elements in the process. We show that there is equifinality in the configurations that lead to purchasing alignment. This means that attaining purchasing alignment is de pendent upon various clusters of contingency elements which must be taken into account when formulating a purchasing strategy. Originality/Value - In contrast with past studies examining purchasing alignment as a result of the isolated impact of several antecedents, we applied a configuration theory approach to demonstrate that it is facilitated by the combined impact of a set of cause-effect relationships. In cases were non-linear and synergistic relationships exist between independent variables, this type of research may be a viable alternative.
Article
Data-centric approaches such as big data and related approaches from business intelligence and analytics (BI&A) have recently attracted major attention due to their promises of huge improvements in organizational performance based on new business insights and improved decision making. Incorporating data-centric approaches into organizational decision processes is challenging, even more so with big data, and it is not self-evident that the expected benefits will be realized. Previous studies have identified the lack of a research focus on the context of decision processes in data-centric approaches. By using a multiple case study approach, the paper investigates different types of BI&A-supported decision processes, and makes three major contributions. First, it shows how different facets of big data and information processing mechanism compositions are utilized in different types of BI&A-supported decision processes. Second, the paper contributes to information processing theory by providing new insights about organizational information processing mechanisms and their complementary relationship to data-centric mechanisms. Third, it demonstrates how information processing theory can be applied to assess the dynamics of mechanism composition across different types of decisions. Finally, the study's implications for theory and practice are discussed.
Article
We illuminate the myriad of opportunities for research where supply chain management (SCM) intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of an SCM data scientist. Such knowledge is crucial to develop future supply chain leaders. We propose definitions of data science and predictive analytics as applied to SCM. We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of SCM and DPB.
Article
This paper investigates linkages between information technology (IT) and firm performance. Although showing recent signs of advance, the existing IT literature still relies heavily on case studies, anecdotes, and consultants’ frameworks, with little solid empirical work or synthesis of findings. This paper examines the IT literature, develops an integrative, resource-based theoretical framework, and presents results from a new empirical study in the retail industry. The findings show that ITs alone have not produced sustainable performance advantages in the retail industry, but that some firms have gained advantages by using ITs to leverage intangible, complementary human and business resources such as flexible culture, strategic planning–IT integration, and supplier relationships. The results support the resource-based approach, and help to explain why some firms outperform others using the same ITs, and why successful IT users often fail to sustain IT-based competitive advantages. © 1997 by John Wiley & Sons, Ltd.
Article
Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships. In this essay, I will describe a few of these tools for manipulating and analyzing big data. I believe that these methods have a lot to offer and should be more widely known and used by economists.
Article
Today’s supply chain professionals are inundated with data, motivating new ways of thinking about how data are produced, organized, and analyzed. This has provided an impetus for organizations to adopt and perfect data analytic functions (e.g. data science, predictive analytics, and big data) in order to enhance supply chain processes and, ultimately, performance. However, management decisions informed by the use of these data analytic methods are only as good as the data on which they are based. In this paper, we introduce the data quality problem in the context of supply chain management (SCM) and propose methods for monitoring and controlling data quality. In addition to advocating for the importance of addressing data quality in supply chain research and practice, we also highlight interdisciplinary research topics based on complementary theory.
Article
Back in the 1990s, computer engineer and Wall Street "quant" were the hot occupations in business. Today data scientists are the hires firms are competing to make. As companies wrestle with unprecedented volumes and types of information, demand for these experts has raced well ahead of supply. Indeed, Greylock Partners, the VC firm that backed Facebook and LinkedIn, is so worried about the shortage of data scientists that it has a recruiting team dedicated to channeling them to the businesses in its portfolio. Data scientists are the key to realizing the opportunities presented by big data. They bring structure to it, find compelling patterns in it, and advise executives on the implications for products, processes, and decisions. They find the story buried in the data and communicate it. And they don't just deliver reports: They get at the questions at the heart of problems and devise creative approaches to them. One data scientist who was studying a fraud problem, for example, realized it was analogous to a type of DNA sequencing problem. Bringing those disparate worlds together, he crafted a solution that dramatically reduced fraud losses. In this article, Harvard Business School's Davenport and Greylock's Patil take a deep dive on what organizations need to know about data scientists: where to look for them, how to attract and develop them, and how to spot a great one.
Article
In this study we revisit some fundamental questions that are increasingly at the heart of current strategic management discourse regarding the relative impact of industry and firm-specific factors on sustainable competitive advantage. We explore this issue by referring to respective assertions of two major perspectives that dominate the literature over the last two decades: the Porter framework of competitive strategy and the more recent resource-based view of the firm. A composite model is proposed which elaborates upon both perspectives' divergent causal logic with respect to the conditions relevant for firm success. Empirical findings suggest that industry and firm specific effects are both important but explain different dimensions of performance. Where industry forces influence market performance and profitability, firm assets act upon accomplishments in the market arena (i.e., market performance), and via the latter, to profitability. The paper concludes with directions for future research that will seek to integrate both content and process aspects of firm behavior. Copyright
Article
The qualitative interview is one of the most important data gathering tools in qualitative research, yet it has remained an unexamined craft in IS research. This paper discusses the potential difficulties, pitfalls and problems of the qualitative interview in IS research. Building on Goffman’s seminal work on social life, the paper proposes a dramaturgical model as a useful way of conceptualizing the qualitative interview. Based on this model the authors suggest guidelines for the conduct of qualitative interviews.
Article
This study develops a research model of how the technical, behavioral, and business capabilities of IT personnel are associated with IT infrastructure capabilities, and how the latter are associated with IT-dependent organizational agility, which is conceptualized as comprising IT-dependent system, information, and strategic agility. Analysis of cross-sectional data collected from 293 IT managers generally corroborates the hypothesized relationships, showing that the technical and behavioral capabilities of IT personnel have a positive effect on infrastructure capabilities. The analysis also provides evidence that the effect of infrastructure capabilities on IT-dependent strategic agility is direct, as well as mediated by IT-dependent system and information agility. The validity of the findings is strengthened by demonstrating that the hypothesized research model fits the data better than two alternative theoretically-anchored models describing different relationships between the same constructs. This study advances understanding of the interrelationships between two major subsets of IT capabilities, and their relationships with the agility afforded by IT.
Article
This research aims at improving our understanding of the concept of the business competence of information technology professionals and at exploring the contribution of this competence to the development of partnerships between IT professionals and their business clients. Business competence focuses on the areas of knowledge that are not specifically IT-related. At a broad level, it comprises the organization-specific knowledge and the interpersonal and management knowledge possessed by IT professionals. Each of these categories is in turn inclusive of more specific areas of knowledge. Organizational overview, organizational unit, organizational responsibility, and IT-business integration form the organization-specific knowledge, while interpersonal communication, leadership, and knowledge networking form the interpersonal and management knowledge. Such competence is hypothesized to be instrumental in increasing the intentions of IT professionals to develop and strengthen the relationship with their clients. The first step in the study was to develop a scale to measure business competence of IT professionals. The scale was validated and then used to test the model that relates competence to intentions to form IT-business partnerships. The results support the suggested structure for business competence and indicate that business competence significantly influences the intentions of IT professionals to develop partnerships with their business clients.
Analytics as a Source of Business Innovation MIT Sloan Management ReviewData scientist: The Sexiest Job of the 21st Century
  • S Ransbotham
  • D Kiron Davenport
  • D Patil
S. Ransbotham and D. Kiron, "Analytics as a Source of Business Innovation," MIT Sloan Management Review, February 2017. [6] T. H. Davenport and D. Patil, "Data scientist: The Sexiest Job of the 21st Century," Harvard business review, vol. 90, no. 5, pp. 70-76, 2012. [7]
Big Data and Strategy: A research FrameworkData science and its relationship to big data and data-driven decision making
  • P Mikalef
  • I O Pappas
  • M N Giannakos
  • J Krogstie
  • G Lekakos Provost
  • T Fawcett
P. Mikalef, I. O. Pappas, M. N. Giannakos, J. Krogstie, and G. Lekakos, "Big Data and Strategy: A research Framework," in MCIS, 2016, p. 50. [25] F. Provost and T. Fawcett, "Data science and its relationship to big data and data-driven decision making," Big Data, vol. 1, no. 1, pp. 51-59, 2013. [26]
Analytics as a Source of Business Innovation
  • S Ransbotham
  • D Kiron
S. Ransbotham and D. Kiron, "Analytics as a Source of Business Innovation," MIT Sloan Management Review, February 2017.
ICT for work: Digital skills in the workplace
  • M Curtarelli
  • V Gualtieri
  • M S Jannati
  • V Donlevy
M. Curtarelli, V. Gualtieri, M. S. Jannati, and V. Donlevy, "ICT for work: Digital skills in the workplace," European UnionMay 2017 2017, Available: http://ec.europa.eu/newsroom/dae/document.cfm?doc _id=44434.
Encyclopedia of Educational Philosophy and Theory
  • M Savin-Baden
M. Savin-Baden, "Education and Big Data," Encyclopedia of Educational Philosophy and Theory, pp. 1-7, 2017.
Big Data and Strategy: A research Framework
  • P Mikalef
  • I O Pappas
  • M N Giannakos
  • J Krogstie
  • G Lekakos
P. Mikalef, I. O. Pappas, M. N. Giannakos, J. Krogstie, and G. Lekakos, "Big Data and Strategy: A research Framework," in MCIS, 2016, p. 50.
  • C M Ringle
  • S Wende
  • J.-M Becker
C. M. Ringle, S. Wende, and J.-M. Becker, "SmartPLS 3," Boenningstedt: SmartPLS GmbH, http://www. smartpls. com, 2015.