Big Data and Analytics: Pathways to Maturity

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    Helping Data Science Students Develop Task Modularity
    ( 2019-01-08) saltz, jeff ; Heckman, Robert ; Crowston, Kevin ; You, Sangseok ; Hegde, Yatish
    This paper explores the skills needed to be a data scientist. Specifically, we report on a mixed method study of a project-based data science class, where we evaluated student effectiveness with respect to dividing a project into appropriately sized modular tasks, which we termed task modularity. Our results suggest that while data science students can appreciate the value of task modularity, they struggle to achieve effective task modularity. As a first step, based our study, we identified six task decomposition best practices. However, these best practices do not fully address this gap of how to enable data science students to effectively use task modularity. We note that while computer science/information system programs typically teach modularity (e.g., the decomposition process and abstraction), and there remains a need identify a corresponding model to that used for computer science / information system students, to teach modularity to data science students.
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    How to Cope with Change? - Preserving Validity of Predictive Services over Time
    ( 2019-01-08) Baier, Lucas ; Kühl, Niklas ; Satzger, Gerhard
    Companies more and more rely on predictive services which are constantly monitoring and analyzing the available data streams for better service offerings. However, sudden or incremental changes in those streams are a challenge for the validity and proper functionality of the predictive service over time. We develop a framework which allows to characterize and differentiate predictive services with regard to their ongoing validity. Furthermore, this work proposes a research agenda of worthwhile research topics to improve the long-term validity of predictive services. In our work, we especially focus on different scenarios of true label availability for predictive services as well as the integration of expert knowledge. With these insights at hand, we lay an important foundation for future research in the field of valid predictive services.
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    Finding the Next Unicorn: When Big Data Meets Venture Capital
    ( 2019-01-08) Weibl, Johannes ; Hess, Thomas
    Venture capital (VC) has been growing rapidly in recent years. So far the screening and evaluation of potential startups as investment objects largely depends on the venture capitalist’s personal experience, network and qualitative evaluations. In the era of big data, the advent of new data sources and analytic techniques enables a data-driven investment process. Grounded in systems theory and the theory of complementarity, this study reports the findings from an exploratory study of 13 VC firms that synthesize and use novel data sources. Our analysis shows that the data-driven approach, in particular, impacts the deal origination and screening stages of investment. It leads to informational and transactional benefits, which lower operational costs in the short term and enlarge the potential return on investment of a VC firm in the long term. We contribute to the literature by shedding light on how various data sources complementarily lead to additional business value.
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    Big Data Redux: New Issues and Challenges Moving Forward
    ( 2019-01-08) Espinosa, J. Alberto ; Kaisler, Stephen ; Armour, Frank ; Money, William
    As of the time of this writing, our HICSS-46 proceedings article has enjoyed over 520 Google Scholar citations. We have published several HICSS proceedings, articles and a book on this subject, but none of them have generated this level of interest. In an effort to update our findings six years later, and to understand what is driving this interest, we have downloaded the first 500 citations to our article and the corresponding citing article, when available. We conducted an in-depth literature review of the articles published in top journals and leading conference proceedings, along with articles with a high volume of citations. This paper provides a brief summary of the key concepts in our original paper and reports on the key aspects of interest we found in our review, and also updates our original paper with new directions for future practice and research in big data and analytics.
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    Don’t Get the Cart before the Horse: There Are No Shortcuts to Prescriptive Analytics
    ( 2019-01-08) St Louis Junior, Robert ; Shao, Benjamin ; Glassman, Jeremy
    Davenport [5] argues that the most important component for putting big data into action within an organization is talent management, and this opinion is widely shared among academics. We interviewed the chief purchasing officers (CPOs) of 15 major corporations and found that they did not feel it was problematic to find the right people for data analytics teams, and did not feel it was difficult to get resources to support data analytics efforts. Instead, they were frustrated by data issues such as granularity, accuracy, and integration. They also were intimidated by what they perceived to be the requirements for prescriptive analytics, and generally had not progressed beyond descriptive analytics. This article summarizes the roadblocks that the CPOs encountered as they attempted to move from descriptive to predictive to prescriptive analytics, and presents a set of steps which must be followed if organizations are to move up the analytics hierarchy.