Jasmien Lismont

Defining analytics maturity indicators

HighlightsDescriptive survey on the application of analytics for each DELTA factor.Prevalence of well-known analytics applications and understandable techniques.4 analytics maturity stages were discovered by means of clustering.Propagation of analytics changes as companies grow more analytically mature.Analytics has still many unexplored opportunities. The ability to derive new insights from data using advanced machine learning or analytics techniques can enhance the decision-making process in companies. Nevertheless, researchers have found that the actual application of analytics in companies is still in its initial stages. Therefore, this paper studies by means of a descriptive survey the application of analytics with regards to five different aspects as defined by the DELTA model: data, enterprise or organization, leadership, targets or techniques and applications, and the analysts who apply the techniques themselves. We found that the analytics organization in companies matures with regards to these aspects. As such, if companies started earlier with analytics, they apply nowadays more complex techniques such as neural networks, and more advanced applications such as HR analytics and predictive analytics. Moreover, analytics is differently propagated throughout companies as they mature with a larger focus on department-wide or organization-wide analytics and a more advanced data governance policy. Next, we research by means of clustering how these characteristics can indicate the analytics maturity stage of companies. As such, we discover four clusters with a clear growth path: no analytics, analytics bootstrappers, sustainable analytics adopters and disruptive analytics innovators.

A guide for the application of analytics on healthcare processes

OBJECTIVE The aim of this study is to guide healthcare instances in applying process analytics on healthcare processes. Process analytics techniques can offer new insights in patient pathways, workflow processes, adherence to medical guidelines and compliance with clinical pathways, but also bring along specific challenges which will be examined and addressed in this paper. METHODS The following methodology is proposed: log preparation, log inspection, abstraction and selection, clustering, process mining, and validation. It was applied on a case study in the type 2 diabetes mellitus domain. RESULTS Several data pre-processing steps are applied and clarify the usefulness of process analytics in a healthcare setting. Healthcare utilization, such as diabetes education, is analyzed and compared with diabetes guidelines. Furthermore, we take a look at the organizational perspective and the central role of the GP. This research addresses four challenges: healthcare processes are often patient and hospital specific which leads to unique traces and unstructured processes; data is not recorded in the right format, with the right level of abstraction and time granularity; an overflow of medical activities may cloud the analysis; and analysts need to deal with data not recorded for this purpose. These challenges complicate the application of process analytics. It is explained how our methodology takes them into account. CONCLUSION Process analytics offers new insights into the medical services patients follow, how medical resources relate to each other and whether patients and healthcare processes comply with guidelines and regulations.

On the gap between reality and registration: a business event analysis classification framework

This paper presents a business event analysis classification framework, based on five business criteria. As a result, we are able to distinguish thirteen event types distributed over four categories, i.e. truthful, invisible, false and unobserved events. Currently, several of these event types are not commonly dealt with in business process management (BPM) and analytics (BPA) research. Based on the proposed framework we situate the different BPM and BPA research areas and indicate the potential issues for each field. A business case is elaborated to demonstrate the relevance of the event classification framework.

Predicting tax avoidance by means of social network analytics

Abstract This study predicts tax avoidance by means of social network analytics. We extend previous literature by being the first to build a predictive model including a larger variation of network features. We construct a network of firms connected through shared board membership. Then, we apply three analytical techniques, logistic regression, decision trees, and random forests; to create five models using either firm characteristics, network characteristics or different combinations of both. A random forest including firm characteristics, network characteristics of firms and network characteristics of board members provides the best performance with a minimal increase of 7 pp in AUC. Hence, including network effects significantly improves the predictive ability of tax avoidance models, implying that board members exhibit specific knowledge which can carry over across firms. We find that having board members with no connections to low-tax companies lowers the likelihood of being a low-tax firm. Similarly, the higher the average tax rate of the companies a board member is connected to, the lower the chance of being low-tax. On the other hand, being connected to more low-tax firms increases the probability of being low-tax. Consistent with prior literature on firm-specific variables, PP&E has a positive influence on the probability of being low-tax, while EBITDA has a negative effect. Our results are informative for companies as to the director expertise they want to attract in their boards. Additionally, financial analysts and regulatory agencies can use our insights to predict which firms are likely to be low-tax and potentially at risk.

Predicting interpurchase time in a retail environment using customer-product networks: An empirical study and evaluation

Abstract In predictive analytics and statistics, entities are frequently treated as individual actors. However, in reality this assumption is not valid. In the context of retail, similar customers will behave and thus also purchase similarly to each other. By combining their behavior in an intelligent way, based on transaction history, we can leverage these connections and improve our ability to predict purchase outcomes. As such, we can create customer-product networks from which we can deduce information on customers expressing similar purchasing behavior. This allows us to exploit their preferences and predict which products are going to be sold significantly less often. We want to use this information mainly for gaining novel marketing insights on products. For example, if customers refrain from buying products this might be due to contextual reasons such as new complements or supplements, or new nearby shops. By using these networks on data from an offline European retail corporation, we are able to boost performance of the predictive models by 6% and the identification of these specific products by 20%. This indicates that the development of customer-product graphs in retail can lead to improved marketing intelligence. To our knowledge, this is one of the first studies to use customer-product networks for predictive modeling in an offline retail setting. Furthermore, we suggest an extensive set of product and network features which can guide future researchers and practitioners in their model development.