user2016

During the last decades, the logging of events in a business context has increased massively. Information concerning activities within a broad range of business processes is recorded in so-called event logs. Connecting the domains of business process management and data mining, process mining aims at extracting process-related knowledge from these event logs, in order to gain competitive advantages. Over the last years, many tools for process mining analyses have been developed, having both commercial and academic origins. Nevertheless, most of them leave little room for extensions or interactive use. Moreover, they are not able to use existing data manipulation and visualization tools. In order to meet these shortcomings, the R-package edeaR was developed to enable the creation and analysis of event logs in R. It provides functionality to read and write logs from .XES-files, the eXtensible Event Stream format, which is the generally-acknowledged format for the interchange of event log data. By using the extensive spectrum of data manipulation methods in R, edeaR provides a very convenient way to build .XES-files from raw data, which is a cumbersome task in most existing process mining tools. Furthermore, the package contains a wide set of functions to describe and select event data, thereby facilitating exploratory and descriptive analysis. Being able to handle event data in R both empowers process miners to exploit the vast area of data analysis methods in R, and invites R-users to contribute to this rapidly emerging and promising field of process mining.

In old economy companies, the introduction of R is typically a button-up process that follows a pattern of three major stages of maturity: At the first stage, guerrilla projects use R parallel to the "official" IT environment. The usage of R is often initiated by interns, student assistants or newly recruited graduates. At the second stage, when the results of the guerrilla projects attract the attention of business departments, R is used as analytic language in proof-of-concept projects. When the proof-of-concept has been successful, the outcome shall be transferred to the production system. At this stage R is being introduced “officially” to the IT environment. While the first and second level of maturity usually do not cause any major problems, the step to the third level is most crucial for the long term success of the implementation of R. This talk will focus on how to master the switch from proof-of-concept to production. It will show based on real world experiences typical road blocks as well as the most important success factors.

During the last decade, data has changed the behaviors of individuals and corporations alike. On the latter, Advanced Analytics has gained considerable momentum – not only as a source of competitive advantage in the short-term, but also the risk of becoming obsolete in the medium-term. In this context, data scientists cannot offer solutions on data-reach problems without leveraging the opportunities of statistical learning with a tool like R, which allows to rapidly transform prototypes into useful solutions, thanks to the functional nature of R. To be specific we will focus on a particular and pressing issue across industries, geographies and organizations: collections & bad debt. We will show how machine learning algorithms leveraging R helped shape better solutions on a “millennial” problem (i.e. how am I getting paid back?) During this talk, we will show how, with the help of R as our main power horse, we approach a collection problem from its inception to the actual business implementation. First, we will describe how we can preprocess the data that may be useful for the purpose of predicting which customers are going to fail to pay their bills. Then, we will explore the relationship between the past payment behavior of a customer and his ability to satisfy future obligations. Finally, we will conclude sharing briefly how the output of a prediction model can be translated into effective business strategies using a project we have been involved on recently as an example.

As consultants, many of the projects that we work on are similar, with many steps repeated verbatim across projects. Previously, our workflow was based largely in Microsoft Office, with our analysis done manually in Excel, our reports written in Word, and our presentations in Powerpoint. In 2015, we began using R for much of our analysis, including making slide decks and reports in RMarkdown. Our presentation discusses why we made the change, how we managed it, and advice for other consulting firms looking to do the same.

Learning R is dangerous. It entices us in by presenting an incredibly powerful tool to solve our particular problem; for free! And as we learn how to do that, we uncover more things that make our solution even better. But then we start to look around our organisation or institution and see how it could make everyone's lives better too. And that's the dangerous part; R's got us hooked and we can't give up the belief that everyone else should be using this, right now. Even though R is free, open source software, there are often barriers to introducing it organisation-wide. This could be because of such things as IT or quality policies, the need for management buy-in or because of perceptions in learning the language. This presentation will first discuss the aspects required to understand these barriers to entry, and the different types of resolution for these. It will then use three projects to show how, by understanding the requirements of the organisation, and developing situation-specific roll-out strategies, these barriers to entry can be overcome. The first example is a large organisation who wanted to quickly (within 6 weeks) show management how Shiny could improve information dissemination. As server policies made a proof of concept difficult to run internally, this project used a cloud hosted environment for R, Shiny and a source database. The second example is around two SME's who required access to a validated version of R, which was provided via the Amazon and Azure marketplaces. The key aspect of these projects is the value to IT departments of being able to distribute a pre-configured machine around the organisation.