# Automated Business Goal Extraction from E-mail Repositories to Bootstrap Business Understanding
## 1 Introduction  
The emergence of Data Mining (DM) as a specific domain led to the creation of high-level models, structuring the data mining processes. CRISP-DM, being the most popular method and the de facto worldwide standard for data mining [1, 2] provides a roadmap for DM projects as illustrated in Figure 1, by specifying their individual phases--Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment--and going deeper into particular tasks. It is however widely known that CRISP-DM does not provide sufficient support for the first step of the data mining process, namely the understanding of the problem owner's (i.e., business insider's) concerns [3]. This step, which is referred to as the Business Understanding phase within CRISP-DM, is considered to be critical, to the point where it can make or break the entire project [4]. There is a particular contrast of the perceived importance of business understanding to its actual implementation, which is often conducted in an ad-hoc manner [5]. [PERSON] et al. [6] explain this phenomenon by describing the general lack of support towards how the implementation of this phase should be performed. Ref. [7] conclude that, even though CRISP-DM is an improvement when compared to the previous standards in the data mining community, it is still not mature enough to deal with the complex problems it needs to address, with the apparent lack of business modelling procedures, formal tools, or methods for the business understanding phase. As a result, there is muchroom for improvement when it comes to structuring the initial stages of the data mining process. This also explains the many task-specific extensions to the generic knowledge discovery process, which have been proposed over the years, including those in the domains of interactive data mining [8], big data processing [9], reproducible research [10] and personalised recommendation systems [11]. In this paper, we propose the NAMBU method for automatic business goal extraction from an organization's textual resources. This method can be embedded within the business understanding phase of CRISP-DM, and thus serve as a bootstrap for the current standards. Natural Language Processing (NLP) is an area of research and application of a theoretically motivated range of computational techniques for analyzing, understanding, and manipulating naturally occurring texts at one or more levels of linguistic analysis for the purpose of performing and achieving desired tasks with human-like language processing accuracy [12]. Information extraction is a domain, where natural language processing methods are applied to identify and isolate relevant information from unstructured textual data. In our case, business goals are the target information. As for the choice of sources, organizational e-mail exchange repositories are considered to be a valuable source of expertise, with an easy-to-mine set of communications between people in the social network [13]. These semi-anonymous communications, through media such as e-mail, also referred to as \"weak ties\" in businesses, are crucial to the flow of knowledge within organizations [14]. The general knowledge management strategy of an organization can be supported by capturing the employees' communication records and subjecting them to further analysis when needed [15]. The objective of this research was to develop the Meta-Algorithmic Model (MAM) shown as a recipe for using these textual resources to identify business goals explicitly stated in communications between the members of the organizations, to facilitate the business understanding phase of CRISP-DM [16; 17]. Thus, the scientific contribution of  
Figure 1: The CRISP-DM process starts with the Business Understanding phase, but does not specify how to determine business goals. this research is threefold. First, we present the NLP-Automated Method for Business Understanding (NAMBU) as a reusable meta-algorithmic model for other researchers.