# Automated Business Goal Extraction from E-mail Repositories to Bootstrap Business Understanding
## 4 Discussion  
The results of the application of the NAMBU goal extraction method are quite promising. The main advantage of applying the method is a significant time gain by reducing the vast amounts of text found in 266 e-mails to 96 potential matches, including 74 correct ones. We expect the usefulness of the method to increase in parallel with the size of the dataset. The modular design of the artefact also provides the opportunity for further adjustments of the keywords, as well as the filters, which may be needed after further analyses of formulas \begin{table}
\begin{tabular}{c c c c c c} \hline \hline _True_ & _False_ & _False_ & & & \\ _Positives_ & _Positives_ & _Negatives_ & _Precision_ & _Recall_ & _F-Measure_ \\ \hline
74 & 22 & 50 & 0.77 & 0.597 & 0.672 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the application of the artefact to the Enron CEO’s _sent_ folder. \begin{table}
\begin{tabular}{l l} \hline \hline _Reason for Omission_ & _Example_ \\ \hline no keywords & deal with issues that impede, or facilitate, market development (...) \\ no verb phrase & reduc = e = 20 the risk of black outs this summer \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Examples of reasons for false negatives. for business goals, or just for the application of the method at a business within a specific industry. At this stage, it is inevitable for some encoded business goals to slip through the algorithm and not be recorded as matches, and the keywords and filters are the main cause of this limitation. The potential practical implications of the development of this method cannot be understated. Even in case of a business goal not being directly translatable to a data mining goal, thus not working within the business understanding phase of CRISP-DM, the method can be applied within a wider context of the overall knowledge management or even master data management of the enterprise (e.g., [29; 30]). Systematic deployment of the artefact at an organization may provide a significant boost to the automation of business goal tracking. The usage of a relatively dated dataset is still a significant limitation of this research. As this type of data is usually extremely sensitive, we had to conduct our research using the only available resources of this type, that is, the publicly available Enron Corpus. The content of the e-mails within this dataset is quite outdated, when compared to the e-mail information flow in modern organizations. A lack of insider information about the organization is also quite apparent, when it comes to business goals with no context around them. As the manual business goal identification was conducted internally, and there exists no specific formula for a business goal, this identification was performed in a subjective manner, with an inherent possibility of categorization bias. This validity threat of the study should be addressed in future research, preferably using more recent datasets. Multiple directions for future research can be taken [31]. We believe that more theoretical, linguistic research on a clear definition of a business goal, or a definition of a business goal with data mining capabilities could take place. Undoubtedly, if such a definition existed, it would be more feasibly translatable to the appropriate natural language processing terms, which would make the business goals easier to extract with higher precision and recall results. Considering how beneficial a framework for goal extraction through text analysis could be for organizations, not only for data mining, our first recommendation for future research is the development of a clearer formula for encoding business goals. Concerning more natural extensions to this study, its precision can be enhanced by the application of sentiment analysis and negation handling to entire sentences containing the suspected business goals. When it comes specifically to data mining, and CRISP-DM, the business goals identified through the tool are not always translatable to data mining goals.