INTELLIGENCE: CONNECTING DATA AND PROCESSES,” BY
VAN DER AALST, W., ZHAO, J. L. AND WANG, H. J.
A Critique of the Paper: “Editorial: Business Process
Intelligence: Connecting Data And Processes,” by
Van Der Aalst, W., Zhao, J. L. and Wang, H. J.
The central theme of the paper deals with the relation (or rather the non-relation) between processes and data. It speaks particularly about the massive amounts of data that can cause difficulty in processes. Hence, the need is for the two aspects to communicate with each other at an optimal level. The paper reveals a focus on the improvement of processes. These processes are related particularly to Business Process Intelligence, which is the focus of the Editorial (article). It has to do with the relation the extraction of the data. This is why the relation between the data and processes is important.
It is also about the difficulty in extracting valuable data from the overall data. This is where the usefulness of actionable information is needed. In order for this to happen, the focus should be on improvement of the processes and not on improving information systems or data recording, as the article suggests. It shows the importance of the capacity to store and the process data, and its scientific application; that is, to compute the information (Hilbert, 2011). The suggestion is a Business Process Intelligence (BPI) that would assist with the communication between data and processing.
There are suggestions on how to improve the retrieval of data through processes. It specifies the need for a Data Scientist (DS), especially in Business Process Intelligence. It would be the role of the Data Scientist to create accessible data in that it would focus on the recording and storage of the correct data.
Furthermore, the article presents ideas on Business Process Intelligence (BPI). This is in fact the central message for the article in that it presents a proposal for the way in which the BPI should operate in order to establish the relation between data and processes. This is especially necessary with the avalanche of data that is present globally. This is especially in the light of the title of the paper, which clearly wished to illustrate the connection between data and processes.
Another focus is on Process Mining (PM), which is in close relation to data science (Rozina et al, 2015) PM is used as a means to “discover, monitor, and improve real processes.” The article further describes event logs and what its purposes are. It identifies three basic types of PM. It places PM as a bridge between the approaches that are process-centric, and approaches that are data-centric (Van der Aalst et al, 2015).
Furthermore, the relation between data and processes require a discipline that is called Business Process Management (BPM) (Van der Aalst, 2013). BPM is illustrated with a diagram that shows how it is implemented in a business setting in particular. Data mining and machine learning flanks BPM. Central to that are process discovery, process mining, conformance checking, enhancement, and predictive analytics. As an Editorial, the article introduces the other articles that are pertinent to the publication as a whole.
These are just some of the issues discussed by the various authors addressed in the Editorial (article). The author also thanks everyone involved in assembling the publication. Even though it mentions the other articles, these too concentrate on the retrieval processes as opposed to the data capturing and recording.
Critique
The paper reveals a focus on the improvement of processes. However, the improvement of the process can be difficult if there is not a focus also, on how the data is captured and stored. The Editorial neglects to give clear evidence of the connection between data and processes.
The improvement of the process can be difficult if there is not a focus also, on how the data is captured and stored. This is why the word extraction is used here, as the article does not focus on the data, but on the processes. The processing is vital to the extraction, however, the data input needs to be an aid to the extraction processes as well (Moss, 2005). Even though there is mention of the data and the capturing of it, the article leans more toward the processes and how these could be improved in terms of BPI. The content is based on suggestions only, and there are no clear examples of where applications actually worked. The article is an introduction to the BPI, but one would expect more of an analysis than a suggestion of the processes and, largely, data.
The role of data scientists is, therefore. a necessity as well, as the article suggests. One would expect, though, that the authors could have addressed the practical applications rather than making mere suggestions. Perhaps a few examples of how the processes and data could have benefited the research and the article. The expectancy in reading the article is that one could find tried and tested answers as opposed to mere suggestions. The suggestions do, however, contribute to the general information around BPI.
The article evokes the need for more reading and research, and perhaps for other authors to take the information a step further. That is, to give evidence of the research in terms of valid examples where the suggestions were applied. Since the article also stresses the fact that conformance checking or compliance checking is part of BPI, it is important that data and processes speak to each other. Hence, there has to be compliance in the organizational interaction.
The importance of the processes is that it needs to have compliance (which is mentioned) but, again, it needs to be addressed in terms of the relation between the data and the processes. It needs to address the issue of the raw data being captured in a way that would make extraction or retrieval much easier. The article does mention several software programs that would be of assistance to data capturing as well as making the retrieval easier.
The article does give an overview of how the Internet deals with “Big Data.” The “Big Data” should serve as an example to operational business processes, and how the extraction of this data is possible. There is a suggestion as to how this should be implemented. As the above critique stated, there is lack in the way data is captured and stored, making it difficult to extract. Hence, the article gives ideas on how this matter should be improved. The suggestion is a Business Process Intelligence (BPI) that would assist with the communication between data and processing. These are, however, only suggestions, and there are no clear examples of where applications actually worked.
This is an introduction to the BPI, but one would expect more of an analysis than a suggestion of the processes and, to a lesser extent, data. This is especially in the light of the title of the paper, which clearly wished to illustrate the connection between data and processes. The expectancy in reading the article is that one could find tried and tested answers as opposed to mere suggestions. The suggestions do, however, contribute to the general information around BPI. One would expect, though, that the authors could have addressed the practical applications rather than making mere suggestions. Perhaps a few examples of how the processes and data could have benefited the research and the article.
Multiple Choice Question:
The article: “Business Process Intelligence: Connecting Data and Processes” proposes that Data Science work in conjunction with Business Process Management (BPM) (Van der Aalst, 2013). Choose the answer that best illustrates the interface between Data Science and Business Process Management.
The specific connection, and interface, between Business Process Management and Data Science involves Business Intelligence and Process Mining, where BI “is a set of methodologies, processes, architecture, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision making.” (According to Evelson et al, 2015).
The interface between Business Process Management and Data Science is easy. The reason for this is that it is easy to distinguish between Process Mining and Business Intelligence. It is especially so, as Process Mining and Business Intelligence is about paying particular attention to processes.
According to this particular article, BPM and Data Science interface through its focus on database algorithms, domain knowledge, data mining, visual analytics, large scale distributed computing, Behavioral/social sciences, privacy, statistics, stochastics, industrial engineering, visualization, and machine learning in particular.
Data Science is an age-old discipline that has been used by Business Process Management “to collect, analyze, and interpret data from a variety of sources (social interaction, business processes, cyber physical systems) [Van der Aalst 2014a; Chiang et al. 2012].
Reasons for the question:
The reason for the choice of the question is that is proposed in the paper that the data and processes should draw attention to further investigation into its relation. This would be in the hands of the two aspects: Data Science and Business Process Management (BPM). The question relates to Figure 2 in the article. The question wishes to highlight the interface and interconnection of the two disciplines, but also highlight its importance.
The first answer is correct as Figure 2 indicates the important connection between Data Science (DS) and Business Process Management (BPM). The connection is particularly in terms of Process Mining and Business Process Intelligence. It offshoots into the other aspects that is connected to the data science.
The second answer is incorrect as the Process Mining and Business Intelligence are both about paying particular attention to processes, which makes it difficult to distinguish between the two. Thus, it is often so that the two are used interchangeably, as Figure 2 illustrates. It is, therefore, not that easy to make the distinction. It is therefore important to know what the different types of Process Mining are. These are (process discovery, conformance checking, and enhancement). In this way it can somehow be separated from BI, but it would still be difficult, as the processes might be the same as PM.
The third answer is partially true, but the focus of this article is on the demarcated area that can be seen in Figure 2 in the article. That means that the relation or the interface is between BPM and data science, which is focused on process mining and business process intelligence. However, the rest of the aspects are focused around data science that does not necessarily interface with Business Process Management. For example, data visualization is specifically part of data science. The same applies to all in the list, mentioned in the question (and seen in Figure 2 in the article,) but it does not necessarily fall within the ambit of BPM. There is, therefore, interface between the Data science and database algorithms, domain knowledge, data mining, visual analytics, large scale distributed computing, Behavioral/social sciences, privacy, statistics, stochastics, industrial engineering, visualization, and machine learning. These, however, do not interface with BPM.
Answer four is not correct as this discipline is new, and interfaces with Business Process Management in a new and innovative way. The idea for it has been devised, indeed, “to collect, analyze, and interpret data from a variety of sources (social interaction, business processes, cyberphysical systems). Thus, the proposed use of the data scientist is new and is, therefore not an age-old discipline. It has emerged as a means of creating better data, as well as better retrieval of such data.
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