Transition from Traditional BPM to iBPM

Article: Gao, X., 2013. Towards the next generation intelligent BPM–in the era of big data. In Business Process Management.

Business process Engineering is one of the most widely used technical terms in business process system. From small to internationalised organisation business process management or business, process engineering is essential components to develop the organisational architecture and to execute enterprise operations efficiently. It is the major cause behind selecting the article Gao, X. (2013), Towards the next generation intelligent BPM–in the era of big data, for this essay. The purpose of the essay is to analyse the fundamentals of Business Process Management and its utility in organisational operation in the light of this chosen article. The major part of this essay covered all the essential and introductory components of Information system based business operation in Enterprise Architecture including business environment, Usefulness of Business Process Engineering, Automated workflow as well the its utilisations in executing process, monitoring and controlling operation.

In this article the author Gao, X has focused on the transaction from traditional Business Process Management to Intelligent Business Process Engineering. The author also discussed how this advanced BPM became more powerful tool through the utilization of Big Data. Big Data has been described as a dynamic, open and new world of storage system and memory space. The paper shades light on the essentiality of automation and Intelligent business process management (iBPM) in this era of digital technology, especially in information technology. The paper describes the utilisation and potential growth of this information processing and handling tool in E-communication and data managing industry.  The research provides an in-depth analysis of how the IBPM can take over the place of BPM in advanced business model architecture.

As opined by Trkman 2013, starting from the beginning of the digital communication and its use in Enterprise architecture Business Process Engineering is in a consistent development and evolving phase. Author said that the Business Process Management has become ‘holistic management approach in enterprise architecture’. According to the Gao 2013, the utilisation of BPM in the enterprise architecture for a long time is the result of its non-linear, dynamic and robust data processing and handling ability. On the contrary, from the application infrastructure and middleware (AIM) point of view, there are certain limitation in its file handling system that sometimes causes excess latency time in data transfer and computing operation. The traditional BPM Suite comprised the major 4 areas of functionality namely Analytical, Real time operation, Flexibility and Compatibility.

Considering all the robust system operation and customisability the traditional approach and implementation of BPM lacks data processing speed and accuracy. According to the Gao 2013, the operational anomaly is not noticeable from the beginning of its operation. Though the operating performance usually gets slow when it has to handle larger volume of data input from multidimensional inflow database. Therefore, BPM needs an regular cache clear operation to reduce its garbage value to gain some extra space for new, comparatively larger volume of data. According to the Gao 2013, being the most popular approach of enterprise architectural development and control traditional business process management required a new web based language named Web Ontology Language or OWL for further customisability. In more simple term in the traditional BPM it can be said that the longer the data chain is the lengthier the data processing will become.

As per the analysis of Rosemann and vom Brocke 2015, Intelligent data operation however provide the business operations a wider space to make the architecture more useful without caring about the latency time and space complexity. The intelligence system can normalize the data relation with additional extra data space created virtually within the BPM suit. It is also true, that flexibly captured and responded by resorting is not the ultimate reason behind the operational transformation to advance and intelligent Process Engineering, rather larger storage capacity allows to add more inflow data structure that enable the business operation to process more data in the shorter amount of time. With this regards Big data can be the significant and most influencing perspective in business process management and automatic data handling and processing platform.

According to Weske 2012, The concept of beg data was born at the early phase of 2000s, however the implementation procedure was not efficient enough to operate with Business Process Engineering Till 2009. According to the Gao 2013, the big data means the storage of dynamic, robust coherent and both linear and non-linear data sate under a single umbrella of storage system. Nowadays along with the utilisation of cloud, computing and cloud storage system the operation of Big Data became more diverse and comprehensive. Considering the data interaction with advance Database Management System iBPM can be seen as the most efficient way to utilise the vast data storage and efficient processing system simultaneously. According to the Gao 2013, driven by the operational process data and other related database components, it can be a more advanced platform for several business operations including the Research and development, business intelligence, logistics operation, manufacturing and even in human resource. This network discipline can be applied in a data monitoring, controlling of iBPM system to look into the applications and outcomes as well as to develop the data governance and monitoring report.

Big Data and Intelligent Process Engineering

According to Weske 2012, adaptability is one of the most essential features of Intelligent Business Process Engineering that allows the data processing system to adopt the data input, output and quarry repetition through its internal adoptive intelligence. At the same time for random file handling the iBPM can be most flexible and effective platform to develop an independent as well as reliable business process architecture. The Software Operation Architecture and multilayered data integrity is also a part of these advanced Data handling system. According to the Gao 2013, cconsidering the data interaction with advance Database Management System iBPM can be seen as the most efficient way to utilise the vast data storage and efficient processing system simultaneously. Through these abilities, the business network management (BNM) as a part of core Business process management can attempt to make operational network while joining up and collaborating with big data based business processes.

As opined by Trkman 2013, Analytical quality is another impressive point of this Big Data based iBPM system. This feature is also called as “art of data operation” that allows the core governance system to automatically prioritise the mostly used and potential database and develop a comprehensive sequential index handling architecture.  The core operation of this facility is its log governance that enables the core data monitoring system to keep track of all the data flow activities including input, output, processing, dropping, cardinality and others. Through these log based data history, IBPM can develop a data mapping system where the files and records are scattered as per their utility as well as potential usefulness. According to the Gao 2013, the next operation is to select the most appropriate set of data chain or records and set them at the surface position of the file indexing system. Big data will offer advancing trends in expertise that unlock the access to a new approach for rapid improvement of the theory and functional level of iBPM.

Intelligent Business Process Management can be profitable for its extreme tangibility in execution, monitoring and controlling operation in any organisational architecture.  It will help to develop appropriate product life cycle while minimising the total operation cost including the material, equipment and maintenance cost.  At the same time, it can help to plan best legalistic planning where the transportation would cover the optimum amount of distance in minimum time limit to reach the nearest material hub or warehouse. According to the Gao 2013, the major advantage of this architecture is it can be visible within network views with certain encryptions and combined automatic discovery, data mining and inference utility capabilities. Only the expert knowledge in complex, dynamic and heterogeneous enterprise domains can alter the operational warpath of this system. 

Adaptability, Software Operation Architecture and Multilayered Data Integrity

In terms of monitoring, appropriate business architecture needs two types of monitoring systems namely operational monitoring and data governance. In this case, data governance is the major advantage of using an Intelligent Business Process Management. The perception of an expansive assortment of complex frameworks regularly reflects a singular informational indexes and decentralized connections, which can be additionally incorporated and combined together into the Big data storage system. According to the Gao 2013, this enormous monitoring information are also connected with such sequential and interconnected information indexing to engage real time monitoring data while quickly forming into appropriate documentations or records.  This network discipline can be additionally applied in a data monitoring iBPM system to look into the applications and outcomes as well as to develop the data governance and monitoring report.

Consequently, in data controlling and automation system the iBPM can easily large amount of data in a real time data processing environment while processing the right set of data to their appropriate position of storing. Furthermore, operational architecture of data controlling system can also be customised with customisable authentication system. As a part of memory buffer, the Big data can act as a virtual paging unit while data controlling operation is under execution. As per the analysis of Rosemann and vom Brocke 2015, the major advantage of using the iBPM instead of traditional Business Process Management is that in data controlling operation, the iBPM can engage the dataflow with certain correlation that increase the accuracy of data controlling system. At the same time for random file handling, the iBPM can be most flexible and effective platform to develop an independent as well as reliable business process architecture. According to the Gao 2013, the NoSQL database is based on a distributed storage system instead of a relational cardinality that display the significant advantages in CRUD based data controlling operation. This features allow the iBPM model to gain reimbursement concerning the extensibility, data model elasticity, cost-effectivenwaa, accessibility and essential factors.

The Article is focused on the transaction from traditional Business Process Management to Intelligent Business Process Engineering. At the same time the descriptive research structure shows how the Intelligent BPM became more powerful tool through the utilization of Big Data. Through focusing on data indexing and record handling operation the Big Data has been described as a dynamic, open and new world of storage system and memory space. However, the most limitation of this paper is it lack of real time data presentation. The article neither presents a set of sample of data complexity and efficiency report regarding iBPM, nor represents any survey based real time data analysis to strengthen the content of this paper. The research provides an in-depth analysis of how the IBPM can take over the place of BPM in advanced business model architecture.

From this article review it can be concluded that Business process Engineering is one of the most widely used technical terms in business process system. Starting from small to the internationalised corporate organisation business process management or business process engineering is essential components to develop the organisational architecture and to execute enterprise operations efficiently. At the same time, Big Data is a dynamic, open and new world of storage system and memory space. The formation of Enterprise architecture defines the essentiality of automation and Intelligent business process management (iBPM) in this era of digital technology, especially in information technology.

As per the paper, according to the application infrastructure and middleware (AIM) point of view, there are certain limitation in its file handling system that sometimes causes excess latency time in data transfer and computing operation. Along with that through log based data records and history management, IBPM can develop a data mapping system where the files and records are scattered as per their utility as well as potential usefulness. In can be concluded that Big data will offer advancing trends in expertise that unlock the access to a new approach for rapid improvement of the theory and functional level of iBPM. At the same time, the NoSQL database can provide a distributed storage system instead of a relational cardinality, which also shows the significant advantages in CRUD based data management.

Reference:

Gao, X., 2013. Towards the next generation intelligent BPM–in the era of big data. In Business Process Management (pp. 4-9). Springer, Berlin, Heidelberg.

Rosemann, M. and vom Brocke, J., 2015. The six core elements of business process management. In Handbook on business process management 1 (pp. 105-122). Springer, Berlin, Heidelberg.

Trkman, P., 2013. Increasing process orientation with business process management: Critical practices’. International journal of information management, 33(1), pp.48-60.

Weske, M., 2012. Business process management architectures. In Business Process Management (pp. 333-371). Springer, Berlin, Heidelberg.