Introduction
Background of Study
The call for Intelligent Business Operation with the support of the business intelligence process is pushing for new face or the convergence of business technologies that is able to drive a positive change in Business Management Process led by iBPMS. The iBPMS is mandated with the responsibility of changing the process to assist organizations to help keep up with business management change. It is noted that the Intelligence business management process is noted to be the next step towards enterprise BPM (Business Management Process). This takes this stand in order to leverage the current technologies to be able to pursue a degree of operational response not possible to traditional business platforms of frameworks. Therefore, this paper will be an analysis of the 11th international Conference of Business Process Management in Beijing China.
Purpose of the Study
Today, companies and firms of all nature calls for faster and better use of large amount data to generate faster and insightful decisions about their operations. The demand and need for operational business intelligence information has led to the rise of smarter ways of business process management processes or suites (BPMSs). An intelligent Business Process Management suite is there to offer functionality to support the business operations, real-time analytics, complex event processing as well as business activity monitoring approaches and technologies. In addition, it also offers an enhanced mobile framework that helps e-commerce (Trkman, 2010). Due to the above concerns and the significance of iBPMs, I consider undertaking this research as suitable study with great importance to the business world.
Outline of the Study
This section indicates the approach that the paper will use in order to elaborate the sequence or the process that this paper will undertake. The start of the paper will explain the Evolution of Intelligent BPM, this will help the analysis of the topic as it will explain the origin or the start of the implementation of Big Data as a cloud computing platform. The second part will explain how Intelligent BPM Leverages Big Data. This is a transitional approach of how iBPM is able to move from management point of view to cloud computing point of view. The third section is now the explanation of how Intelligent BPM as Business Process Management Approach. There is a gap between impregnation of iBPM and Business management, thus, this section will help fit the gap in literature amid the two. The last section if the conclusion, this will be a synopsis of the entire paper and the loopholes evident in the study. Recommendation of future research will be advised in this section.
Methodology
The research was based on qualitative research. There was only one source of data collection that was used in the research and this was basically a primary data collection method was composed of only book, journals, article, and newspapers amid other sources in the primary category. Therefore, it can be concluded that the data used in the research were based on previous research undertaken.
The Evolution of Intelligent BPM
To be able to understand the chances or the opportunities that come with the adoption of Intelligence Business Process Management, it is significant to consider the stages that the process went through overtime over the last decades. During the technology phase that started in the mid-90s, the management of business process was only limited to repetitive sequencing of acts with tough applications such as the ERP systems. Any introduction of a complex degree of workflow management only led to the imposition of the other costs of integration that accounted for more than 80 percent of the project costs with no chance of recycling or reusing. In addition, integration was limited to data retrieval documentation (Harmon, 2010).
The traditional process management project focused on automating and integration of repetitive processes, this was generally within a standardized environment that offered business or forms or companies a conducive environment that could be able to offer opportunities for success via decision making. Whether the nature of the Straight-Through process was in a discrete form such as Account activation, they are considered as applicable applications that have a sequence and flow of acts or activities that are only immutable or predetermined. The roles that exception handling was to allow human intervention was used to correct any problem that arose from the break of data. Organizations in the past had Big Data issues that cost the decision making of the organizations to an extent of not allowing such organizations to undertake a reliable sequence of data from one department to the other. Therefore, it is important to consider that the process of Managing Big Data is just a start of other issues in Business Process management, thus, traditional business management process was worth their start as it gave rise to the contemporary cloud computing.
Intelligent BPM Leverages Big Data
Since its start, the introduction of Information Technology (IT), has been noted in the introduction of frameworks of relational databases. The advances that have been noted over the years such as in cloud computing, is all entangled in the evolution of what is known as internet architecture. This was all but a derivative of relational databases. This has changed over the last 50 years, with the change of monolithic software to comparative agile and simple applications that have ever been built in Business Process Management models. Currently, the world is on the eve of meeting an inflexion point, stirring to a post-relational era that is able to be more aptly “Big Data Era”.
The business intelligence that comes from the big event data and riding greater understanding of innovation via simulation is known as the Big Data scenario. This cannot be attained with a narrow vision of data, nor can it be done through a limited internal and external boundaries such as department and organizational constraints. It is noted that successful adaptation of iBPM calls for the ability to handle sophisticated Multi-Enterprise systems. This is able to expand the window of all the analysis for an approach beyond business unit. Via customer interaction, peer production, and partner value chains, iBPM process are often spread beyond just discrete businesses amid the customers and the suppliers.
This level of integration of Big Data calls for standard conventions, the close understanding meaning of information exchanged amid stakeholders, but with concrete structure and formalization. In this manner, there is a huge gap in the shift to Big Data that has been largely parallel and in various ways it has led to the implementation of Big Data. In various ways the change of the Business Process Management is driven by the same conditions and conditions behind intelligence adaptability. The idea behind the Big Data is to expand the limits of the process that limits data capture and structures to better and richer context that defines business events. The paper notes that one of the most influential Big Data initiative is Hadoop; this is a flat-file database very important and notable of traditional databases with hierarchical structure. However, it is different to NoSQLI that is a potential anti-SQL that denotes for No Only SQL. In the post-relational adjustment that allows the integration of Business intelligence with Business Process Management, there must of a serious analysis of the intelligence process so that this can be covered for the better good of organizations and firms that prepare or those that are currently using Big Data cloud computing programs.
One of the most significant force that drives the success of Big Data is the vast growth of digital information, especially one that starts and exists the lifecycle of digital formatted information or data. This does not deal with the magnitude of data “Big”, but the broader view of data which must be managed differently. The idea behind the data management goal is not to create or develop a monolithic structure, but to arrange data in ways that can be attributed to be semantics-this deals with the meaning of the data within an accessible framework.
With very few limitations, reporting on business process management and business performance was previously undertaken after the process had been executed, or under a distinct environment fragmented from the rest of the process. The main aim of fragmentation of the two processes is to give room for better analysis and decision making, in order to come up with the right information to be incorporated as Big Data. This is a clear path that was used to signify the right path to follow in the process of adopting an iBPM. This was, however, limited to, software and system framework. Especially, when it came to BPM process models, they were known as proprietor structures and most of them were compiled into the software in the long run. Therefore, in order to come up with a better BPM tool, it was important to consider the worth of recompiling systems and applications, or were limited to discrete points such as yes/no decision points.
Intelligent BPM as Business Process Management Approach
Within the frameworks of iBPM processes, each business organization process has both a discrete cycle and the suitability to inform the larger process. This means that intelligence Business Process management are defined in a manner that is in the series of workflows that ensures the process of Big Data setup is up to consideration in the forefront of business analysis and decision making. This informs the direction of the organizations help to bring up the reality of the organizations as it helps firms to come up with significant analysis of “Big Data.” This informs that iBPMS technology in terms of all organizational business processes, management approaches that are able to leverage data from a variety of sources that explains the importance of intelligence in business growth and management productivity. The ability to collect and capture data and to get all the information needed from it from various repositories as described as Big Data cloud computing (See the figure below):
Figure 1: Business Event Management Approach
From software and a systems perspective, it can be considered as they envision of iBPM as a connecting pipe. This helps in delivering and receiving data from a larger source which is later used in decision making. It can be deduced that proper decision results come from well-developed Cloud methodologies that are often attributed to better performance and result. The organization is working under various tiers that are concurrent to better output or result. Therefore Big Data process of iBPM is ideal for both bigger and smaller organizations. For examples, the events that define clients are often attributed to a mixture of data sources, for both external and internal data. With the adoption of such data, it is therefore considerate to get outstanding output.
Working within the frameworks of intelligence Business Process Management network is what counts towards the success of the contemporary organizations or firms (Hallerbach, Bauer & Reichert, 2010). This is because business intelligence processes and events are managed and received across their cycle and data enrichment process. This allows the process implantation to be understood in a larger context that helps organizations to benefit from the attributes that leads to the success of Big Data integration. This is a clear cut-out standardization process that ensures all the Business Process Management process is up to the task. Adoption of the cloud that can be able to accommodate Big Data is still not a possible affair to most organization recording that there are various steps to be considered before and organizations denote it fully able to accept the change process amid other issues. Intelligent Business Process Management is data have driven and also an adaptive tool for many organizations.
Conclusion
It is clear that the adoption of cloud computing of Big Data is a step towards iBPM. This offers organizations the chance to expound the details of how organizations are able to benefit from the use of cloud Big Data. Therefore, it can be concluded that Big Data analysis is significant research and thanks to the 11th international Conference on Business Process Management in Beijing China.
References
Hallerbach, A., Bauer, T., & Reichert, M. (2010). Capturing variability in business process models: the Provop approach. Journal of Software Maintenance and Evolution: Research and Practice, 22(6‐7), 519-546.
Harmon, P. (2010). The scope and evolution of business process management. In Handbook on Business Process Management 1 (pp. 37-81). Springer Berlin Heidelberg.
Trkman, P. (2010). The critical success factors of business process management. International Journal of Information Management, 30(2), 125-134.
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