Foundations of Information Systems
MIS500 Foundations of Information Systems
Assessment Three – Reflective Portfolio
Table of contents
Description of research and learning process 3
Feeling during the learning process 4
Relativity between business intelligence and big data 5
Professional implications of big data and business intelligence 6
Evaluation of the learning process and outcomes 8
Introduction
The subject of business intelligence and big data is emerging as an essential fragment across the current dimensions. It has been majorly useful for facilitating smooth transitions across a variety of business practices by enhancing their decision making and planning process. This self-reflection is based on my personal experiences based on the learning process linked with business intelligence and big data dimensions (Chen et al. 2017). This reflective report will help guide through the whole learning process and including better ideologies linked to the processing of business intelligence and big data usage across the innovative decisions making process. I have been involved in a number of incidents which specify different types of situations and outcomes based on the utilization of business intelligence and big data factors. These interventions will be discussed, and a detailed assessment will be promoted to achieve a better understanding of the relevant sections. The overall operability factor that is linked to the usage of big data and business intelligence is directly linked to initiating improvised formats of learning under these constituents and thus improve the operational significance (Hayit et al. 2016).
A reflection is based on underlining the accomplishments as well as the lacking attributes that are available across a learning process. Self-reflection justifies the analysis of the mistakes and accomplishments that an individual has achieved during any learning process. Self-reflection holds an essential position in a research or learning process as it helps an individual by highlighting their own mistakes and strengthening their theoretical understandings across the entire process of development. It includes a different set of information based on various operations that are carried out and examines the overall durability of the processes that are involved (Lengelle, Luken & Meijers, 2016). The authenticity of approaches and particular concerns are acknowledged to build a more useful idealization for the individuals. The theoretical approach for formulated self-reflection signifies its relativeness and coordination possibilities, all along with the process outcomes. The entire situation held under this specific direction is based on analyzing the concerns and framing of better self-reflection possibilities.
Description of research and learning process
The course attributes included in this research process were based on the subject of business intelligence and big data. The question is quite informative and advances. This particular subject can influence the professionally operational framework for an individual learner. The course was stuffed with real-time knowledge and practical implications possibilities. I got involved with this course, as there were specific business development possibilities linked to this (Pérez & Batista, 2016). I was initially fascinated by the practical implications that can be learned through this process of assessment and thus got involved in this program. The support factor which this course was going to offer was directly linked to associate a better understanding of the business operations. Therefore the sizeable scaled help that was linked to this course in opinion and managing the business concepts initiated a better understanding of concepts and their relative implications. I was constantly provided with a different set of complications under this course, which made me adaptive to the real-time issues and situations under a business process (Winkel et al. 2017). A self-reflection is the roadmap that is placed to facilitate better outcomes for a learning process along with its operational jurisdictions. It helps in guiding the whole process of information management and aligns the concepts which are suitably placed to include better results and attract more meaningful analysis for the relative subject. An efficient learning plan is liable to provide direction and guidance for the related learner, and thus the availability of self-reflection is highly essential to keep it on track (Sriramoju, 2017).
As I have mentioned in the earlier sections, I had a significant interest in business operations and management processes. Thus, this course was a better opportunity to expand my knowledge base and assess the practical implications that are linked to business operations and management. I consider this course a key to my first ever business study as it uses segmented and simplified ways of propagating information that ultimately increases the justifications and accomplishments (Yan & Brown, 2017). The learning process made me realize the actual importance of major business management concepts, such as big data and business intelligence. Factors such as improved decision making and information-driven processing of ideas are the core attributes that helped me gain a more selective approach to the concepts. I was always attached to informative learning and practical illustrations, which are the two major attributed that play a significant role in justifying the growth possibilities all along the entire business management process.
Feeling during the learning process
Initially, during the early stages of this learning process, I was a little confused with the initiation process and further development opportunities linked to the process. The fact that there has been a tremendous amount of planning and management during the initial sections of a research-based learning process led to uncertainty and depression. Some of the specific parts of the entire learning process were utterly new to my knowledge and thus required assessment of suitable information from scratch. In addition to this, the methods used for different information collection and analysis were also doubtful, which increased the chances of a failed learning process for me (Beckers, Dolmans & Van Merriënboer, 2016). These attributes made me less patient and more anxious about the entire process of learning and development. In addition to this, some specific areas were lacking information, and thus the analysis process was significantly less effective across these regions. The availability of these dimensions was operational and strategic; thus, their inclusion was crucial. However, I was able to overcome these limitations over time. But these limitations helped me realize the entire process and its complexity in terms of analyzing the desired networks of business management.
Relativity between business intelligence and big data
Throughout my entire learning process, I have developed a clear understanding of the relational approach that is available across business intelligence and big data. According to the views of Alharthi, Krotov & Bowman (2017), big data is the primary platform that helps in facilitation intelligence attribute across the process of business. The entire segment of business intelligence is directly based on the insights and ideas developed through big data (Blaschke & Hase, 2016). The fact that big data capture processes and analyses both structured as well as an unstructured set of data across a particular system helps institutions to make improved decisions across sections such as ERP database, Dashboard, customer outcomes, data warehouses, and operating system. As each of these attributes is an essential section of the whole process, and any positive influence across these can be evident in helping the desired obligations and association advantages (Erickson & Rothberg, 2019). Business intelligence and big data are related to the process, which is associated with the segmentation process and provides them with some of the most attractive sections across the business. These segments are the most profitable across all the other fragments of expansion and customer acquisition (Panadero, Klug & Järvelä, 2016). It also illustrates liabilities for performing better expansion possibilities, which can be used to justify the growing operational limits all along with the relative business sector.
The relativity between both of these attributes is quite sufficient to frame better results and acknowledge the growing needs of businesses under current circumstances. Factors such as changes across customer management, growth requirements to the companies, and the availability of a robust relatable system driven by real-time data can be most effective in managing the results. The overall operational significance across the possible segments of the business are strengthened through the incorporation of big data (Sirin & Karacan, 2017). These requirements and attributes are also based on justifying the drastically changing demands across the operational channels and application of profitable outcomes to achieve better results for them. I personally believe the relationship between big data and business intelligence are attached explicitly to each other for their collective growth. This is due to the fact that there are limitations and accomplishments that are liable to undertake better approaches and institutional design for managing the provisions across the relative segments.
Professional implications of big data and business intelligence
Growth and expansion are some of the key adjectives that are related to any business across the world. As the inclusion of business intelligence and big data directly influences the growth possibilities across an organizational structure, they hold a crucial position across professional designs. A business professional in the current time must be efficient in acknowledging the usage of big data and make intelligent decisions across the business (Silahtaroğlu & Alayoglu, 2016). These are some of the most primary requirements across a professional overview. Apart from improved decision making across business designs and associations, one crucial factor that is positively affected by the usage of big data is customer acquisition and retention. A professional must be able to correlate with different divisions and analytics fields of big data. Implications of big data and business intelligence are also based on on-boarding newer clients and achieving better results to attract a significant amount of business through employing sufficient initiatives and attributes (Sun, Sun & Strang, 2018). The fact that big data has its implications and importance in almost every dimension of business makes it a crucial entity among the professionals.
The businesses also experience a diversified consumer market, which is perfectly inclined with its broad range of business intelligence and prominent data utilization possibilities. It helps the companies to get an ideal hold over the available customer groups along with their respective requirements and achieve better results to attract revenues and profits. Another major advantageous factor that is associated with the usage of big data and business intelligence is its highly efficient supply chain and logistical structure management processes (Srivastava, 2016). It provides the businesses with an ability to deliver its products and services flawlessly and also boosts the entire structure of operation related to the service system.
The tools and techniques related to business intelligence and big data are justified with support mechanisms and are linked to enhance a better understanding of the desired implications. Big data and business intelligence have the potential to provide businesses with suitable ideologies and outcomes to assess its valuable customers by offering them appropriate and effective products as well as services. The process-related to this business management segment is primarily supported by the fact that it is intended to make businesses as customer-centric organizations (Ram, Zhang & Koronios, 2016). This factor depicts the amount of impact which the customers have in their overall operations and product development initiatives with the inclusion of business intelligence and big data.
Business is an entity that is drastically growing, and thus the possibilities and association fragments related to its growth must be relevant to this growth. Therefore, factors such as business intelligence and big data that play a significant role in acknowledging the desired decision making and customer management potential must be assessed accordingly. These concepts of business are professionally important as these play a vital role in justifying the increased demand for possibilities and enhance professional expansion attributes all along the process of business management (Larson & Chang, 2016). In case there is a limited acknowledgment of these attributes, the professional advantages can be lacked behind, and thus the desired growth is significantly less than the actual growth. The growing usage of data and its practical usage across different dimensions of business are a significant research factor for this learning process. The overall acceptability index for big data is associated with the business development initiatives and are mostly initiated with accepting the desire and approach to support the organization with a positive response from its available customer services (Reshmi & Balakrishnan, 2018). This analysis depicts the current state of operations that are associated with this organization and the accountabilities, which are based on future associations.
There have been huge developments all along the process of using big data in terms of customer services and products based operations across the organizational development processes. This can be assessed by the fact that businesses achieve better results and attract a significant amount of support mechanisms due to the availability of business intelligence. In the past few years, governments across several countries have been ideal for managing better results and attracting initiatives for achieving profound ways that can be put in place for smooth operational guidelines across business based data usage (Abai, Yahaya & Deraman, 2018). This has led to an increased possibility to attract better outcomes and initiate a better design for attracting newer markets as well as more new customer groups.
Evaluation of the learning process and outcomes
The learning process used under this course is based on a number of decisions that are taken in order to facilitate a better approach across the selected subject. The basic ideology behind this learning process was sufficient and reliable under theoretical as well as practical implications. Thus, the increased demand for real-time information and analysis was fulfilled, and a detailed assessment is carried out (Vargas et al. 2016). I personally believe that if the learning subjects are selected on personal interest, the overall burden of understanding and analysis is significantly less hectic than any other subject-based learning. The ability of the individual learner to attain a well researched and analyzed set of information improves the overall operability across the relative research-based education. In addition to this, communication and interaction are two other fragments that are linked to utilize and assess the operational obligations related to the process of learning (Balachandran & Prasad, 2017). The learning process is also based on acknowledging the incidences and allocating practical research possibilities all along the process of learning.
My entire learning process was based on a variety of experiences. I have had bad experiences as well as good experiences. There were times when some of the concepts were not at all assessed, and thus I had to make constant learning processes to accomplish it. Practicing with honest intentions are the key factors that can be considered as the basic operational attribute across the learning process (Labonte-LeMoyne et al. 2017). A variety of practical as well as theoretical information association was carried out that improved the overall usability linked to the learning process. In addition to this, as the learning process is based on business-related attributes, finding sufficient information was a straightforward task. The usability extents and results for big data, as well as business intelligence, helped facilitate improved decision making and understanding of the concepts across the entire learning process. A direction was established initially to the start of this process of learning that helped me from getting diverted as the whole process of business intelligence, and big data is quite vast and varied. I used a unidirectional approach for accessing the requirements and managing the segmented data segments linked to the whole learning process. The practical implications helped me in making a quick understanding of based decisions and also saved a significant amount of time by reducing the knowledge of time for me (Cambria et al. 2016). The challenges were vast, and thus, their respective elimination was required eventually in order to carryout a serious learning process.
Conclusion
Learning process in any form is highly important process as it initiates a process to undertake better approaches and attributes for building the design segments. Availability of a reflection is capable of framing better results and formulating improved form of approach to correlate with the design sections. A reflection is based on promoting ideal analysis of individual along with its decision making processes across the related sections of learning process. An improved learning process is directly based on development of a reflective analysis (Salih, Wongthongtham & Zajabbari, 2019). The obligations and attributes that are based on promoting better results are directly highlighted by a self reflection process. This process is underlined to put a check on the proceedings and decisions made under a process. The whole system of reflection improvises the ability to improve across different areas and thus the possibility to indulge in to improved modes of operations.
I have carried out this self reflection to assess the limitations and advantages related to the process of learning. The learning process involved under this section is based on distributing the desired obligations for process. This process helped me analyse myself in terms of my researching and analysing aspects. The self reflection possibilities initiate an encounter of ideas that ultimately increases the durability of the research. The possibilities for research and self evaluation is based on acknowledging the relative areas of improvements (Fiaz et al. 2016). It has also helped me to improvise me in terms of skills and additional abilities across the region. The described negative outcomes are also highlighted to keep a clear understanding of the resources and requirements that hinder the process. A number of improvement possibilities are accessed through the self reflection process which can be of huge help under the future research possibilities. The ability to intervene the outcomes that are linked to assess the operational abilities and skills based on self reflection attributes. The self reflection process involved under this particular learning process is acknowledged to increase the durability and acceptability of the whole process.
Conducting this self reflection is important as it has helped me idealize my requirements under a researching process and attain a better outlook of the business intelligence and big data usage. This will also help in improving my professional life in future. In addition to this, the issues and limitations assessed under this self reflection process is helpful in acknowledging further research possibilities (Vidal-García, Vidal & Barros, 2019). The operational obligations based on this self reflection is quite supportive in nature and are linked to process better results exclusively. The factors for improvised set of attribute is associated to promote significant attention to the real time objectives linked to the process of learning.
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