Enterprise architecture is a visual representation of its individual components and the ways these components interact. The main idea behind this “blueprint” is to efficiently analyze and understand how an enterprise can perform its tasks and achieve its goals in the most efficient way possible. Business intelligence and analytics system is directly responsible for this investigation. To perform such a thorough investigation, this system needs to work with very secretive data about businesses’ employers, clients, employees, supply paths, stocks, and others. Due to handling data that could be a danger to the business if obtained by its competitors, the business intelligence and analytics system itself is considered sensitive. Because of that, these systems can only be accessed only through the company’s local network. After considering the above-formulated definitions and tasks, it is inevitable for a number of questions to arise. If the analyzed sources are already individual and well-protected, how does a business analytic system gather data? This paper will identify and elaborate upon the abilities of a business intelligence and analytics system.
The Inner Works of an Analysis Function
As a business grows and becomes an enterprise, the amount of data accumulated over time becomes harder to handle. In corporate management, when the team becomes too big to work within one workspace, it is divided into multiple offices together with the corresponding paperwork and materials. Similarly, data has to be separated among various localized storages and may take forms suitable for the particular division that works on it. To organize the gross data, also known as big data, and see the enterprise’s performance, a business analytics system is integrated. Marjani (2017) explains that such systems’ primary objective is to help business associations make well-informed and efficient decisions with a better understanding of the overall landscape. As mentioned above, to accumulate all of the needed data for analysis, the system must reach into every enterprise database and have a unique way of cultivating it. Apart from straightforward information, such as spreadsheets, an analytic system must use back-end databases, message buses, and business applications’ metadata.
One of the many specific ways to collect information is known as “screen scraping”. In big data analysis, “screen scraping” is used to accumulate information from websites, listing, and applications, which cannot be accessed otherwise (Marjani et al., 2017). Being able to read metadata and code is another unique way an analytic system can collect information. It is useful for adequately structuring a report on the said database and relating it to data from other sources. By digging into the preexisting structure of a database and “translating” it, a system can present information in a neat, preorganized manner.
Overcoming Restrictions
As a structure that collects and analyzes data from all of the businesses’ secretive databases, the business analysis system becomes the most sensitive system in the architecture of an enterprise. Apart from storing the confidential information, a business intelligence analysis system has the keys to accessing it (Llave, 2017). While some applications and datasets of an enterprise are available for public access, their information would not be enough to produce a full-scale analysis of the situation. It is easy to come to a logical conclusion that in order to obtain all of the required credentials, besides having a unique way of access, the system must pass the company’s security.
Business applications, documents, and storages often use high-end encryption systems and multi-layered verification. It is not uncommon that even the management of a firm is denied access to some databases from different departments. In order to log into said databases, a business analysis system must have special permissions and encoded ways of going around verification processes (Llave, 2017). An analogy can be made with a security guard holding keys to each and every room in the building. While having access to everything is beneficial in terms of keeping the place safe, if the su=ecurity guard themselves fails, a catastrophe will follow. Although this is done to make data collection more efficient, it creates a high risk of failure of the entire security structure if the intelligence system falls into the wrong hands.
Conclusion
A business intelligence analysis system can be compared to an octopus. Similar to the mollusk, it has many “tentacles” reaching into every database, application, message, and even outside information. The resemblance follows in the system’s ability to have an individual approach of accessing each source. Some sources require “screen scraping”, some can be reached into directly, and sometimes, to reach the data, the interaction must occur. An analysis system is a product of the information revolution that has brought enterprises incredible expansion opportunities, since managing big data ceased to be a problem. However, with great power comes great responsibility, and with the power to organize and manage big data comes the risk of a security system breach. The business intelligence system must be the most protected unit on the enterprise architecture map. If treated responsibly, the benefits are plentiful, and they will make a great foundation for the development of an enterprise.
References
Llave, M. R. (2017). Business intelligence and analytics in small and medium-sized enterprises: A systematic literature review. Procedia Computer Science, 121, 194-205. Web.
Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I. A. T., Siddiqa, A., & Yaqoob, I. (2017). Big IoT data analytics: Architecture, opportunities, and open research challenges. IEEE Access, 5, 5247-5261. Web.