The business environment is undergoing continuous changes as it becomes more complicated. Consequently, both private and public organization is under pressure to execute quick responses to the new conditions and come up with creative means in their operations. Organizations need to make smart and strategic decisions, which may affect their existing processes. Before the said decisions are reached, a significant amount of data, information, and knowledge will have to be factored. There may be a need for computerized support to ensure that the process is quick, in real-time and is done efficiently and effectively. Data management process issues have continued to exist upstream, making exclusive self-service business intelligence cumbersome due to the continuation of poor data quality (Marvin, 2015).
For a business to survive in the modern world, they should organize themselves ethically and legally so that they can serve their customers, stakeholders, and perform their business processes efficiently. Data collection is the beginning of the entire process.
The business intelligence
Business intelligence is a combination of several processes, architecture, and technologies that allows the conversion of data into a form that is meaningful to enable a business to learn from it for profit maximization (Pratt & Fruhlinger, 2019). In business intelligence, the software is used to transform data into a form that can be acted upon and communicates viable information. It affects all the operational and management aspects of an organization as it enables fact-based decision-making b the application of historical data. The business intelligence software has allowed diversity in its usage as it provides for people who are not tech-savvy to process data with ease.
According to Knight (2019), business intelligence is mainly dependent on proper data management implementation. Data management provides the foundation on which good business intelligence rests and determines the business intelligence firm. Effective business intelligence depends mainly on an organization’s structure and its data needs. If the data is poorly managed, the report builders, on the other end, will be frustrated with business intelligence. Successful business intelligence is supposed to be beneficial to the enterprise as a whole. Different users will be involved with some focusing on the strategic level, while some will be more oriented on the tactical level.
Business intelligence is important as it creates a measure of measure where an organization can utilize past data to create performance indicators. It also creates the basis for benchmarking as it locates business problems and notifies on trends in the market. Visualization of data to enhance data quality and allow quality and informed decision making is enabled by the application of business intelligence. The systems are versatile as they can be customized to be used by small and medium business enterprises.
There has been a very big shift towards the management, analysis, and application of predictive analysis when dealing with data. Businesses have previously leveraged on self-service in data analytics, which has not borne much success (Marvin, 2015). For a businessperson to make a decision, a considerable amount of relevant data is necessary. The available data should be processed within minimum time, in real-time, and accurately to offer viable information for managerial decision-making. This is where computerized support using computer software is necessary.
According to Kaspi & Venkatraman (2014), online transaction processing (OLTP) is a system that can be used in a number of ways in data management and analysis. For example, product information such as the line and price can be stored here or be manipulated. The system can also be used in advertising to change the options. Customer demographic data, such as an increase in their credit limit or adjustment of income, are some of the customer information that can be kept and manipulated in intelligent business systems.
Today data analysis and processing can be done online by the application of online analytical processing (OLAP), which allows data to be processed for the end-user and ad hoc reports, queries, and analysis. OLAP is a computer processing system that allows the extraction and viewing of data from diverse viewpoints. OLAP system stores data is in a multidimensional database where every data attribute is considered as a separate dimension. The software has the capability of locating the intersection of dimensions and displays them as it also allows the breakdown of certain attributes to sub-attributes (Kaspi & Venkatraman, 2014).
Data management and business intelligence are applied in Vignette Magpie sensing, a system that applies analytics in the management of supply and safety of vaccines. Maintenance of the cold chain is very critical in the transportation and storage of vaccines, which need to be stored at low and regulated temperatures (Sinha, 2017). The magpie sensing is a project aimed at ensuring that vaccines are maintained at constant low temperatures. The system relies on robust algorithms that allow gathering and scrutiny of data from the monitoring devices to enable improvement of efficiency of the system as well as to detect the eminent problem in the cold storage. In the system, the descriptive, predictive, and prescriptive analytical techniques are used, which allows the taking of the necessary action based on the raw data that is obtained from the monitoring devices. Besides, prescriptive analytics is used as a guide towards making informed purchase decisions as it continually analyses the performance of the storage compartments. Consequently, the system allows saving on costs and time as it guarantees the presence of safe products, and thus, the administration of useful products to the patients (Sharda et al., 2014).
According to Sharda et al. (2014), Vignette is a good illustration of how business data can be used to give insight at different levels of business operation. The system has graphical analysis, which enables the user to have a feel of the situation. Data mining is in the project is predictive as it enables us to make an informed prediction of future performance. From the predictive analysis, necessary recommendations for the operators can be created. Innovative applications can create new business ventures, as evidenced in the case of the opening Vignette.
The proliferation of cloud business intelligence for new projects has increased due to a shift in data capacity, perception of value, and the emergence of new major players such as Amazon and Microsoft (Marvin, 2015).
The use of business intelligence is advantageous as it boosts productivity by allowing businesses to create easily and conveniently create reports and thereby saving on time and resources. Thus, the increased productivity of the worker is ensured. The visibility of processes is increased as it makes it possible for the identification of areas which attention. Consequently, accountability in an organization is ensured in terms of goal setting and evaluating performance. Business intelligence facilitates business in the making of decisions as they can have a better perspective using the available characteristics of the software. The business processes are streamlined as all the complexities that are due to processes in the business are eliminated.
Data preprocessing is a data mining technique that enables the transformation of non-processed data into a form that information can be derived. The available raw data does not tell us much as it lacks certain behaviors or trends and thus may contain numerous errors. Data preprocessing is involved in addressing such issues as the data is prepared for advanced processing. In data preprocessing that, data goes through a cleaning, integration, transformation, reduction, and discretization (Huffaker, Bittelli & Rosa, 2018).
Data preparation is a vital function within the entire data management, as an organization cannot have sufficient business intelligence if it does not have data that is good enough. The higher the quality of data that is fed into the business intelligence model, the more the information and benefits will be derived from the system. Finding the best business intelligence for a specific organization is critical to generate the right solutions that satisfy the organizational needs (Knight, 2020).
Huffaker, R., Bittelli, M. & Rosa, R., 2018. Data Preprocessing. Oxford Scholarship Online.
Kaspi, S. & Venkatraman, S., 2014. Performance Analysis of Concurrency Control Mechanisms for OLTP Databases. International Journal of Information and Education Technology, 4(4), pp.313–318.
Knight, M., 2019. Data Management vs. Business Intelligence. DATAVERSITY. Available at: https://www.dataversity.net/data-management-vs-business-intelligence/# [Accessed May 2, 2020].
Marvin, R., 2015. 10 Business Intelligence Trends for 2016. PCMag UK. Available at: https://uk.pcmag.com/cloud-services/73741/10-business-intelligence-trends-for-2016 [Accessed May 2, 2020].
Pratt, M.K. & Fruhlinger, J., 2019. What is business intelligence? Turning data into business insights. CIO. Available at: https://www.cio.com/article/2439504/business-intelligence-definition-and-solutions.html [Accessed May 3, 2020].
Sharda, R. et al., 2014. Business intelligence: a managerial perspective on analytics, Harlow, Essex: Pearson Education Limited.
Sinha, S., 2017. Clinical Vignette. Oxford Medicine Online.
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