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The Gap between Theory and Practice in Business Intelligence

Business intelligence is a compound terminology used to refer to distinct business management models associated with informed decision making, to spark considerably significant advancement in organizational performance and overall productivity. The contemporary market dynamics are very competitive, and the flexibility of an organization to the ever-rising constraints in the market establishment can be realized through optimizing performance. According to Arnott, Lizama, and Song(2017), corporate datasets represent an incredibly valuable asset that is critical for legislators in an organization. Through time, companies have depended on business intelligence models to transform collected data to draw productive insights to boost overall performance. However, giving the advancing nature of business intelligence theories, there is the existence of a considerably significant gap between theory and practice of the business intelligence theories (Moscoso-Zea et al., 2016). Among the broadly applied approaches in business intelligence is data mining. In this text, I take a closer look at the data mining theory and going further to analyze the present gap between theory and practice, while outlining the origins of this concern.

Since the terminology business intelligence was initially coined in 1958, there have been considerable evolutions in the discipline of information systems and technology, especially in the decision support frameworks segment. Business intelligence structures are broadly employed variously in companies. These models express themselves as critical sections of an entire knowledge-based business and a formidable instrument useful in backing decisions made within the business front (Arnott, Lizama & Song, 2017). The diffusion of instruments in data mining is growing popularity in the business intelligence front, with several studies conducted on the same acknowledging the appropriateness of using this tool in enterprise business intelligence structures. Business intelligence instruments are far welcoming, repetitive, and engaging, granting business the ease of access. In this case, the user can directly modify given datasets, possessing the potential to obtain all the importance held in the given business datasets (Richards et al., 2019). However, the main concern is that data mining models demonstrate some level of sophistication aside from the theoretical aspect to be implemented in practice.

Data mining is among the widely adopted terminologies in business intelligence. It is used to speak of the evaluation and analysis of vast volumes of datasets to recognize considerable models and laws (Fink, Yogev & Even, 2017). Within the organizational set-up, data mining is employed to boost marketing strategies, sales, and consumer promotion activities via the generation of a more profound understanding of their consumer base. Progressive business expansion is accompanied by an increase in the volume of data sets passing through the organization’s database (Kasemsap, 2018). To draw some sense from the vast data volumes, the developed data mining algorithms could come in handy to assist in the various data mining functionalities. In a market space wherein there is freedom for the clients to switch suppliers with reduced cost implications, companies are routinely employing data mining approaches to maintain their client base (De Masellis et al., 2017). Through the utilization of algorithms that point out the trends in vast volumes of datasets, the business can master same client factors and in doing so, develop an ideal marketing plan that will appeal to their target market segment, besides, the other notables benefits of optimizing sales and cutting down on charges. For the success of data mining models, there should be effective data gathering and storage, coupled with a proficient computer processing system.

Since its inception, data mining has expanded considerably with companies like the United States market mogul Walmart being the first firm to adopt the data mining model while trying to institute unnoticeable trends in shoppers’ buying habits as their primary drive (Fink, Yogev & Even, 2017). Through this approach, Walmart realized that there is a potential business gap in selling more alcohol during the evening hours through a simple trick of positioning the beer alongside baby products (Moscoso-Zea et al., 2016). This approach was very active as mothers occasionally request their husbands to pass by the retail and shop some baby products while on their way home from work. In this process, there was a considerably significant likelihood that men would purchase some drinks when they noticed it was being sold at the retail. While in the formative days, this model was employed in preserving large organizations that featured immense volumes of datasets (Fink, Yogev & Even, 2017). Even so, the astounding expansion of mobile usages, social network plexus, and cloud-based networking in the subsequent days, there has a marked rise of raw datasets wherein lies hidden treasure to optimize organizational performance. Despite the increase in the data volumes, businesses have upped their game with adequate resourcing to handle such data traffic.

The Gap between Theory and Practice

The advent of disruptive tech has had an impact on software and hardware costs, and the global internet revolution has empowered companies to be best positioned to develop, gather, store, process, evaluate, disburse and make use of vast volumes of datasets, faster than ever before (Liang & Liu, 2018). Currently, a vast population of consumers is making use of eCommerce for their product buying and conduct banking. The rise in web and eCommerce usage had fronted companies and state bodies the opportunities to collect and conduct a data analysis of given sets of information variously, in approaches that the before analog business society stood no chance to benefit from (Moscoso-Zea et al., 2016).  For a long time, customer information has been present in offline platforms. Still, with the advent of technology and internet networking, coupled with computer processing systems, the data is now within a click-away reach for company analysis.

The challenge in the analysis of vast volumes of data is drawn from the massive scalability coupled with the existence of mixed datasets based on distinct trends and laws –heterogeneity in the gathered or warehoused datasets, this sets forth a gap, for the practicability of the mined data (De Masellis et al., 2017). The sophisticated heterogeneity of mixed datasets, which is mined, has multiple patterns and dominions, with the data, attributes highly differing. The challenge in practice is primarily drawn from the theoretical aspect that a vast percentage of mined data from organizations is broadly unstructured, with high dynamicity, and lacks a format. This may be present in various forms from emails, to voice mails to audios and videos (Kasemsap, 2018). The challenge in practice is to analyze this unstructured and mixed data in trying to affect the data mining theory

Also, the user interface is another notable gap. The discovered knowledge based on the data mining instruments is only feasible in practice if it is valuable or interesting to the user. The whole theory lacks the ideal visualization data interpretation to ease the data mining process and bolster the understanding of their requirements. Perfect visualization of the mined datasets would be useful in practice to demonstrate and modify the extracted knowledge, but this remains to be a gap that ought to be bridged (Kasemsap, 2018). A practical approach to bridge the difference would be to conduct data mining based on the abstraction level. This would be collaborative, in terms of allowing the users to focus on identifying the trend, presentation, and optimization of data requests for the mined data as per the feedback findings. The institution of background information would be critical for the visualization of data interpretation to bridge this gap in data mining theory.

In practice, the performance of data mining and systems is another notable gap in the theory. The overall effectivity theory is dependent on the algorithms and models. In this case, the algorithms and models developed express some practical insufficiency, and this might consequently affect the whole process (Liang & Liu, 2018). A more realistic approach to the algorithm’s efficiency and scalability would be to redesign and tailor the models to obtain information from the large volumes database, with more efficiency for better performance.

There is a probable prediction that several advancing organizations and firms are chasing to genre BI by adopting contemporary models like eCommerce, CRM packing, and continual acquisition as top market collaborations (Liang & Liu, 2018). Such collaborations grant the present-day clients a chance to get involved in new mastery. The adoption of novel approaches of business like the data mining theory has some benefits that encompass some level of pitching business intelligence for the prediction of supply and demands within the market dynamics. To bridge the gap between the data mining theory and practice of business intelligence, the mentioned gaps need to be addressed in detail. This will be of overall organizational productivity in appreciating key client variables that might as well the market trick firms have been chasing after.

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