How will natural language processing (NLP) systems change the business intelligence arena and enhance measurement systems?
A firm’s strategic success is bound by the potentiality of the firm to retail its products and services in a perfectly competitive market scenario. When a firm heads towards establishing its set goals and objectives, it is assigned with the reward of greater margin that adds value towards the business, increased volume of revenue and a significant increase in the general value of the shareholder. Plenty of human technocrats to computer information is available in the form of text language. A huge number of human labors are required to perform the research and work out on the relevant information then do a refined compilation of the findings into a knowledge database (Gross & Murphy, 2014). This paper mainly evaluates the process by which natural language processing aids the performance of business intelligence and facilitates the development of performance dashboards.
Natural language processing is more of a computerized approach that is applied in the text analysis procedure. Generally, it applies the concept of theories and a given set of technologies to perform text analysis and present exclusive information to the user. This occurs when the system identifies relationships as well as concepts through a given domain ontology. One of the examples of NLP is the semantic web, this technology facilitate the scope of work that is presented. Machines, through this kind of technology, carry out data processing directly or indirectly. The result is usually a significant reduction in the number of people-hours and acceleration of decision-making
Business intelligence involves software tools that are used for querying, reporting and analyzing. Concisely, it can be generalized as a basic process that can be used to convert data into information (Gross & Murphy, 2014). A stochastic approach towards creating knowledge and plans that can be used to drive effective business ventures if facilitated by the information that is obtained. Business operations cannot be equated to operating systems; it is independent on its own. Both systems generate qualitative reports, but business intelligence generates effective solution to the scope of inefficiencies of information gathering rather than applying the principle of people-hours annually. Business intelligence generally produces credible and accountable data that facilitates sales and general credibility of the firm.
Natural language processing techniques gives the managers the basic ability to describe their situational awareness using natural language and to input the situational awareness description to the given system in a simple manner. The applicability of natural language processing systems decreases the basic need for the management to apply structured language in order to obtain raw information into the system (Mills et al, 2014). Natural language processing system facilitates data capture because managers relates to the system the same way another human would perform in a natural conversation.
The basic combination of business intelligence and natural language processing facilitates a temporal approach to situational awareness that can be used by managers (Fisher et al. 2014). This can be applied widely in the decision-making process. Natural language processing systems designs a unique input-output function to business intelligence (Mills et al. 2014). The crafted business intelligence system leads to capability of data analysis and output visualization. Additionally, business intelligence systems that diffuse into the entire scope of departments of the firm provide a simplistic and reliable approach first-hand work that can be used for the establishment of a performance dashboard.
What metrics (up to 8 for each position) do you think would be appropriate for the following positions: CEO, CFO, VP Manufacturing, and VP Sales? Why?
A variety of metrics is available to ascertain for different business effectiveness in terms of achieving market demand at a minimum cost (Dijkman et al. 2014). The resultant effect of this is that, there is clear need to have a refined understanding of the relevant available metrics that can allow a specific choice. This evaluation sought to derive appropriate metrics that can be used for different managerial positions in a given firm. CFO, CEO, and VP are some of the significant managerial positions that determine the stochastic placement of a given entity.
Brand value metrics is often appropriate for CEO and CFO management positions. Brand equity relates positively to the firm value and the main task of the stated CEO towards increasing the firm’s value. The information concerning the performance of the firm is the basic task of any given CEO. Brand equity estimates the knowledge metrics that is applied in brand awareness and various stages of recognition within the firm (Gross & Murphy, 2014). In reality, successful brands own a significantly high score on general awareness and association. Performance metrics is another brand equity metric that can be used in measuring the competitiveness of a given brand in the market scene in respect to the competing brands. CFO position basically measures the effectiveness of its strategic setup through the use of financial metrics that measure the actual monetary value of the brand (Dijkman et al. 2014).
Customer value metrics is used to measure the customer lifetime value towards the interest of the firm for the general basis of identifying the most profitable customers during marketing campaigns. Referral value metrics and word of mouth are appropriate tools for VP sales because they measure the basic effectiveness of the sales department of a given firm. Customers who are satisfied are prone to engaging through word of mouth referrals of the given company than the underlying unsatisfied customers. Acquisition metrics and customer retention acts as tools for sufficing the VP sales positions and CFO positions because they measure the general number of customers the entity acquires, and the number of loyal customers who remain put to the business (Xu et al. 2014). Up-buying and cross-buying metrics offers a general classification to the nature of customers that a given firm attains into given categories based on the nature of the spending pattern and is, therefore, classified under the VP sales position.
Multi-channel shopping metrics determines how the different channels that belong to the firm influencing the customer. This type of metric determines the customer behavior and the range of profitability that leads to the formation of prominence. Product return metrics forms the VP manufacturing docket. This type of metric measures the total number of faulty products that are destined to the market scene (Dijkman et al. 2011). It depicts the efficiency of the manufacturing department in avoidance of the manufacturing department in avoiding errors and ensuring timely delivery of quality products. The superiority of the firm’s product quality in the market is arrived at by word of mouth and customer value, as well as referral metrics. This can only be arrived at though the use of manufacturing position effectiveness. All these approaches using procedures allows the VP; CEO and CFO to attain their set goals and objectives.
References
Dijkman, R., Dumas, M., van Dongen, B., Käärik, R., & Mendling, J. (2011). Similarity of business process models: Metrics and evaluation. Information Systems, 36(2), 498-516.
Fisher, D., Drucker, S., & Czerwinski, M. (2014). Business Intelligence Analytics [Guest editors' introduction]. IEEE Computer Graphics & Applications, 34(5), 22-24.
Gross, A., & Murthy, D. (2014). Modeling virtual organizations with Latent Dirichlet Allocation: A case for natural language processing. Neural Networks, 5838-49.
Mills, M. T., & Bourbakis, N. G. (2014). Graph-Based Methods for Natural Language Processing and Understanding—A Survey and Analysis. IEEE Transactions On Systems, Man & Cybernetics. Systems, 44(1), 59-71.
Xu, S., Wenjie, L., Houfeng, W., & Qin, L. (2014). Feature-Frequency--Adaptive On-line Training for Fast and Accurate Natural Language Processing. Computational Linguistics, 40(3), 563-586.
