Posted: September 19th, 2020
We are currently living in a digital era driven by technology as a facet and an asset that determines our day-to-day operations. In this digital age, business operations are taking advantage of sophisticated tools and information to streamline their operations and achieve business success (Heinze et al., 2020). One of the valuable assets used by business ventures is data that gives good insights on market behavior, which is fundamental in the decision-making process. In this regard, business ventures use data to analyze to understand consumers better, make a prudent evaluation on ad campaigns, provide personalized content, develop products and create content strategies (Yerpude & Singhal, 2017). However, data relevance is only harnessed when business ventures use reliable data mining tools, which helps in analyzing data from a different perspective and consequentially creating useful information. Data mining envisions a process finding patterns or correlations among enormous fields in a diverse relational database. Data mining helps extract useful patterns and information from a database to use this data in future decision making.
Therefore data mining is an extricable component of business analytics; this is the process of analyzing big data that has been initially collected and finding a way of turning this data into information that as a business can use in determining some relationship between the data or spotting a trend hence data mining is effecting due to its potential business benefits (Shmueli et al., 2017). This envisions why data mining has increasing benefits in determining the relationship between data; data mining can also be used to sporting a trend between data that can help predict a continuation of a trend or sporting new trend as it emerges, or changing the behavior of the consumers. However, the data mining process’s output stands to the fundamental component that determines the success of the data mining process; this is determined by several factors that include the quality of data used, its validity, and the robustness of the algorithms. Therefore this paper will seek to elaborate on how these factors mentioned above are critical in determining the output of data mining despite its robustness.
This factor is fundamental in determining the success of the business operation. Data quality is used to envision the measure of data based on factors like completeness, accuracy, reliability, consistency, and whether the data used is up to date (Robinson, 2017). They determine the quality of data to help a business venture identify whether available can be relevant in solving underpinning challenges (Wynn & Sadiq, 2019). Moreover, the emphasis on data quality has risen as data processing has become inseparably associated with business operations and business operations are capitalizing on data analytics as a tool for driving business decisions making. If the data default, it will have significant negative implications for the business; for instance, poor data is attributed to be a source of inaccurate analytics and consequential half-baked business analytics.
Data accuracy stands at the vital building component of data with high quality. To mitigate challenges associated with faulty analytics, data that is being used must be correct. Besides other aspects of include data completeness, this envisions data with all elements necessary for analytics. Data quality is also measured based on consistency, which envisions the absence of conflicts between the same data values stored on different data sets or different systems. Besides data quality, data currency also demines data quality as it envisages the data that is up to date and gives information on current affairs. With all these in place, it is easier to produce data sets the is trustworthy and reliable. Data quality is fundamental in determining the success of business analytics. However, the Harvard business review mentioned that data quality stands to be challenging aspects that business operations seek to obtain (Rouse, 2019). This is depicted by the fact that businesses struggle much to overcome the hurdle of obtaining valuable data required to provide good insights on consumers’ current wants and needs. The absence of such valuable data may lead to business operation failure due to poor decision-making; this includes poor performance emanating from improper communications and marketing efforts and incomplete customer data.
According to global (2020), “Business analysts are increasingly critical in the modern data-driven economy. They determine market trends, analyze performance data, and even present insights to executives that will help direct the future of the company. And as the world becomes even more data-driven, it becomes vitally important for business and data analysts to have the right data, in the right form, at the right time so they can turn it into insight.” However, these businesses analytic have a hard time seeking data with high quality. This presents a challenge despite the richness in tools and algorithms and manipulation models used in performing the analysis itself. The challenge of data quality is also envisioned by the flood of big data that has consequentially resulted in the exportation of raw information for analysis. Most businesses have no time ensuring proper data clean up hence affecting the entire analytics operation.
In practice, the data quality affects the success of some practices where data mining is used in improving business competitiveness. For example, its use in sales forecasting, based on identifying the relationship between datasets, can determine data sets that have a close relationship with another dataset and use that information to forecast by predicting what happens. For example, the Pune Maharashtra in India undertook a sales forecast of the Hydroxypropyl Methacrylate market and produced an in-depth insight of sales analysis. The analysis was based on the value growth prospects of Hydroxypropyl Methacrylate, which was fundamental in making market projections (Johnson, 2020). Some of the analyzed factors include the participant Hydroxypropyl Methacrylate principal’s products, geologic areas, participants, end-user applications, and product sort. The end product is a strategic report on global Hydroxypropyl Methacrylate research, and it contains details that are vital for the Hydroxypropyl Methacrylate supply business. The information’s constrained to include Hydroxypropyl Methacrylate market frequency, supply-demand quantitative relation, driving factors, the main actors of the Hydroxypropyl Methacrylate market, and information on challenges and restraints. However, the viability of this report’s output does not depend much on the richness of the data mining process but mostly on the data quality used. In this case, the robustness of data quality played a significant role in determining sales, market revenue, and the production value of Hydroxypropyl Methacrylate and to predict the market share. This will consequently help Hydroxypropyl Methacrylate lead prodding firms to undertake proper strength, weakness, opportunities, and Strength (SWOT) analysis. Conversely, if the input data used was of poor quality; therefore, the output would give misleading information despite the viability of the sales forecasting process.
Data mining has extended from the normal use by commercial sites, social, news outlets, news platforms, and retailers to use in for political interests (Hitlin & Rainie, 2019). A good illustration is the 2016 Cambridge Analytica scandal, where the London based data mining and analytics firm abused personal information for over 50 million Facebook users to facilitate political campaigns (Confessore, 2018). The data comprised of the psychological profiles of American voters. Despite the scandal that landed Cambridge in trouble, the later was achieved as the data was instrumental in ensuring successful Camping for Trump and consequential elections to office. The campaign became a success because of the quality of data initially used by Cambridge Analytica. This data gave a prudent account of prospective voters’ psychological profile, and the campaigners have an easy task customizing their campaign messages effectively and efficiently.
Data validity affects the output of the data mining process as business ventures seek to make informed decisions based on the information obtained from data mining operations. For instance, consumer data is important in market segmentation; data mining has helped businesses become much more effective and efficient in segmenting by breaking down their market into small groups, enabling them to target products and services more effectively or position products successfully. Therefore, data mining has a robust impact on segmenting a market with ease based on occupation, age, gender, and occupation.
Traditional mass marketing was carried out by advertising, price promotion, packaging, and mass advertisements on television, magazines, billboards, and radios (Belch & Belch, 2003). This marketing has been transformed with the new age of online generations addicted to social media. Social media is a convenient platform for brand advertisement as it captures an age group defined by the online culture in the digital age. Based on the Coca-Cola market for coke- zero product, segmentation would capture only consumers who are health sensitive and gravitated to watching their diet. The brand developed coke zero this for an audience who are sensitive about their health and weight and who were not interested in products that could increase their waistline. Specifically, it targeted male adults between 18-29 years (William & Goldsworthy, 2011).
When segmenting a mass market, a business can use various criteria to fragment the market to specific groups, including demographics like age and gender. However, age has limited Coca-Cola segmentations has it has only targeted a particular segment of the population. The coke zero brands work on a marketing strategy that focuses on a male between 18-29 years. Therefore the company should ensure it has data for these consumers to easily make customized advertisements specifically targeted to the identified age bracket. Data mining plays a critical role in this stage, but the robustness of its output depends on the validity of the data used for this case. If the data used in data mining for coke-zero had low validity, then it would affect the entire operation of market segmentation. Consequentially the Coca-Cola Company will end targeting the wrong market segment with inappropriate advertisements leading to lower sales and a small profit margin. Conversely, if Coca-Cola decides to subsidize their market segmentation by capitalizing on sophisticated data mining processes but still use data with low validity, this will be an only a good illustration of the end justifies the means.
In business analytics, where business ventures rely on data mining as a tool for extracting critical knowledge from a substantial amount of data, classification Algorithms are mostly relied on as data mining models. Data mining is collecting relevant information from unstructured data. Hence it helps to achieve specific objectives. “The purpose of data mining effort is normally either to create a descriptive model or a predictive model. A descriptive model presents, in a concise form, the main characteristics of the data set. The purpose of a predictive model is to allow the data miner to predict an unknown often future value of a specific variable.” (Pushpam & Jayanthi, 2017). Therefore, the algorithm is a data mining tool that creates a model by analyzing the input provided and performing an extraction for specific types of correlation. Identifying the best algorithm for a specific analytic task can be challenging because different algorithm produces different results, and another algorithm can give more than one result. Therefore with a diverse range of algorithms available, one has to develop and apply an algorithm that is robust and can lead to extractions of tangible data that can help in prudent decision making.
A good illustration is a study undertaken by Pushpin & Jayanthi (2017) to determine the critical factors that impact Facebook usage time and access frequency. Based on this study’s findings, it is evident that the SVM algorithm emerged as the best algorithm that gave the most accurate predictions for the two target variables. This algorithm was tasted alongside CHAID, C5, CART, and artificial neural network algorithm and was built into SPSS clementine 12. As the dataset consisted of discrete valuables, the false and true predictions were listed as indicated in figure one below. These variables play a critical role in explaining the robustness of the SVM algorithm in creating accurate predictions. After determining the most robust algorithm, this study concluded that usage time, membership in students group, and age are the fundamental factors that affect access frequency to Facebook. With this illustration, it is evident that the richness of the data mining process has little influence in determining the output; instead, the employment of a robust and valid algorithm can suffice as far as the output quality is concerned.
Generally, the significance of data in this digital era forms an inextricable part of business operations and determines business operations’ success. This includes the use of data to determine consumer behavior and consequentially make a decision based on appropriate goods and services that are to be delivered to these consumers. Obtaining this consumer data envisions a data mining process in which several factors determine its output quality. From this study, it is evident that the statement “No matter how sophisticated the data mining processes are, the output fundamentally depends on the quality of the data used and the validity as well as the robustness of the algorithms developed and applied.” It is not only true theoretically but also viable in practice. The data mining for Hydroxypropyl Methacrylate, coca-cola market segmentation, and Cambridge Analytica affirms the importance of data quality in determining data mining output. Besides the case of determining the critical factors that impact Facebook usage time and access frequency, it is evident that the robustness and validity of algorithms used also plays a significant role in determining the output of data mining operations.
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