Business Intelligence Approaches
Business intelligence is a growing field in the modern-day business environment as a tool for information gathering and decision-making. As customers become more knowledgeable and demanding, businesses have to keep up by providing them with matching goods or services. It is imperative that businesses conduct prior research work before making their decisions and such a process can be facilitated with factual data and information that comes in the form of market intelligence. Business intelligence has varied definitions but can be defined in simple terms as a set of mechanisms and technologies that foster the improvement of business operations and support decision-making systems to achieve competitive advantage (El-Adaileh and Foster, 2019). In essence, business intelligence is the kind of information that a business gathers for decision-making purposes, which is then used to generate and maintain a competitive edge.
Competition on a global scale has pushed businesses to gather accurate information to be used in decision-making processes. Most organizations are turning to business intelligence systems for their decision-makers internally and externally. Internally, business intelligence promotes greater management and encourages a greater sense of responsibility to all employees. Externally, business intelligence is used to build stronger relationships with strategic partners and suppliers by sharing key performance indicators for mutual gain. However, any involvement with business intelligence is not always straightforward as it is expensive to gather, complex to use, may not be flexible in a dynamic business environment, and demands investing in hardware infrastructure (Cruz-Jesus et al., 2018). As a consequence, many businesses, especially small medium enterprises (SMEs) have found it difficult to use and implement business intelligence effectively.
While there are many theories, models, and frameworks for approaching business intelligence, the most popular ones are the diffusion of innovation (DOI) theory and the technology organization environment (TOE) framework. The DOI theory considers all the perceptions about an innovation before it has been adopted and this gives a chance for awareness to be created. As a result, DOI has become a prominent adoption model in most information system intelligence research work (Dearing and Cox, 2018). The theory provides a thorough analysis of the drivers and constraints of innovation diffusion and gives insights into the process of whether or not an innovation should be adopted. More importantly, DOI covers both technological and non-technological innovation at the individual and firm levels (Cruz-Jesus et al., 2018). The DOI theory takes into account five factors – observability, compatibility, complexity, relative advantage, and trialability – to determine the relative speed with which an innovation will be adopted (Stjepić et al., 2021). Therefore, with DOI, business intelligence can be used to determine whether a business innovation, process, or idea will be successful when introduced into a certain marketplace.
Besides DOI, the technology organization environment (TOE) framework is another popular approach to business intelligence. The TOE framework employs three elements –technological, organizational, and environmental – of a given company to assess the organizational process by which technological innovation will be adopted (Stjepić et al., 2021). The technological element covers the technologies within and outside a company while the organization element refers to features such as size, linking structure, leadership, and centralization that determine the makeup of an institution (Cruz-Jesus et al., 2018). Lastly, the environmental element covers the areas within which the business operates, including suppliers, competitors, and government bodies. TOE in business intelligence recommends that important information is gathered on these three elements and a context for adopting a certain type of technology is then prescribed. With such information in place, it is possible for a business to modify its operations and make improvements in ways that upgrade the business enterprise wholesomely.
TOE and DOI are not inextricably linked concepts but have an array of similarities and differences. Firstly, both DOI and TOE take into account the technological factors that influence the adoption and implementation of a given business decision. DOI encourages business intelligence to take into account how technology will make a certain business decision beneficial or detrimental to a company (Dearing and Cox, 2018). Similarly, TOE recommends that business intelligence takes a look at the role that technology plays in generating a competitive advantage for a given business. Like DOI, TOE also encourages that internal and external technologies relevant to a company are examined and their ability to influence an adoption decision is then determined (Cruz-Jesus et al., 2018). Therefore, both DOI and TOE are technologically sensitive approaches to business intelligence.
The other similarity between DOI and TOE is the focus on organizational elements. DOI focuses on both internal and external organizational elements that may drive or constrain the implementation of business decisions (Cruz-Jesus et al., 2018). Consequently, DOI presents business decision makers with a chance to weigh the pros and cons of a given idea or concept before it can be fully taken on board. On the other hand, TOE recognizes organizational factors as necessary ingredients when determining the validity of adopting a certain piece of technology in business. Organizational factors in TOE are varied and are not limited to relationships, management support, and size of the company in question (Cruz-Jesus et al., 2018). In the business intelligence world, focusing directly on the organizational elements, as recommended by the TOE and DOI frameworks, ensures that businesses will always make the most informed decisions.
In terms of the best approach to business intelligence, both DOI and TOE are instrumental tools. Since they have an array of overlapping characteristics, both TOE and DOI can fit in many different business intelligence settings. As a result, the preference on which business intelligence approach to use can simply be narrowed down to the individual’s taste. However, one major difference that would make DOI stand out is that it incorporates the important element of leader characteristics, popularly referred to as management support. Such inclusion makes DOI a better suited theory for analyzing intra-form technological innovations than TOE (Cruz-Jesus et al., 2018). Therefore, when the priority is on the intra-firm elements, DOI is the stand out favorite. For any firm that places value on business intelligence, the choice of DOI or TOE should be guided by different parameters. Since the main objective of business intelligence is to acquire important internal and external business-related information, the theory that promotes easier unification of information from multiple sources should be the most desirable. Unification of multiple data sources fosters operational efficiency and saves time used when tracking down information.
Data Mining
Data mining refers to the nontrivial process of extracting previously unknown useful information as well as patterns from large data. The process is also called knowledge mining of data, knowledge discovery in database (KDD), and knowledge extraction of data (Mughal, 2018). Data mining involves seven main steps, data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation, and knowledge presentation (Jassim and Abdulwahid, 2021). Some of the techniques and algorithms used in KDD include association rule, artificial intelligence, genetic algorithms, regression and neural networks among others. Knowledge extracted can be utilized in different areas such as customer retention, science exploration, production control, fraud detection, and market analysis.
Databases are an essential component in day-to-day business operations in modern organizations. Database maintains large sets of information about different objects or subjects which can later be used as a point of reference. Businesses can have a database that includes, the number of employees, the different suppliers, inventory on raw materials, price of products, salary and wages, events, transactions, and many other areas. Notably, businesses generate new data every day and store them in databases (Bhojaraju and Koganurmath, 2003). There are different types of databases, including network, hierarchical and relational. Database searches cover the process of extraction information from well-maintained databases using keyword searches with the aim of acquiring the most relevant information. Database searching methods need to consider the sensitivity and selectivity of the data being sought after. Selectivity refers to the ability to find the right type of data, while sensitivity is finding data that is correlated. The common heuristic search tools include FASTA and BLAST.
Data analysis refers to the process of applying logical or statistical techniques to illustrate, recap, evaluate, and recap data. It is used to draw inductive references. A significant component of maintaining data integrity is accurate data analysis. The five core steps of data analysis is identifying, collecting, cleaning, analyzing and interpreting data. The types of data analysis utilized in the business and technological world include prescriptive analysis, predictive analysis, diagnostic analysis, and statistical analysis. Moreover, there are different methods of analyzing data. The method is dependent on whether the data is qualitative or quantitative. Some of the methods include factor analysis, cluster analysis, neural networks, regression analysis, cohort analysis, and data mining.
The major advantage of data mining is that it provides the ability to look through a large pool of data as compared to data analysis and database searches. Given that Big Data is being generated on a daily basis, data mining is an effective tool to sieve through Big Data to derive meaningful information (Lan et al. 2018). Also, it provides forecast on a given problem in different fields such as genetics, agriculture, engineering, and electric power. Utilizing the available data, data mining identifies changes in pattern and its repetition is used to predict the future. Additionally, data mining enhances decision-making capabilities of organization by providing concise and clear information. The major disadvantage of data mining is privacy issues (Lan et al. 2018). For example, data being used to assess a company’s performance will likely have employees’ personal details. Data mined might also be misused leading to significant ethical issues. Consequently, data mining might lead to privacy legal concerns.
The advantage of database searches is that they provide structural information. After setting up the keyword for searching, the database provides correlated and information that is relevant to the same. For example, if a human resource manager wants to know how many employees are on study leave he or she needs to type ‘study leave’. The HR database will accurately provide this information: both of employees who are currently in study leave and those who went in the past. Also, database searches provide ready-made information making it easy to use (Bhojaraju and Koganurmath, 2003). Compared to other methods of data retrieval, such as data mining and analysis, database searches are less time-consuming and offer a fast and efficient way to acquire information.
Database searches also lead to unexpected discoveries. In the search example above, the HR can have information on employees on study leave and those receiving study leave bonuses. Thus, he or she might discover that there are employees who are receiving study leave bonuses yet their study period has ended as The disadvantagethe database provides. Disadvantage of using database searches is that keywords semantics might bring ambiguity. Finding relevant answers depends on accurate keywords semantics, thus, the keywords should be as accurate as possible. Furthermore, database searches provide numerous results and are ranked based on relevance which at times might not offer the result one is looking for (Bhojaraju and Koganurmath, 2003). Using the example above, if the human resource manager searches ‘study leave’ on the company database, it is likely that the results will include study leave programs the company offers or company policies on study leave, among others.
The other advantage of data analysis is that it improves the functions of data mining. Data mining only enables one to acquire data but it is only after analysis that it can be meaningfully used. For instance, data mining is applicable to interactions, images, social relations, and text (Iwatani, 2018). However, it is only with data analysis that such information can be applied meaningfully. For experienced researchers, it is important that both data analysis and data mining are used concurrently.
Strategic decision-making for any business requires information that is translated to strategic actions which ensure the success of a company. Data mining and data analysis are significant as they provide business intelligence which is essential in financial budgeting, human resource management, product design and competitive pricing. One of the tools used in data mining and analysis is the corporate performance management (CPM) tool aids in corporate decision-making (Nazier et al., 2013). One of the companies that utilize business intelligence is Apple. The product design of the iPhone is made to ensure that it meets customer preferences. In recent years, the ‘vlogging’ has become a popular action among consumers. Hence, the need for quality, affordable cameras creates a demand for this. Apple has ensured that the iPhone has great camera quality to meet this demand. By doing so, the company retains its competitive advantage.
Business and corporates are generating data on a continuous basis. Data from daily operations, product designs, human resources, financial statements and strategic decisions are being created by organizations. To foster effective business intelligence, companies use data mining to respond to business problems, identify influential customers, facilitate proactive planning, and develop a recommendation system Damasceno et al., 2021). The need to acquire new information that makes companies competitive is of importance as many organizations seek to maximize profits in the highly dynamic market. Agricultural companies, for instance, have to find ways to produce crops on limited lands whereas tech organizations have to find how they can incorporate technology in daily human activities. Such dynamism paves way for data mining to be applied alongside other business intelligence tools in a bid to sustain healthy competition.
Ethics in Business Intelligence
Ethics is useful for any firm that seeks to improve its business decisions. In gathering business intelligence, it is necessary that companies adopt measures that do not infringe upon the privacy rights of competitors, customers, or suppliers. As a result, the ethical principles of business intelligence are in place to differentiate information gathering that constitutes business intelligence or industrial espionage (McBride, 2015). Similarly, these principles clarify the kind of business intelligence activities that are considered legal or illegal. Companies need to stay abreast of these principles in order to stay away from suits and other legal consequences. In essence, the underlying aspect of business intelligence is using all available means either from specialist software, publicly accessible sources, or subscription sources to make the best decisions for a company while simultaneously abiding by the provisions of the law.
Given the influential power of business intelligence to drive decision making and resource allocation, it is required of practitioners to be committed to ethical data collection and use techniques. The ethical concerns of business intelligence cannot be resolved with abidance to corporate compliance and codes of practice, but requires individual commitment and maturity in business intelligence practices (McBride, 2015). Therefore, observing ethical practices in business intelligence is a matter rooted in the ethical practices of a company by virtue of the kind of employees it has.
The main legal and ethical concern with business intelligence is the issue of privacy. Today, all big companies are usually collecting information from competitors, customers, and suppliers that are used for business intelligence and for big data purposes. Data-driven businesses, in particular, are known to sell their customers’ information to other companies for critical business decisions (McBride, 2015). While the point of defence is that such customers’ information is only used for business decisions, there is a loophole left for abuse of such information. Since customers have no control over what happens to the information they share with these companies, their privacy rights are usually abused.
The other concern with business intelligence is the security of data. Business intelligence information is susceptible to attacks especially when it is stored in unencrypted format. It is possible that sensitive information acquired for purposes of business intelligence could end up in the wrong hands and be used for unethical practices. A prime example as to how sensitive information can be misused is when Hilary Clinton’s emails were leaked as she ran for presidency in 2016 and used against her by her political opponents (Leung et al., 2020). While such information was used in a political field, the same unethical practice could befall the business sector.
Another area in which data mining possibly invades privacy is in marketing. For companies to ensure effective marketing strategies they need information on certain aspects such as customers peak hours for eating or using the phone, the most ordered food in the menu, the demographic and fashion trends in the market among others (Wahlstrom et al., 2006). Such information is crucial for organizations to create personalized marketing whether through print or social media. For companies to have such information they might access personal data on consumers eating habits through means such as the frequency of payment to a branch during a certain period. Taking this into consideration, privacy concerns arise significantly.
The practice of business intelligence leaves room for the amplification of corporate vices and virtues. For instance, companies that do not respect their customers are highly likely to use business intelligence of accelerating sales as unethically as possible. There have been instances where hospitals use business intelligence in unethical ways where well-insured patients are given preferential treatment to those without insurance (McBride, 2015). For the hospitals, the use of business intelligence is such a manner reflects poorly on their ethical sensitivity as the focus is not on providing the best care for all patients and in an equal manner, but on generating the maximum profit by targeting specific customers. It is important that businesses do not abuse information acquired from data mining services for exclusion of marginalized groups or exploitation of its affluent members.
In conclusion, business intelligence is a critical component in any organization. BI ensures that an organization stays competitive by having the right type of information to implement the right strategic plans. For business intelligence to be useful for a company, intelligence gathering techniques should also be efficient. Data mining and data analysis are considered the most effective methods of meaningful information acquisition. Through these techniques, companies can find necessary knowledge on aspects such as marketing, financial budgeting, and human resource management. Nonetheless, the major ethical issue arising from using these techniques is data privacy. Thus, finding ways to ensure consumer privacy rights are protected while maintaining effective data usage is essential.
References
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