Data is a key aspect in business intelligence and analytics. Business intelligence focuses on the present, taking data to apply it towards enhanced decision-making through aggregation, visualization, and analysis which can improve operational efficiency. This paper continues building on the example of a medium-sized business offering business development education services. As previously noted, data is collected and flows in the business between data entities and major departments. Business intelligence helps to visualize this flow of data and make necessary improvements in the moment which will have immediate effect (Import.io., 2019). For example, it may be viable to share survey results with the sales department to modify or improve the sales process. Meanwhile, data analytics is focused on future. It utilizes data mining to analyze sets of information to determine patterns and potentially identify future trends that inform business decisions. Therefore, it is based on predictive analytics using previous data, but is inherently more complex and requires more input and development (Import.io., 2019). Combined with business intelligence, these aspects can have a wide range of influence and benefits for business decision-making to improve the quality and types of services offered to the consumers.
As discussed in previous papers, a wide range of data is collected including consumer names, their identification, their sales orders, satisfaction with purchase, willingness to recommend the service. There is also employee data being collected including name, personal documentation, and identification. As mentioned above, it is probably best to use a combination of live data for business intelligence aspects combined with data mining for long-term data analytics and pattern identification. Some examples of such data use can include use of satisfaction scores to modify course content or methods of service offerings or identifying patterns in consumer choices of courses to define better course listings for the future while removing redundant ones, streamlining the platform. Data is used extensively and consistently by most types of businesses, particularly where any sort of digital interaction occurs (i.e. registration, payment, or use of the service itself).
The U.S. does not have federal-level consumer data privacy laws, and while some states do, the requirements are law. Generally, businesses are legally allowed to collect data on consumers if such elements exist in the terms of service or other agreements per registration. It is also legal to store consumer data and sell it to third parties for aspects such as ad targeting, but some nuances may remain. However, recent state laws such as the CCPA in California provide the consumer with the right to access, delete, and opt-out of data processing at any time (Ramirez, 2020).. It is ethical to use consumer data for business improvement and growth as this data is securely protected, often anonymized and aims at improving the consumer experience. However, data use aimed at any sort of consumer manipulation and profitability based off personal data has potential ethical concerns.
There is an increased recognition that knowledge management is vital for sustainability and growth of business. Artificial intelligence in aspect dealing with machine intelligence, utilizing cognitive science, neural, systems, and machine learning to make systems behave in similar manner to humans in different situations. Expert systems are subset application/program which uses AI that builds a knowledge base and seeks to solve a problem within a specialized domain that typically requires human expertise, replicating the judgment and decision-making processes of human experts (Birkett, n.d.). The medium-sized business does not require such complexities of expert systems but may benefit from AI knowledge management. The AI system can automatically categorize customer data, form schedules, management student databases. This type of knowledge management is systemic and can contribute greatly to efficiency of operational processes. AI is known to contribute more informed and personalized education services to students that consider ways of learning and training processes. AI processes also contribute strongly in the back end of service provision including budgeting and expense management, course management, purchasing and procurement, and even HR-related issues (Schmelzer, 2019). AI systems ultimately benefit efficiency of a business with the purpose of lowering operating costs while improving transparency and overall responsiveness to consumers.
Birkett, A. (n.d.). Knowledge management systems: The ultimate guide. Web.
Import.io. (2019). Business intelligence vs data analytics. Web.
Ramirez, N. (2020). Data privacy laws: What you need to know in 2020. Web.
Schmelzer, R. (2019). AI applications in education. Forbes. Web.