Introduction
Technology has revolutionized how businesses operate and has enormously impacted the global economy. As technology rapidly evolves, so do the opportunities available to businesses. The challenge for businesses is assessing new technologies in their field and deciding which one best suits their needs. This report will determine three new technologies in the field of business analytics and recommend which one is best suited for businesses. This report will assess three technologies: self-service business intelligence, predictive analytics, and machine learning.
Self-service Business Intelligence
Passlick et al. (2020) state that self-service business intelligence (BI) is a powerful technology that enables businesses to quickly and efficiently access, analyze, and interpret data to gain insights, identify trends and make better decisions. It is rapidly becoming an essential tool for organizations as it is relatively cheap, easy to use, and can be implemented quickly. One of the key benefits of self-service BI is its affordability; it does not require expensive software or IT personnel to run, making it an attractive and cost-effective option for many businesses (Passlick et al.,2020). This is especially true for small to mid-sized businesses that often cannot afford to invest in traditional BI solutions. Additionally, self-service BI is relatively easy to use and can be used by non-technical personnel. This makes it an ideal choice for businesses with limited IT resources, as it requires minimal training and expertise. Self-service BI also enables businesses to identify trends and anomalies in their data quickly. For example, a business may be able to quickly identify a trend in customer buying habits or a sudden decrease in sales. This data can then be used to inform decisions and develop strategies for improving business performance. By monitoring key performance indicators (KPIs) over time, businesses can gain valuable insights into their operations and make informed decisions.
Self-service BI also enables businesses to identify new opportunities quickly. This can include identifying potential new markets, developing new products and services, or expanding into new areas. By analyzing data and spotting trends in customer behavior, businesses can quickly identify new opportunities that have the potential to generate significant revenue (Bani Hani,2017). Finally, self-service BI can help businesses improve their decision-making process. Businesses can make better decisions based on sound data rather than guesswork by quickly and accurately analyzing data. This can be especially helpful in product development, marketing, and customer service, where decisions are often made without adequate data.
Predictive Analytics
Predictive analytics is a technology that enables businesses to gain insights into their operations with the help of historical data. In essence, predictive analytics is a powerful tool for businesses because it can better understand their operations and help them make better decisions. Predictive analytics can be used to predict customer demand, identify potential opportunities, and optimize operations. For example, predictive analytics can be used to forecast customer demand by analyzing historical sales data. This can help businesses create accurate forecasts and adjust their operations accordingly (Attaran et al.,2019). Additionally, predictive analytics can be used to identify potential opportunities that may arise due to changes in the market or customer needs. For instance, a business may use predictive analytics to identify new product development opportunities or target markets.
However, predictive analytics can be challenging to use and requires much knowledge and expertise. This makes it difficult for businesses to implement predictive analytics without hiring an experienced professional or team. Additionally, predictive analytics can be expensive as it requires specialized software and personnel. Furthermore, predictive analytics relies heavily on data accuracy and quality, meaning that businesses must ensure that their data is accurate and up to date. Finally, predictive analytics is only sometimes reliable as it can only predict events that are likely to occur and may not be 100% accurate.
Machine learning
Machine learning is a type of artificial intelligence that is used to automate processes, identify potential opportunities, and optimize operations. It is a computer-based technology that uses algorithms to identify data patterns and learn from them. This allows machines to make decisions and predictions without requiring human input (Canhoto et al.,2020). Machine learning can be used for various tasks, including text and voice recognition, image processing, and forecasting. The process of machine learning involves gathering data and building an algorithm, or a set of instructions, that can be used to identify patterns and make decisions. The algorithm is then tested against real-world data to check its accuracy. This process of testing and refining the algorithm is known as “training” and is essential to the success of the machine learning process.
According to research, the main advantage of machine learning is that it can be used to automate processes and identify potential opportunities. Machine learning algorithms are designed to improve continually, meaning they can learn from their mistakes and become more accurate over time (Canhoto et al.,2020). This makes them ideal for tasks such as predictive analytics, where they can be used to identify trends and patterns in large datasets. Additionally, machine learning can be used to optimize operations, such as improving customer service by identifying customer needs and making suggestions for improvement.
However, there are several potential drawbacks to using machine learning. The first is that the technology can be difficult to implement and requires great expertise. This is because the algorithms used in machine learning are complex and require an understanding of both data and computer science (Lee,2020). Additionally, machine learning can be expensive as it requires specialized hardware and software. As a result, businesses must be willing to invest significant resources into developing machine learning solutions to make the most of the technology.
After assessing the three technologies, it is recommended that businesses use self-service business intelligence. This technology is inexpensive, easy to use, and can be implemented quickly. For example, a small business can use self-service BI to gain insights into its customer behaviors. By using self-service BI, businesses can quickly and easily access their customer data and identify trends in their purchases. This can help the business identify customer needs and adjust its marketing campaigns accordingly.
Additionally, self-service business intelligence does not require IT personnel or expensive software solutions. In comparison, predictive analytics and machine learning are more complex technologies that require a great deal of expertise and resources. Predictive analytics uses data to identify patterns and predict future trends, while machine learning is used to automate data analysis. These technologies can be challenging to use and require a great deal of expertise to use effectively. Additionally, these technologies can be expensive and require businesses to invest in an expensive software solution. As such, self-service business intelligence is the most suitable technology for businesses.
Conclusion
In conclusion, self-service business intelligence is the most suitable technology for businesses in business analytics. It is inexpensive, easy to use, and can be implemented quickly. This makes it an ideal choice for small to mid-sized businesses that often cannot afford to invest in traditional BI solutions. Additionally, self-service BI can be used by non-technical personnel, enabling businesses to identify trends and anomalies in their data quickly. This can help them make better decisions and develop strategies for improving business performance. Furthermore, self-service BI can help businesses identify new opportunities that have the potential to generate significant revenue. Predictive analytics and machine learning are more costly and require much expertise to use effectively. Therefore, self-service business intelligence is the most suitable technology for businesses in business analytics.
References
Attaran, M., & Attaran, S. (2019). Opportunities and challenges of implementing predictive analytics for competitive advantage. Applying Business Intelligence Initiatives in Healthcare and Organizational Settings, 64-90.
Bani Hani, I., Deniz, S., & Carlsson, S. (2017). Enabling organizational agility through self-service business intelligence: The case of a digital marketplace.
Canhoto, A. I., & Clear, F. (2020). Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential. Business Horizons, 63(2), 183-193.
Lee, I., & Shin, Y. J. (2020). Machine learning for enterprises: Applications, algorithm selection, and challenges. Business Horizons, 63(2), 157–170.
Passlick, J., Guhr, N., Lebek, B., & Breitner, M. H. (2020). Encouraging the use of self-service business intelligence–an examination of employee-related influencing factors. Journal of Decision Systems, 29(1), 1-26.
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