Reflection and Literature Review
Business analytics strives to allow companies to make decisions faster, cheaper, and smarter, with the goal of generating market value. To date, the main emphasis on descriptive and predictive analytics in the academic and industrial realms. However, prescriptive analytics, which aims to identify the best approach for the future, has gathered growing interest in the study. Prescriptive analytics is also seen as the next step toward increasing the sophistication of data analytics and contributing to automated decision-making in advance for improving business efficiency.
Data analysis can be conducted using different types of computational methods and various technologies, including machine learning, exploratory analysis, and data mining (Baum et al, 2018). The approaches can be classified as descriptive, analytical, predictive or prescriptive depending on the way the data is analyzed and the information intended to be obtained from the study.
According to Baum et al, (2018), Descriptive analytics indicates use of data analytics in describing and understanding situation based on the present and the past. In addition, diagnostic analytics is use of data to find the cause of an event or situation. Predictive analytics is an application of data analysis using mathematical principles and data to explain the relationship between data to predict future outcomes based on changes in the data Server (Baum et al., 2018).
Descriptive analysis summed up in MDM’s research progress by synthesizing numbers of literary works by years, sources, and types of publications. Meanwhile, the technique of text analysis using a 1-gram and 2-gram model displays the most common terms and subject of interest in MDM literature (Haneem, et al., 2017). Significantly, this review’s technique of descriptive analysis and text analysis is very useful to other researchers particularly when they plan to embark on a new research subject. Overall, the most attention has been paid to the topic of master data management, how MDM solves data quality issues, and MDM is a foundation for effective business intelligence.
The issue of the MDM-business process partnership, data integration, and big data follows it. In addition, the topic of data governance, information governance, and data management had also raised many concerns of MDM research. Finally, it is also noted that the information commonly managed in MDM is data about the product (Haneem et al., 2017).
According to Edwards, Predictive analytics can help a company predict potential outcomes based on machine learning techniques and historical data. Predictive analytics is a category of data analytics, which aims to make predictions of future results based on machine learning, analytical techniques and historical data such as statistical modeling. The science of predictive analytics with a large degree of precision will produce potential insights (Edwards, 2020). With the assistance of advanced predictive analytics software and models, any company can now accurately forecast patterns and behaviors milliseconds, days, or years into the future using past and current data.
Predictive analytics has captured support from a wide range of organizations, with a global market projected to reach around $10.95 billion by 2022, growing at a compound annual growth rate (CAGR) of around 21 per cent between 2016 and 2022, according to a 2017 Zion Market Research report (Edwards, 2020).
Applied Learning Exercises
- Data warehouse.
A data warehouse is a data management facility explicitly built for query and analysis allowing organizations to turn data into actionable insight. The right cloud solution for data warehouse could help you realize your data’s full potential. However, many companies are switching to a cloud data storage model to address the growing challenges of traditional on-premises data warehousing. Major players in data warehousing include SnowflakeDM, Microsoft Azure, Google BIgQuery, and Amazon Redshift. Other data warehouse vendors include, Oracle, SAS institutes, Cognos, Software A&G. Since companies rely on this data for analytics or reporting purposes, the data need to be correctly structured and easily available – two attributes that characterize data warehousing and make it important for businesses today (SAS, 2020).
Oracle data warehouse
Oracle offers on-site, in-cloud, and hybrid applications with four decades of data processing and analytics innovation. Oracle stands out from competition by giving clients a genuinely unique value proposition, and its ability to implement its plan efficiently and effectively. The In-Memory database provides speed-of-thought retrieval for sophisticated analytical queries. Database In-Memory incorporates state-of-the-art columnar data processing to speed up the data warehouse analytics by magnitude commands. Answers that used to take minutes to get are now immediately accessible. Questions you used to dream of asking can now be answered quickly and easily. By providing total autonomy, mobility and economies of scale, it offers faster consolidation of data marts and data warehouses.
Oracle Multitenant provides additional benefits by offering a simple and efficient management mechanism within the overall Oracle Big Data Management System for the distribution of sandboxes and data discovery platforms. As data warehouse grows with Oracle Partitioning that improves the handling, efficiency, and availability of large data marts and data warehouses. It increases query efficiency by only operating on relevant data, improves availability by manageability of individual partitions and decreases costs by storing data in the most suitable manner. With Automatic Data Optimization (ADO), it allows you to create smart data policies for data movement and compression making it possible for data compression and storage tiering. The database is able to leverage on all hardware resource through multiple storage units and cluster nodes. The Oracle Optimizer will automatically decide whether a query will run in parallel, and the degree of parallel usage based on the statement’s resource requirements.
Conclusions
References
Baum, J., Laroque, C., Oeser, B., Skoogh, A., & Subramaniyan, M. (2018). Applications of Big Data analytics and Related Technologies in Maintenance—Literature-Based Research. Machines, 6(4), 54.
Haneem, F., Ali, R., Kama, N., & Basri, S. (2017, July). Descriptive analysis and text analysis in Systematic Literature Review: A review of Master Data Management. In 2017 International Conference on Research and Innovation in Information Systems (ICRIIS) (pp. 1-6). IEEE.
Edwards, J. (2020). Predictive analytics: Transforming data into future insights. Retrieved from https://www.cio.com/article/3273114/what-is-predictive-analytics-transforming-data-into-future-insights.html
SAS. (2020). Data warehouse. Retrieved from https://www.sas.com/en_us/insights/data-management/data-warehouse.html