Analytical Review is a method of data analysis that helps businesses review their data with an eye for anomalies, trends, and new insights. With Analytical Review, businesses leverage statistical methods to detect variations and areas that require further investigation. This approach helps businesses identify possible data errors, inconsistencies, or weaknesses in their current processes. By scrutinizing the data, businesses can identify ways to improve their operations and decision-making processes.
Analytical Review works by analyzing financial data from a variety of sources, both internal and external. This data is then compared to historical data, industry benchmarks, and other relevant data points. The results of this analysis can be used to identify any anomalies, trends, or patterns in the data. Machine learning algorithms can be used to help automate this process and improve accuracy.
Analytical Review is important because it can help businesses detect any irregularities or inconsistencies in their financial data. This can help to prevent fraud, uncover errors and inaccuracies, and identify areas where performance can be improved. By identifying patterns and trends, businesses can make informed decisions about how to allocate resources and plan for the future.
Some of the most important Analytical Review use cases include:
Fraud detection and prevention
Financial forecasting and planning
Internal auditing and control testing
Identifying cost savings opportunities
Enhancing risk management strategies
Some other technologies that are closely related to Analytical Review include data mining, machine learning, and artificial intelligence. While Analytical Review provides a high-level overview of data, data mining aims to discover hidden patterns and relationships between data sets that may not be visible to the naked eye. Machine learning and artificial intelligence techniques can automate the Analytical Review process to a certain degree and provide businesses with more accurate findings and better insights into their data.
H2O users would be interested in Analytical Review as it provides an efficient and structured way to analyze their data. Although H2O provides critical functionality for machine learning and AI, the output generated by these models is only as good as the data that goes into them. By leveraging Analytical Review, H2O users can ensure that their data is of high quality, reducing the risk of flawed results and making the most of their H2O models.