Skip to content

DA7. Advanced Analytics Techniques

Statement

Advanced analytics plays a major role in big data analysis and reporting. It allows analysts to apply advanced techniques to their data analysis resulting in findings that may not have been revealed using legacy data analysis procedures/algorithms.

Discuss any two techniques employed by advanced analytics and how they have influenced big data analysis and data gathering.

Answer

Introduction

Big data analytics is the process of mining useful information from large and complex data sets. There is already a set of tools called Business Intelligence (BI) tools that are used to analyze data and generate reports. However, BI tools are either too generic or unable to handle the complexity of big data. Advanced analytics techniques are used to analyze data sets that are beyond the limits of traditional BI tools (Farmer & Hanna, 2023).

The research for advanced analytics techniques has returned a list that includes progressive analysis, factor analysis, cluster analysis, time series analysis, cohort analysis, Monte Carlo simulation (Prachi Palkhiwala, 2023), data mining, sentiment analysis, recommendation systems, machine learning, data visualization (Farmer & Hanna, 2023), predictive modeling, and complex event processing (Staff, 2024).

The text will discuss two advanced analytics techniques: data visualization and predictive analysis.

Data Visualization

The main goal of big data analytics is to present facts and information about the business in an easy way that non-data-experts can understand; those are usually the decision-makers. Data visualization is used to present information such as Key Performance Indicators (KPIs), relationships, comparisons, distributions, and trends in a visual format such as charts, graphs, and maps (AWS, 2024).

Data visualization allows for condensing large amounts in a single figure that is easy to comprehend which is a big part of making a good decision. Under the hood Data visualization tools use algorithms to cluster data points, detect patterns and relationships between those points, and later put them into the process of visualization.

Predictive Analysis

Predictive analysis is a way to make an informed prediction about the future based on historical or near-real-time data. It is still a prediction and may not happen as expected, but it presents the possibility to the decision-makers and gives the business a chance to prepare for the future.

Predictive analysis allows for mitigating potential risks, identifying new opportunities, or suggestions to improve existing processes. The process of predictive analysis involves data mining, statistical algorithms, and machine learning techniques to work deeply on historical data and extract patterns that may repeat or emerge in the future.

Conclusion

The two techniques discussed above have significantly influenced big data analysis as the results of the analysis are more important to the decision-makers, but they also altered the processes of data gathering as the data collected should transformed to meet the requirements of the selected tool.

Data visualization allows for condensing large amounts of data in a single figure that is easy to comprehend which is a big part of making a good decision. Predictive analysis allows for mitigating potential risks, identifying new opportunities, or suggestions to improve existing processes.

References

‌ ‌ ‌