Augmented Analytic’s Impact on Business Intelligence (BI):
Augmented analytics plays a crucial role in reducing manual efforts, automating data scientists’ tasks, and generating valuable insights while minimizing inconsistencies and errors. It facilitates decision-making processes, enhances transparency, and transforms customer engagement with data. Let’s explore the three critical stages of business intelligence that typically involve manual operations and are prone to errors.
1. Data Preparation:
Advanced analytics systems excel at handling large volumes of data. However, data cleansing and preparation, a necessary step before analysis, often require manual intervention by data scientists. They must create metadata, perform data profiling, modeling, data quality checks, and data manipulation, which increases the likelihood of human errors.
Augmented analytics automates the data preparation process by leveraging machine learning capabilities. Machine learning algorithms can identify relevant information, provide optimal data profiling techniques, and streamline the data refinement process. As a result, the speed of data preparation improves, and data scientists become more productive.
2. Identifying Data Patterns:
While modern analytic systems can identify and analyze various patterns and relationships in data, there is a risk of overlooking crucial trends and distortions that impact businesses, especially as the data’s size and complexity increase.
Data exploration often occurs with biased elements and preconceptions, and exhaustive permutations and combinations must be examined, making the process time-consuming and prone to oversight. Augmented data discovery overcomes these challenges by employing powerful algorithms to automatically identify relationships and data outliers, ensuring that critical insights receive the necessary attention.
3. Utilizing Data Insights:
Modern BI platforms provide highly interactive and visual dashboards. However, interpreting and extracting meaningful information from these dashboards is not always straightforward for all users. Augmented analytics addresses this issue by incorporating Natural Language Generation (NLG) capabilities to inform users about noteworthy observations, making insights more accessible and actionable.