Video : Better Manage and Visualize Data

Managing and visualizing data is important for several reasons:

• Informed decision-making: Effective data management ensures that data is accurate, consistent, and up-to-date, which is crucial for making informed business decisions. Data visualization helps present complex data in a digestible format, enabling decision-makers to identify patterns, trends, and insights more quickly.

• Improved communication: Data visualization can help communicate complex information more effectively to team members, stakeholders, or clients. By presenting data in an appealing and understandable manner, it becomes easier to convey key insights and support strategic decision-making.

• Efficiency: Proper data management and visualization can save time and resources by streamlining data analysis and interpretation. Data visualization tools can automate creating charts, graphs, and other visual representations, reducing the time spent on manual data analysis.

• Identifying trends and patterns: Data visualization enables users to identify trends, patterns, and anomalies in large datasets that might be difficult to detect through traditional data analysis methods.

• Performance monitoring: Data visualization can help organizations monitor their performance, track KPIs, and identify areas for improvement. By visualizing data, it becomes easier to spot trends and make data-driven adjustments to strategies and processes.

Data quality: Proper data management ensures that data quality is maintained, reducing the risk of errors and inconsistencies. High-quality data is essential for accurate analysis and reliable decision-making.

Proper processing and analysis of data is the only way to get deeper insights for more informed decisions.

Watch this video to learn how to:

• Edit data and result sets of run SQL queries by using graphical, Excel-like grids.

• Import and export data of different formats from and to data sources, tables, files, and other objects.

• Protect sensitive data by masking selected columns of result sets to prevent exposure.

• Generate test data to facilitate tests without compromising sensitive data.

• Run visual data analytics by dragging and dropping data into advanced visualizations.