BI & Data Visualisation Tool
Easy-to-Use Dashboards and Customisable Reports to Make the Most of Your Data.
Decision Intelligence Powered by Effective Data Visualisation
With Corestrat’s data visualisation and automated data insights, you can easily identify intriguing statistical insights and understand problems with data quality. You can also increase the accuracy and agility of your machine learning models by using outlier identification, dataset splitting, and missing value management.
We use data science, analytics, technology, and our risk management expertise to help you understand data and make fast, profitable decisions that are aligned with your larger business goals. We help you increase revenue by building the right customer base, verifying identity, detecting fraud, balancing risk and reward, and much more.
Powerful Monitoring & Reporting Capabilities for Better Decision-Making
Easy Integration
Easy Integration
Metadata Management
Metadata Management
Collaboration
Collaboration
Data Mapping
Data Mapping
Complement Your Decision Workflows with Data Visualisation
Easy Detection of Trends and Patterns
Business users can identify and improve trends and patterns important to your business early on.
Better Communication
Data insights and trends can be easily communicated to stakeholders using data visualisation tools than raw data.
Increased Accuracy
Improve data quality by identifying and solving inconsistencies and inaccuracies through visualisation tools.
Endless Applications of BI and Data Visualisation Across Industries
Decisioning and credit risk management
Enhance decision processes to deliver profitable portfolio and lending management decisions through the right reports and insights.
Customer acquisition
Boost profitability by building lasting customer relationships while decreasing customer acquisition costs through the right decisioning tools.
Discover Corestrat's Complete Product Suite
Why Choose Corestrat?
Talented Team with Decades of Global Experience in Multiple Business Sectors.
AI Powered Digital Transformation with Expanded Control and Visibility.
Intelligent Automation Smart Platforms and Analytics-Based Advisory.
Intuitive Technology Architecture & Streamlined Business Processes.
FAQ's
Data visualisation is the graphical representation of data and information using visual elements such as charts, graphs, maps, and infographics. It helps to communicate complex data in a clear and intuitive manner, making it easier to understand patterns, trends, and insights.
Data visualisation is a powerful tool that aids in the understanding of complex data by presenting it in a visual format that is easier to comprehend. It goes beyond raw numbers and text, enabling the identification of patterns, trends, and relationships that may not be apparent otherwise. By translating data into visually appealing and intuitive representations, data visualisation facilitates data-driven decision-making, providing actionable insights that can be effectively communicated to stakeholders. Moreover, it enhances data storytelling, making it more engaging and memorable, ultimately leading to a deeper understanding of the data and its implications.
Data visualisation encompasses various widely utilised methods of representation, including bar charts, column charts, line charts, heatmaps, pie charts, scatter plots, and infographics. When selecting a suitable means of representation, one can consider their specific requirements and target audience.
To create effective data visualisation, it is important to adhere to several best practices. Keep the visualisation simple, using clear and concise visuals while avoiding clutter to focus on conveying the key message. Ensure that the scales and axes accurately represent the data and provide proper context. Clearly label data points, axes, and titles to aid viewers in understanding the visualisation.
Choose appropriate colour palettes that are visually appealing, easily distinguishable, and align with the intended message. Add context, explanations, and annotations to provide further guidance for interpretation. Continuously test and iterate the visualisations, gathering feedback from representative users to improve clarity and effectiveness. By following these practices, data visualisations can effectively communicate insights and facilitate a better understanding of the underlying data.
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