Visualise Data Relationships
In today’s dynamic business landscape, the world of data is evolving rapidly, growing in complexity, accessibility and volume. At Billigence, we specialise in harnessing the power of data modelling to provide a clear, organised and insightful perspective on complex data relationships within organisations and industries. Our proven expertise allows businesses to unlock the full potential of their data.
Understanding Data Modelling
Data modelling involves identifying entities, relationships and data elements within a business area of focus or its segments. It is a pivotal initial step in designing and developing effective data warehouses and reporting solutions. This approach helps in conveying how different data elements are connected. Based on the specific use case, Billigence adopts 3nf, data vault and star schema approaches.
Why Businesses are Embracing it
Efficiency and accuracy have become paramount in strategic decision-making and data modelling serves as a critical enabler. Business queries, reporting and analytics and data science initiatives rely upon the correct and appropriate storage of financial and operational data. Beyond this, the adaptive nature of data models allows for seamless integration of new data sources and optimisation of queries, streamlining the decision-making process.
Why should I invest in it?
Business Process Optimisation
Data modelling helps when mapping out business processes to identify inefficiencies and bottlenecks. Optimised models can then be implemented, improving workflows and ultimately reducing costs, time and resources.
When migrating, modelling can provide a structural blueprint when moving data from one system to another. It can ensure that data retains its integrity and relationships during the migration process, reducing errors and downtime.
Data Security / Compliance
Data modelling outlines the architecture where security measures are implemented effectively. This ensures compliance with regulations like GDPR by identifying where sensitive data resides and how it is accessed, managed and monitored.
When undertaking data integration, models are used to understand how disparate data sources can be cohesively combined. This allows for a unified view of data, aiding in better-informed decision-making and quality analytics.
A long-term comprehensive framework guides how data is stored, responsibly accessed and governed within an organisation. It standardises data relationships, making it faster and easier to enforce governance policies and perform analytics.
When warehousing the emphasis is on designing structures that can organise data for complex queries. The models may include star schemas, snowflake schemas or other multidimensional models to optimise for read-heavy operations.