Analytics & BIEnhancing Analytics and Business Intelligence with Safe Synthetic Data

Using cloud service provider (CSP) analytics tools is a game-changer in the modern analytics and BI landscape. However, data privacy concerns often limit their adoption. Synthetic data provides a secure path, enabling organizations to use the power and elasticity of CSP analytics tools without compromising data integrity or compliance.

Stricter data privacy regulations and heightened cybersecurity concerns bring up the need for robust, more secure data handling practices. Synthetic data reduces compliance-related costs and usage constraints by providing privacy-preserving datasets with the same properties as real-world data.

Access to sensitive data is often restricted due to regulatory constraints, data silos, compliance barriers, or privacy considerations, which holds back effective analytics and leads to missed opportunities and revenue shortfalls. Synthetic data enhances data accessibility, allowing organizations to leverage operational analytics tools and make well-informed decisions with confidence.

Traditional data storage methods are expensive, especially for large datasets. However, with synthetic data, organizations significantly reduce their storage costs. Synthetic data is generated on demand and does not require physical storage space, making it a cost-effective solution for companies looking to scale up their analytics capabilities without breaking the bank.

Data scarcity and bias in available datasets are major roadblocks in the world of operational analytics and business intelligence. Synthetic data resolves these challenges by enabling organizations to generate large amounts of high-quality, diverse data. Since this data is not affected by human biases, it allows for fairer analytics outcomes.