The startup develops automated software testing and data quality engineering tools that enhance software quality assurance through automated test management, data cleansing, and bug fixing. By improving the efficiency of complex data testing projects, the company helps businesses reduce errors and increase operational resilience.
Funding
$2.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Many organizations struggle with data quality issues during data migration, integration, and digital transformation projects, leading to budget overruns, project delays, and increased operational risks. Inaccurate, incomplete, and inconsistent data across disparate systems hinders effective decision-making and impedes business performance.
Solution
IDS provides end-to-end data certainty solutions that ensure accurate, complete, and consistent data throughout the entire data lifecycle. Leveraging the Kovenant™ methodology and the iData toolkit, IDS automates data ingestion, cleansing, transformation, migration, and quality assurance processes. This approach eliminates manual errors, reduces project costs and timelines, and enables organizations to achieve a single source of truth for improved business insights and decision-making. IDS's solutions support various use cases, including data migration, data integration, master data management, and test data management, ensuring data is fit for purpose across diverse environments.
Target Audience
IDS primarily serves organizations in regulated industries such as legal, healthcare, financial services, government, and higher education, that require high levels of data quality and compliance.
Features
- Kovenant™ methodology: A structured approach to data quality management that ensures accurate, complete, and enriched data.
- iData toolkit: A bespoke data transformation and migration assurance engine that automates data validation and transformation processes.
- Data cleansing and deduplication: Identification and removal of duplicate and inaccurate data entries.
- Data profiling: Auditing data against metadata and existing standards to ensure data quality.
- Data obfuscation and synthesis: Replacing sensitive data with synthetic data to maintain GDPR compliance in non-production environments.
- Automated testing: Ensuring data is fit for purpose through continuous testing and quality assurance.
- SnapLogic partnership: Leveraging SnapLogic's integration platform to deliver faster and more reliable data pipelines.