In this era of big data, cloud computing, and AI, data problems have grown beyond its volume or how it is used. If the data quality is compromised, advanced analytics loses its purpose. To ensure this doesn’t happen, enterprises will need end-to-end data quality management solutions. They need to be able to make their data reliable, from cleansing and validation to enrichment and governance, to reduce the time taken from insights to action
With Sancus, our AI-powered Data Quality management system, you can create your master data from diverse sources while maintaining and tracking data quality and data governance over time. Continue deriving actionable insights from reliable data that is automatically captured, non-redundant, and cost-effective to maintain, for trustworthy intelligence to make the right decisions at the right time.
See how we helped a global CPG major streamline supplier network located across the globe, create a unified database to improve procurement and create comprehensive supplier scorecards & identify priority suppliers
Improved data quality KPIs (data completeness, product & revenue mapping etc.) by 28%
Revamped procurement processes and 7% reduction in shipment times
15% removal of non-performing suppliers through data rationalization efforts
Equip your data with a self-learning algorithm to keep track of quality and reduce time-to-value
Leverage our intuitive dashboard with insights into quality metrics that can be configured to fit your data quality and data governance workflows. Accelerate the process of standardizing, cleansing, and deduplicating data by proactively keeping track of it through a single master record.
Validate your lead or customer data using our postal directories and third-party APIs to address any corrections across phone contacts and emails. Enrich it further through insights on third-party partnerships. Automate web scraping, image processing, and analysis of unstructured product data to derive continuous value.
Leverage insights into creation, validation, and management through third-party partnerships of flexible customer and vendor hierarchies that you can reflect on. Address product or material hierarchy assignment with insights into scaling across the value chain, using our ML algorithms.
The ML models that drive Sancus learn actively through a reinforcement loop. Integrate your data with our active learning-based feedback module to pass feedback and override the algorithm’s result for deep learning and insights.
Get individual, easy-to-use modules for the data quality management of metadata, data quality, metrics, and more to scale standardized processes across the value chain. Ensure on-going data is cleansed and governed by managing stewardship, policies, and more, with insights to help maintain a data-driven culture.
Why choose us?
Go-to-market in 6-8 weeks
Master data engine creation suitable for various use cases
Auto-scale on Azure Databricks