Data teams avoid error sprawl and remediation. Errors are handled proactively at inception when cheapest to resolve by the person or system that created them. Saves time and money otherwise spent reacting to errors late in the data lifecycle.

Total cost of ownership is low because setup is simple, and creation and maintenance of rules are not necessary. They are optional. Typo can run maintenance-free without a data steward in the loop.

Typo provides automated data quality monitoring to avoid risk and uncertainty. With consistent accuracy, a foundation of data credibility and trust is built. From this foundation leaders can make decisions with confidence.

With rule-based tools, a new error is often known after you have been subjected to its consequences. Root cause investigation, remediation, and creation of a rule to stop future occurrences is estimated to cost $20,000. Typo can detect new errors upon the first attempt to enter information systems. Avoid costs, lost opportunities, and damaged reputations.

that need to ensure data is a competitive advantage instead of an error prone liability.

that desire transparency into data quality and cutting time to insights by avoiding data cleansing.

that strive for data quality automation and monitoring that aligns with governance policies without any investment of time.

that need prebuilt validators or reusable custom validators on multiple applications to prevent repeat implementation, testing, and deployment.

Traditional data quality tools are reactive because they attempt to address errors after they are persisted to an application data store.  During transfer from an application data store to a data lake or warehouse, data quality tools identify errors and attempt to resolve them in the destination data store.  This transfer may occur days or months later.  By this time the user that entered the data is unlikely to recall a single record out of the thousands entered that month. Meanwhile the application data store continues to serve erroneous information. Problems pile up and the data team is overloaded with work.  Typo is a proactive solution that prompts the user to fix the error at the time of entry.

Typo is available as a cloud service that is simple to setup and use. Traditional data quality tools require a team of experts for installation, configuration and maintenance.  These tools require rules to be created for each error that you have experienced or anticipate.  Who is watching for errors that you cannot predict?  With artificial intelligence that uses machine learning and advanced algorithms, Typo is able to detect errors that you may not foresee. Unlike traditional data quality tools that require updates to rules when data changes, Typo adapts to change by learning from data and responses from users.