Data mesh is an approach to organizational data management that helps data-driven companies become more agile and prudent in managing, analyzing, and distributing data.
The following principles are fundamental in a data mesh.
However, to effectively manage a decentralized data solution, you must ensure quality data, starting at inception and then throughout the data lifecycle.
Mistakes in data quality can erode the value of a mesh and the self-serve data products. A small amount of poor data that originated from one domain data source has the potential to spread throughout your enterprise and wreak havoc.
Since quality data is necessary to deliver the best end-user experience with data products, F4 includes data quality as the fifth principle for a data mesh.
The Current Challenges with Data Quality
Managing data quality with traditional tools is a reactive paradigm that increases the burden on domain owners. These tools scan domain data sources to provide a list of problems and quarantined errors, which are then added to the domain owner’s workload.
To avoid these quarantined errors in the future, data rules are created, tested, approved, and deployed. Continuous change of data products is expected in data-driven cultures and data meshes.
Keeping up with this change requires the maintenance of data rules.
For this reason, F4 developed Typo to enable customers to implement an automated proactive data quality paradigm. Typo is a patent pending data quality barrier® surrounding enterprise information systems.
Unlike reactive data quality tools that detect errors after they are saved and rule-based tools that fail to block new errors, Typo uses artificial intelligence to handle errors at origin, in real-time, and before storage. The result is fully automated data quality provided at the beginning of the data lifecycle without requiring anyone from a data team.
Typo can be used on databases, web applications, mobile apps, devices, and data integration tools. Typo places the burden of data error resolution on the user or system that created the data instead of the domain owner that maintains the information systems storing and processing the data. This frees teams from the burden of error identification, remediation, and rule maintenance.
How Typo Benefits Organizations
High ROI: 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. It saves time and money otherwise spent reacting to errors late in the data lifecycle.
Low TCO: Total cost of ownership is low because the setup is simple, and the creation and maintenance of rules are not necessary. They are optional. Typo can run maintenance-free without a data steward in the loop. Artificial intelligence adapts to change by learning from new data and user feedback at inception.
Fast ROI: After one installation step, Typo starts quality protection when enough records are observed. Correcting data in real-time provides immediate value to customers, partners, data brokers, internal business intelligence teams, and other data consumers.
Prevent first-time exposure to errors: With rule-based tools, a new error is often known after you have been subjected to its consequences. Investigating the root cause and creating a rule to stop future occurrences is estimated to cost $15,000. Typo can detect new errors upon the first attempt to enter information systems. Avoid costs, lost opportunities, and damaged reputations.
Accelerate time to analysis: Data scientists spend 60% of their time cleaning and organizing data. Typo can cut the mind-numbing time spent cleansing, so data experts can do what they love sooner.
Maximize Existing Investments: Typo will optimize the return on previous investments by enhancing data quality in IT systems through flexible integration. Open-source components and REST API are available.
Confident decisions: With consistent accuracy, a foundation of data credibility and trust is built. From this foundation, leaders can make decisions with confidence.
Data Mesh and Typo
Building a data mesh with Typo allows a data-driven enterprise to stay agile in high-growth environments while maintaining trust and credibility of data. Notably, agility does not come at a high operational cost; combining a data mesh and Typo data quality management creates a malleable data environment where:
- Domain owners avoid error remediation tasks and rule maintenance
- Data products sustain quality and credibility
- Self-serve platform provides data quality metrics, monitoring, and management
- Automated federated governance policies include data quality assurance and monitoring
To learn more about how to optimize your organization with a data mesh and Typo’s automated proactive data quality management, contact us here.