Equipped with a strong data ecosystem and host of data transformation tools to ensure high data quality output, the healthcare provider aimed to invest in a data quality stack with specialized data quality tools that would stand up to the company’s changing business use case needs.
The company sought to implement a data quality stack to enable:
Real time decision support for real time quality
Use cases require real time data, and our customer found their data was moving too quickly for traditional reject, recycle, and cleansing processes.
Clinical and business workflow must also be alerted to any lack of quality data, as clinical use cases could impact patient outcomes.
Preventing and correcting data issues before they impacted the business
Early detection of source system issues is critical
Aimed to shift from reactive to preventive methods in order to understand their data quality before the end user does
Measuring and managing data to a quality standard
Without measuring data, the company found it impossible to improve data quality. The company recognized that data assets needed to be evaluated constantly to validate quality and completeness
Ad-hoc approaches were dividing resources and unnecessarily segmenting information
Data assets were growing at a faster pace each year
The healthcare provider leverages Ataccama’s complete suite for data quality and governance:
Data Quality Center (DQC) – a powerful, business rule driven engine for complex data management that enables you to transform, standardize, cleanse, validate, correct, and enrich your data, and prevent incorrect data from entering your systems.
Running on top of the DQC engine, there are two user-friendly applications for data stewards and business users:
Data Quality Issue Tracker (DQIT) – allows data stewards to resolve various data quality issues that cannot be processed automatically and require their approval or other manual intervention.
Data Quality Dashboard (DQD) – enables data stewards and business users to monitor data quality continuously, see its current state, track trends, and use this insight to make informed business decisions.
The company also works with Ataccama’s Master Data Management and Reference Data Management tools.
Master Data Center (MDC) – a high-performance platform, optimized for demanding transactional environments, that makes consistent, accurate, and up-to-date master data instantly available across a broad range of enterprise applications and internal systems.
Reference Data Manager (RDM) – a web-based app that keeps your reference data up to date, valid, consistent, and available throughout your organization, with powerful Data Quality capabilities.
Ataccama tools proved to be a significant advantage in terms of easy integration with applications already in use in the company’s environment (e.g. databases, version control systems, schedulers, ETL tools, and more).
The healthcare provider worked towards building their capacity through quality programs to support clinical, information protection, and financial domains. They also further enabled field operations with improved tools.
Cooperation with Ataccama has laid the foundation for the company to move towards:
Business-driven quality management
An organizational structure for information quality management that is institutionalized and viewed as critical to business across all functions
Business taking full ownership for data content and data policy making
33 rules were created to monitor our EDW Patient Financial subject area
Measuring their ability to convert local codes to industry ontologies
Measuring ranges in lab values, in particular to determine the quality of lab results for patients
Validating provider identities against the CMS master
Validating provider credentialing and privileges
Together with implementing Ataccama’s data quality suite, the healthcare provider established a data quality team to ensure tools were fully utilized, trained business units to develop their own quality rules, and created a framework for data quality deployment. They were increasingly able to detect which BI teams were underperforming, and IT no longer needed to wait to hear from business users that information was corrupted or missing.
As a whole, the company found that instituting a data quality program compelled data governance and stewardship to take shape, as data stewards naturally emerged to take ownership of the data.
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