Intelligent automation democratises data quality

The adoption of cloud computing has accelerated rapidly, and data quality is a top concern among organisations moving workloads to the cloud. Improved data quality increases the value of data, speeds up cloud migrations and cloud data warehouse modernisation, improves user adoption of SaaS applications and reduces risk and potential business disruptions. Yet according to an IDC survey, 60% of organisations are challenged by data quality.

Informatica’s Cloud Data Quality solution solves those challenges by using a consistent process and methodology, automating and scaling data profiling, empowering self-service and business ownership, enabling centralised rule management across sources in on-premises and multi-cloud hybrid environments, and providing continuous insight into the quality of data.

This democratises data quality for everyone by empowering users across the enterprise to identify and resolve data quality issues themselves. Involving end users in data quality initiatives can be pivotal to the successful adoption of cloud applications and trust in insights from analytics.

Improving data quality will also enable enterprises to increase cloud data warehouse productivity and value. With Informatica, enterprises can simplify data cleansing, build a high-quality data pipeline, and deliver trusted insights from a cloud data warehouse. Instituting proven methods for validation, cleansing, parsing, standardisation and deduplication of data is key to generating market-shaping insights from cloud data warehouses.

Cloud native features and capabilities of the enhanced Cloud Data Quality solution include:

• Intelligent Data Profiling: With intelligence powered by CLAIRE®, automatically assign best practice data quality rules based on cloud application data sources. Profile data to examine its structure and context using out-of-the-box templates. Then, drill down to see details and filter on results, and compare profile runs to identify trends over time.
• Business Rule Definitions: Empower the business to lead data quality initiatives while reducing project cycles and enabling IT to focus on strategic projects.
• Centralised Re-Usable Rules: Consistently apply data quality rules across the enterprise in support of data governance. Reduce cost through re-use of centrally managed data quality rules and streamline the resolution of data quality issues.
• Manage and Monitor: Provide continuous insight by aligning data quality and data governance efforts, and track data quality improvements over time.