Deliver Trusted Data to Data Consumers
An Integrated View into Data Quality
Data consumers get a consolidated view of enterprise data assets and their data quality, and can view data quality metrics, rules, and warnings. The Alation Open Data Quality Initiative adds the ability to instantly access data trustworthiness, extending what Alation already collects to support data users’ evaluation of data sources and creating a one-stop shop to find, understand and trust their data.
Accelerate Informed Decision-Making with Trusted Data
EASILY ASSESS DATA TRUSTWORTHINESS
OPEN & EXTENSIBLE
REDUCE RISKS & COSTS
MAKE DISCOVERY MORE VALUABLE
Provide Choice and Flexibility
Concepts like “data fabric” suggest that customers should be able to assemble the best heterogeneous data systems together. The Open DQ API Initiative extends Alation’s existing commitment to platform openness and to an ecosystem approach to the marketplace.
Reduce Risks and Costs
Data consumers need context about an asset’s quality to use it wisely. Alation catalogs valuable data quality information and metadata (metrics, reports, and lineage) with its parent asset so users can confidently leverage. With this information, a more complete data governance solution is created. DG App can relate data quality rules to overarching data quality policies captured within Alation.
Open and Extensible
The Open DQ Initiative gives vendors, partners, and customers a starter kit including the API, documentation, an onboarding walkthrough, and integration best-practices.These enable all your data consumers to access trusted, reliable data. This empowers all people — from business users to analysts, data scientists, and stewards — with real-time visibility into data health.
Alation + Anomalo
Anomalo automatically detects data issues and their root causes, before anyone else. Alation customers can set up data monitoring for any table in five minutes or less using Anomalo and see results directly in Alation. Anyone on the team can check that data is up-to-date, complete, accurate, and consistent—and get alerted with an automatic root cause analysis to understand any issues.
Alation + Bigeye
Ensure reliable data pipelines at all times. Bigeye’s data observability platform detects issues instantly by automatically monitoring potential issues with more than 50 data quality metrics. If a problem arises, the data team is immediately alerted, enabling them to proactively address the issue and alert those affected.
Alation + Soda
Soda provides full observability across all customer data. Features like easy instrumentation, anomaly detection, and intelligent monitoring minimize risk. Instrument datasets for any data workload, whether it’s a warehouse or data lake. With Soda, it’s easy to observe and monitor how data behaves over time, both at scale and in depth. Embedded alerting surfaces data issues before they grow.
Alation + Databuck
DataBuck’s unified Data Observability & Trustability platform autonomously monitors and validates data in all locations – Lakes, Warehouses, Pipelines, on-prem and cloud. With Machine Learning it auto discovers 100’s of data pattern fingerprints and automatically calculates objective Data Trust Scores (DTS) for every schema, table, and file with no human inputs. This end-to-end data visibility and reliability instills trust in data by catching issues before they break downstream processes and impact data users.
Alation + Lightup
Bad data has become a significant issue for businesses, creating operational inefficiencies and impacting business outcomes. Alation’s integration with Lightup enables the end user to recognize whether their insights are based on valid data or bad data. Whether you are an analyst or an executive, the insights you depend on are only as good as the data that drives them. The Lightup integration surfaces data issues to the end user, enabling trust in data and business outcomes based on confirmed quality data.
Alation + Acceldata
Trusted data needs to be accurate, reliable and performant. To that end, data teams leverage Acceldata’s Enterprise Data Observability platform to eliminate performance issues, improve data quality, scale data platforms, and even reduce cost. Data consumers benefit by gaining visibility into the complete health profile of data assets – including quality, reliability, and performance right inside Alation.
What is data quality and why does it matter?
Data quality represents the degree to which data meets a company’s expectations of accuracy, validity, completeness, and consistency. Tracking data quality enables a business to identify potential issues and ensure that shared data is fit for purpose.
When shared data fails to meet these standards, it can negatively impact customer service, employee productivity, and other key business strategies. It can also make an organization vulnerable to regulatory fines and negative publicity, resulting in a loss of customers.
How does data quality relate to data catalogs?
Data catalogs are the platforms where data consumers discover data. Adding data quality information empowers users to determine if data is trustworthy before making a decision, building a report, or creating a ML dataset.
Why did Alation create the Open Data Quality Initiative?
Data-driven organizations need the freedom to choose the best data quality and observability tools for their unique needs and goals. This is because data quality tools have significantly different capabilities and applications. The Open Data Quality Initiative grants organizations this freedom of choice, with the added confidence that a wide range of DQ tools integrate seamlessly with the Alation Data Catalog and Data Governance app
"We are moving from a culture of reporting to a culture of analysis. It's about people transformation."
VP, Information & Data Management, American Family Insurance
"We want to use Alation for our privacy and security, like for classification and retention of data."
Data Governance Manager, Ancestry
"Alation is very good at helping us understand who uses which tables the most."
Head of Data Governance, eBay