IBM named a leader in the 2022 Gartner® Magic Quadrant™ for Data Quality Solutions

By | 3 minute read | November 4, 2022

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Data is the new oil and organizations of all stripes are tapping this resource to fuel growth. However, poor data quality is a barrier faced by organizations in their quest to become more data-driven. So, it is imperative to have a clear data quality strategy that relies on proactive data quality management as data moves from producers to consumers.

Unlock quality data with IBM

We are excited to share that Gartner recently named IBM a Leader in the 2022 Gartner® Magic Quadrant™ for Data Quality Solutions.

Access the full report here.

We believe, this is a testament to IBM’s vision to empower data professionals with trusted information through data quality capabilities including data cleansing, data lineage, data observability, and master data management.

IBM recently expanded its data quality capabilities with the acquisition of and its leading data observability offerings. This complements IBM’s partnership with MANTA to integrate automated data lineage capabilities from MANTA with IBM Watson Knowledge Catalog on Cloud Pak for Data.

Why does data quality matter across the data lifecycle?

Data quality issues can have far-reaching consequences across the lifecycle of data:

1. Analytics and AI

When a sophisticated AI/ML model confronts bad-quality data, it is the latter that usually wins. As organizations increasingly rely on AI/ML for critical business decisions, the role of a trusted data foundation that delivers high-quality data is paramount. So, it is important to provide data consumers with a curated set of high-quality data and allow them to search for relevant data through a well-defined data catalog.

2. Data Engineering

Data engineers spend a disproportionate amount of their time firefighting bad data. This could be because a lot of the current data quality approaches are reactive, triggered only when data consumers complain about data quality. Once poor-quality data moves from data sources into downstream processes, it gets challenging to remediate quality issues. A smarter approach would be to plug data quality issues upstream through active monitoring and automated data cleansing at the source. Data observability capability makes data quality checks upstream possible.

3. Data Governance

Ensuring data quality is critical for data governance initiatives. Increasingly enterprise data is spread across multiple environments which contributes to inconsistent data silos that complicate data governance initiatives and create data integrity issues that could impact Business Intelligence and analytics applications. It is critical to promote a common business language across the enterprise to break down these silos. One effective way to identify bad-quality data before it flows into downstream processes is with the use of active metadata to foster greater understanding and trust in data and ensure that only high-quality data makes its way to data consumers. Equally important is the ability to understand data lineage by tracking the flow of data back to its source which can prove handy when remediating data quality issues.

IBM’s holistic approach to Data Quality

With a strong end-to-end data management experience combined with innovation in metadata and AI-driven automation, IBM differentiates itself by offering integrated quality and governance capabilities.

IBM Watson Knowledge Catalog, QualityStage, and Match360 services on Cloud Pak for Data offer a composable data quality solution with an easy way to start small and expand your data quality program across the full enterprise data ecosystem.  Watson Knowledge Catalog serves as an automated, metadata-driven foundation that assigns data quality scores to assets and improves curation through automated data quality rules. The solution offers out-of-the-box automation rules to simplify the addressing of data quality issues.

With the recent acquisition of,  a leading provider of data observability solutions, IBM can elevate traditional DataOps by using historical trends to compute statistics about data workloads and data pipelines directly at the source, determining if they are working, and pinpointing where any problems may exist. IBM’s partnership with Manta for automated data lineage capabilities further strengthens its ability to help clients find, track and prevent issues closer to the source and for a more streamlined operational approach to managing data.

IBM offers a wide range of capabilities necessary for end-to-end data quality management including data profiling (both at rest and in-flight), data cleansing, data monitoring, data matching (discovering duplicated records or linking master records), and data enrichment to ensure data consumers have access to high-quality data.


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