Organizations are making rapid strides in deploying decision-making systems based on Artificial Intelligence (AI), and this is underpinned by the ability to tap vast amounts of data across distributed data landscapes. However, poor data quality can be a powerful barrier to maximizing value from AI.
Regulations around the use of AI have emphasized high data quality as a key imperative for the success of AI systems. Reputational damage and missed revenue opportunities are just a few of the consequences of using bad quality data in business decision-making. Advancements in generative AI have further fueled the need for strong data quality management practices to deliver trust in AI outcomes.
A holistic approach to data quality with IBM
We are excited to share that Gartner recently named IBM a Leader in the 2024 Gartner® Magic Quadrant™ for Augmented Data Quality Solutions for the 17th time in a row.
We believe this is a testament to IBM’s holistic approach to data quality management to deliver trust in data sources, pipelines and data outcomes.
IBM helps organizations scale AI through the IBM watsonx™ platform. With IBM data fabric, clients can build the right data infrastructure for AI using data integration and data governance capabilities to acquire, prepare and organize data. IBM helps accelerate data quality initiatives by embedding generative AI to automate and simplify critical data quality tasks.
Adopt the right architecture for data quality
Embracing the right data architecture strategy is critical for effective data quality management. With the right architecture, organizations can design data quality initiatives that deliver not just on accuracy of data but also accessibility, timeliness and relevance.
Accuracy
To deliver accurate data as data volumes and complexity multiply, enterprises require the ability to automate data profiling, conduct data quality analysis and enforce data quality rules. With a data fabric architecture, organizations can use active metadata to gather insights into data across their data landscape. They can act on those insights to deliver the right quality data to the right data consumers in a compliant manner. Advanced data quality SLA rules can strengthen trust in data through efficient monitoring and management of data quality.
Accessibility
As enterprises increasingly adopt data marketplaces to share data products, including data sets and machine learning (ML) models, there is a growing demand for managing data quality to meet service level agreements (SLAs) between data producers and consumers including business analysts, data scientists and business users. A data fabric simplifies the orchestration of data required to build high-quality data products, which can then be published on a marketplace for large-scale data sharing.
Timeliness
End-to-end monitoring of data health across data pipelines, with ML-based anomaly detection, can reduce the time needed to detect and resolve pipeline issues. This requires data observability capabilities to continuously detect and resolve data quality incidents in real-time. It also provides visibility into pipeline quality issues. Also, a data fabric simplifies the delivery of end-to-end data lineage, so organizations can gain visibility into the entire journey of their data, from source systems to end use.
Relevance
A modern data architecture, such as data fabric, can help deliver the right data for each business use case by providing a shared semantic knowledge layer that helps to enable a consistent understanding of data across the organization. It also helps to enable automation to act on these insights. Data fabric enables in-depth analysis of data relationships and provides automated entity resolution capabilities to improve data quality on a larger scale.
IBM’s approach to data quality
IBM data fabric enables a holistic approach to data quality management with integrated data quality and governance capabilities. With IBM Knowledge Catalog (rebranded from IBM Watson® Knowledge Catalog), Match 360 on IBM Cloud Pak® for Data, Data Quality for AI library or API and IBM® QualityStage® via IBM DataStage, organizations can gain a composable data quality solution within a unified platform that facilitates automated data quality, along with data governance, data lineage and data protection.
The recent acquisition of Manta has further strengthened IBM’s data quality credentials with the ability to deliver greater transparency into data flows and determine whether the right data was used for AI and other decision-making systems. When combined with the data observability capabilities delivered by IBM® Databand®, IBM offers a holistic data quality approach to help accelerate data and AI outcomes.
With IBM data fabric services and watsonx, enterprises gain access to high-quality, trusted data, whether they build or tune generative AI models or traditional ML models. This is underpinned by a semantic layer powered by generative AI to help organizations discover, understand, cleanse and augment data.
IBM offers a wide range of data quality management capabilities, including data profiling, data cleansing, data monitoring, data matching and metadata enrichment powered by AI/ML. The unified data quality experience within IBM Knowledge Catalog is designed to accelerate the identification and remediation of quality issues.
IBM continues to introduce new product innovations that simplify the curation of high-quality data for self-service consumption by data consumers. Through AI-powered data quality rules, support for SLA rules to monitor the quality of critical data elements, and intelligent matching algorithms to deliver a single, trusted view of organizational master data entities, IBM continues to deliver powerful data quality management capabilities to data teams.
If you’re ready to try IBM data governance capabilities, access the free trial.
Read the report to learn why IBM is a Leader in the 2024 Gartner Magic Quadrant for Augmented Data Quality Solutions.
Gartner, Magic Quadrant for Augmented Data Quality Solutions, Melody Chien, Jason Medd, 6 March 2024
The report was previously named as Magic Quadrant for Data Quality Solutions
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