Data quality management tools can automate many of the processes needed to ensure that data remains fit for purpose in analytics, data science, and machine learning use cases. Organizations need to find the most suitable tool to assess existing data pipelines, identify quality bottlenecks, and automate various remediation steps.
Processes associated with data quality assurance include data profiling, data lineage tracking, and data cleansing. Overall, it might be a good idea to identify a tool or set of tools that aligns with the company’s existing data pipeline workflows. In the short term, it is sometimes useful to identify specific gaps or challenges in the data quality process.
“It’s best to focus first on the tools and systems that need to be replaced,” said Jeff Brown, team leader for BI projects at Syntax, a managed services provider.
It starts with working with teams to determine what will have the most significant effect on improving the data-driven culture.
Key considerations include overall cost, ability to audit, ease of setting up policies and standards, amount of training required to use the tool, ability of the tool to scale to keep up with the increase and evolving data sources and ease of use, said Terri Sage. , CTO of 1010data, a provider of analytical intelligence for financial, retail and consumer markets.
Similarities and differences of tools
Each data quality tool has its own set of features and workflows. Most tools include data profiling, cleaning, and normalization features.
Christopher AnthonyVice President of Global Solutions Engineering, Talend
Data profiling, measurement, and visualization features help teams understand the format and values of the collected data set. These tools will flag outliers and mixed formats. Data profiling serves as a quality correction filter in the data analysis pipeline.
“You can learn a lot about the quality of your data by using the right profiling tools,” said Christophe Antoine, vice president of global solutions engineering at Talend, an open-source data integration platform.
Data normalization capabilities help identify inconsistent data formats, values, names, and outliers in current data sets. Then they can apply a normalization, such as an address validator, pattern check, formatter, or synonym database, to the data pipeline to always return a normalized value. This is useful when the same data is entered in different ways.
Data cleansing features can help fix structural issues, remove outliers, fill in missing data fields, and ensure required fields are filled in correctly. Data cleansing can be expensive, considering the extra hours and tools required. Look for tools that can fill data cleansing gaps in existing workflows, Sage said.
Analytical capabilities help break data down into its component parts. This can help track the cause of data quality issues and flag downstream datasets when issues arise in a data pipeline.
Monitoring capabilities track data quality metrics. When issues are detected, they can alert data management teams to investigate sooner, when they’re easier to fix.
Here are seven of the best data quality management tools:
Ataccama One Data Quality Suite
Ataccama was founded in 2007 to market the data preparation tools developed internally by Adastra, a data analysis and AI platform. Ataccama specializes in transforming raw data into reusable data products that can support various artificial intelligence, analytics, and operational tasks within an enterprise. An important aspect of this is ensuring data quality for large data pipelines through self-driving capabilities for automated data classification, automated data quality, and access policy documentation.
Freemium data quality tools include Ataccama Data Quality Analyzer and Ataccama One Profiler. The company has also developed a comprehensive set of AI tools for detecting data anomalies, creating new data quality rules, and automating various data quality processes. While these tools can streamline many aspects of data quality, it may require additional work for companies that need to support processes that operate outside of existing workflows.
IBM InfoSphere Information Server for data quality
Over the years, IBM has developed many data quality tools to complement its various enterprise database offerings. Current tools include IBM InfoSphere Information Server for Data Quality for end-to-end quality, IBM InfoSphere QualityStage for data cleansing and standardization, and IBM Watson Knowledge Catalog for AI metadata management. InfoSphere tools are part of IBM’s legacy offerings.
Watson Knowledge Catalog is a newer offering that promises to streamline the quality aspects of AI and machine learning workflows into a single platform. This approach promises to help harmonize workflows that traditionally spanned multiple data science, ModelOps, and data management tools.
Informatica Data Quality
Informatica has a long history of improving data transformation and quality. Its current portfolio includes a range of data quality tools, including Informatica Data Quality, Informatica Cloud Data Quality, Informatica Axon Data Governance, Informatica Data Engineering Quality, and Informatica Data as a Service.
Each tool focuses on different aspects of data quality. This breadth of offerings allows the toolset to support a variety of use cases. Its extensive cloud capabilities also allow enterprises to ensure data quality when migrating to hybrid or cloud-native data management tools. However, its cloud-based tools are still catching up in available functionality compared to on-premises tool versions.
Additionally, Informatica simplifies data quality in modern AI and machine learning workflows. The company also supports a rich offering of metadata management, data cataloging, and data governance capabilities aligned with its data quality capabilities.
Precisely is the modern name of an old provider of data transformation tools that has been around since the late 1960s. The company started life as Whitlow Computer Systems to focus on high-speed data transformation ; it was renamed Syncsort in the 1980s and then Precisely in 2020. Each new branding represented a change in current industry needs and new technologies.
It recently acquired Infogix and its data quality, governance and metadata capabilities. Current data quality offerings include Precisely Trillium, Precisely Spectrum Quality, and Precisely Data360. One of its great strengths is a comprehensive set of geocoding and spatial data standardization and enrichment features. It should be noted that these separate tools were separate acquisitions. Therefore, companies may need to budget for additional integration for workflows that span these different tools.
SAP Data Intelligence
SAP is the established leader in ERP software, a data-heavy application. It has acquired or developed various data quality capabilities to enhance its core platform. Current data quality offerings include SAP Information Steward, SAP Data Services, and SAP Data Intelligence Cloud.
The company has undergone many significant platform changes for its core offerings, with the development of the S/4HANA intelligent ERP platform. It is currently undergoing a similar evolution to next-generation data quality capabilities with SAP Data Intelligence Cloud. This new offering centralizes access to data quality capabilities across on-premises and cloud environments. It supports data integration, governance, metadata management, and data pipelines.
Several third-party applications also enhance SAP’s master data quality offerings. Teams may need to consider these third-party tools to improve data quality, especially when working with data outside of the SAP platform.
SAS data quality
SAS has long reigned as the leading provider of analytics tools. In the mid-1960s, the company launched its first analytics tools, which continue to evolve as it has expanded its core tools to handle nearly every aspect of the data preparation pipeline, including quality Datas.
Its core data quality offering is SAS Data Quality. It works in concert with a multitude of complementary tools, such as SAS Data Management, SAS Data Loader, SAS Data Governance and SAS Data Preparation. Its data quality tools are integrated with the SAS Viya platform for AI, analytics and data management for the cloud at no additional cost. This helps streamline the data quality aspects of various data science workflows.
The company also offers SAS Quality Knowledge Base, which provides several data quality functions such as extraction, pattern analysis and normalization. Real-time quality enhancements included in the SAS Event Stream Processing service can perform a variety of data cleansing tasks on data streams from IoT devices, operations, or third-party sources.
Talend Data Fabric
Talend was founded in 2006 as an open source data integration company. The company has developed an extensive library of tools for data integration, data preparation, application integration, and master data management. Current freemium data quality offerings include Talend Open Studio for Data Quality and Talend Data Preparation Free Desktop. Other offerings include Talend Data Catalog and Talend Data Fabric.
Talend Data Catalog automates various aspects of data inventory and metadata management. Talend Data Fabric helps streamline data quality processes as part of automated data pipelines with support for data preparation, data enrichment, and data quality monitoring. These tools are often used in conjunction with other data analysis and data science tools.