A Review of the 5 Top Data Quality Tools

Informatica

Headquartered in Redwood City, California, Informatica is a consistently-ranked leader in data quality. Their products include Informatica Data Quality, Big Data Quality, Axon Data Governance, and Data as a Service. They are well established in the market with over 5,000 customers.

Perhaps one of Informatica’s strongest selling points is a large global partner ecosystem. Their partners include the likes of Accenture, Amazon, Cognizant, Deloitte, Google, and Microsoft. If there’s any part of your data governance project outside the scope of their services, a partner is certainly going to fill in the gaps!

Be forewarned, however, that with such a large ecosystem to support, they won’t be the least expensive option. What they lack in terms of usability and price-point, they make up in a deep understanding of the data quality market.

Warnings:

• Resource intensive
• Complex transformations are hard to configure / debug
• No job archival

Commonly Used In:

• Insurance
• Financial services
• Information technology services
• Enterprise

Most Used Features:

• Address validation / standardization
• Records deduplication
• Integration of data from SAP and Salesforce
• Real-time information
• Data profiling
• Character set mapping

Least Used Features:

• Scheduling
• Alerts
• Corporate support / training materials
• Scorecards / exporting scorecards
• Integration with other Informatica tools

IBM

Headquartered in Armonk, New York, IBM is also a top-ranked leader in data tools. Their product, IBM InfoSphere Information Server for Data Quality commands an established market of well over 2,000 customers.

IBM has operations in over 170 countries and provides its own ecosystem of software applications. They certainly have a deep understanding of the data quality market and have proven innovations in data science capabilities.

While offering a lower price-point than some of their competitors, being the giant they are, ease of upgrades and support seem to be lagging.

Warnings:

• Difficult to integrate with other products
• No integration with NoSQL
• Limited search
• Slower at processing large volumes of information
• Limited cloud capability for data stage

Commonly Used In:

• Education / Government
• Financial services
• Legal
• Mid-market
• Enterprise

Most Used Features:

• Address validation / standardization
• Data quality monitoring
• Scorecards
• Metadata management
• Full IBM database stack

Least Used Features:

• BIG Insights
• XML
• Customizations
• Web user interface
• Corporate support / training materials

SAP

Headquartered in Walldorf, Germany, SAP is also a well-known European multinational software corporation. Their products include SAP Smart Data Quality, SAP Information Steward, SAP Data Services, and SAP Hub.

Best known as an enterprise resource planning solution, their corporate strategy includes a recent shift to focus on cloud-based offerings. With a customer base of over 14,000 customers, they are one of the top three providers identified by Gartner, Inc.

Warnings:

• Resource intensive
• Slower at processing big data
• Limited collaboration with multiple developers
• Limited functionality with some browsers, i.e. Chrome

Commonly Used In:

• Manufacturing
• Healthcare
• Education
• Consumer products
• Information technology services
• Enterprise

Most Used Features:

• Scorecards
• Metadata management
• Address validation / standardization
• Rules and controls
• Integration between SAP tools

Least Used Features:

• Customizations
• Clustering / load balancing
• Cloud connectivity
• Scheduler

SAS

Headquartered in Cary, North Carolina, SAS provides tools and services through two core products; SAS Data Management, and SAS Data Quality. SAS has deep industry experience as evidenced by over 2,500 customers for their data quality products.

What makes SAS quite different from their closest competitors is an open-source support for their cloud-ready platform.

From a business-user perspective, they have simplified the user experience with advances in artificial intelligence and automation built on a massive parallel processing architecture.

Without a foothold in existing lines of business, SAS seems to be the choice for the largest variety of use-cases. Be forewarned – you absolutely must have an experienced SAS developer to achieve rapid success.

Warnings:

• Resource intensive
• Requires object based programming skills
• Sluggish with more complex machine learning algorithms
• Expertise in statistical mathematics is required

Commonly Used In:

• Healthcare
• Education
• Financial services
• Enterprise

Most Used Features:

• ArcGIS
• Customizations
• Statistical analysis
• Data analysis
• Metadata management

Least Used Features:

• Corporate support / training materials
• Real-time processing
• MAC compatible version
• Point and click interface

Talend

Headquartered in Redwood City, California, Talend offers two products; Talend Open Studio, and Talend Data Management Platform. Talend has a very large and active user community which has helped grow their user-base to over 1,500 customers.

Talend has enjoyed a massive increase in customer since 2017. This is likely due to the overall ease of use of their tools and the active user community. As an open-source tool, it provides deep integration with outside information sources.

With a free version to get started on, it’s easy to get your feet wet with Talend. Custom-code is Java, and that makes finding development support a smaller lift than other solutions.

Warnings:

• Resource intensive
• Limited search
• Requires Java programming skills
• Trouble with joblets

Commonly Used In:

• Healthcare
• Information technology services
• Education
• Mid-market

Most Used Features:

• Open-source
• Fast daily integrations
• Custom Java components
• Real-time data

Least Used Features:

• Scheduling
• Corporate support / training materials
• Deduplication
• Fuzzy matching
• Unit testing

Do you already use other software applications from the provider?

If you already use IBM or SAP products, for example, it is a logical choice to extend the family of software to their data tools. This will often be more economical and result in better integration throughout the products.

What in-house expertise do you already have?

Many data tools offer deep customizations, or in the case of SAS, are almost entirely code-based. Consider what in-house expertise you already have.

Getting developers who are familiar with the back-end and coding languages required can be expensive if you don’t already have that expertise.

Do you have a clear use-case with intended results?

One thing is clear – the more you know about the content you’ll be working with, and the intended results, the better your selection process and future outcomes.

Consider also the volumes of information you'll be working with and how quickly you need results. Are you processing data for analytics, reporting, or in accordance with a master data management? 

While each tool does a little of everything, they don't all offer the same throughput, load balancing for hyperscaling, or support for back-end databases.