Data quality software refer to a wide range of tools and services specifically designed to deliver comprehensive and precise data to organizations. Data quality software vendors offer a broad range of functions and capabilities, which include data cleansing, profiling, parsing, monitoring, and enrichment. The data quality software allow organizations to comprehend, standardize, and monitor the data over the course of its lifecycle, ensuring continuous operation within the system. Ensuring data quality should be a paramount concern to the authorities, as a good dataset within an organization can be a key enabler to gain competitive advantage in the market by conducting better analysis and craft business strategies that will have long-term implication on realizing organizational goals. The 6 key considerations of data quality, which every enterprise should seek for, include consistency, conformity, completeness, uniqueness, accuracy, and integrity of data.

COMPETITIVE LEADERSHIP MAPPING TERMINOLOGY

The vendors are placed into 4 categories based on their performance in each criterion: “visionary leaders,” “innovators,” “dynamic differentiators,” and “emerging companies.” The top 25 vendors evaluated in the data quality software market include Alteryx, Ataccama, BackOffice Associates, Data Ladder, DQ Global, Experian, IBM, Informatica, Information Builders, Infosolve Technologies, Ixsight Technologies, Innovative Systems, Microsoft, Oracle, QFire Software, Pitney Bowes, Neopost, RedPoint Global, SAS Institute, Syncsort, Trianz, Talend, Tamr, and Uniserv.

VISIONARY LEADERS

Vendors who fall into this category receive high scores for most of the evaluation criteria. They have strong and established product portfolios and a very strong market presence. They provide mature and reputable data integration tools. They also have strong business strategies. The visionary leaders in data quality software include IBM, Informatica, Oracle, SAP, SAS Institute, and Talend.

INNOVATORS

Innovators in the data quality software are the vendors who have demonstrated substantial product innovations as compared to their competitors. They have very focused product portfolios. However, they do not have very strong growth strategies for their overall businesses. Vendors who fall in this category are Alteryx, Ataccama, BackOffice Associates, Data Ladder, Innovative Systems, QFire Software, Syncsort, Tamr, and Trianz.

DYNAMIC DIFFERENTIATORS

They are established vendors with very strong business strategies. However, they are low in their product portfolios. They focus on a specific type of technology related to the product. Data quality software vendors who fall in this category are Experian, Information Builders, Microsoft, and Pitney Bowes.

EMERGING COMPANIES

These are vendors with niche product offerings who are starting to gain their positions in the data quality tools market. They do not have very strong business strategies as compared to other established vendors. They might be new entrants in the market and require some more time before getting a significant traction. Data quality software vendors who fall in this category are DQ Global, Infosolve Technologies, Neopost, RedPoint Global, and Uniserv.

Organizations worldwide have adopted data quality tools in order to reap the best possible value from the raw content. The exponential rise in the number of desktop and mobile device users and the easy accessibility to the desired data has enabled organizations to leverage businesses. Further, an enormous amount of data continually generated and accessed through multiple sources has inevitably led to the occurrence of data redundancies and inconsistencies. Furthermore, the ability to sort information from heterogeneous data sources has given significant traction to the data quality tools market. Lately, a collection of various types of data in an organization has led to an unavoidable need to maintain the quality of data in organizations. Considering the intense competition in different industry verticals, organizations are encouraged to utilize advanced data management tools to enhance their business decisions and operational workflows. The data types include customer data, product data, financial data, compliance data, and supplier data.

CUSTOMER DATA

Customer data is data that is pertaining to the customer. It is one of the major factors which enable organizations to sustain in the competitive market. The integration of critical information pertaining to their clients/customers helps organizations in order to govern their operations and formulate their business plans. Further, with data quality tools integrated over a single platform provides a complete view of client-related content. Customer data is generated and compiled through the Customer Relationship Management (CRM) systems installed among the organizations which allow collection and maintenance of reliable, precise, and updated customer records enabling integrity and authenticity. The streamlined centralization of customer data helps organizations to analyze customer preferences, strengthen customer relationships, augment sales, and strategize on building new clientele as well.

PRODUCT DATA

The product data comprises the information related to the products and offerings. The product data creates value for an organization by managing data that describes their tangible and intangible assets, which helps in managing the business operations process such as sales, purchase, planning, production, and associated processes. Additionally, the centralization of product data offers the executives to have an integrated view of the entire lifecycle of their product. Product data extensively help organizations to administer operational performance by analyzing complete data through single point of assistance. Moreover, product data facilitates both the front-office and back-office functions.

FINANCIAL DATA

Poor quality of financial data can have a long-term impact on the organizations’ finances. With higher accuracy of data, better decisions can be made, and finance being the critical deciding factor, it is one of the major reasons for fueling the growth among various organizations and finance institutions. Financial data comprises sets of information related to the financial operations of a business. These sets of data are used by management to study business performance and identify whether there is need for defining new business strategies. In a recent study, 35% of banks say they are having trouble accumulating customer data and in managing all the requirements that come with this data. Additionally, according to another survey in 2016, 33% of participants stated that data quality was significant challenge and hence, increased adoption can be witnessed among organizations.

COMPLIANCE DATA

Regulatory pressures are crucial factors for organization in every industry. Compliance data can be referred to as the data that is collected under these stated regulations, which can be used to recognize and predict future outcomes. This data helps organizations in the assessment of potential risks and useful for controlling the entire risk landscape, by balancing the risks through required actions.

SUPPLIER DATA

Today supplier data is considered as the base upon which an organization operates. Supplier data also called vendor data is information provided to the enterprises about the suppliers. It delivers a compiled view of the vendor/supplier comprising information such as supplier offerings, procurement sourcing, accounts, and deliverables, among various others. The companies are using the data quality tools to manage and improve the supplier data, to transform it into supplier intelligence to empower the organization and help in making more confident decisions; moreover, good quality supplier data enables enterprise in monitoring supplier records eventually improving the supplier relationships. According to a survey in 2014, 67% of participants agreed that poor supplier data quality was a key barrier that impacted their operations. Similarly, 95% of procurement professionals recognized supplier data quality as an extremely important factor to meet the procurement objectives.

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EVALUATION CRITERIA

Below criteria are most commonly used for comparing Data Quality Software tools.
  • Breadth and Depth of Product Offerings
    • Products Offered
    • licenses
    • Services
      • Education & Training
      • Support & Maintenance
      • Consulting Services
      • Deployment & Integration Services
    • Data Quality tools provided
      • De-Duplication Tools
      • Noramalization Tools
      • Other Tools
  • Product Features and Functionality
    • Features Offered
      • Data Governance
      • Data Integration
      • Data Migration
      • Data Enrichment
      • Metadata Management
      • Data Stewardship
      • Data Profiling
      • Data Monitoring
      • Data Validation and Standardization
      • Other Feautures
  • Delivery
    • Deployment Model
      • On-Premise
      • Hosted / On-Cloud
    • Channel of Delivery
      • Through Partners / Third-Party Vendors / MSSPs
      • Directly
  • Business Applications
    • End Users/Workstations
      • Other
  • Support and Services
    • Level of Support
    • Customer Redressal Mechanism/Program
    • Support Services
      • Technical Support
      • Customer Support
      • Sales Support
    • Channel for Delivery of Support Services
      • On-Site Support
      • Remote Support

TOP VENDORS (41)

  • 1

    Informatica offers Informatica Data Quality software as the core product in data quality tools market. Informatica Data Quality delivers ensures data quality for all business applications, whether they are on-premises or cloud applications. Informatica Data Quality includes unified role-based tools that facilitate the participation of business in the data quality process and deliver comprehensive support for applying data quality rules to products, assets, and customer data. These quality rules can applied across the enterprise for data quality purposes.

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    • Enterprise
    • California, USA
    • Founded: 1993
    • $1BN to $5BN
    • 1,001 to 5,000
  • 2

    IBM focuses on various aspects of data quality. IBM InfoSphere Information Server offers several capabilities that address data quality needs of businesses. IBM InfoSphere Information Server is an IBM data quality software which helps enterprises extract more value from complex, heterogeneous information spread across systems. IBM offers a substantial range of products for managing the end-to-end data quality requirements of organizations.

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    • Enterprise
    • New York, USA
    • Founded: 1911
    • $50BN to $100BN
    • 1,00,001 to 5,00,000
  • 3

    SAP offers SAP Data Management in the data quality tools market. SAP Data Management offers a broad range of functionalities for data quality, data integration, and data cleansing. It offers flexibility to the business users, maintains data quality, and ensures data consistency across the enterprise. SAP Data Management helps access data on-premises and on the cloud. SAP Data Management can be integrated with SAP Information Steward to assist enterprises in tracking data records for enhanced information governance.

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    • Enterprise
    • Weinheim, Germany
    • Founded: 1972
    • $10BN to $50BN
    • 75,001 to 1,00,000
  • 4

    SAS offers  SAS data quality tools through its data management suite. The suite comprises various features including data integration and access, data preparation for Hadoop, data governance, and event stream processing. The company’s data management solutions help businesses analyze and maintain the data generated by different devices, sensors, and software.

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    • Enterprise
    • North Carolina, USA
    • Founded: 1976
    • $1BN to $5BN
    • 10,001 to 15,000
  • 5

    Through the acquisition of Trillium Software, Syncsort gained unparalleled advantage in offering comprehensive solutions for the data integration and data quality tools markets. Trillium Software has a strong foothold in the data quality tools market and offers TS Quality, a single solution which is capable of ingesting both structured and unstructured data.

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    • SME
    • New Jersey, USA
    • Founded: 1968
    • $101MN to $500MN
    • 501 to 1,000
  • 6

    Talend offers Talend Open Studio for Data Quality as its prime solution in the data quality tools market. The software offers intrinsic features, such as data profiling tools, which are instrumental in providing a comprehensive view of enterprise data, and help to recognize grey areas in the organization where the data is either incomplete or duplicated or out of conformance with standards.

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    • SME
    • California, USA
    • Founded: 2005
    • $51MN to $100MN
    • 501 to 1,000
  • 7

    Experian Data Quality is a global provider of data quality software and services. These software and services are offered via flexible Software-as-a-Service (SaaS) and on-premises deployment models. The company offers comprehensive data management solutions which help organizations achieve their business goals. Experian Data Quality assists in maintaining the accuracy of the collected data which leads to an efficient operational workflow. It offers a real-time capturing of the customer information which helps in keeping data up-to-date.

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    • Enterprise
    • California, USA
    • Founded: 1996
    • $1BN to $5BN
    • 15,001 to 20,000
  • 8

    Oracle offers various tools for maintaining the data quality of an organization. Oracle Data Quality is a critical component of Oracle data integration and Master Data Management (MDM) solutions. The Oracle Enterprise Data Quality product suite helps organizations achieve maximum business value by providing high-quality data.

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    • Enterprise
    • California, USA
    • Founded: 1977
    • $10BN to $50BN
    • 1,00,001 to 5,00,000
    • SME
    • California, USA
    • Founded: 1997
    • $101MN to $500MN
    • 101 to 500
  • 10

    Microsoft Data Quality Services is a knowledge-based product that provides both computer-assisted and interactive methods to govern integrity and quality of the data sources. The DQS solution performs data cleansing through cloud-based reference data services provided by reference data providers. Microsoft Data Quality Services also provides data profiling integrated with data quality tasks, allowing analysis of the integrity of enterprise data. The data quality tools offered by Microsoft Server DQS empower an information steward or an IT expert to keep up the quality of data.

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    • Enterprise
    • Washington, USA
    • Founded: 1975
    • More than $100 BN
    • 1,00,001 to 5,00,000
  • 11

    Pitney Bowes offers a wide range of products in the data quality tools market. These products include Spectrum Data Discovery Module, Spectrum Advanced Matching Module, Spectrum Data Normalization Module, Spectrum Universal Name Module, and Spectrum Universal Addressing Module. The various capabilities offered by the company’s products are data profiling, advanced data matching and consolidation, data enrichment with reference data, standardization and normalization of data, monitoring trends, and Key Performance Indicators (KPIs).

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    • Enterprise
    • Connecticut, USA
    • Founded: 1920
    • Below $10 MN
    • 501 to 1,000
    • Enterprise
    • Ontario, Canada
    • Founded: 2007
    • $11MN to $50MN
    • 501 to 1,000
  • 13

    Information Builders offer a comprehensive product suite that helps organizations monitor and manage their information assets. The company offers the iWay Omni-Gen Data Management platform which has Omni-Gen Data Quality Edition as one of its chief components. Omni-Gen Data Quality Edition is instrumental in offering all-inclusive features and capabilities that are designed to optimize the integrity of the enterprise data irrespective of the data source, location, or format.

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    • SME
    • New York, USA
    • Founded: 1975
    • $101MN to $500MN
    • 1,001 to 5,000
    • Enterprise
    • Connecticut, USA
    • Founded: 2006
    • Below $10 MN
    • 501 to 1,000
  • 15

    Trianz offers DataVision as its core offering in the data quality tools market. DataVision+ is an automated solution, designed to meet the needs of data comparison across various sources. The cloud and mobileenabled platform enables users to write custom SQL queries, compare data between source and target environments, and generate meaningful reports. DataVision+ is also capable of synchronizing metadata and generating queries automatically to compare. In addition, the solution can test crystal reports and big data.

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    • Enterprise
    • California, USA
    • Founded: 2001
    • $10BN to $50BN
    • 1,001 to 5,000
  • 16

    Tamr offers a patented enterprise data unification platform that combines machine learning with human expertise for combining numerous data sources to provide unmatched scalability, speed, and accuracy. The company distinguishes itself from other market players by offering a bottom-up, machine-learning based approach which is instrumental in unifying disparate enterprise datasets.

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    • Enterprise
    • Massachusetts, USA
    • Founded: 2012
    • Below $10 MN
    • 501 to 1,000
    • SME
    • Massachusetts, USA
    • Founded: 1996
    • $51MN to $100MN
    • 101 to 500
    • Enterprise
    • 501 to 1,000
    • Enterprise
    • New Jersey, USA
    • Founded: 2003
    • Below $10 MN
    • 51 to 100
    • Enterprise
    • Maharashtra, India
    • Founded: 2007
    • 501 to 1,000
    • Enterprise
    • Pennsylvania, USA
    • Founded: 1968
    • Below $10 MN
    • 501 to 1,000
    • Enterprise
    • Massachusetts, USA
    • Founded: 2006
    • $11MN to $50MN
    • 501 to 1,000
    • Enterprise
    • England, UK
    • Founded: 2003
    • Below $10 MN
    • 501 to 1,000
    • Enterprise
    • Bagneux, France
    • Founded: 1924
    • $101MN to $500MN
    • 501 to 1,000
    • Startup
    • Vancouver, Canada
    • Founded: 1988
    • Below $10 MN
    • 51 to 100
    • Startup
    • California, USA
    • Founded: 2013
    • Below $10 MN
    • 101 to 500
    • Startup
    • California, USA
    • Founded: 2005
    • $11MN to $50MN
    • 51 to 100
    • Startup
    • Ontario, Canada
    • Founded: 1984
    • Below $10 MN
    • 1 to 50
    • Startup
    • Illinois, USA
    • Founded: 2013
    • Below $10 MN
    • 101 to 500
    • Startup
    • California, USA
    • Founded: 2013
    • Below $10 MN
    • 101 to 500
    • Startup
    • Connecticut, USA
    • Founded: 2006
    • Below $10 MN
    • 51 to 100
    • Enterprise
    • California, USA
    • Founded: 1956
    • $500MN to $1BN
    • 1,001 to 5,000
    • Startup
    • California, USA
    • Founded: 2007
    • Below $10 MN
    • 51 to 100
    • Startup
    • California, USA
    • Founded: 2008
    • $101MN to $500MN
    • 101 to 500
    • Startup
    • Bracknell Forest, UK
    • Founded: 1987
    • Below $10 MN
    • 51 to 100
    • Enterprise
    • Pennsylvania, USA
    • Founded: 1993
    • $1BN to $5BN
    • 1,001 to 5,000
    • Startup
    • California, USA
    • Founded: 2012
    • $11MN to $50MN
    • 501 to 1,000
    • Startup
    • New York, USA
    • Founded: 2004
    • Below $10 MN
    • 501 to 1,000
    • Enterprise
    • Washington, USA
    • Founded: 2003
    • $500MN to $1BN
    • 1,001 to 5,000
    • Enterprise
    • California, USA
    • Founded: 1979
    • $1BN to $5BN
    • 10,001 to 15,000
    • Enterprise
    • California, USA
    • Founded: 1997
    • $5BN to $10BN
    • 5,001 to 10,000

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