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Magic Quadrant for Data Quality Tools
Published 24 October 2017 - ID G00321125 - 46 min read
By Analysts Mei Selvage, Saul Judah, Ankush Jain
The data quality tools market continues to innovate, fueled by desire for cost reductions,
information governance and digital business. This Magic Quadrant evaluates 16 vendors to
help you find the most suitable one for your organization's needs and deliver greater business
value.
Market Definition/Description
The discipline of data quality assurance ensures that data is "fit for purpose" in the context of existing
business operations, analytics and emerging digital business scenarios. It covers much more than just
technology. It includes program management, roles, organizational structures, use cases and processes
(such as those for monitoring, reporting and remediating data quality issues). It is also linked to broader
initiatives in the field of enterprise information management (EIM), including information governance
and master data management (MDM).
Gartner's definition of the market for data quality tools focuses on innovative technologies and
approaches intended to meet the needs of end-user organizations in the next 12 to 18 months. As digital
business requires innovations in data quality tools, vendors are competing fiercely by enhancing existing
capabilities and creating new capabilities in eight key areas: audience, governance, data diversity,
latency, analytics, intelligence, deployment and pricing (see "Evaluate and Adopt Modern Data Quality
Tools Based on Eight Changing Trends").
As a result of a significant shift in the market over the past few years from traditional IT-driven data
quality tools to modern business-driven ones, Gartner has redesigned this Magic Quadrant to reflect
changing market dynamics and the necessity for innovation.
This market includes vendors of stand-alone software products that address the following data quality
capabilities:
■ Connectivity: The capability to access, and apply data quality rules to, a wide range of data sources.
These include internal and external data, on-premises and cloud data, and structured and
unstructured data.
■ Data profiling, measurement and visualization: Data analysis capabilities that give business and IT
users (especially those supporting business users) insight into the quality of data, and that help them
identify and understand data quality issues.
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■ Monitoring: Capabilities to assist the ongoing understanding and assurance of data quality through
monitoring.
■ Parsing: Built-in capabilities for decomposing data into its component parts.
■ Standardization and cleaning: Built-in capabilities for applying industry or local standards, business
rules or knowledge bases to modify data for specific formats, values and layouts.
■ Matching, linking and merging: Built-in capabilities for matching, linking and merging related data
entries within or across datasets, using a variety of techniques, such as rules, algorithms, metadata
and machine learning.
■ Multidomain support: Packaged capabilities for specific data subject areas, such as customer,
product, asset and location.
■ Address validation/geocoding: Support for location-related data standardization and cleansing.
■ Data curation and enrichment: The capability to integrate externally sourced data to improve
completeness and add value.
■ Issue resolution and workflow: The process flow and user interface that enables business users to
identify, quarantine, assign, escalate and resolve data quality issues.
■ Metadata management: The capability to capture, reconcile and interoperate metadata relating to the
data quality process.
■ DevOps environment: Capabilities that facilitate configuration of data quality operations.
■ Deployment environment: Styles of deployment and hardware and operating system options for
deploying data quality operations.
■ Architecture and integration: Commonality, consistency and interoperability among the various
components of the data quality toolset and third-party tools.
■ Usability: Suitability of the tools to engage and support the various roles (especially business roles)
required by a data quality initiative.
Magic Quadrant
Figure 1. Magic Quadrant for Data Quality Tools
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Source: Gartner (October 2017)
Vendor Strengths and Cautions
Ataccama
Ataccama (http://www.ataccama.com/) has headquarters in Stamford, Connecticut, U.S. and Prague,
Czech Republic. Its data quality product is Ataccama One. Ataccama has an estimated 285 customers
for this product (see Note 1).
Strengths
■ Sales strategy and licensing model: Ataccama's freemium license for data profiling continues to
attract new customers. Its reference customers highlighted the company's attractive pricing/licensing
approach and indicated that it offers very good value for money.
■ Entity resolution, monitoring and business-driven workflow: Reference customers reported positive
experiences, especially with entity resolution, monitoring and workflow. These capabilities, in
combination with data profiling and visualization features, have enabled Ataccama to support
business audiences well.
■ Improved integration with Master Data Center (MDC): Ataccama has improved integration between
its data quality tool and its MDC technology. This enhances its support for MDM and information
governance use cases across multiple data domains.
Cautions
■ User experience: Ataccama's surveyed reference customers scored the ease of use of its data quality
tools lower than those of other competitors (for details of the survey, see the Evidence section). They
also highlighted a need for better support for complex implementations.
■ Support for location enrichment and address standardization: Ataccama's customers scored its
support for location and spatial data enrichment and address standardization and validation lower
than the survey average.
■ Availability of skills: Ataccama's small size and small customer base limit the availability of skilled
resources and erect an adoption barrier. Ataccama is, however, revamping its educational resources
and extending its partnerships to address this issue.
BackOffice Associates
BackOffice Associates (http://www.boaweb.com/) has headquarters in Hyannis, Massachusetts, U.S.
Its data quality products are dspMigrate, dspMonitor, dspCompose, dspCloud and SAP Data Quality
Accelerator by BackOffice Associates. BackOffice Associates has an estimated 250 customers for this
product set.
Strengths
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Multidomain support across a wide range of use cases: BackOffice Associates' data quality tools
provide good support for all data domains, with particular depth in the product data domain. They are
applied to a wide variety of use cases.
■ Pricing model: Refinements to the pricing and licensing structure of BackOffice Associates' data
quality tools have been well received by reference customers. These improvements helped its scores
for overall customer experience and perceived value.
■ Support for SAP implementations: BackOffice Associates provides tools and processes designed and
optimized specifically for SAP implementations. This shortens the time to value for SAP-focused
customers.
Cautions
■ User experience and implementation: Reference customers expressed concerns about challenges in
terms of the user experience and the implementation of BackOffice Associates' data quality tools.
■ Performance, scalability and throughput: Reference customers scored the performance, scalability
and throughput of BackOffice Associates' data quality tools lower than the survey average.
■ Innovation and functionality: BackOffice Associates has yet to demonstrate innovation in areas such
as data preparation, machine learning, streaming data and predictive analytics for data quality.
Innovations in these areas are increasingly required for digital business.
Experian
Experian (http://www.qas.com/) has its corporate headquarters in Dublin, Ireland. Its data quality
products include Experian Pandora, and the Capture, Clean and Enhance data quality tools. Experian has
an estimated 7,500 customers for this product set.
Strengths
■ Ease of implementation, upgrade and migration: Reference customers praised Experian Pandora for
its ease of implementation, upgrade and migration. They also commended its ease of use and
business-friendly interface.
■ Pricing and licensing model: Reference customers identified Experian's pricing and licensing
approach as a strength. They indicated that a favorable total cost of ownership contributed to an
overall positive customer experience.
■ Data profiling: Experian Pandora has strong data-profiling capabilities. It provides rich out-of-the-box
functionality in this area.
Cautions
■
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■ Limited progress in certain areas of technology: Experian has made limited technological progress in
machine learning, Internet of Things (IoT) connectivity and deployment, and connections to NoSQL
repositories.
■ Reliability of some software versions: Reference customers highlighted issues with some versions of
Experian's products. They indicated a higher incidence of software bugs than the survey average.
■ Business-driven workflow: Reference customers scored Experian's business-driven workflow features
lower than the survey average, and highlighted the need for improvement in this area.
IBM
IBM (http://www.ibm.com/) has headquarters in Armonk, New York, U.S. Its data quality product is IBM
InfoSphere Information Server for Data Quality. IBM has an estimated 2,500 customers for this product.
Strengths
■ Open integration and architecture: Reference customers observed that IBM's data quality product
integrates well with both IBM and non-IBM products. IBM's shared metadata platform and open APIs
enable this easy integration.
■ Market understanding and mind share: IBM has demonstrated strong market understanding, and has
aligned its sales and marketing activities accordingly. IBM is among the data quality vendors
evaluated most frequently by reference customers and most discussed during Gartner's interactions
with clients.
■ Comprehensive and innovative product capabilities: IBM's data quality product offers the breadth and
depth of a modern data quality platform. IBM continues to invest in innovation, such as in information
governance and machine-learning capabilities (using IBM Watson).
Cautions
■ Installation, upgrading and migration: Referencecustomers expressed dissatisfaction with IBM in
terms of product installation, upgrading and migration. IBM is addressing this issue through a
standardized in-place upgrade process, a thin client and a container-based deployment approach.
■ Performance issues with newer releases: Although IBM's data quality products have traditionally
delivered good performance and scalability, some reference customers with large datasets and
complex analysisto perform have reported performance and scalability problems with version 11.5.
■ Support for small and midsize enterprises: Although large IBM customers reported good customer
support and service, smaller ones identified challenges with getting support and professional
services.
Informatica
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Informatica (http://www.informatica.com/) has headquarters in Redwood City, California, U.S. Its data
quality products are Informatica Data Quality (IDQ), Cloud Data Quality Radar and Data as a Service.
Informatica has an estimated 3,800 customers for this product set.
Strengths
■ Innovation and product strategy: Based on its Intelligent Data Platform strategy, Informatica offers
broad and deep data quality capabilities for a variety of use cases and data domains. Furthermore, its
capabilities extend to emerging areas, such as big data, cloud computing, data governance, the IoT
and machine learning.
■ Market understanding and global partnerships: Informatica has a deep understanding of the data
quality market, which informs its optimized strategy for sales, marketing and product development. It
has also built global partnerships to support its customers' data quality and data governance
programs.
■ Technical and sales support: Reference customerspraised Informatica's timely and effective
technical and sales support, as well as its account teams.
Cautions
■ Licensing complexity for existing customers: Although Informatica has simplified its product
packaging and introduced new licensing models (primarily based on subscriptions), existing
customers still experience issues with licensing and pricing models.
■ Support for business audiences: Reference customers voiced a need for better, deeper business
support in terms of Informatica's reporting, rules, workflows and interfaces. Informatica is addressing
this requirement through further integration with Informatica Axon (a cloud data governance tool) and
improvements to the business user experience.
■ Challenges prior to version 10.2: Reference customers reported that some versions of IDQ prior to
version 10.2 are unstable, with a number of bugs and a poor user experience. Informatica has fixed
some of these problems in version 10.2.
Information Builders
Information Builders (http://www.informationbuilders.com/) has headquarters in New York City, New
York, U.S. It offers the iWay Data Quality Suite. Information Builders has an estimated 270 customers for
this product set.
Strengths
■ Market understanding and marketing strategy: Information Builders has a good understanding of the
data quality market and adjacent markets, such as the data integration market. Its marketing strategy
focuses on business users and on aligning with changing market trends.
■
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Multidomain support and diverse use cases: Deployments by Information Builders' reference
customers indicate a diversity of usage scenarios and data domains, such as customer, product and
financial data.
■ Price and value: Customers view Information Builders' tools as being reasonably priced and offering
good value for money.
Cautions
■ Market share and mind share: Information Builders has only a limited share of the data quality market
and suffers from a lack of visibility outside its customer base. It appears infrequently in competitive
evaluations seen by Gartner and the survey participants.
■ Service, technical support and skill availability: Reference customers voiced concerns about
Information Builders' service and technical support. They also identified a lack of documentation and
limited availability of skilled resources.
■ Customer satisfaction and limited progress in certain areas: Reference customers scored many of
Information Builders' core data quality functionalities below the survey average. Moreover,
Information Builders lacks out-of-the-box machine learning, predictive analytics and a SaaS
deployment model.
Innovative Systems
Innovative Systems (http://www.innovativesystems.com/) has headquarters in Pittsburgh,
Pennsylvania, U.S. Its data quality products include the Enlighten Data Quality Suite and FinScan, which
reside on the Synchronos platform. Innovative Systems has an estimated 1,000 customers for this
product set.
Strengths
■ Reliable products and good pricing/licensing: Reference customers indicated that Innovative
Systems' products are stable, reliable and customizable. In addition, its pricing and licensing approach
meets customers' expectations and offers good value overall.
■ Customer satisfaction: Innovative Systems retains a loyal customer base and achieves high levels of
customer satisfaction, especially with it support and professional services. Many customers have
been using its products for years.
■ Visualization capabilities: Reference customers praised Innovative Systems' visualization capabilities,
especially for reporting and monitoring. These capabilities are especially beneficial for business
users.
Cautions
■
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Limited mind share and market presence: Innovative Systems commands limited mind share, as
indicated by its comparatively rare presence in competitive situations and infrequent mention by
users of Gartner's client inquiry service.
■ Innovation and product strategy: Although Innovative Systems offers solid traditional data quality
functions, it lacks innovative functionality in support of Apache Hadoop, the IoT (connectivity and
deployment) and machine learning.
■ Narrow focus on party data: Gartner sees limited adoption of Innovative Systems' products by
reference customers outside the party data domain. Its multidomain support functionality scored
below the survey average.
MIOsoft
MIOsoft (http://www.miosoft.com/) has headquarters in Madison, Wisconsin, U.S. Its data quality
product is MIOvantage. MIOsoft has an estimated 360 customers for this product.
Strengths
■ Product strategy and innovation: MIOsoft has a strong vision for the provision of contextual
technology that uses graph analytics and machine learning to address data quality issues in big data
and IoT use cases.
■ Robust, high-performing functionality: About 90% of MIOsoft's reference customers reported no
problems with its software. Their scores for MIOsoft's overall data quality functionality and customer
experience were among the highest in the survey.
■ Rapid deployment: The average time to production deployment reported by MIOsoft's reference
customers was among the shortest in the survey.
Cautions
■ Marketing execution and mind share: MIOsoft's sales and marketing execution and presence are
limited in comparison with its close competitors. MIOsoft appears very infrequently in competitive
evaluations seen by Gartner.
■ Configuration and usability: MIOsoft's reference customers expressed a desire for simpler
configuration and improved usability, which would enable them to become self-sufficient more
quickly.
■ Availability of product expertise: The relative smallness of MIOsoft's customer base has resulted in
limited availability of relevant product expertise. However, MIOsoft is expanding its partnerships to
address this issue.
Oracle
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Oracle (http://www.oracle.com/) has headquarters in Redwood Shores, California, U.S. Its data quality
product is Oracle Enterprise Data Quality (EDQ). Oracle has an estimated 550 customers for this product.
Strengths
■ Diverse use cases and data domains: Oracle EDQ provides broad, versatile data quality functionality,
which can be applied easily to a wide variety of use cases and data domains.
■ Data profiling: Reference customers highlighted the strength and ease of use of Oracle EDQ's data
profiling functionality. They gave it among the highest scores in the survey.
■ Address standardization and location data support: Recent investment to improve EDQ's address
standardization and verification functionality, as well as location and spatial data enrichment, resulted
in positive feedback from Oracle's customers.
Cautions
■ Pricing and licensing models: Reference customers' scores for Oracle's pricing and licensing models
were among the lowest in the survey. These remain areas of concern.
■ Integration complexity: Reference customers indicated that the process of integrating EDQ with other
Oracle and non-Oracle products was complex and in need of improvement.
■ Performance and scalability: Reference customers scored Oracle EDQ's performance, scalability and
throughput capabilities below the survey average. They expressed a desire for improvements in this
area.
Pitney Bowes
Pitney Bowes (http://www.pb.com/software) has headquarters in Stamford, Connecticut, U.S. Its data
quality product is the Spectrum Technology Platform. Pitney Bowes has an estimated 2,750 customers
for this product.
Strengths
■ Address standardization and validation: Reference customers scored highly the Spectrum
Technology Platform's support for address standardization and validation, as well as geocoding and
spatial data enrichment.
■ Depth in customer/party data domain: Pitney Bowes focuses on support for customer/party data. Its
workflow, real-time processing and machine learning for the party data domain add further value for
customers who focus on party data.
■ Hybrid deployment models: Pitney Bowes customers are increasingly opting for SaaS and cloud-
based deployments of the Spectrum Technology Platform.
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Cautions
■ Support for emerging technologies: Reference customers indicated that Pitney Bowes' data quality
functions do not yet fully support some of their emerging requirements, such as the IoT (connectivity
and deployment) and mobile platform connectivity.
■ Customer experience: Reference customers gave Pitney Bowes scores below the survey average for
overall customer experience (such as documentation, professional services and technical support).
■ Performance and throughput: Some reference customers scored the performance and throughput of
Pitney Bowes' data quality tool below the survey average.
Quadient
Quadient (https://www.quadient.com/) (formerly Neopost) has headquarters near Paris, France. Its data
quality products are DataCleaner, DataHub, Data Services and DataEntry. Quadient has an estimated 850
customers for this product set.
Strengths
■ Experience with customer/party data domain: Customers benefit from Quadient's deep experience of,
and strategic focus on, the customer/party data domain.
■ Adoption of emerging deployment models: SaaS and other cloud-based deployments are widely
adopted by Quadient reference customers. This enables them to use Quadient's data quality
functionality at scale in a cost-effective manner.
■ Technical support: Reference customers reported high levels of satisfaction with Quadient's technical
support. It provides timely and high-quality assistance.
Cautions
■ Mind share and brand recognition: Quadient is mentioned infrequently by users of Gartner's client
inquiry service and appears infrequently in competitive situations seen by Gartner. The recent
rebranding from Neopost to Quadient aims to improve the company's brand recognition.
■ Functionality improvements: Reference customers gave many of Quadient's foundational data quality
functionalities scores below the survey average.
■ Support for new data types: Quadient lacks connectivity to emerging data sources and data types,
such as mobile platforms, in-memory databases, machine data and streaming data. These are
becoming increasingly important.
RedPoint Global
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RedPoint Global (http://www.redpoint.net/) has headquarters in Wellesley Hills, Massachusetts, U.S. Its
data quality product is RedPoint Data Management. RedPoint Global has an estimated 230 customers
for this product.
Strengths
■ Customer satisfaction: Reference customers reported high levels of satisfaction, particularly with
RedPoint Global's support, services and training. They also like its product's ease of installation,
upgrade and migration.
■ Business knowledge: RedPoint Global has strong business domain knowledge in terms of marketing
and customer engagement. Its customers view it as a trusted business partner.
■ Rich, easy-to-use data quality functions: Reference customers indicated that RedPoint Global
provides a broad set of functionality, which is easy to learn and use.
Cautions
■ Pricing and licensing models: Reference customers expressed a desire for more flexible pricing,
especially for cloud environments. Gartner sees a gap between RedPoint Global's sales strategy and
execution.
■ Out-of-the-box visualization and monitoring: RedPoint Global relies primarily on third-party tools for
dashboard, interactive visualization and monitoring capabilities. Its customers expressed a desire for
more out-of-the-box functions for visualization and monitoring.
■ Documentation and skill availability: Although RedPoint Global has improved the availability of
documentation and skills, reference customers still identified getting access to these as challenging.
SAP
SAP (http://www.sap.com/) has headquarters in Walldorf, Germany. Its data quality products include
Data Quality Management and Data Services. SAP has an estimated 10,000 customers for this product
set.
Strengths
■ Product strategy and innovation: SAP offers comprehensive data quality functions and broad
connectivity, through both SAP data quality solutions and its SAP Hana platform. It innovates in many
areas, such as big data, cloud computing, in-memory computing, the IoT and machine learning.
■ Marketing strategy and execution: SAP's data quality products are rapidly increasing their market
share and are often shortlisted in competitive situations seen by Gartner.
■ Tight integration with other SAP products: On both on-premises and cloud platforms, SAP's data
quality products are preintegrated with other SAP products and modules. This is especially beneficial
for customers with SAP-centric infrastructure and application portfolios.
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Cautions
■ User experience: Reference customers indicated that SAP's data management tools should be more
user-friendly and need better interfaces, especially for a business audience.
■ Support and services: SAP scored below the survey average for overall customer experience. The
quality of its support and services — including training and consultants — have caused some concern.
SAP is improving in this area through various means — for example, Expert Chat and more selflearning resources.
■ Pricing and licensing model: Reference customers voiced concerns about SAP's complicated
licensing models and relatively high prices. They scored its pricing and licensing models below the
survey average.
SAS
SAS (http://www.sas.com/) has headquarters in Cary, North Carolina, U.S. Its data quality products are
SAS Data Management, SAS Data Quality and SAS Data Quality Desktop. SAS has an estimated 2,500
customers for this product set.
Strengths
■ Business orientation: SAS's data quality tools enable business roles (such as information stewards)
to perform data quality functions by providing business-centric visualization. SAS has a strong
knowledge base for the contact and product data domains.
■ Customer experience: Reference customers revealed high levels of satisfaction with SAS's support
and services. The overall customer experience has improved significantly, compared with last year.
■ Comprehensive data management portfolio: SAS's data quality products are undergoing a strategic
transformation centered on SAS Viya, an extension of the SAS Platform. This transformation brings
tighter integration with SAS's analytics, data integration, data preparation and data governance
functionality.
Cautions
■ Lack of native support in certain emerging areas: Although SAS can support machine learning,
machine data and automated issue resolution through its analytics and event stream processing
applications, its data quality tools have yet to gain these capabilities natively.
■ Workflow and address standardization/validation: Reference customers scored SAS's workflow and
address standardization/validation capabilities below the survey average.
■ Pricing and licensing: Reference customers identified challenges with SAS's pricing and licensing
models, especially during the renewal process.
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Syncsort
Syncsort (https://www.syncsort.com/en/Home) has headquarters in Pearl River, New York. It acquired
Trillium Software in December 2016. Its data quality products are the Trillium Software System, Trillium
Director, Trillium Global Locator, Trillium Cloud, Trillium Precise, Trillium Quality for Big Data and Trillium
Quality for Microsoft CRM. Syncsort has an estimated 1,150 customers for this product set.
Strengths
■ Core functionality: Syncsort offers solid, stable core data quality functionality, both on-premises and
in the cloud. Its reference customers reported fewer functionality and integration issues than the
survey average.
■ Enhanced capabilities to support big data: Syncsort's acquisition of Trillium Software enables Trillium
products to benefit from Syncsort's investment in big data, including the "Intelligent Execution" that
underpins its DMX-h technology. This improves the scalability and speed of data quality improvement
for big data integration.
■ Address validation and geocoding capabilities: The Trillium Precise data-as-a-service solution
complements Syncsort's offerings. It enables Syncsort's customers to benefit from strengthened and
more integrated address validation and geocoding enrichment.
Cautions
■ Marketing messages and vision: Although Syncsort has done much to integrate Trillium into its
organization, its reference customers want it to communicate its roadmap better in order to clarify its
vision for the data quality market.
■ User experience: Reference customers scored the ease of use of Syncsort's data quality tools below
the survey average. They especially highlighted a need for improvements in terms of user interface,
installation and upgrade.
■ Technical support and documentation: Reference customers scored Syncsort's technical support and
availability of documentation lower than many other vendors in the survey.
Talend
Talend (http://www.talend.com/) has headquarters in Redwood City, California, U.S. Its data quality
products are Talend Open Studio for Data Quality and Talend Data Management Platform. Talend has an
estimated 760 customers for this product set.
Strengths
■ Sales, marketing strategy and market understanding: Talend demonstrates good market
understanding, healthy sales and a marketing strategy aligned with emerging trends. Talend
increasingly appears in competitive situations seen by Gartner.
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Business audience enablement: Talend's data quality product set enables business users to perform
self-service data preparation, data stewardship and data governance.
■ Broad data connectivity and multidomain support: Talend's data quality tools support a broad set of
connectors for all types of data source. Its tools are used for multiple data domains in a wide range of
industries and regions.
Cautions
■ Market share: Although sales of its data quality products have grown rapidly recently, Talend's share
of the data quality market remains limited, compared with many of the big competitors.
■ Profiling of diverse datasets: Reference customers highlighted a need for improvement to Talend's
profiling function, especially for diverse sets of data used by business users.
■ Confusion caused by license changes: Reference customers identified Talend's changes to licensing
and pricing as a source of confusion. They want clearer communications about these changes,
especially the resulting differences between cloud and on-premises versions.
Vendors Added and Dropped
We review and adjust our inclusion criteria for Magic Quadrants as markets change. As a result of these
adjustments, the mix of vendors in any Magic Quadrant may change over time. A vendor's appearance in
a Magic Quadrant one year and not the next does not necessarily indicate that we have changed our
opinion of that vendor. It may be a reflection of a change in the market and, therefore, changed
evaluation criteria, or of a change of focus by that vendor.
Added
■ None.
There are, however, three changes of name:
■ Quadient replaces Neopost.
■ RedPoint Global replaces RedPoint.
■ Syncsort replaces Trillium Software.
Dropped
■ Uniserv.
Inclusion and Exclusion Criteria
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■
The inclusion criteria represent the specific attributes that vendors had to have to be included in this
Magic Quadrant.
To be included in this Magic Quadrant, vendors had to fulfill all of following criteria. They must:
■ Offer stand-alone on-premises software tools and/or cloud-based services that (1) are not embedded
in, or dependent on, other products and services, and (2) are positioned, marketed and sold
specifically for general-purpose data quality applications. Vendors that provide several data quality
products had to demonstrate that these are integrated, and that they collectively meet the full
inclusion criteria.
■ Deliver core data quality functions for the following, at minimum: profiling, core functions (parsing,
standardization and cleansing), interactive visualization, matching, multidomain support and
business-driven workflow.
■ Support multiple domains and diverse use cases across different industries.
■ Support data quality functionality with packaged capabilities for data in more than one language and
for more than one country.
■ Support the above functions in both scheduled (batch) and interactive (real-time) modes.
■ Enable large-scale deployment via server-based runtime architectures that can support concurrent
users and applications.
■ Maintain an installed base of at least 100 production, maintenance/subscription-paying customers for
the data quality products.
■ Have a production customer base that includes customers in more than one region (North America,
Latin America, EMEA, Asia/Pacific).
The following types of vendor were excluded from this Magic Quadrant, even if their products met the
above criteria:
■ Vendors offering narrow functionality (for example, these that support only address cleansing and
validation, or that deal only with matching). They are excluded because they do not provide all the
core functionality of modern data quality tools.
■ Vendors limited to deployments in a specific application environment, industry, language or data
domain. They are excluded because they do not provide complete market coverage.
■ Vendors operating in only a single country or supporting only narrow implementations.
■ Vendors possessing fewer than 100 production customers (perhaps because products are very early
in their life cycle).
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Evaluation Criteria
Ability to Execute
Gartner analysts evaluate technology vendors on the quality and efficacy of the processes, systems,
methods and procedures that enable their performance to be competitive, efficient and effective, and to
positively affect their revenue, retention and reputation. Gartner evaluates vendors' Ability to Execute in
the data quality tools market by using the following criteria:
■ Product or service: The vendor's core goods and services that compete in and/or serve the defined
market. Included are current product and service capabilities, quality, feature sets, skills and so on.
Products and services can be offered natively or through OEM agreements/partnerships, as defined in
the market definition and detailed in the subcriteria.
■ Overall viability: Viability includes an assessment of the overall organization's financial health, the
financial and practical success of the business unit, and the likelihood that the individual business
unit will continue offering and investing in the product(s). The vendor's financial strength (as
assessed by revenue growth, profitability and cash flow) and the strength and stability of its people
and organizational structure are considered. This criterion reflects buyers' increased openness to
considering newer, less-established and smaller providers with differentiated offerings.
■ Sales execution/pricing: The organization's capabilities in all presales activities and the structure that
supports them. Included are deal management, pricing and negotiation, presales support and the
overall effectiveness of the sales channel. We evaluate the effectiveness of the vendor's pricing
model in light of current and future customer demand trends and spending patterns (for example,
operating expenditure and flexible pricing), as well as the effectiveness of its direct and indirect sales
channels.
■ Market responsiveness/record: The vendor's ability to respond, change direction, be flexible and
achieve competitive success as opportunities develop, competitors act, customer needs evolve and
market dynamics change. This criterion also considers the vendor's history of responsiveness to
changing market demands. We evaluate the degree to which the vendor has demonstrated the ability
to respond successfully to market demand for data quality capabilities over an extended period.
■ Marketing execution: The clarity, quality, creativity and efficacy of programs designed to deliver the
organization's message in order to influence the market, promote the brand and the business,
increase brand awareness, and establish a positive identification with the product/brand and
organization in the minds of buyers. This "mind share" can be driven by a combination of publicity,
partnerships, promotional initiatives, thought leadership, social media, referrals and sales activities.
We evaluate the overall effectiveness of a vendor's marketing efforts, the degree to which it has
generated mind share, and the magnitude of the market share achieved as a result.
■ Customer experience: Relationships, products and services/programs that enable clients to be
successful with the products evaluated. Specifically, we include the quality of technical and account
support that customers receive. We may also include ancillary tools, customer support programs,
availability of user groups, SLAs and so on. We evaluate the level of satisfaction expressed by
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customers with a vendor's product support and professional services, and their overall relationship
with the vendor, as well as customers' perceptions of the value of the vendor's data quality tools
relative to costs and expectations.
■ Operations: The vendor's ability to consistently meet its goals and commitments. Factors considered
include the quality of the organizational structure, skills, experiences, programs, the stability of key
staff and other means that enable the vendor to operate effectively and efficiently.
Table 1: Ability to Execute Evaluation Criteria
Source: Gartner (October 2017)
Completeness of Vision
Gartner analysts evaluate vendors on their ability to convincingly articulate logical statements. The
evaluation covers current and future market direction, innovation, customer needs and competitive
forces, and how well they correspond to Gartner's view of the market. Gartner assesses vendors'
Completeness of Vision in the data quality tools market by using the following criteria:
■ Market understanding: The degree to which the vendor leads the market in new directions (in terms
of technologies, products, services or otherwise); and its ability to adapt to significant market
changes and disruptions, such as by supporting business-centric roles and providing advanced data
quality functionality for the IoT (connectivity and deployment), data lakes, streaming data and external
data. Also considered are the degree to which vendors are aligned with the significant trend of
convergence with other data management-related markets — specifically, the markets for data
integration tools and MDM solutions.
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Marketing strategy: We look for clear, differentiated messages, consistently communicated internally
and externally through channels, social media, advertising, customer programs and positioning
statements. Also considered are the degree to which the vendor's marketing approach aligns with
and/or exploits emerging trends (such as bimodal data governance and business-centric data quality
programs) and the overall direction of the market.
■ Sales strategy: We look for a sound strategy for selling products that uses an appropriate network of
direct and indirect sales resources, partnerships, and marketing, service and communication affiliates
to extend the scope and depth of the vendor's market reach, skills, expertise, technologies, services
and customer base. We particularly assess the use of partnerships. A sound sales strategy also aligns
sales models with customers' preferred buying approaches, such as freemium programs and
subscription-based pricing.
■ Offering (product) strategy: This criterion concerns the vendor's product development and delivery
approach, emphasizing differentiation, functionality, product portfolio, methodology and features as
these map to current and future requirements. It also covers the degree to which the vendor's product
roadmap reflects demand trends, fills current gaps or weaknesses and emphasizes competitive
differentiation. Also considered are the breadth of the vendor's strategy regarding a range of product
and service delivery models, from traditional on-premises deployment to SaaS and cloud-based
models.
■ Business model: This criterion concerns the design, logic and execution of the organization's
business proposition for revenue growth and sustained success. We consider the vendor's overall
approach to executing its strategy for the data quality tools market, including delivery models, funding
models (public or private), development strategy, packaging and pricing options, and partnership
types (such as joint marketing, reselling, OEM and system integration/implementation).
■ Vertical/industry strategy: We assess the vendor's strategy to direct resources, skills and offerings to
meet the specific needs of individual market segments, including vertical markets. The degree of
emphasis that the vendor places on vertical-market solutions is considered, as is the vendor's depth
of vertical-market expertise, including certifications.
■ Innovation: We assess the extent to which the vendor demonstrates creative energy in thought-
leadership and in differentiating ideas and product roadmaps that could significantly extend or even
reshape the market in a way that adds value for customers. Particularly, we examine how well vendors
support, or plan to support, key trends with regard to personas, data diversity, latency, data quality
analytics, intelligent capabilities and deployment, for example.
■ Geographic strategy: We evaluate the vendor's strategy to direct resources, skills and offerings to
meet the specific needs of geographies outside its "home" or native geography, either directly or
through partners, channels and subsidiaries, as appropriate. We do so in light of global demand for
data quality capabilities and expertise.
Table 2: Completeness of Vision Evaluation Criteria
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■
Source: Gartner (October 2017)
Quadrant Descriptions
Leaders
Leaders demonstrate strength in depth across the full range of data quality functions, including core
functions (parsing, standardization and cleansing), profiling, interactive visualization, matching,
multidomain support and business-driven workflow.
Leaders exhibit a clear understanding of dynamic trends in the data quality market; they explore and
execute thought-leading and differentiating ideas; and they deliver product innovations based on the
market's demands.
Leaders align their product strategies with the latest market trends, such as business audience focus,
trust-based governance, growth in data diversity, low data latency, data quality analytics (not just
reporting), intelligent capabilities (such as machine learning and artificial intelligence), new deployment
options (such as cloud and IoT edge deployment), and alternative pricing and licensing models (such as
open-source and subscriptions).
Leaders address all industries, geographies, data domains and use cases. Their products support
multidomain, alternative deployment options such as SaaS, offer excellent support for business roles
and easy-to-use visualization, and include out-of-the-box machine learning and predictive analytics.
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Leaders offer extensive support for a variety of traditional and new data sources (including IoT
platforms, Hadoop and mobile devices), a trust-based governance model, and delivery of enterprise-level
data quality implementations.
Leaders have significant size, an established market presence and a multinational presence (either
directly or through a parent company).
Leaders undertake clear, creative and effective marketing, which influences the market, promotes their
brand, and increases their mind share.
Challengers
Challengers have established presence, credibility and viability, along with robust product capabilities
and solid sales and marketing execution.
Challengers may not have the same breadth of offering as Leaders, and/or in some areas they may not
demonstrate as much thought-leadership and innovation. For example, they may focus on a limited
number of data domains (customer, product and location data, for example).
Challengers may lack capabilities in areas such as streaming data, machine-learning predictive analysis
or support for new data sources.
Compared with Leaders, Challengers often exhibit less understanding of some areas of the market, and
their product strategies may suffer from a lack of differentiation.
Visionaries
Visionaries are innovators.
Visionaries demonstrate a strong understanding of trends in the market, such as business audience
focus, trust-based governance, growth in data diversity, low data latency, data quality analytics,
intelligent capabilities (such as machine learning), new deployment options (such as cloud and IoT edge
deployment), and alternative pricing models (such as open-source and subscriptions). Visionaries'
product capabilities are mostly aligned with these trends, but not as completely as Leaders.
Although Visionaries may deliver good customer experiences, they may lack the scale, market presence,
brand recognition, customer base and resources of Leaders.
Niche Players
Niche Players often specialize in a limited number of industries, geographic areas, market segments
(such as small and midsize businesses) or data domains (such as customer data or location data). They
often have strong offerings for their chosen areas of focus and deliver substantial value for customers in
those areas.
However, Niche Players typically have limited market share and presence, limited functionalities, or lack
financial strength.
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Niche Players often have to catch up with the latest innovations, such as the IoT (connectivity and
deployment), machine learning and interactive visualization.
Context
Gartner's 2017 CEO Survey asked CEOs to identify their top five strategic business priorities for 2017 and
2018. We then categorized their replies, the top five categories being growth, IT-related, corporate,
customer and product (see "2017 CEO Survey: CIOs Must Scale Up Digital Business"). To achieve CEOs'
business priorities in these categories, data and analytics leaders — including chief data officers and
CIOs — must ensure that the quality of their data about customers, employees, products, suppliers and
assets is "fit for purpose" and trusted by users. Without trusted data, efforts to achieve these objectives
will be impeded, which will result in less value for shareholders, reduced competitiveness, rising
operational costs, loss of customers to competitors and, potentially, fines for noncompliance with
regulations.
The accelerating pace of digital business is disrupting traditional approaches to data quality by creating
a complex data landscape and generating new and urgent business requirements. Data quality vendors
are competing by enhancing existing capabilities and introducing new capabilities, such as machine
learning, interactive visualization and predictive/prescriptive analytics.
As a result of this significant shift in the market, we have redesigned the "Magic Quadrant for Data
Quality." To evaluate and adopt modern data quality tools, data and analytics leaders must consider
eight key market trends generated by both the demand side of the market and the supply side (see
"Evaluate and Adopt Modern Data Quality Tools Based on Eight Changing Trends"). The demand side —
driven by end-user organizations — has generated four key trends:
■ Business users as the primary audience: Increasingly, business users — represented by the maturing
roles of information steward and data quality analyst — are fueling data quality improvement. This is
creating a trend toward the democratization of data quality tools and practices. Business users have
become the primary audience for modern data quality tools and the agents of information
governance.
■ Trust-based information governance: Forward-looking data and analytics leaders are embracing
bimodal and trust-based information governance to prioritize data quality improvement. They are
developing a graduated trust model by comparing and rating observed and desired levels of trust in
data sources and data itself. This approach helps prioritize governance efforts and mitigate risks (see
"Reset Your Information Governance Approach by Moving From Truth to Trust").
■ Growing data diversity: Organizations face major challenges to ingest and analyze massive amounts
of data from diverse sources and in varied formats (data streams, cloud data, NoSQL databases,
social data and sensor data, for example). They are also increasingly curating external data to enrich
and augment their internal data. Finally, they are expanding their data quality domains from traditional
party domains (such as customer and organization data) to other domains (such as product, location
and financial data).
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Low-latency data requirement: The speed of digital business requires data quality operations to be
performed in real time and interactively. With new and emerging use cases such as the IoT and data
streams, real-time data quality services are mandatory.
The supply side — driven by data quality tool vendors — has also produced four key trends:
■ Advanced data quality analytics: Modern data quality tools (along with external visualization tools)
enable business users to identify and investigate data quality issues through a wide variety of visual
interfaces ranging from dashboards to scorecards and interactive graphics. Moreover, some data
quality tools provide predictive analytics by looking at past trends and predicting future trends in data
quality. They can also automate the best remedies through machine learning and prescriptive
analytics. Applying predictive and prescriptive analytics to data quality fields enables business users
to proactively and effectively prevent data quality issues from happening or escalating.
■ Intelligent capabilities: With the volume, variety and velocity of data generated by digital business
increasing, the task of managing data quality effectively and efficiently can be overwhelming for
humans using traditional data quality tools and approaches. To help organizations overcome this
challenge, modern data quality tools deliver an array of intelligent capabilities, such as machine
learning and natural-language processing.
■ New deployment options, such as cloud and IoT edge deployment: Cloud and hybrid deployments of
data quality tools are growing increasingly attractive for many organizations due to their numerous
benefits, such as simplicity, scalability, elasticity and subscription-based pricing. In addition, digital
business fuels the need for distributed data quality processing to IoT edges and gateways. Although
the deployment of data quality functions to edge devices and gateways is immature at this point, this
could change rapidly in the next few years as adoption of the IoT becomes mainstream.
■ Alternative pricing models: Many organizations prefer alternative pricing models, such as open-
source and subscription-based pricing, to traditional perpetual pricing. These pricing models enable
data quality tools to be counted as operating expenditure, which reduces the entry cost and is more
nimble.
Due to these eight trends, data and analytics leaders must take the following actions to improve data
quality practice and make the most of modern data quality tools:
■ Embrace bimodal and trust-based information governance by certifying the trust levels of data
sources and data itself. This will enable a more focused approach to prioritizing data quality
improvement efforts.
■ Expand data quality usage from traditional data management environments to frontiers like the IoT,
data lakes, data streams and external data. Do so by fully piloting and exploiting capabilities from
cutting-edge data quality tools in order to prepare for new and emerging data quality use cases.
■
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■ Gauge the technical innovativeness of data quality vendors by evaluating their data quality intelligence
capabilities, such as machine learning and predictive/prescriptive analytics, to meet the challenges
posed by data of increasing volume, variety and velocity.
Use this Magic Quadrant to help you find the right vendor and product for your organization's needs.
Gartner strongly advises against selecting a vendor simply because it is in the Leaders quadrant. A
Challenger, Niche Player or Visionary could be the best match for your requirements. Use this Magic
Quadrant in combination with "Critical Capabilities for Data Quality Tools,""How to Overcome the Top
Four Data Quality Practice Challenges" and "Toolkit: RFP Template for Data Quality Tools," as well as
Gartner's client inquiry service.
Given the current economic and market conditions, it is also important to deeply analyze
nontechnological characteristics of vendors, such as acquisitions, pricing models, speed of deployment,
total cost of ownership, availability of skills, and support and service capabilities.
Market Overview
The data quality tools market remains vibrant, owing to greater adoption on the demand side and
consequent growth in revenue on the supply side. We continue to see high demand for data quality tools
from many industries and organization sizes, including midsize organizations (which traditionally tended
not to buy them). This demand is driven both by organizations continuing to invest in digital business
initiatives and by organizations seeking to cut costs, optimize business operations and monetize data
assets. Therefore, we see data quality tools being applied to a wide range of use cases, including big
data and analytics, data integration, data migrations, information governance and MDM.
Consequently, too, the data quality tools market has continued to show strong revenue growth — 7% in
2016 in constant currency, compared with 5.2% in 2015 (see "Market Share: Data Integration Tools and
Data Quality Tools, Worldwide, 2016"); its revenue is estimated to have reached $1.37 billion in 2016, up
from $1.28 billion in 2015. This market is still among the fastest-growing in the infrastructure software
subsector of the enterprise software market (see "Market Share: All Software Markets, Worldwide,
2016"). We forecast compound annual revenue growth of 10% in this market for the period 2016 to 2021.
In 2016, approximately 60% of the market (up from 50% in 2016) was controlled by four large, wellestablished vendors: SAP, Experian, Informatica and Pitney Bowes. The remaining 40% was divided
between a large number of providers, including megavendors (such as SAS, IBM and Oracle) and smaller
vendors (such as Neopost, Syncsort/Trillium, Innovative Systems and Talend). The consolidation of
market share suggests that the market is becoming saturated and difficult for newcomers to break into.
This agrees with Gartner's assessment of the maturity of data quality tools, which are classified as "early
mainstream" in "Hype Cycle for Data Management, 2017." Technologies in the early mainstream typically
offer more out-of-the-box functionality and proven methodologies. Consequently, the market's smaller
vendors are increasingly challenged to maintain high levels of investment and are often forced to
become, or stay as, niche vendors.
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This market continues to interact with related markets, such as those for MDM, data integration
products, and analytics and business intelligence (BI), as demand shifts to broader capabilities that span
the EIM discipline. As a result, a growing number of vendors — big and small — are positioning
themselves in these related markets. For example, RedPoint Global recently released an MDM solution,
and Informatica, having acquired technologies from Diaku, offers Axon (an enterprise data governance
software). In short, the percentage of vendors dedicated solely to data quality offerings continues to
lessen. Ideally, customers should evaluate their long-term EIM requirements first, and then select data
quality tools accordingly.
Finally, a review of the responses from surveyed reference customers, as well as of vendor briefings and
client inquiries over the past few years yields some important findings:
■ Data domains: Data quality initiatives address a wide variety of data domains. However, party data
(for existing customers, prospective customers, citizens or patients, for example) remains the No. 1
priority: 80% of reference customers considered it the top priority among their three most important
domains. Transactional data came second highest, with 45% of reference customers naming it
among their top three. Financial/quantitative data was third, with 39% of reference customers naming
it. The figure for product data was 34%.
■ Usage scenarios: Overall usage of data quality tools has increased since the 2016 survey. Ongoing
operation of business applications — used by 60% of reference customers this year, up from 54% in
2016 — has become the most important usage scenario. Information governance has climbed to 52%
in 2017, from 40% in 2016. Analytics and BI usage also has risen, to 50% in 2017, from 41% in 2016.
We continue to see strong usage of data quality tools for MDM (41%), as well as for data migrations
and consolidations (37%). Big data and analytics has risen to 20% in 2017, from 12% in 2016.
Interenterprise data sharing remains around 18%.
■ Adoption of financial metrics: Similar to past years, the majority (65%) of the reference customers do
not measure the financial impact of data quality at all. Only 7% have formal internal metrics to
measure the financial impact of poor data quality, and the remaining 28% use informal metrics.
Failure to measure this impact can result in reactive responses to data quality issues, missed
business growth opportunities, reduced business buy-in and increased risk. Data and analytics
leaders can improve their data quality initiatives by connecting business cases directly with business
outcomes and adopting formal financial metrics (see "Five Steps to Creating a Business Case for Data
Quality Improvement").
■ Estimated financial impact of poor data quality: We asked the reference customers to estimate the
annual financial impact of poor data quality on their organizations. This year's estimation has jumped
to $15 million on average, compared with $9.7 million last year. The increase shows increased
awareness on the part of organizations of the financial impact of poor data quality.
■ On-premises and cloud deployment: 82% of the reference customers are still using on-premises
deployments of data quality tools. Ten percent use cloud deployments (including SaaS, platform as a
service and infrastructure as a service), which is essentially the same as last year. Nine percent use
hybrid deployments. Although customers are still hesitant about embracing cloud deployments of
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data quality tools, Gartner observes that data quality vendors have increasingly shifted their
investment and innovation priorities to the cloud in the past 12 months. This points to a trend for
more cloud deployments in the next few years.
■ Important factors for vendor evaluation: We asked reference customers their three most important
factors when evaluating vendors. "Functional capabilities" was identified by 61% of customers as the
top factor. Both "pricing model" and "understanding of their business needs" were mentioned by 40%.
"Performance and scalability" was mentioned by 36%, "vendor's overall expertise in the data quality" by
22%, and "experience in their specific industry" by 18%. These results show that customers evaluate
vendors on the basis of many factors, not just technical functions.
■ Usability concerns: 13% of the reference customers reported that their data quality tools are difficult
to implement and use. This problem exceeds other problems, such as inadequate performance and
scalability (10%) and unreliable software (9%). Interestingly, absent or weak functionality was
mentioned by only 5% of the reference customers. One important lesson for data quality vendors is
the need to focus on improving the usability of their products, instead of simply focusing on adding
new functionality.
Gartner clients should take these findings into account in their strategies for selecting and deploying
data quality tools in order to optimize their investments.
Evidence
The analysis in this document is based on information from a number of sources, including:
■ Extensive data on functional capabilities, customer base demographics, financial status, pricing and
other quantitative attributes, gained via an RFI process that engaged vendors in this market.
■ Interactive briefings in which vendors provided Gartner with updates on their strategy, market
positioning, recent key developments and product roadmap.
■ A web-based survey of reference customers identified by each vendor. This captured data on usage
patterns, levels of satisfaction with major product functionality categories, various non-technologyrelated vendor attributes (such as pricing, product support and overall service delivery), and more. In
total, 201 organizations from 19 vendors across all major regions provided input on their experiences
with vendors and their tools.
■ Feedback about tools and vendors captured during conversations with users of Gartner's client inquiry
service.
■ Market share and revenue growth estimates developed by Gartner's technology and service provider
research unit.
Note 1
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Estimates of Vendors' Data Quality Customers
Gartner's estimates for the number of customers that each vendor has for its data quality products are
based on a number of data points. These include RFI responses by the vendors, market surveys,
discussions with users of Gartner's client inquiry service, market share data (such as "Market Share: All
Software Markets, Worldwide, 2016") and publicly available sources.
As part of our formal research process, we reviewed our customer estimates with the featured
vendors. In most cases, vendors either confirmed that the Gartner estimate was accurate or provided no
comment, in which case Gartner stood by its estimates.
Evaluation Criteria Definitions
Ability to Execute
Product/Service: Core goods and services offered by the vendor for the defined market. This includes
current product/service capabilities, quality, feature sets, skills and so on, whether offered natively or
through OEM agreements/partnerships as defined in the market definition and detailed in the subcriteria.
Overall Viability: Viability includes an assessment of the overall organization's financial health, the
financial and practical success of the business unit, and the likelihood that the individual business unit
will continue investing in the product, will continue offering the product and will advance the state of the
art within the organization's portfolio of products.
Sales Execution/Pricing: The vendor's capabilities in all presales activities and the structure that
supports them. This includes deal management, pricing and negotiation, presales support, and the
overall effectiveness of the sales channel.
Market Responsiveness/Record: Ability to respond, change direction, be flexible and achieve
competitive success as opportunities develop, competitors act, customer needs evolve and market
dynamics change. This criterion also considers the vendor's history of responsiveness.
Marketing Execution: The clarity, quality, creativity and efficacy of programs designed to deliver the
organization's message to influence the market, promote the brand and business, increase awareness of
the products, and establish a positive identification with the product/brand and organization in the minds
of buyers. This "mind share" can be driven by a combination of publicity, promotional initiatives, thought
leadership, word of mouth and sales activities.
Customer Experience: Relationships, products and services/programs that enable clients to be
successful with the products evaluated. Specifically, this includes the ways customers receive technical
support or account support. This can also include ancillary tools, customer support programs (and the
quality thereof), availability of user groups, service-level agreements and so on.
Operations: The ability of the organization to meet its goals and commitments. Factors include the
quality of the organizational structure, including skills, experiences, programs, systems and other
vehicles that enable the organization to operate effectively and efficiently on an ongoing basis.
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Completeness of Vision
Market Understanding: Ability of the vendor to understand buyers' wants and needs and to translate
those into products and services. Vendors that show the highest degree of vision listen to and
understand buyers' wants and needs, and can shape or enhance those with their added vision.
Marketing Strategy: A clear, differentiated set of messages consistently communicated throughout the
organization and externalized through the website, advertising, customer programs and positioning
statements.
Sales Strategy: The strategy for selling products that uses the appropriate network of direct and indirect
sales, marketing, service, and communication affiliates that extend the scope and depth of market reach,
skills, expertise, technologies, services and the customer base.
Offering (Product) Strategy: The vendor's approach to product development and delivery that
emphasizes differentiation, functionality, methodology and feature sets as they map to current and
future requirements.
Business Model: The soundness and logic of the vendor's underlying business proposition.
Vertical/Industry Strategy: The vendor's strategy to direct resources, skills and offerings to meet the
specific needs of individual market segments, including vertical markets.
Innovation: Direct, related, complementary and synergistic layouts of resources, expertise or capital for
investment, consolidation, defensive or pre-emptive purposes.
Geographic Strategy: The vendor's strategy to direct resources, skills and offerings to meet the specific
needs of geographies outside the "home" or native geography, either directly or through partners,
channels and subsidiaries as appropriate for that geography and market.
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