Magic Quadrant for Data Quality Tools, = 2007

Data quality technology continues to see increased demand = from=20 technology buyers and vendors in related markets. As a = result, the=20 market for data quality tools is growing at a solid pace, = and new=20 vendors are entering this market. Large vendors, such as = Business=20 Objects, IBM and Pitney Bowes, have entered via = acquisitions, while=20 numerous small vendors have entered via the organic = development of=20 new data quality technology. The entry of larger vendors = signals a=20 change in the competitive landscape and also raises = questions as to=20 the long-term outlook for vendors of pure-play data quality = tools.=20 In addition, various aspects of data quality functionality,=20 beginning with profiling, are appearing in related = technology=20 markets, such as the data integration tools market, and in = the=20 enterprise application vendors' platforms.

When evaluating offerings in this market, organizations = must=20 consider the breadth of functional capabilities (for = example, data=20 profiling, matching, cleansing and enrichment) relative to = their=20 requirements. Other key criteria include the degree of = integration=20 of these capabilities into a single architecture and = product, as=20 well as the size, viability and partnerships of the=20 vendors.

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Magic Quadrant

Figure 1. Magic Quadrant for Data Quality Tools, = 2007

=20

Source: Gartner (June=20 2007)

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Market = Overview

Throughout its history, the discipline of data quality = has=20 focused generally on specific domains, such as customer = information=20 files and campaign management. This domain specificity has = often=20 resulted in highly regionalized or narrow solutions that = ignore the=20 breadth and global nature of today's business environment. = Many=20 contemporary data quality initiatives still focus on a = single=20 domain, but there has been a clear move toward multifaceted = efforts=20 that address multiple domains. Companies are discovering = that data=20 quality has a significant impact on their most strategic = business=20 initiatives, and, as such, are raising the visibility of = data=20 quality activity within their enterprise information = management=20 programs. Compliance and transparency are at the top of the = list of=20 concerns, along with ensuring the capture of expected = benefits from=20 investments in business intelligence and master data = management. A=20 common thread underpins successful initiatives in each of = these=20 areas: the prerequisite for high-quality data to add = accuracy and=20 value to the relevant business processes.

Data quality is often understood as "data cleansing" = only.=20 However, this is only one area of the overall data quality=20 continuum. A more complete view includes areas such as = profiling,=20 standardization, matching and enrichment. In addition to = offering a=20 range of functional capabilities, data quality tools support = multiple modes of deployment. Historically, the tools have = been=20 deployed in batch-mode silos =97 as one step in an overall = flow or as=20 a stand-alone, offline process. Increasingly, the tools are = being=20 deployed in a real-time manner, integrated into operational = systems=20 with the goal of standardizing and cleansing data at the = point of=20 capture and maintenance. Well-documented application = programming=20 interfaces (both proprietary and standards-based) to the = tools are=20 facilitating their integration with other packaged software = =97 from=20 business applications, such as ERP and CRM, to = infrastructure=20 components, such as data integration tools. In addition, = data=20 quality is playing an important role in master data = management (MDM)=20 environments; in particular, matching, deduplication and=20 standardization technologies are often used to define master = records. Most tool use has been for improving the quality of = customer data, but organizations are increasingly looking to = deploy=20 the tools against other data subject areas =97 in = particular, product=20 data (often within the context of a product information = management=20 [PIM] initiative) and financial data (as it relates to = compliance=20 directives in financial reporting). An additional emerging = trend is=20 the application of data quality tools in the domain of = content=20 management, to understand and improve the quality of = less-structured=20 data.

The vendors in this market offer a broad range of data = quality=20 functionality. However, within their portfolios, many still = retain=20 the various data quality components as separate product = offerings,=20 with some degree of integration between them. Data quality=20 technology must further evolve toward a model where a common = set of=20 business rules drives all the various data quality = components (from=20 profiling, matching and cleansing, to validation, = standardization=20 and enrichment). In addition, these data quality = capabilities must=20 be able to behave in a service-oriented manner, existing as = an=20 enterprise service that is consumed by all applications, = processes,=20 infrastructure tools and users that require data quality = operations=20 (see "The Emerging Vision for Data Services: Use Data = Quality,=20 Profiling and Mining to Achieve Policy-Based Business = Rules").=20

The market for data quality tools is modest in size but = is=20 growing at a rapid pace (see "Forecast: Data Quality Tools,=20 Worldwide, 2006-2011") compared with many other software = markets.=20 Interestingly, much of the innovation is coming from outside = the=20 United States. As a result, the veteran data quality tool = vendors=20 are being challenged by entrants that have an international = focus=20 and propensity toward designing and deploying = domain-agnostic data=20 quality services (stand-alone or embedded in applications), = based on=20 a centrally managed set of business rules. However, with the = increasing trend toward embedding data quality capabilities = in=20 business applications, data integration tools and other = software=20 offerings from larger vendors, these small competitors will = face=20 significant challenges in survival and growth. Several of = the "mega=20 vendors" have started to build rudimentary data quality = capabilities=20 into their data management products, but for the most part = they are=20 leveraging third-party applications to provide deeper data = quality.=20 Within the next 24 months, the mega vendors, as well as = business=20 intelligence and data integration vendors that are still = lacking=20 data quality capabilities, will look to expand their = portfolios.=20 This will lead to more acquisitions and consolidation in the = data=20 quality tools market.

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Market=20 Definition/Description

The data quality tool market comprises vendors that offer = stand-alone software products for addressing various aspects = of the=20 data quality problem:

Parsing and standardization: decomposition of = text=20 fields into component parts and formatting of values into=20 consistent layouts based on industry standards, local = standards=20 (for example, postal authority standards for address = data),=20 user-defined business rules, and knowledge bases of values = and=20 patterns.=20
Generalized "cleansing": modification of data = values to=20 meet domain restrictions, integrity constraints or other = business=20 rules that define sufficient data quality for the = organization.=20
Matching: identification, linking or merging = related=20 entries within or across sets of data.=20
Profiling: analysis of data to capture = statistics=20 (metadata) that provide insight into the quality of the = data and=20 aid in the identification of data quality issues.=20
Monitoring: deployment of controls to ensure = ongoing=20 conformance of data to business rules that define data = quality for=20 the organization.=20
Enrichment: enhancing the value of internally = held data=20 by appending related attributes from external sources (for = example, consumer demographic attributes or geographic=20 descriptors).

The tools provided by vendors in this market are = generally=20 consumed by technology users for internal deployment in = their IT=20 infrastructure, although hosted data quality solutions are=20 continuing to emerge and grow in popularity.

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Inclusion and = Exclusion=20 Criteria

For vendors to be included in the Magic Quadrant, they = must meet=20 the following criteria:

Offer stand-alone (not only embedded in, or dependent = on,=20 other products and services) packaged tools that are = positioned,=20 marketed and sold specifically for data quality = applications.=20
Deliver functionality that addresses, at a minimum, = profiling,=20 parsing, standardization, cleansing and matching. Vendors = offering=20 only narrow functionality (for example, only address = cleansing and=20 validation, or only matching) are excluded because they do = not=20 provide complete data quality tool suites.=20
Support this functionality for data in more than one = language,=20 and specific to more than one country (in the case of = address=20 standardization).=20
Maintain an installed base of at least 50 production = customers=20 for their data quality products.=20
Demonstrate, via customer references, the use of the = tools at=20 an enterprise (cross-departmental or multiapplication) = level.=20

A vendor that does not meet the above criteria may be = considered=20 for inclusion if it is a new entrant to the market that is=20 demonstrably different from established vendors and = represents a=20 future direction for data quality tools.

It should be noted that a large number of data quality = tools=20 vendors currently exist, but most do not meet the above = criteria and=20 are, therefore, not included in the Magic Quadrant. Many = vendors=20 provide products to address one very specific data quality = problem,=20 such as address cleansing and validation, but cannot support = other=20 types of applications, or lack the full breadth of = functionality=20 expected in today's modern data quality solutions. Others = provide a=20 range of functionality, but operate only in a single country = or=20 support only narrow, departmental implementations. Others = may meet=20 all the functional, deployment and geographic requirements = but are=20 at a very early stage in their life span and, therefore, = have few,=20 if any, production customers. The following vendors are = occasionally=20 considered by Gartner clients alongside those appearing in = the Magic=20 Quadrant when deployment needs are aligned with their = specific=20 capabilities, or are newer market entrants beginning to gain = visibility in the market but lacking a significant customer=20 base:

AddressDoctor, Maxdorf, Germany, http://www.addressdoctor.com/=20
BackOffice Associates, Harwich, Massachusetts, = http://www.boaweb.com/=20
Business Data Quality, London, U.K., http://www.businessdataquality.com/=20
Certica Solutions, Wakefield, Massachusetts, http://www.certicasolutions.com/=20
ChoiceMaker, New York, New York, http://www.choicemaker.com/=20
DQ Global, Fareham, U.K., http://www.dqglobal.com/=20
Identity Systems (a division of Nokia), Old = Greenwich,=20 Connecticut, http://www.identitysystems.com/=20
Infogix, Naperville, Illinois, http://www.infogix.com/=20
InQuera, Tefen, Israel, http://www.inquera.com/=20
Omikron, Pforzheim, Germany, http://www.omikron.net/=20
Melissa Data, Rancho Santa Margarita, = California, http://www.melissadata.com/=20
QAS (a subsidiary of Experian), London, U.K., = http://www.qas.com/=20
Silver Creek Systems, Louisville, Colorado, http://www.silvercreeksystems.com/=20
SRC, Orange, California, http://www.extendthereach.com/=20
Stalworth, San Mateo, California, http://www.stalworth.com/=20
Udex, Newport, U.K., http://www.udex.com/=20
Utopia, Mundelein, Illlinois, http://www.utopiainc.com/=20
Zoomix, Jerusalem, Israel, http://www.zoomix.com/ =

Gartner will continue to monitor the status of these = vendors for=20 consideration as new entrants to the data quality tool Magic = Quadrant during subsequent updates.

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Added

Datactics, Belfast, Northern Ireland, http://www.datactics.com/

DataMentors, Wesley Chapel, Florida, http://www.datamentors.com/

Datanomic, Cambridge, U.K., http://www.datanomic.com/

Fuzzy! Informatik, Ludwigsburg, Germany, http://www.fazi.de/

Netrics, Princeton, New Jersey, http://www.netrics.com/

Uniserv, Pforzheim, Germany, http://www.uniserv.com/

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Dropped

No vendors were dropped from the previous version of the = Magic=20 Quadrant.

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Evaluation = Criteria

Ability to = Execute

Vendors' ability to execute in the data quality tools = market is=20 determined by the following criteria:

Product/Service: how well the vendor supports = the range=20 of data quality functionality required by the market, the = manner=20 (architecture) in which this functionality is delivered, = and the=20 overall usability of the tools. Product capabilities are = critical=20 to the success of data quality tool deployments, and, = therefore,=20 receive a high weighting.=20
Overall Viability: the magnitude of the = vendor's=20 financial resources and the strength of its people and=20 organizational structure.=20
Sales Execution/Pricing: the effectiveness of = the=20 vendor's pricing model, and the effectiveness of its = direct and=20 indirect sales channels.=20
Market Responsiveness and Track Record: the = degree to=20 which the vendor has demonstrated the ability to = successfully=20 respond to market demand for data quality capabilities = over time.=20
Marketing Execution: the overall effectiveness = of the=20 vendor's marketing efforts, and degree of "mind share," = market=20 share and account penetration that the vendor has achieved = as a=20 result. Marketing execution is a significant driver of = sales,=20 growth and brand awareness, and, therefore, receives a = high=20 weighting.=20
Customer Experience: the quality of the = vendor's=20 general customer service, implementation service and = technical=20 support, and customers' perception of overall value.=20

Table 1.=20 Ability to Execute Evaluation Criteria
Evaluation Criteria	Weighting
Product/Service	high
Overall Viability = (Business=20 Unit, Financial, Strategy, Organization)	standard
Sales=20 Execution/Pricing	standard
Market = Responsiveness and=20 Track Record	standard
Marketing = Execution	high
Customer = Experience	standard
Operations	no=20 rating

Source: Gartner (June = 2007)

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Completeness = of=20 Vision

Vendors' completeness of vision for the data quality = tools market=20 is determined by the following criteria:

Market Understanding: the degree to which the = vendor=20 leads the market in new directions (technology, product, = services=20 or otherwise) and its ability to adapt to significant = market=20 changes and disruptions. Given the dynamic nature of this = market,=20 this item receives a high weighting.=20
Marketing Strategy: the degree to which the = vendor's=20 marketing approach aligns with and/or leverages emerging = trends=20 and the overall direction of the market.=20
Sales Strategy: the alignment of the vendor's = sales=20 model to how customers' preferred buying approaches will = evolve=20 over time.=20
Offering (Product) Strategy: the degree to = which the=20 vendor's product road map reflects demand trends in the = market and=20 fills current gaps or weaknesses. Also, the strength of = the=20 vendor's strategy regarding alliances of different types = is=20 considered.=20
Business Model: the overall approach the vendor = takes=20 to execute on its strategy for the data quality market. = With a=20 reasonably high degree of similarity across the vendors in = this=20 market, this item receives a low weighting.=20
Vertical/Industry Strategy: the level of = emphasis the=20 vendor places on vertical solutions, and the vendor's = depth of=20 vertical expertise. Given the broad cross-industry nature = of the=20 data quality discipline, vertical strategies are less = critical=20 and, therefore, this item receives a low weighting.=20
Innovation: the degree to which the vendor has=20 demonstrated a willingness to make new investments to = support the=20 strategy and enhance product capabilities, the level of = investment=20 in R&D directed toward development of the tools, and = the=20 extent to which the vendor demonstrates creative energy. = With=20 rapidly evolving technology requirements =97 in the face = of trends=20 such as service-oriented architecture (SOA) =97 and = increased=20 competition in the market from large vendors, this item = receives a=20 high weighting.=20
Geographic Strategy: the global presence of the = vendor=20 and the manner in which it is achieved (direct local = presence,=20 resellers, distributors and so on), in light of emerging = demand=20 for data quality capabilities in regions such as Asia, and = the=20 desire of multinational enterprises to leverage common = tools=20 worldwide.

Table 2.=20 Completeness of Vision Evaluation = Criteria
Evaluation Criteria	Weighting
Market=20 Understanding	high
Marketing = Strategy	standard
Sales = Strategy	standard
Offering (Product) = Strategy	standard
Business = Model	low
Vertical/Industry=20 Strategy	low
Innovation	high
Geographic = Strategy	standard

Source: Gartner (June = 2007)

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Leaders

Leaders in the market demonstrate strength across a = complete=20 range of data quality functionality, including profiling, = parsing,=20 standardization, matching, validation and enrichment. They = exhibit a=20 clear understanding and vision for where the market is = headed,=20 including recognition of noncustomer data quality issues and = the=20 delivery of enterprise-level data quality implementations. = Leaders=20 have an established market presence, significant size and a=20 multinational presence (directly or as a result of a parent=20 company).

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Challengers

Challengers in the market provide strong product = capabilities but=20 may not have the same breadth of offering as Leaders. For = example,=20 they may lack several functional capabilities of a complete = data=20 quality solution. Challengers have an established presence,=20 credibility and viability, but may demonstrate strength only = in a=20 specific domain (for example, only customer name and address = cleansing) and/or may not demonstrate thought leadership and = innovation to a significant degree.

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Visionaries

Visionaries in the market demonstrate a strong = understanding of=20 current and future market trends and directions, such as the = importance of ongoing monitoring of data quality, engagement = of=20 business subject matter experts and delivery of data quality = services. They exhibit capabilities aligned with these = trends, but=20 may lack the market presence, brand recognition, customer = base and=20 resources of larger vendors.

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Niche = Players

Niche Players often have limited breadth of functional=20 capabilities and may lack strength in rapidly evolving = functional=20 areas such as data profiling and international support. In = addition,=20 they may tend to focus solely on a specific market segment = (such as=20 midsize businesses), limited geographies or a single domain = (such as=20 customer data), as opposed to positioning toward broader = use. Niche=20 Players may also have good functional breadth, but may have = an=20 early-stage presence in the market with a small customer = base and=20 limited resources. Niche Players that specialize in a = particular=20 geography or data domain may have very strong offerings for = their=20 chosen area of focus and deliver substantial value for their = customers in that segment.

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Vendor = Strengths and=20 Cautions

Business = Objects

Strengths

Business Objects entered the data quality tools market = through=20 its acquisition of Firstlogic in 1Q06. Business Object's=20 significant global presence, combined with Firstlogic's = brand=20 recognition in North America, large installed base (over = 2,500=20 customers) and market share leadership, creates = significant=20 leverage and cross-sell opportunities for the vendor to = grow its=20 data quality tools business.=20
Business Objects provides good breadth of functional = data=20 quality capabilities, including data profiling (via Data = Insight=20 XI) and the common data cleansing operations (via Data = Quality=20 XI). The core data quality functionality in Data Quality = XI, based=20 on the new IQ8 architecture of the Firstlogic technology, = enables=20 the delivery of data quality services in an SOA context. = The=20 vendor continues to increase the depth of integration = across the=20 product portfolio with each major release.=20
With the addition of the Firstlogic's heritage, = technology,=20 customer base, people and experience, Business Objects' = strength=20 remains very much in applications of customer data = quality,=20 specifically in matching/linking, de-duplication, and name = and=20 address standardization and validation. =

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Cautions

Because Firstlogic's technology was heavily biased = toward=20 customer data quality, Business Objects is lagging behind = many of=20 its competitors with regard to product capabilities and = experience=20 in addressing other data domains. It will attempt to close = this=20 gap with the delivery of more generalized parsing and = cleansing=20 operations, planned for 3Q07.=20
Since acquiring Firstlogic, Business Objects has = appeared in=20 competitive situations less frequently than Firstlogic did = prior=20 to 2006. This is likely due to the removal of the = Firstlogic=20 branding and Business Objects' limited recognition as a = data=20 quality tools vendor.=20
Business Objects is making many value-added = infrastructural=20 enhancements to its "EIM" line of products (such as = scalability=20 improvements via grid computing and caching, and = productivity=20 improvements via enhanced reporting and automated tuning). = This=20 will initially benefit the vendor's data integration tools = (primarily, Data Integrator), and will not impact the data = quality=20 tools until slightly later in the product road map. =

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Datactics

Strengths

Datactics is a small data quality vendor based in = Northern=20 Ireland. Its software is used in a range of subject areas, = not=20 limited to typical name/address verification scenarios. = Datactics=20 operates primarily in Europe, but there are a number of=20 value-added resellers in the Americas and Asia.=20
The flagship product, DataTrawler, is fully 64-bit and = Unicode=20 enabled, supports most of the European languages, runs on = many=20 platforms and supplies broad capabilities in profiling,=20 matching/merging, cleansing and monitoring. Data quality=20 scorecards can be constructed to monitor quality-related = metrics.=20
Datactics has partnerships with consultancies and = system=20 integrators that have used the DataTrawler product in some = strategic data quality programs, which is quick to = implement at a=20 reasonable cost, mostly in the government sector. = Datactics has=20 also built an alliance with ETI, a data integration tools = vendor,=20 and other software companies that include DataTrawler = services.=20

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Cautions

With only four sales employees, limited marketing = budgets and=20 relatively low-profile partnerships, Datactics is "flying=20 underneath the radar" for most organizations looking for a = provider of data quality tools. Datactics needs to find = stronger,=20 more visible partners in the software arena, such as in = business=20 intelligence.=20
The current system integrator channel does not provide = sufficient inroads and introductions to potential new = accounts, as=20 they are only working as intermediaries. A strong reseller = agreement is required to build stronger bonds between = Datactics=20 and its partners.=20
Although the DataTrawler product is service-enabled,=20 references indicate that the main use of the technology is = in=20 batch environments only, with a focus on matching. Ease of = use is=20 repeatedly cited as a challenge.

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DataFlux

Strengths

DataFlux has been moving out of the large shadow of = its parent=20 company, SAS. It is demonstrating high growth and has = begun=20 to be seen as a major competitor, most recently in = Europe. The=20 company is investing heavily in R&D, and is = establishing key=20 relationships with large system integrators.=20
The vendor has expanded its functionality beyond the = core=20 competency areas of data cleansing and matching, in = particular in=20 data profiling, rooftop geocoding, team development,=20 internationalization and service-oriented architecture = (SOA)=20 support. It has also added data quality monitoring, = including some=20 basic dashboarding capabilities.=20
The DataFlux platform includes profiling, cleansing = and=20 monitoring capabilities in a single architecture, thereby = reducing=20 the efforts to integrate discrete products and increasing=20 usability through a consistent user experience. =

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Cautions

DataFlux has been seen mostly as a stand-alone data = quality=20 technology provider. However, through the new customer = data=20 integration (CDI) solution, and a broader data integration = positioning with its Integration Server product, DataFlux = will=20 increasingly try to address needs beyond its core market = and into=20 the world of MDM. But this may put DataFlux at odds with = its=20 parent company and could generate some confusion in the = market, as=20 SAS is also targeting data integration opportunities.=20
To become a data quality name brand independent of = SAS,=20 DataFlux will need to strike partnerships and build a = channel with=20 other vendors in the business intelligence and data = integration=20 space, possibly companies that compete directly with SAS.=20
Its attempt to become a more globally visible data = quality=20 player is still in its early stages. Unicode support is = very new=20 and has shown little traction.

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DataLever

Strengths

DataLever provides integrated data profiling and data=20 cleansing functionality in a single product. All = operations can be=20 readily deployed in both batch and real-time modes.=20
DataLever takes a domain-agnostic view of data quality = issues,=20 enabling its technology to be applied toward various data = domains,=20 including customer, product and others. Much of the = current=20 installed base applies DataLever's technology toward = customer data=20 quality issues, with some users working in a combination = of other=20 areas.=20
Customers cite overall ease of use, relatively short=20 implementation times and lower cost in comparison to = alternative=20 offerings as the main selling points of DataLever's = products. In=20 addition, the lower complexity of the product enables its = use by=20 business subject matter experts in addition to IT = personnel.=20

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Cautions

Still a relative newcomer in this market, DataLever = supports a=20 small customer base of approximately 150, with virtually = no=20 presence outside North America.=20
DataLever currently has very limited runtime platform = support=20 (Windows and Linux only), but expects to deliver Unix = support in=20 4Q07. Its lack of unicode capabilities and lack of support = for=20 languages and address data outside the U.S. will continue = to=20 hamper its traction with large multinational prospects. = Initial=20 steps toward Unicode compatibility, as well as = partnerships to=20 support international address cleansing, are also planned = by YE07.=20
DataLever's customer base is oriented toward = departmental=20 projects or midsize businesses. However, the vendor is = beginning=20 to demonstrate success in larger-scale, enterprise-class=20 implementations.

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DataMentors

Strengths

DataMentors specializes in customer data quality = applications,=20 providing matching, linking, standardization and cleansing = operations via its DataFuse product, and data profiling=20 capabilities via ValiData. Its partnership with smartFocus = enables=20 the vendor to offer campaign management, analytics and = mapping=20 capabilities (branded as DataMentors PinPoint). The = vendor's roots=20 are in database marketing, with the management team having = been=20 involved in large-scale applications of this type for over = 20=20 years.=20
Customer references cite accuracy of matching, ease of = use,=20 and attractive pricing relative to some of the more = prominent=20 vendors in the market as key strengths and reasons for = their=20 selection of DataMentors' technology.=20
DataMentors demonstrates strong knowledge and = experience in=20 the financial services industry, and the majority of its = customer=20 base is in this industry.

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Cautions

DataMentors' focus on customer data quality issues = places the=20 vendor at a competitive disadvantage when prospects have = broader=20 requirements, including quality issues in non-customer = data=20 domains.=20
With a small installed base (approximately 60 = customers) and=20 limited resources for marketing, DataMentors will be = challenged to=20 gain mind share in a market increasingly populated with = much=20 larger providers.=20
From a product functionality perspective, DataMentors = has=20 weaknesses in runtime platform support (Windows is the = only=20 deployment option) and in international capabilities due = to lack=20 of unicode support and absence of prepackaged = functionality for=20 non-English language and non-U.S. address data. Unicode=20 capabilities are planned for delivery by YE07. =

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Datanomic

Strengths

Datanomic is a relatively new entrant in the data = quality=20 tools market. Since it was founded in 2001, the vendor has = grown=20 to about 60 customers, most of which are in the U.K., some = in=20 mainland Europe, and a handful in the U.S. and Asia. As a = new=20 player, Datanomic has been able to build its Director = platform on=20 modern technology and with an attractive user interface, = without=20 any major legacy baggage.=20
With the dn:Director and dn:Dashboard products, = Datanomic has=20 brought the separate components =97 such as dn:Clean, = dn:Match or=20 dn:Audit =97 into a comprehensive platform that is = Unicode-enabled=20 and runs on many platforms, both in batch and interactive = modes.=20
Datanomic has a strong focus on the financial = industry, with a=20 few clients also in the telecommunications sector. = Datanomic=20 products are domain-agnostic and not specifically targeted = at=20 customer data, though most implementations are found in a = CRM=20 context.

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Cautions

In addition to its direct sales channel, Datanomic = boasts=20 partnerships with Tier 1 system integrators, such as = Accenture and=20 PricewaterhouseCoopers. These partners are introducing = Datanomic=20 to their customers. They are also using the product in = client=20 engagements for initial evaluations of clients' data, = though they=20 aren't actively selling the dn:Director product. Despite = the=20 international reach of the system integrators, Datanomic = has been=20 unable to benefit from it, and has virtually no visibility = outside=20 its home market in the United Kingdom.=20
While the dn:Director product is built on an SOA, its=20 connectivity is somewhat limited. While Java Database = Connectivity=20 (JDBC), Java Messaging Service (JMS) and Web services are=20 supported, adapters for native database access are = missing.=20
Although the vendor maintains an alliance with Oracle, = it does=20 not participate in a sales and marketing "ecosystem" with = a number=20 of data integration or business intelligence platform = companies,=20 thereby missing out on channel sales opportunities. =

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Fuzzy! = Informatik

Strengths

Fuzzy! Informatik is an established data quality = vendor in the=20 "D/A/CH" region (made up of the German speaking countries = of=20 Germany, Austria and Switzerland). It was founded in 1994, = and has=20 its roots in Daimler Benz, which go as far back as 1980.=20
With about 350 customers, virtually all of which are = in=20 Europe, Fuzzy! Informatik focuses mainly on CRM = implementations=20 with name and address profiling and correction, through = certified=20 integration with SAP and Siebel. The validation of = address,=20 banking and telephone information, as well as blacklist = matching,=20 are some of the core capabilities of the various toolsets. =
Fuzzy! Informatik maintains various partnerships with=20 universities in Germany and the U.S., and raises data = quality=20 awareness through board membership at the German Data = Quality=20 Association (DGIQ).

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Cautions

The merging and deduplication capabilities are = somewhat=20 incomplete, which puts Fuzzy! Informatik at a disadvantage = when=20 compared with other vendors. Although both of those = functions are=20 supported, the final merging and deduplication through = reduction=20 of records is not executed. Potential record duplicates = and=20 candidates for merging are merely indicated, and it is = left to the=20 end user to act appropriately.=20
Address data is the only subject area that is = available for=20 profiling, cleansing and enrichment. To become a more = all-purpose=20 data quality vendor, Fuzzy! Informatik must expand into = areas such=20 as product data or financial data.=20
Fuzzy! Informatik currently maintains no major channel = partnerships. In order to grow visibility, in terms of = both mind=20 share and market share, particularly outside Germany, the = vendor=20 has to engage, train and build relationships with system=20 integrators and potential OEM partners that can then be = introduced=20 into new accounts.

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Group 1 = Software

Strengths

Pitney Bowes Group 1 Software continues to focus on = its=20 traditional positioning of "Customer Data Quality." The = vendor=20 specializes in global name and address standardization and = validation, matching-related capabilities (including = linking and=20 deduplication), and geocoding. It has significant strength = in each=20 of these areas, and although the underlying technology can = be=20 considered domain-agnostic, customer data quality = applications are=20 Group 1's sole focus.=20
Group1 retains a large installed base (over 2,400 = customers),=20 making it one of the market share leaders for data quality = tools.=20 While the majority of the customer base is in North = America, the=20 vendor has established a foothold in Asia/Pacific, where = it now=20 has several hundred customers.=20
With the significant financial resources of Pitney = Bowes,=20 Group 1 continues to expand its capabilities through = acquisitions=20 =97 such as the 2007 addition of MapInfo, which brings = further=20 geospatial and mapping services to the portfolio. Also, = the vendor=20 continues to fund organic development of its core data = quality=20 technology, as evidenced by the recent addition of=20 service-oriented capabilities, SaaS delivery models, and = increased=20 depth of integration with the Group 1 Data Flow data = integration=20 tools.

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Cautions

Group 1's focus on customer data places the vendor at = a=20 competitive disadvantage when prospects have broader = requirements.=20 While it offers data profiling capabilities via its = professional=20 services engagements, the vendor does not directly market = and sell=20 a data profiling product.=20
Lack of clear communication regarding the status of = the Group=20 1 data quality products relative to the existing Pitney = Bowes name=20 and address cleansing products continues to create = confusion and=20 concern among customers. The vendor needs to publish a = clear and=20 consistent product road map showing the plans and = milestones=20 regarding integration, support or decommissioning of the = various=20 products.=20
While Group 1 offers a range of pricing models and = options,=20 mainframe-based customers (which represent the core of its = customer base) occasionally report challenges in = negotiating the=20 cost of upgrades and ongoing support/maintenance. =

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Human = Inference

Strengths

Human Inference has been providing data quality = solutions to=20 large customers mostly in the European financial services = industry=20 (banking and insurance), where the vendor has some = long-standing=20 relationships with approximately 220 clients.=20
The HIquality components include technology for = inspection and=20 profiling, name and address cleansing, matching, merging = and=20 enrichment. One of the key differentiators for Human = Inference is=20 its maintenance of reference datasets, which serve as a = knowledge=20 base for names, addresses and other specific meanings from = a=20 variety of contexts. The SOA-based data quality platform = also=20 integrates well in heterogeneous environments through = relevant=20 technology adapters for Java, .NET, SOAP and = vendor-specific=20 adapters for MQ, Oracle and Microsoft databases, among = others.=20
As one of the larger European data quality vendors, = Human=20 Inference has high mind share in the Netherlands, and is=20 increasingly active in other European countries, mostly = driven by=20 successful marketing programs. SAP NetWeaver certification = indicates the vendor's strategy to invest more in = integration with=20 application vendors such as SAP.

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Cautions

Human Inference focuses almost exclusively on = customers in=20 Western Europe through its offices in the Netherlands, = Belgium,=20 Germany and the United Kingdom, in addition to a number of = distributors in France, Italy and Switzerland. While the = direct=20 sales force is successful, the vendor's channel strategy = is in its=20 infancy, which will hamper growth.=20
Human Inference needs to build a channel and establish = partnerships with data integration, process integration = and=20 application vendors to extend its presence. That way, it = can=20 better compete with the product offerings from large = international=20 infrastructure companies that, because of their holistic = approach,=20 regularly win deals over small providers.=20
The vendor has the technical capability to provide = data=20 quality in an SaaS fashion, and other independent software = vendors=20 (ISVs) are leveraging the software in a services model. = But Human=20 Inference does not provide those data quality services = itself=20 through a formal hosted offering or specific branding.=20

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IBM

Strengths

IBM sees data quality functionality as a key component = within=20 its integration portfolio, and has started to deliver data = quality=20 products under the Information Server packaging. As one of = the=20 best-known brands with worldwide consulting, service, and = support=20 functions, IBM is well equipped to position its vision of = data=20 quality in organizations worldwide.=20
As part of its vision for information-on-demand, IBM = built=20 software components named Information Analyzer (the = successor of=20 ProfileStage, for discovery, profiling and analysis) and = updated=20 QualityStage (parsing, standardization and sophisticated=20 matching). The rearchitected products that were delivered = with=20 IBM's release of Information Server underwent significant=20 development to harmonize the user interfaces and = administration=20 functionality, and increase ease of use and developer=20 productivity, which were known challenges for previous = versions.=20
IBM has a broad data management portfolio including=20 extraction, transformation and loading (ETL) tools, = federation,=20 replication and metadata, which creates an opportunity and = advantage for IBM in cross-selling data quality products, = as data=20 quality is increasingly being recognized as critical to = data=20 integration activities of all types. Information Analyzer = and=20 QualityStage can be deployed as individually selectable = modules on=20 top of the metadata-driven Information Server = infrastructure,=20 thereby increasing the consistency and interoperability = among the=20 various data quality and data integration components.=20

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Cautions

Ascential had better brand recognition in terms of = data=20 integration and data quality. However, when it was = acquired by IBM=20 its strong focus on data profiling and cleansing was = somewhat=20 diluted, as it fell under the large umbrella brand of = WebSphere=20 and was subsequently repositioned within the new = Information=20 Server. Although IBM has the highest overall software = brand=20 recognition of all vendors evaluated in this market, it = does not=20 exercise a strong positioning as a data quality tools = provider.=20
The Information Server versions of the data quality = products=20 are seeing slow adoption in the market, with virtually no = customer=20 references available that use these versions of the = products in a=20 production environment. This is mostly because the new = product=20 version is still very new and customers with prior = versions are=20 early in their planning and analysis stages of the upgrade = effort.=20
Although the data quality products are service-enabled = and IBM=20 has extensive hosting capabilities, it has not embarked on = a=20 go-to-market strategy with a service offering of hosted = profiling,=20 matching, cleansing or enrichment services, leaving this = field=20 open for competitors.

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Informatica

Strengths

Informatica stepped into the data quality market with = its=20 acquisition of Similarity Systems in January 2006, which = itself=20 had acquired the assets of Evoke Software in 3Q05. At that = time,=20 the established partnerships between Informatica and = Trillium and=20 Firstlogic ended. Through the acquisitions, Informatica = now has=20 all the key pieces of profiling, standardization, matching = and=20 cleansing. The company has since moved all = data-quality-related=20 development into a separate business unit.=20
The data quality arena represents a big market = opportunity for=20 Informatica, and the vendor's strategy of cross-selling = the data=20 quality products into its large installed base appears to = be=20 working. In addition, the data quality capabilities = further=20 enhance Informatica's strategy for developing a = comprehensive data=20 integration and data quality offering.=20
The tools have been used in several significant data = quality=20 implementations inside large global companies, supporting=20 initiatives such as MDM in addition to traditional = customer data=20 quality applications. The Informatica Data Quality product = is well=20 positioned for domain-agnostic cleansing, and the Data = Explorer is=20 one of the most mature profiling engines in the market.=20

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Cautions

Informatica maintains two separate profiling solutions = =97 the=20 PowerCenter profiling option and Informatica Data = Explorer. While=20 the company positions these offerings for specific and = different=20 scenarios, the overlap in functionality can sometimes = create=20 confusion for customers. Informatica plans to unify these = into a=20 single offering by 2008, thereby removing any redundancy.=20
Organizational integration following the acquisitions = has been=20 completed and progress has been made with product = integration.=20 However, architectural rationalization of the various=20 data-quality-relevant components into one concise product = family=20 is still under way.=20
Informatica has traditionally sold to IT buyers. But = because=20 data quality improvement requires heavy business buy-in = and=20 involvement, in order to raise the data quality products'=20 currently low market share, the Informatica sales force = must be=20 trained to sell to buyers on the business side. =

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Innovative = Systems

Strengths

Innovative Systems has the most longevity of the = vendors in=20 this market, with a history spanning nearly 35 years. = Innovative's=20 i/Lytics platform, released two years ago and now on = version 2.1,=20 provides proven capabilities based on its deep experience = in=20 customer data matching and cleansing applications. = i/Lytics=20 provides strong support for both mainframe and distributed = platforms, and enables data quality functionality to be = exposed=20 via service interfaces.=20
Innovative's customer base (approximately 160 = customers)=20 reflects the vendor's strong experience in the banking and = insurance industries =97 these verticals include about = two-thirds of=20 the vendor's customers. While most of these customers are = in North=20 America, Innovative also supports customers in Europe and = is=20 experiencing growth in Latin America (a region in which it = has=20 significant experience).=20
Complementary to its financial services depth, = Innovative=20 continues to focus on its compliance watchlist screening=20 offerings, as there remains strong demand for these in the = market.=20 In addition, the vendor increasingly emphasizes the = quality of its=20 professional services and the ability to deliver complete=20 solutions rather than just implementations of its tools.=20

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Cautions

With a strong emphasis on customer data quality = issues,=20 Innovative will be challenged to win new business or = expand its=20 presence in existing accounts when multidomain data = quality=20 improvement initiatives are required. While the vendor = supports=20 data quality activities in other domains, such = applications are a=20 small portion of the installed base.=20
Given its long history in the market, Innovative's = relatively=20 small installed base indicates limited growth in recent = years. It=20 has been generally successful in retaining its longtime = customers,=20 but will need to increase the pace of new customer = acquisition in=20 order to remain competitive.=20
Innovative has expanded its product portfolio with the = addition of a data profiling product, but it has limited=20 capabilities and a bias toward customer data, rendering it = immature compared with the market leaders. In addition, = while=20 Innovative's technology can support multilingual data, the = lack of=20 full unicode capabilities limits Innovative's ability to = compete=20 on a global basis.

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Netrics

Strengths

Netrics, a relatively new entrant to the data quality = tools=20 market, provides a range of capabilities with a specific = focus on=20 matching. The vendor utilizes a machine learning approach = to=20 implementing matching and standardization, based on the = customer=20 working through a sample set of data in order to train the = technology.=20
Customer references claim better accuracy in highly = complex=20 matching problems as compared to more traditional matching = approaches, with a shorter time to implementation because=20 comparatively less "programming" is needed.=20
Netrics' technology is essentially an embeddable data = quality=20 and matching engine, enabling the deployment of=20 data-quality-related services inside any type of = application. This=20 is a significant differentiation from most other vendors = in the=20 market, and enables Netrics to focus primarily on an = indirect=20 channel strategy with OEM and system integration (SI) = partners.=20

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Cautions

Netrics' strong emphasis on matching comes at the = expense of=20 other data quality operations, such as profiling and = address=20 validation, where it has limited capabilities. In = addition, the=20 lack of a user interface means the vendor does not provide = out-of-the-box functionality for exposing profiling = results,=20 matching results or runtime statistics.=20
With a small installed base (approximately 100 = customers) and=20 limited resources for marketing, Netrics will be = challenged to=20 gain mind share in a market increasingly populated by much = larger=20 providers.

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Trillium = Software

Strengths

Harte-Hanks Trillium Software provides a broad data = quality=20 tool suite, including data profiling (TS Discovery), core = data=20 quality components (TS Quality) and a new data quality = dashboard=20 offering (TS Insight). Its data enrichment capabilities = are=20 focused on customer data (addresses, geocoding and = watch-list=20 compliance).=20
Trillium continues to enjoy strong brand recognition = and=20 remains a market share leader with a large installed base = of=20 nearly 1,600 customers. The large size of its parent = company,=20 Harte-Hanks, provides a solid financial basis relative to = many=20 other vendors in this market.=20
Trillium continues to expand its focus on alternative = delivery=20 models for its data quality capabilities. Its Diamond Data = IS=20 offering provides the TS Quality functionality in a hosted = model=20 for customers of software as a service (SaaS) application=20 providers such as salesforce.com. Trillium also markets = and sells=20 directly its TS On-Demand solution, giving customers a = choice of=20 on-premise or hosted deployment of TS Quality. =

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Cautions

Trillium's functionality, marketing and product road = map are=20 largely geared toward data quality issues in customer = data, and=20 only a small fraction of the customer base is applying TS = Quality=20 in other data domains. While customer data quality will = remain a=20 mainstay of market demand, Trillium will be increasingly=20 challenged by newer domain-agnostic competitors and pushed = by its=20 customers for greater extensibility to other data types. = Trillium=20 will expand upon its non-customer data quality = support=20 with the introduction of Universal Data Libraries = (pre-built=20 functionality for common data attributes including units = of=20 measure, currencies and package types) in the forthcoming = v11=20 release, planned for 3Q07.=20
Trillium's large customer base is heavily skewed = toward North=20 America. To ensure long-term market leadership, the vendor = will=20 need to generate significant growth in other regions in = response=20 to competition from larger and more globally visible = vendors.=20
In 3Q06, Harte-Hanks acquired U.K.-based address = cleansing=20 specialist Global Address, creating some overlap of = functionality=20 with TS Quality. Harte-Hanks has not provided a clear road = map=20 indicating how, or indeed if, these redundant capabilities = will be=20 rationalized.

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Uniserv

Strengths

Uniserv is a longtime provider of data quality = solutions with=20 a solid track record. Based in Germany, the vendor focuses = almost=20 exclusively on customer data, name and address = verification, and=20 geocoding. Most of Uniserv's customers are in = German-speaking=20 countries, though the vendor also has a large customer = base in=20 France, and has also branched out into other European = countries=20 and North America.=20
Uniserv has more than 60 employees in technical roles, = such as=20 development, services and support. This allows its = customers to=20 easily and quickly see the benefits of its mailBatch,=20 mailRetrieval and postal applications. A relatively low = average=20 software license per implementation also makes it easy for = customers to deploy Uniserv software.=20
Partnerships with SAP and Oracle, both of which have = limited=20 data quality capabilities, give Uniserv a large potential = to=20 integrate its software within business applications.=20

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Cautions

As many organizations start to view data quality as a=20 domain-agnostic issue, the strong customer data focus will = put=20 Uniserv at a disadvantage compared with other providers = that=20 market themselves with a broader data quality view toward, = for=20 example, product data or financial data.=20
Although Uniserv is an established brand for matching, = merging, cleansing, or address and bank data verification=20 technologies, increasingly popular areas such as data = profiling or=20 quality monitoring are currently limited or unsupported = functions.=20 This will make it more difficult for the vendor to compete = with=20 other functionally rich data quality platforms.=20
Uniserv software is regularly implemented by partners. = However, the strong concentration on its direct sales = force, and=20 the lack of large international alliances with system = integrators=20 and software companies that leverage Uniserv technology as = an OEM,=20 puts the vendor under increasing pressure from highly = networked=20 and larger international competitors.

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The Magic Quadrant is copyrighted 29 = June 2007 by=20 Gartner, Inc. and is reused with permission. The Magic = Quadrant is a=20 graphical representation of a marketplace at and for a = specific time=20 period. It depicts Gartner=92s analysis of how certain = vendors measure=20 against criteria for that marketplace, as defined by = Gartner.=20 Gartner does not endorse any vendor, product or service = depicted in=20 the Magic Quadrant, and does not advise technology users to = select=20 only those vendors placed in the =93Leaders=94 quadrant. The = Magic=20 Quadrant is intended solely as a research tool, and is not = meant to=20 be a specific guide to action. Gartner disclaims all = warranties,=20 express or implied, with respect to this research, including = any=20 warranties of merchantability or fitness for a particular=20 purpose.

=A9 2007 Gartner, Inc. and/or its Affiliates. = All Rights=20 Reserved. Reproduction and distribution of this publication = in any=20 form without prior written permission is forbidden. The = information=20 contained herein has been obtained from sources believed to = be=20 reliable. Gartner disclaims all warranties as to the = accuracy,=20 completeness or adequacy of such information. Although = Gartner's=20 research may discuss legal issues related to the information = technology business, Gartner does not provide legal advice = or=20 services and its research should not be construed or used as = such.=20 Gartner shall have no liability for errors, omissions or=20 inadequacies in the information contained herein or for=20 interpretations thereof. The opinions expressed herein are = subject=20 to change without notice.