Informatica

Innovative Systems, Inc.

DataFluData QualityManagement SoftwareProduct Directory2009 EDITION

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Welcome to SearchDataManagement.com’s Data Quality Management SoftwareProduct Directory. This directory is designed to be a valuable resource for thosegetting started with research or evaluating vendors in the data quality market.

Inside, you’ll find basic information about the major vendors in the data qualitymarket and the products they sell. Each listing is accompanied by a short descrip-tion and a long description including limited information about functionality andproduct use. You’ll find products for businesses of all sizes as well as productsthat can be deployed on-demand and on-premise. Use this list to get startedwith the evaluation process. For more information about any of the productsor to speak to a sales representative, please visit the vendor website or productwebsite.

SearchDataManagement.com will launch a series of directories throughoutthe year to address unique segments of the data management market. Wantto see your product listed in one of our directories? Go here to submit a product.Need to update product or pricing information? Email us here. For questionsfor the editors or to make suggestions for improving the directory, write to usat editor@searchdatamanagement.com.

Happy shopping!

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 2

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

Welcome!

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 3

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

Data quality management andchoosing data quality tools: A primer

s more business analysts recognize the relationship between high-quality data and the success of the business, there is a growing inter-est in integrating data quality management within the organization.And while the lion’s share of the effort involves putting good datamanagement practices in place and establishing data governance,there will always be a requirement for technology to supportdata quality maturity.

That being said, until relatively recently, most people equated the phrases “dataquality” and “data cleansing” with the expectation that data quality tools wereintended only to help identify data errors and then correct those errors. In reality,there are many techniques applied within the context of a data quality managementprogram, with different types of tools used to support those techniques.Data quality management incorporates a “virtuous cycle,” shown in FIGURE 1,

essentially consisting of two phases: analysis and assessment, followed by monitor-ing and improvement.

AFIGURE 1: The virtuous cycle of data quality management.

MONITORING/

IMPR

OVEM

ENT

DATA

ANALYSIS

/ASSESSM

ENT

Monitor results ofimprovement methods

against targets

Data cleansingand

enhancement

Implementquality

improvementmethods andprocesses

Design qualityimprovement processesand set performance

targets

Definebusiness-related data

quality rules

qIdentify andmeasure howpoor dataqualityimpedesbusinessobjectives

Data quality tools and technology are necessary to support both the analysis andassessment phases. Data profiling tools are used for data analysis and identificationof potential anomalies, while parsing and standardization tools are employed for rec-ognizing errors, normalizing representations and values, and some degree of datacorrection. These tools can be used to define data quality rules that assert validityof data and are used to flag non-conformant values and to aid the correction process.A commonly used technology for cus-

tomer and business name correction isidentity resolution, which helps in linkingand resolving variant representations ofthe same entities. After normalization andidentity resolution have been performed,data enhancements such as address stan-dardization and enhancement and geo-coding are applied. Lastly, the data qualityrules can be integrated within a data quali-ty auditing tool that measures compliancewith defined data quality expectations. Theresults of these measurements can be fed into a data quality metrics scorecard, andif the metrics are defined in relation to the business impacts incurred by violating theexpectations, that scorecard will provide an accurate gauge of how improving dataquality goes straight to the bottom line.

DATA PROFILINGThe initial attempts to evaluate data quality are processes of analysis and discovery,and this analysis is predicated on an objective review of the actual data values. Thevalues populating the data sets under review are assessed through quantitativemeasures and analyst review. While a data analyst may not necessarily be able topinpoint all instances of flawed data, the ability to document situations where theremay be anomalies provides a means to communicate these instances with subject-matter experts whose business knowledge can confirm the existence of data prob-lems.Data profiling is a set of algorithms for statistical analysis and assessment of the

quality of data values within a data set, as well as exploring relationships that existbetween value collections both within and across data sets. For each column in atable, a data profiling tool will provide a frequency distribution of the different values,providing insight into the type and use of each column. Cross-column analysis canexpose embedded value dependencies, while inter-table analysis explores overlap-ping value sets that may represent foreign key relationships between entities, and itis in this way that profiling can be used for anomaly analysis and assessment.The data profiling process often sheds light on business rules inherent to each

business process’s use of the data. These rules can be documented and used duringthe auditing and monitoring activity to measure validity of data.

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 4

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

“Data quality toolsand technology arenecessary to supportboth the analysis andassessment phases.”—DAVID LOSHIN

DATA PARSING AND STANDARDIZATIONIn any data set, slight variations in representation of data values easily lead to situa-tions of confusion or ambiguity for both individuals and other applications. For exam-ple, consider these different character strings:

q 301-754-6350q (301) 754-6350q 301.754.6350q 866-BIZRULE

All of these formats use digits, some have alphabetic characters, and all use differ-ent special characters for separation, but to the human eye they are all recognized asreasonable telephone number formats. To determine whether these numbers areaccurate or to investigate whether duplicate telephone numbers exist, the valuesmust be parsed into their component segments (area code, exchange and line) andthen transformed into a standard format.When analysts are able to describe the different component and format patterns

used to represent a data object (person's name, product description, etc.), data qual-ity tools can parse data values that conform to any of those patterns and even trans-form them into a single, standardized form that feeds the assessment, matching andcleansing processes. Pattern-based parsing can automate the recognition and subse-quent standardization of meaningful value components.In general, parsing uses defined patterns managed within a rules engine used to

distinguish between valid and invalid data values. When patterns are recognized,other rules and actions can be triggered, either to standardize the representation(presuming a valid representation) or to correct the values (should known errors beidentified).

IDENTITY RESOLUTION:

SIMILARITY, LINKAGE AND MATCHINGA common requirement for data quality management involves two sides of the samecoin: when multiple data instances actually refer to the same real-world entity, asopposed to the perception that a record does not exist for a real-world entity when infact it really does. Both of these problems indicate the need for techniques to helpidentify approximate matches to determine similarity between different records. Inthe first situation, similar, yet slightly variant representations in data values mayhave been inadvertently introduced into the system, while in the second situation, aslight variation in representation prevents the identification of an exact match of theexisting record in the data set.Both of these issues are addressed through a process called identity resolution, in

which the degree of similarity between any two records is scored, most often basedon weighted approximate matching between a set of attribute values between thetwo records. If the score is above a specific threshold, the two records are deemed to

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 5

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

be a match and are presented to the end client as most likely to represent the sameentity. Identity resolution is used to recognize when only slight variations suggestthat different records are connected and where values may be cleansed.Attempting to compare each record against all the others to provide a similarity

score is not only ambitious but also time-consuming and computationally intensive.Most data quality tool suites use advanced algorithms for blocking records that aremost likely to contain matches into smaller sets, whereupon different approachesare taken to measure similarity. In addition, there are different approaches to match-ing—a deterministic approach relies on a broad knowledge base for matching, whileprobabilistic approaches employ statistical techniques to contribute to the similarityscoring process. Identifying similar records within the same data set probably meansthat the records are most likely duplicatedand may be subjected to cleansing and/orelimination. Identifying similar records in dif-ferent sets may indicate a link across thedata sets, which helps facilitate cleansing,knowledge discovery, reverse engineeringand master data aggregation.

DATA CLEANSING AND ENHANCEMENTData cleansing incorporates techniques suchas data imputation, address correction, elim-ination of extraneous data, and duplicateelimination, as well as pattern-based transformations. Data cleansing complements(and relies on) parsing and standardization as well as identity resolution and recordlinkage. Data enhancement is a data improvement process that relies on record link-age, along with value-added improvement from third-party data sets (such asaddress correction, geo-demographic/psychographic imports, list appends). This isoften performed by partnering with data providers, using their aggregated data as a“source of truth” against which records are matched and then enhanced.

DATA AUDITING AND MONITORINGThe same types of data quality rules exposed through conversations with subject-matter experts and profiling can be used to describe end-user data quality expecta-tions. Monitoring defined data quality rules and auditing the results provides a proac-tive assessment of compliance with expectations, and the results of these audits canfeed data quality metrics populating management dashboards and scorecards.Data profiling tools, as well as standalone auditing utilities, often provide capabili-

ties for proactively validating data against a set of defined (or discovered) businessrules. In this way, the analysts can distinguish those records that conform to defineddata quality expectations from those that don’t, which in turn can contribute tobaseline measurements and ongoing auditing for data quality reporting.

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 6

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

“Consider whetheror not a specific tooloffering necessitatespurchasing a suiteof products.”—DAVID LOSHIN

WHAT TO LOOK FOR IN DATA QUALITY TOOLS AND VENDORSThere are two interesting notions to keep in mind when considering data qualitytools. First, every organization’s needs are different, depending on the type of com-pany, industry and business processes and their corresponding dependence on theuse of high-quality data. Second, while the needs may be different, the ways thatthose can be addressed are often very similar, although different vendors mayaddress those issues with greater degrees of accuracy and precision (and, naturally,cost). Weighing both of these notions together, the conclusion is that what will dis-tinguish the suitability of one product over the others is more than just functionality,especially as data quality technology becomes more of a commodity capability.Along with functionality, consider cost, installed base, vendor stability, training

and support capabilities, as well as the pool of talent that can be tapped to help inte-grate the tools within a governed data quality program. In addition, because therehave been many corporate acquisitions within the data quality market, considerwhether or not a specific tool offering necessitates purchasing a full-blown suite ofproducts. Alternatively, one must consider the level of comfort of purchasing onecomponent of a vendor’s tools suite with the expectation that it will integrate wellwith other tools already in use within the environment.

BUSINESS NEEDS ASSESSMENT AND DATA QUALITY TOOL REQUIREMENTSThe desire to acquire data quality tools should be tempered by the assessmentprocess—too often, the technology is purchased long before the specific businessneeds have been determined. Therefore, it is worthwhile to perform a high-level dataquality assessment with these specific objectives:

q Identify business processes that are affected by data quality issues.q Identify the data elements that are critical to the successful execution of

those business processes.q Evaluate the types of errors and data flaws that can occur.qQuantify business impacts associated with each of those errors.q Prioritize issues based on potential business impacts.q Consider the data quality improvements that can be applied to alleviate the

business impacts.

While this process is presented simply above, there are many subtleties that mayrequire additional expertise. To expedite this assessment, you may consider partner-ing with expert consultants that can perform the rapid assessment while simultane-ously training your staff to replicate the process on other data sets.General requirements for data quality toolsAs a result of this process, companies should arrive at a prioritized list of improve-

ments, which should frame the discussion of requirements analysis, both from thedata quality standpoint and from the systemic and environmental aspects. For exam-ple, determining that duplicated customer records lead to business risks would sug-

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 7

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

gest that duplicate elimination is advisable. This requires tools for determining whenthere are duplicates (identity resolution) and for cleansing (parsing and standardiza-tion, enhancement).There are also degrees of precision, however. If your company is a mail-order ven-

dor sending out duplicate catalogs, the risk is increased costs and lowered response,but 100% de-duplication may not be a requirement. But attempting to identify ter-rorists at the airport security gate may pose a significant risk in terms of passengersafety, necessitating much greater precision in identity resolution. Increased preci-sion is likely to correspond to increased costs, and this is another consideration.In terms of environmental and systemic aspects, one must consider how well

different products can integrate within the organization’s system architecture. Hardrequirements such as platform compliance are relatively easy to specify. Architec-tural expectations are too, such as the different deployment options such as whetherthe tools support real-time operations, whether they only execute in batch, or canthey be integrated “in-line” are also relevant questions. In addition, as more organi-zations migrate toward services-oriented architectures (SOAs), determiningwhether the tools support services also becomes a requirement. From the businessside, one must consider the license, support, training, and ongoing maintenancecosts, as well as the internal staffing requirements to manage and use the products.Because many vendors provide tools that may (or may not) address the organiza-

tion’s issues, it is worthwhile to carefully delineate your business needs and technol-ogy requirements within a request for proposal (RFP). Providing an RFP provides twoclear benefits. First, it clarifies your needs to the vendors so they can more effectivelydetermine if their product will meet them. Second, it provides a framework for com-paring vendor tools, scoring their relative suitability and narrowing the field. It is agood idea to include specific metrics associated with the quality of the data that canbe used to compare and measure effectiveness of the products.

NARROWING DATA QUALITY VENDORSReviewing the RFP responses will help filter out those vendors that make the gradefrom those whose products are not entirely appropriate to address the businessneeds. But to narrow the remaining vendors to a short list, set up meetings for thevendors to present their technology along with a proposal for how their products willbe used to address the business needs. Again, it may be worthwhile to engage indi-viduals with experience in data quality tools and techniques to clarify the distinc-tions among the vendor products, translate any “tech-talk” into terms that areunderstood, and to ask the tough questions to ensure that the vendors are properlyrepresenting what their products can and cannot do.By this time, your team should be able to whittle down the field to at most three

competitors. The final test is to try out the tools yourself—arrange for the installationof an evaluation version of the product and run it over your own data sets. Havingspecified a benchmark data set for comparison, one can compare not just how wellthe products perform but also the ease of use and adoption by internal staff.

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 8

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

SPECIFIC REQUIREMENTSThe full details of what can be expected from the data quality tools described isbeyond the scope of this article, but this table provides some high-level, “no-miss”capabilities for each of the tools described.

TECHNOLOGY CORE CAPABILITIES

Data profiling � Column value frequency analysis and related statistics(number of distinct values, null counts, maximum, minimum,mean, standard deviation)

� Table structure analysis� Cross-table redundancy analysis� Data mapping analysis� Metadata capture� DDL generation� Business rule documentation and validation

Parsing and � Flexible definition of patterns and rules for parsingstandardization � Flexible definition of rules for transformation

� Knowledge base of known patterns� Ability to support multiple data concepts(individual, business, product, etc.)

� Manageable transformation actions

Identity � Entity identificationresolution � Record matching

� Record linkage� Record merging and consolidation� Flexible definition of business rules� Knowledge base of rules and patterns� Integration with parsing and standardization tools� Advanced algorithms for deterministic or probabilistic matching

Cleansing and � Flexible definition of cleansing rulesenhancement � Knowledge base of common patterns (for cleansing)

� Knowledge base of enhancements (e.g., address cleansing,geocoding)

Auditing and � Data validationmonitoring � Data controls

� Services-oriented� Rule management� Rule-based monitoring� Reporting

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 9

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

CONCLUSIONEven though many of the more established data quality tool vendors have beenacquired by even bigger fish, there are still companies emerging with betterapproaches to fill the void. Whether better algorithms packaged in a different way,improvements in performance, better suitability to SOA, or even an open sourceoffering, there is a wide range of vendors, products and tools to fit almost any organi-zation’s needs. Even armed with the knowledge of what you should look for in dataquality tools, there is one last caveat: If your organization has an opportunity for dataquality improvement, make sure that you have done your homework in businessneeds assessment and development of a reasonable RFP before evaluating and pur-chasing tools.

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 10

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

ABOUT THE AUTHOR

David Loshin, president of Knowledge Integrity, Inc, is a recognized thought leader and expert consultantin the areas of data governance, data quality methods, tools, and techniques, master data management,and business intelligence. David is a prolific author regarding BI best practices, either via his B-Eye Net-work expert channel and numerous books on BI and data quality. His book,Master Data Management, hasbeen endorsed by data management industry leaders, and his valuable MDM insights can be reviewed atwww.mdmbook.com. David can be reached at loshin@knowledge-integrity.com, or 301-754-6350.

SAASOR ONPAGE VENDOR PRODUCTS SERVICES PREMISE SW

12 Business Objects BusinessObjects Data Quality, s

(now SAP) Universal Data Cleanse, BusinessObjectsData Quality for SAP, BusinessObjectsData Quality for Oracle’s Siebel CRM,BusinessObjects Data Insight

12 Datactics DataTrawler s

13 DataFlux DataFlux Data Quality Integration s

Platform

13 DataLever DataLever Enterprise Suite s

14 Data Mentors Inc. DataFuse 1 s

14 Datanomic dn:Director s

15 eprentise eprentise Data Quality s

15 Fuzzy! Informatik Fuzzy! Dime, Fuzzy! Analyzer, Fuzzy! s

(now SAP) Double, Fuzzy! Move, Fuzzy!Bank, Fuzzy! Umzug

16 Harte-Hanks Trillium Trillium Software System: 1 s

Software TS Insight, TS Discovery, TS Quality,TS Enrichment

16 Human Inference HIquality Suite 1 s

17 IBM IBM Information Server WebSphere s

QualityStage

17 Informatica Informatica Data Quality s

18 Innovative Systems Inc. i/Lytics Enterprise Data Quality Suite: 1 s

i/Lytics Data Profiler, i/Lytics DataQuality, i/Lytics GLOBAL

18 Netrics Netrics Matching Platform s

19 Oracle Corp Data Quality for Oracle Data Integrator s

19 Pervasive Software Pervasive Data Profiler s

20 Pitney Bowes Group 1 Customer Data Quality Platform, CDQ 1 s

On Demand, Global Sentry, CDQ Platformfor Microsoft Dynamics CRM, CDQPlatform for Salesforce.com, CDQ Platformfor SAP and CDQ Platform for Siebel

20 Stalworth Inc. DQ*Plus, DQ*Plus Batch and 1 s

Persistent, DQ*Plus Interactive

21 Talend Talend Open Profiler s

21 Uniserv Data Quality Batch Suite 1 s

22 Zoomix (nowMicrosoft) Zoomix Accelerator s

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 11

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

Vendor: Vendor/developer of product at directory press time;1 SaaS or services indicates technology available asSaaS, hosted, on-demand, ASP and web services;s On-premise SW indicates software or systems on premise;2 Description was written by the SearchDataManagement.com editorial team based on information gathered fromvendor websites.

Index at a GlanceClick on the product name at left to jump to a longer description.

BUSINESS OBJECTS, AN SAP CO.

• BusinessObjects Data Quality• Universal Data Cleanse• BusinessObjects Data Qualityfor SAP

• BusinessObjects Data Qualityfor Oracle’s Siebel CRM

• BusinessObjects Data Insight

Business Objects has multiple productsfor data quality monitoring, analyzing,standardizing and reporting. s

COMPANY WEBSITE: www.businessobjects.comFOUNDED: 1990SUMMARY: BusinessObjects Data Qualitystandardizes, corrects, enhances andunifies data from various sources. Uni-versal Data Cleanse parses, standardiz-es and identifies non-customer andregion-specific data. BusinessObjectsData Quality for SAP is embedded with-in SAP for global address correction andverification. BusinessObjects DataQuality for Oracle’s Siebel CRM is simi-larly embedded within the Siebel CRM.BusinessObjects Data Insight supportsmonitoring, analyzing and reportingdata quality in a relational database or aflat file in an open system or mainframeenvironment. 2

PRICING: Declined to provide pricing.

DATACTICS

DataTrawler

Datactics DataTrawler is data qualitysoftware with grid technology for largedata volumes. s

COMPANY WEBSITE: www.datactics.comFOUNDED: 1999SUMMARY: DataTrawler combines dataprocessing techniques with the latestgeneration of data management tech-nologies. It is based on “fuzzy” technol-ogy, namely the tolerance of errors perlength of any given string of data, andprovides users with dials to adjust thelevel of errors that they’re prepared totolerate. DataTrawler uses next-genera-tion grid technology to call on availablecomputing power and allow processesto be shared across many computers.Datactics also has a “data managementmethodology” with three phases: ana-lyze, re-engineer and match—and threemanagement elements: report, inte-grate and manage.

PRICING: Declined to provide pricing.

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 12

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 13

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

DATAFLUX CORP.

DataFlux Data QualityIntegration Platform

DataFlux has multiple products support-ing different data quality processes,all based on a single platform. s

COMPANY WEBSITE:www.dataflux.comFOUNDED: 1997SUMMARY: DataFlux data quality inte-gration products have functions fororganizations to: plan and prioritizedata-correction programs; parse datainto components to help identify andresolve problematic data; standardize,correct and normalize data to create aunified view of corporate information;verify and validate data accuracy toimprove the overall accuracy of cus-tomer records, product data and otherinformation; and apply business rulesacross the enterprise to ensure that allcorporate data reflects business needs.All DataFlux products are built from thesame core technology and code base. 2

PRICING:

DataFlux dfPower Studio is available ona per user basis, while the DataFluxIntegration Server utilizes a per servermodel. The pricing of the DataFlux DataQuality Integration Platform starts at$50,000 and varies according to thenumber of users and the processingspeed of the servers involved.

DATALEVER

DataLever Enterprise Suite

DataLever Enterprise Suite is avisual process designer for creatingand running high-performance data-transformation and data qualityprocesses. s

COMPANY WEBSITE: www.datalever.comFOUNDED: 1998SUMMARY:DataLever Enterprise Suiteis a visual process designer allowingorganizations to create and run high-performance data-transformationprocesses. Using a modular approach,components are selected from apalette. Components perform special-ized operations. By connecting the com-ponents, custom data processingengines, which resemble flow charts,can be created. The results of the jobcan be stored in a database, viewed in aspreadsheet or written to a report. TheDataLever framework encompasses abroad range of functionality, includingcomprehensive data re-engineering anddata profiling. 2

PRICING:Depending on database size,pricing begins at $8,000 per monthfor ASP-delivered functions.

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 14

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

DATAMENTORS, INC.

DataFuse

DataFuse is a modular databasepreparation and integration systemthat cleanses, organizes, standardizes,matches and prepares data. 1s

COMPANY WEBSITE:www.datamentors.comFOUNDED: 1998SUMMARY: DataMentors provides data-base preparation and maintenance mar-keting systems featuring DataFuse(which cleanses, organizes, standardiz-es, matches and prepares data), ValiDa-ta and PinPoint. These may be usedindividually or as an integrated end-to-end turnkey database system for aclean, granular and complete view ofthe customer through data validation,transformation, standardization andhouseholding. Offered as either a cus-tomer-premise installation or ASP-delivered system, DataMentors sup-ports proprietary data discovery,reporting and analysis, campaign man-agement, data mining, and modelingpractices.

PRICING:The pricing structure is based onseveral factors including, but not limitedto: number of licenses, database size,length of term, specific client productcustomization and client support crite-ria. Information as of Q1 2009. Declinedto provide additional information.

DATANOMIC

dn:Director

dn:Director is a multi-functiondata quality platform. s

COMPANYWEBSITE:www.datanomic.comFOUNDED: 2001SUMMARY: Datanomic’s dn:Director isa multi-function data quality platform,providing a range of data qualityprocessors for profiling, checking, trans-forming, parsing, matching, enhancing,merging and reporting on data fromdisparate systems, languages and stan-dards, accessible from a single inter-face. dn:Director supports a data-leddiscovery methodology where datasources are examined for their contentand inherent business rules. Deploy-ments might include data migrations,linking records across disparatesources, single view or master data cre-ation and compliance solutions.dn:Director is an all-Java application.

PRICING:Datanomic’s dn:Director offersa flexible pricing model for differentdeployments, with list prices startingat $20,000. Modules of the product(e.g., profiling, audit, transformation,text analysis, matching) may be pur-chased separately. Licenses may alsobe priced by data volume, hardware,users or software purpose.

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 15

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

EPRENTISE

eprentise Data Quality

eprentise Data Quality is softwarethat standardizes, locates and resolvesduplicate data. s

COMPANYWEBSITE:www.eprentise.comFOUNDED: 2006SUMMARY: eprentise Data Quality soft-ware applies standard abbreviationsand naming conventions to enterprisedata with find-and-replace rules –which can be used to change punctua-tion and abbreviations or make globaltext replacements. User-defined criteriaand complex Boolean rule structuresidentify candidate duplicates and oper-ate across tables. The software com-pares records and identifies candidateduplicates based on matches to thesecriteria. Matches are assembled intoduplicate sets for the user to verify andresolve. Software is currently availableon Oracle e-Business Suites.

PRICING:eprentise Data Quality softwarestarts at $150,000.

FUZZY! INFORMATIK, AN SAP CO.

Fuzzy! Dime, Fuzzy! Analyzer,Fuzzy! Double, Fuzzy! Move,Fuzzy! Bank, Fuzzy! Umzug

Fuzzy! Informatik, an SAP Co., hasdata quality products primarily forinternational business partner data. s

COMPANYWEBSITE:www.fazi.de/index.php?enFOUNDED: 1994SUMMARY: Fuzzy! products, availableworldwide, are designed for internation-al business partner data management.Data quality products include: Fuzzy!Dime (monitoring and measuring dataquality); Fuzzy! Analyzer (structuringand analyzing information); Fuzzy!Double (preventing duplicates andfault-tolerant searches); Fuzzy! Post(qualifying addresses); Fuzzy! Move(searching for customers who havemoved to unknown destinations);Fuzzy! Bank (checking and correctingbank details); Fuzzy! Tel (finding currenttelephone numbers); and Fuzzy! Umzug(updating customer databases withaddresses of customers who havemoved). 2

PRICING:Declined to provide pricing.

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 16

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

HARTE-HANKS TRILLIUM SOFTWARE

Trillium Software System:TS Insight, TS Discovery, TS Quality,TS Enrichment

Trillium Software System is anintegrated global data qualitymanagement software suite. 1s

COMPANYWEBSITE:

www.trilliumsoftware.comFOUNDED: 1993SUMMARY: Trillium Software System:Integrated suite of four data qualityproducts, providing data quality detec-tion, construction, repair and mainte-nance. TS Insight is a browser-basedwindow into data and a data qualitydashboard for business users/IT pro-fessionals to manage data. TS Discov-ery is an automated data profiling toolthat analyzes data to reveal weaknessesand gaps. TS Quality cleanses data fromdifferent sources. TS Enrichment sup-plements business/corporate data withdemographic and geographic informa-tion to improve postal performance andmore. Some products available as on-premise software or on-demand.

PRICING:Declined to provide pricing.

HUMAN INFERENCE

HIquality Suite

HIquality Suite is a data qualitymanagement platform based onnatural language processing. 1s

COMPANYWEBSITE:

www.humaninference.comFOUNDED: 1986SUMMARY: Human Inference’s open dataquality software aims to help organiza-tions optimize customer databases forincreased revenue, reduced cost,improved decision-making and regula-tory compliance. The HIquality ProductSuite is applied in call center solutions,data warehouses, customer relationshipmanagement (CRM), and sales andmarketing systems. It is used in de-duplication and matching processes,and for fraud detection and centralizingrelationship databases. Human Infer-ence has data quality products availableas on-premise software or, via partner-ships, as SaaS (may have limited avail-ability). 2

PRICING: License fees are subject tonumber of records in customer data-base(s). The average starting price ofthe products is approximately ¤30,000(approximately $38,361 USD at presstime).

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 17

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

IBM

IBM Information ServerWebSphere QualityStage

IBM Information Server WebSphereQualityStage provides data qualitymanagement for customer and businesspartner address information. s

COMPANYWEBSITE:www.ibm.com/usFOUNDED: 1924SUMMARY: IBM Information ServerWebSphere QualityStage modules aredesigned to support data quality man-agement for customer and businesspartner address information, with:address verification and enrichmentmodules; address certification, includ-ing the US Postal Service, Coding Accu-racy Support Systems (CASS) andmore, which supports verification andenrichment of addresses; Geospatialinformation to pinpoint locations andestablish spatial; and certified dataquality integration for the SAP BusinessAddress Services DES, which providesextensible search and match capabili-ties to SAP customers. 2

PRICING: IBMWebSphere QualityStagefor EIM 50 Concurrent Users License,plus software subscription and supportfor 12 months (D59B1LL), is $25,000(According to IBM’s website as of Q22008).

INFORMATICA

Informatica Data Quality

Informatica Data Quality has dataanalysis, cleansing, matching, reportingand monitoring capabilities to imple-ment and manage enterprise-wide dataquality initiatives. s

COMPANYWEBSITE:www.informatica.com/Pages/index.aspxFOUNDED: 1993SUMMARY: Informatica Data Qualityhas data analysis, cleansing, matching,reporting and monitoring capabilities. Itaddresses master data types, includingcustomer, product, financial, materials,pricing, order and asset. Featuresinclude a data quality workbench(design, build and manage data qualityprograms, create reports and dash-boards, deploy data quality rules in realtime), data quality assistant (edit,review low-quality records, trackchanges for auditing), data quality pro-filing, data cleansing and parsing, datamatching, scalable deployment and aglobal component software develop-ment toolkit and more. 2

PRICING:Declined to provide pricing.

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 18

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

INNOVATIVE SYSTEMS INC.

i/Lytics Enterprise Data Quality Suite:i/Lytics Data Profiler, i/Lytics DataQuality, i/Lytics GLOBAL

i/Lytics Enterprise Data Quality Suite isa data quality platform with functionsfor data profiling and analysis, dataquality and address validation. 1s

COMPANYWEBSITE:

www.innovativesystems.comFOUNDED: 1968SUMMARY: Innovative Systems’ i/LyticsEnterprise Data Quality Suite includes:i/Lytics Data Profiler for data profilingand analysis; i/Lytics Data Quality fordata cleansing, data linking and changemanagement; and i/Lytics GLOBAL, aCASS-certified address validation andGeocoding product. The i/Lytics Enter-prise Data Quality Suite offers multipledeployment styles, available as licensedsoftware in both real-time and batchmode, as a hosted ASP solution or on aservice bureau basis. Innovative Sys-tems has data quality products andservices available as on-premise soft-ware or on-demand.

PRICING:Declined to provide pricing.

NETRICS

Netrics Matching Platform,Netrics Matching Engine, NetricsDecision Engine, Netrics ReportingEngine, Netrics N-Mend DataReconciliation Tool

Netrics Matching Platform is designedfor finding and linking data. s

COMPANYWEBSITE:www.netrics.comFOUNDED: 2000SUMMARY:Netrics Matching Platformincludes: Netrics Matching Engine,which matches data with error toler-ance that approximates human percep-tion; and Netrics Decision Engine,which creates automated decision-models for detecting duplicates, linkingrecords, resolving entities and more.Netrics creates custommodels for thespecific datasets, market conditionsand business requirements of an appli-cation. Netrics Reporting Engine givesdetails on the state of data, in a data-quality reporting tool. Netrics N-MendData Reconciliation Tool is aWeb-based tool that locates/eliminatesduplicates from databases and more.

PRICING:The Netrics Matching Platformand the Netrics Decision Engine eachcost approximately $25,000 per CPU-core.

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 19

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

ORACLE CORP.

Data Quality forOracle Data Integrator

Oracle Data Quality for Oracle DataIntegrator includes multiple tools fordata quality and data governance. s

COMPANYWEBSITE:www.oracle.comFOUNDED: 1997SUMMARY: Oracle Data Quality for OracleData Integrator is a data quality plat-form that covers various data qualityneeds. Its rule-based engine and scala-ble architecture support data qualityand name and address cleansing. Ora-cle Data Quality enables global dataquality support, name and addresscleansing, parsing and standardization,customer data validation with postaldirectory or third-party information,automatic process duplication identity,user-customizable rules, customizabledata quality process and rules, built-inname and address standardization vali-dation, and enrichment.

PRICING:Data Quality for Data Integrator(up to a maximum of 100 millionrecords), Processor License: $60,000;Software Update License and Support:$13,200. Data Quality Rules for DataIntegrator (per rule set): License Price:$20,000; Software Update License andSupport: $4,400 (According to Oracle’swebsite as of Q2 2008).

PERVASIVE SOFTWARE

Pervasive Data Profiler

Pervasive Data Profiler automates thetesting, quality control and regulatorycompliance of critical data. s

COMPANYWEBSITE:www.pervasive.comFOUNDED: 1994SUMMARY: Pervasive Data Profiler helpsensure data quality by enabling users toaudit various types of data and auto-mate testing against changing businessneeds and compliance regulations.Users can assess data across multipledata platforms and quarantine ques-tionable data until it can be cleansed.Pervasive Data Profiler also supportsthe testing of large amounts of transac-tional data.

PRICING:Pricing starts at $5,000.

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 20

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

PITNEY BOWES GROUP 1 SOFTWARE

• Customer Data Quality Platform• CDQOn Demand• CDQGlobal Sentry• CDQ Platform for MicrosoftDynamics CRM

• CDQ Platform for Salesforce.com• CDQ Platform for SAP• CDQ Platform for Siebel

Pitney Bowes Group 1 is a providerof customer data quality and dataprofiling software. 1s

COMPANYWEBSITE:www.g1.comFOUNDED: 1982SUMMARY: Pitney Bowes Group 1 dataquality software creates a view of cus-tomer relationships to support target-ing, generate communications andmore. The vendor features a customerdata quality suite -- Customer DataQuality Platform, CDQ On Demand,Global Sentry, CDQ Platform forMicrosoft Dynamics CRM, CDQ Plat-form for Salesforce.com, CDQ Platformfor SAP and CDQ Platform for Siebel --as well as various standalone applica-tions (Merge/Purge Plus, BusinessMerge/Purge Plus, Dispatcher 4, Gen-eralized Selection Plus, List ConversionPlus and EZ-Case Plus). The vendor hasdata quality products and servicesavailable as on-premise software orservices. 2

PRICING:Declined to provide pricing.

STALWORTH INC.

DQ*Plus

DQ*Plus is a data quality platformwith functions for data cleansingand consolidation for databasesand enterprise applications. 1s

COMPANYWEBSITE:www.stalworth.comFOUNDED: 1998SUMMARY: Stalworth Inc. and MelissaData formed a strategic partnership todeliver DQ*Plus.. DQ*Plus Batch andPersistent cleans data automatically,while DQ*Plus Interactive works withusers inside their applications. DQ*Plushas a service-oriented architecture(SOA) and a web services API that sup-ports interoperability with legacy andthird-party applications. Application-aware connectors, packaged rules, apoint-and-click interface and DQ*PlusSIMULATOR enable businesses todeploy DQ*Plus and enable businessusers to configure data quality rules.Stalworth has data quality products andservices available as on-premise soft-ware or as web services. 2

PRICING:Pricing for the DQ*Plus Enter-prise Suite begins at $50,000.

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 21

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

TALEND

Talend Open Profiler

Talend Open Profiler is an opensource data profiler. s

COMPANYWEBSITE:www.talend.comFOUNDED: 2006SUMMARY: Talend Open Profiler, an opensource data profiler, enables companiesto assess the quality of data and decidewhich actions must be taken to correct“dirty data” can negatively affect anorganization. Talend Open Profiler's fea-tures include: metadata discovery,which identifies the structure of thedatabases that need to be analyzed;statistics definition, which defines thestatistics and metrics that need to bemeasured on each data item; andresults and graphs, which make it easyto view the results and assess the levelof quality of the data.

PRICING:Talend Open Profiler is opensource, available at no cost under aGPL license at www.talend.com.

UNISERV, GMBH

• Data Quality Batch Suite

Uniserv provides data quality soft-ware and services for customer data,particularly for business intelligenceand customer relationship managementapplications. 1s

COMPANYWEBSITE:www.uniserv.comFOUNDED: 1969SUMMARY: Uniserv, GmbH is a Germansupplier of data quality software andservices for the quality assurance ofcustomer data in the areas of businessintelligence (BI), customer relationshipmanagement (CRM) applications, datawarehousing, eBusiness and direct anddatabase marketing. Uniserv’s dataquality products feature: address analy-sis and formatting; merge, purge andduplicate check; intelligent search andduplicate check; postal address check;geo- and micromarketing; postage andmailing optimization; relocationaddresses; direct marketing suite; bank-ing data finder; and telephone numberfinder. 2

PRICING:Declined to provide pricing.

DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 22

INTRO

DATA QUALITYMANAGEMENTAND CHOOSINGDATA QUALITY

TOOLS

INDEX

BUSINESSOBJECTS

(NOW SAP)

DATACTICS

DATAFLUX

DATALEVER

DATAMENTORSINC.

DATANOMIC

EPRENTISE

FUZZY!INFORMATIC(NOW SAP)

HARTE-HANKSTRILLIUM

SOFTWARE

HUMANINFERENCE

IBM

INFORMATICA

INNOVATIVESYSTEMS INC.

NETRICS

ORACLE CORP

PERVASIVESOFTWARE

PITNEY BOWESGROUP 1

STALWORTH INC.

TALEND

UNISERV

ZOOMIX (NOWMICROSOFT)

ABOUTSEARCHDATA-MANAGEMENT

.COM

METHODOLOGY

ZOOMIX (MICROSOFT SUBSIDIARY)

Zoomix Accelerator

Zoomix Accelerator is a dataprocessing server designed to solvedata inconsistencies in-line withbusiness processes.s

COMPANYWEBSITE:www.zoomix.comFOUNDED: 1999SUMMARY: Zoomix, a Microsoft Sub-sidiary, develops and markets softwareto support the implementation of mas-ter data management (MDM) systems.The company’s technology combinessemantic analysis with machine learn-ing to classify, match and standardizecorporate master data (including semi-structured, highly variable product dataand financial data). Zoomix Acceleratoraims to help organizations resolveinconsistencies in data at the sourceand in-line with routine businessworkflows. 2

PRICING:Declined to provide pricing.

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DATA QUALITY MANAGEMENT SOFTWARE PRODUCT DIRECTORY 23

Guide methodology: TechTarget has not evaluated the products listed &/or described in this Directory and does not assume any liabilityarising out of the purchase or use of any product described herein, neither does it convey any license or rights in or to any of the evaluatedor listed products. TechTarget has prepared this Directory from sources deemed reliable (including vendors, research reports and certainpublicly available information). TechTarget has used good faith efforts to indicate when content has been provided by a vendor and, in somecases, has removed what it has deemed to be overt marketing language.

TechTarget is not responsible for any errors or omissions contained in this Directory or for interpretations thereof, and expressly dis-claims all warranties as to the accuracy, completeness or adequacy of all content contained herein. This disclaimer of warranty is in lieu ofall warranties whether expressed, implied or statutory, including implied warranties of merchantability or fitness for a particular purpose.Information in this Directory is current as of June 30, 2008. SearchDataManagement.com editors contacted all vendors for updates on pric-ing and product information in Q1 2009. Any updates vendors supplied were incorporated into the 2009 edition of the directory. For morerecent information, please check the vendor’s websites. The opinions expressed herein are subject to change without notice.

©2009 TechTarget, Inc. All Rights Reserved. Reproduction and distribution of this publication in any form without prior written permis-sion is forbidden. TechTarget and the TechTarget logo are registered trademarks of TechTarget, Inc.; all other trademarks are the property oftheir respective companies.

To compile this guide, our editorial team initially consulted research reports by major analyst firms covering the data quality market andcontacted vendors about products reviewed by those firms. Editors also conducted additional Internet research and solicited feedback fromour expert contacts. A notice about the project was posted on SearchDataManagement.com and listed regularly in our email newsletters.

Vendors were invited to submit listings via a form on the website. For vendors that did not submit listings, our editorial team compiledlistings by excerpting information from the vendor’swebsite. All entries, whether theywere vendor-submitted or compiled by our team,wereedited for length and clarity and to remove overt marketing language. In order to best assist our readers in assessing products, our editorialteam attempted to obtain basic pricing information for all products in this directory—requesting information from vendors multiple timesvia email. Vendors that did not respond, or refused to provide any pricing information, have this statement on their listings: “Declined to pro-vide pricing.”

Collection of data for this directory took place during the second calendar quarter of 2008. As with any directory of this kind, productsand vendors may change substantially at any time. Though every effort was made to make this directory as complete and accurate as pos-sible, theremay be changes, errors, omissions or vendors in this market not included in this guide. Nothing in this guide should be construedas endorsements, professional suggestions or advice. This directory should be used simply as a resource. We strongly urge you to supple-ment this with your own research and to contact vendors for the most up to date information about their companies or products. It is ourintent to update this directory annually, but that is subject to change.