# Best Data Quality Tools

  *By [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)*

   Data quality tools analyze sets of information and identify incorrect, incomplete, or improperly formatted data. After profiling data concerns, data quality tools cleanse or correct that data based on previously established guidelines. Deletion, modification, appending, and merging are all common methods of data set cleansing or correction; data analysts, marketers, and salespeople are just a few positions that benefit from leveraging data quality solutions.

By targeting and cleaning data lists, data quality software allows businesses to establish and maintain high standards for data integrity. These solutions are also helpful for ensuring that data adheres to these standards, based on the required industry, market, or in-house regulations. This process of maintaining data integrity enhances the reliability of such information for business use. Data sets can range from customer contact information to granular financial statistics and much more.

Data quality software products may also share features or coexist with [master data management (MDM) software](https://www.g2.com/categories/master-data-management-mdm), [data integration software](https://www.g2.com/categories/data-integration), or [big data software](https://www.g2.com/categories/big-data). While tangentially related to data quality solutions from a functional standpoint, [address verification software](https://g2.com/categories/address-verification) differs through its distinct use cases, focus on physical location data, and reliance on authoritative location data sourcing to verify correctness.

To qualify for inclusion in the Data Quality category, a product must:

- Enable data profiling and identify data anomalies
- Provide basic data cleansing functionalities like record merge, append, and delete
- Allow data modification and standardization based on predefined rules
- Allow automated and manual cleaning options
- Offer preventive measures to preserve data integrity





## Best Data Quality Tools At A Glance

- **Leader:** [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews)
- **Highest Performer:** [Traction Complete](https://www.g2.com/products/traction-complete/reviews)
- **Easiest to Use:** [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews)
- **Top Trending:** [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews)
- **Best Free Software:** [ZoomInfo Operations](https://www.g2.com/products/zoominfo-operations/reviews)


---

**Sponsored**

### QuerySurge

QuerySurge is an enterprise-grade data quality platform that leverages AI to continuously automate data validation across your entire ecosystem ‐ from data warehouses and big data lakes to BI reports and enterprise applications. With AI-powered test creation, scalable architecture, and the leading DevOps for Data CI/CD integration, QuerySurge ensures data integrity at every stage of the pipeline. Automated Data Validation Use Cases: QuerySurge provides a smart, AI-driven, data validation & ETL testing solution for your automated testing needs. - Data Warehouse / ETL Testing - DevOps for Data / Continuous Testing - Data Migration Testing - Business Intelligence (BI) Report Testing - Big Data Testing - Enterprise Application Data Testing What QuerySurge Provides: - Automation of your manual data validation and testing process - Ease-of-use, low-code/no-code features - Generative AI capabilities for test creation - Testing across 200+ data platforms - Integration into your CI/CD DataOps pipeline - Acceleration of your data analysis - Ensurance of regulatory compliance Key Features: - Data Connection Wizard provides an easy way to link to your data stores - Visual Query Wizard builds table-to-table and column-to-column tests without writing SQL - Generative AI module automatically creates transformation tests in bulk - DevOps for Data provides a RESTful API with 110+ calls and Swagger documentation and integrates into CI/CD pipelines - Create Custom Tests and modularize functions with snippets, set thresholds, stage data, check data types & duplicate rows, full text search, and asset tagging - Schedule tests to run immediately, at a predetermined date & time, or after any event from a build/release, CI/CD, DevOps, or test management solution - Multi-project support in a single instance, new Global Admin user, assign users and agents, import and export projects, and user activity log reports - Webhooks provide real-time integrations with DevOps, CI/CD, test management, and alerting tools - Ready-for-Analytics provides seamless integration with QuerySurge and your BI tool or open-source Metabase to create custom reports and dashboards and gain deeper, real-time insights into your data validation and ETL testing workflows - Data Analytics Dashboards and Data Intelligence Reports track, analyze, and communicate data quality



[Book a Demo](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=74&secure%5Bdisplayable_resource_id%5D=74&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=74&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=54942&secure%5Bresource_id%5D=74&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fdata-quality%3Flocale%3Dpt%26segment%3Dall&secure%5Btoken%5D=557833cd0ba1a7ce88e6602345b6a3c526e806731d1b19c3f035acefbb12cf16&secure%5Burl%5D=https%3A%2F%2Fwww.querysurge.com%2Fget-started%2Fprivate-demo%3Futm_source%3DG2%26utm_medium%3Dcpc%26utm_campaign%3DG2-reviews&secure%5Burl_type%5D=book_demo)

---

## Top-Rated Products (Ranked by G2 Score)
  ### 1. [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews)
  SAS Viya is a cloud-native data and AI platform that enables teams to build, deploy and scale explainable AI that drives trusted, confident decisions. It unites the entire data and AI life cycle and empowers teams to innovate quickly while balancing speed, automation and governance by design. Viya unifies data management, advanced analytics and decisioning in a single platform, so organizations can move from experimentation to production with confidence, delivering measurable business impact that is secure, explainable and scalable across any environment. Key capabilities required to deliver trusted decisions include: • End-to-end clarity across the data and AI life cycle, with built-in lineage, auditability and continuous monitoring to support defensible decisions. • Governance by design, enabling consistent oversight across data, models and decisions to reduce risk and accelerate adoption. • Explainable AI at scale, so insights and outcomes can be understood, validated and trusted by business and regulators alike. • Operationalized analytics, ensuring value continues beyond deployment through monitoring, retraining and life cycle management. • Flexible, cloud-native deployment, allowing organizations to start anywhere and scale everywhere while maintaining control.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 696

**User Satisfaction Scores:**

- **Quality of Support:** 8.3/10 (Category avg: 8.8/10)
- **Automation:** 9.0/10 (Category avg: 8.7/10)
- **Identification:** 8.7/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 8.8/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2)
- **Company Website:** https://www.sas.com/
- **Year Founded:** 1976
- **HQ Location:** Cary, NC
- **Twitter:** @SASsoftware (60,957 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1491/ (18,238 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Student, Statistical Programmer
  - **Top Industries:** Pharmaceuticals, Computer Software
  - **Company Size:** 33% Enterprise, 33% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (316 reviews)
- Features (218 reviews)
- Analytics (196 reviews)
- Data Analysis (166 reviews)
- User Interface (147 reviews)

**Cons:**

- Learning Difficulty (151 reviews)
- Learning Curve (144 reviews)
- Complexity (143 reviews)
- Difficult Learning (117 reviews)
- Expensive (108 reviews)

  ### 2. [Monte Carlo](https://www.g2.com/products/monte-carlo/reviews)
  Monte Carlo, the data + AI observability leader, enables enterprise organizations to drive mission-critical initiatives with trusted foundations. Nasdaq, Honeywell, Roche, and hundreds of leading organizations depend on Monte Carlo's end-to-end platform to easily detect and resolve data + AI issues at scale. Offering thoughtfully automated workflows, intuitive collaboration tools and first-of-their-kind Observability Agents for monitoring and resolution, Monte Carlo extends it's powerful platform into every layer of the data + AI estate—data, system, code, and model—to help teams detect issues immediately, resolve them quickly, and scale coverage faster. Consistently ranked #1 in its category, Monte Carlo sets the industry standard for data + AI reliability, helping enterprise teams everywhere to reduce risk, accelerate innovation, and drive more value from their data + AI products.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 485

**User Satisfaction Scores:**

- **Quality of Support:** 9.0/10 (Category avg: 8.8/10)
- **Automation:** 7.5/10 (Category avg: 8.7/10)
- **Identification:** 8.1/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 6.1/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Monte Carlo](https://www.g2.com/sellers/monte-carlo)
- **Company Website:** https://www.montecarlodata.com/
- **HQ Location:** San Francisco, US
- **Twitter:** @montecarlodata (1,576 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/monte-carlo-data/ (576 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Senior Data Engineer
  - **Top Industries:** Financial Services, Computer Software
  - **Company Size:** 49% Enterprise, 44% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (112 reviews)
- Alerts (107 reviews)
- Monitoring (97 reviews)
- Alerting System (78 reviews)
- Data Quality (53 reviews)

**Cons:**

- Alert Management (68 reviews)
- Alert Overload (62 reviews)
- Inefficient Alert System (53 reviews)
- UX Improvement (49 reviews)
- Limited Functionality (44 reviews)

  ### 3. [ZoomInfo Operations](https://www.g2.com/products/zoominfo-operations/reviews)
  ZoomInfo Operations is a sophisticated data management solution designed to assist organizations in optimizing their go-to-market (GTM) strategies by effectively managing sales and marketing data. This platform serves as the backbone of the GTM intelligence ecosystem, enabling operations teams to automate the processes of cleaning, enriching, and routing their data. By leveraging a no-code automated data management engine, businesses can establish a robust and unified data foundation that enhances their revenue-generating efforts. Targeted primarily at sales and marketing operations teams, ZoomInfo Operations addresses the common challenges associated with data management. Organizations often struggle with maintaining accurate and up-to-date information, which can hinder their ability to make informed decisions and execute effective marketing campaigns. This solution is particularly beneficial for companies that rely heavily on data-driven strategies and need to ensure that their sales and marketing databases are both clean and comprehensive. One of the standout features of ZoomInfo Operations is its ability to automatically clean and complete sales and marketing data. The platform efficiently deduplicates records, standardizes fields, and fills in missing information, all while maintaining data hygiene. This automation eliminates the need for manual intervention and complex workflows, allowing teams to focus on strategic initiatives rather than getting bogged down by data management tasks. Additionally, ZoomInfo Operations enriches existing databases by integrating with ZoomInfo and over 60 third-party data sources. This capability ensures that organizations have access to fresh contact information, firmographics, technographics, and buying signals, which are crucial for effective targeting and engagement. The seamless flow of enriched data into CRM and marketing automation platforms empowers teams to execute campaigns with greater precision and relevance. Routing engagement-ready data to the appropriate sales representatives is another critical function of ZoomInfo Operations. The platform employs intelligent assignment rules to ensure that every lead, account, and opportunity is directed to the right person at the right time. By infusing essential first-party data into the relevant systems, organizations can enhance their responsiveness and improve overall sales efficiency. This targeted approach not only optimizes the sales process but also fosters better relationships with potential customers, ultimately driving revenue growth.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 345

**User Satisfaction Scores:**

- **Quality of Support:** 8.6/10 (Category avg: 8.8/10)
- **Automation:** 8.9/10 (Category avg: 8.7/10)
- **Identification:** 9.0/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 8.8/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [ZoomInfo](https://www.g2.com/sellers/zoominfo-26a9872a-d61e-4832-ab53-5e972b230706)
- **Company Website:** https://www.zoominfo.com/
- **Year Founded:** 2000
- **HQ Location:** Vancouver, WA
- **Twitter:** @ZoomInfo (23,461 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/zoominfo/ (4,353 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Salesforce Administrator, Marketing Operations Manager
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 66% Mid-Market, 20% Small-Business


#### Pros & Cons

**Pros:**

- Data Accuracy (25 reviews)
- Ease of Use (23 reviews)
- Automation (20 reviews)
- Lead Generation (19 reviews)
- Efficiency (18 reviews)

**Cons:**

- Inaccuracy Issues (13 reviews)
- Inaccurate Data (11 reviews)
- Learning Curve (11 reviews)
- Learning Difficulty (11 reviews)
- Expensive (10 reviews)

  ### 4. [HubSpot Data Hub](https://www.g2.com/products/hubspot-data-hub/reviews)
  Operations Hub connects, cleanses, and automates customer data across the HubSpot CRM, providing operations teams with tools to maintain data quality, ensure system integration, and streamline business processes. Core Value Proposition: Operations Hub addresses critical operational challenges: disconnected data across applications, manual data entry consuming team time, data quality issues undermining business decisions, and complex automation requirements existing tools cannot handle. The platform offers native integrations with other applications to create a more efficient, aligned, and agile business. Key Capabilities: Data Integration: Operations Hub connects contacts, leads, and company data between HubSpot and external applications bidirectionally and in real-time. This creates a unified customer data foundation rather than requiring manual data transfer. Data Quality Management: The platform includes tools that maintain a clean database, allowing operations teams to save hours of manual data validation and correction work. Process Automation: Operations Hub enables complex business process automation across systems, connecting trigger events in one application to automated actions in another. This streamlines internal workflows and reduces manual coordination. Unified Customer View: By connecting all customer data sources to the HubSpot CRM platform, Operations Hub creates a single source of truth that sales, marketing, and service teams can reference for customer interactions. Operations Hub vs. Alternatives: Unlike standalone integration platforms (iPaaS) requiring technical expertise to configure and maintain, Operations Hub provides native HubSpot integration with a visual interface designed for operations professionals rather than developers. This reduces implementation time and ongoing maintenance requirements. Operations Hub eliminates manual data entry and data validation by automating these workflows. The platform guarantees up-to-date data and maintains a clean database without constant manual intervention. Who Should Use Operations Hub: Operations Hub serves operations teams managing data across multiple systems, organizations experiencing data quality issues affecting business decisions, and companies needing to automate complex cross-system workflows without extensive technical resources. The platform enables business agility as organizations grow. Outcome: Operations Hub supercharges your HubSpot CRM with a complete toolkit to connect, clean, and automate customer data, uniting all customer data into one connected platform that results in a friction-free customer experience.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 557

**User Satisfaction Scores:**

- **Quality of Support:** 8.8/10 (Category avg: 8.8/10)
- **Automation:** 8.9/10 (Category avg: 8.7/10)
- **Identification:** 8.8/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 8.7/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [HubSpot](https://www.g2.com/sellers/hubspot)
- **Company Website:** https://www.HubSpot.com
- **Year Founded:** 2006
- **HQ Location:** Cambridge, MA
- **Twitter:** @HubSpot (785,472 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/68529/ (11,979 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** CEO, Owner
  - **Top Industries:** Information Technology and Services, Marketing and Advertising
  - **Company Size:** 67% Small-Business, 29% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (119 reviews)
- Automation (90 reviews)
- Data Management (88 reviews)
- Integrations (65 reviews)
- Efficiency (48 reviews)

**Cons:**

- Limitations (58 reviews)
- Missing Features (40 reviews)
- Learning Curve (37 reviews)
- Expensive (33 reviews)
- Complexity (27 reviews)

  ### 5. [dbt](https://www.g2.com/products/dbt/reviews)
  dbt is a transformation workflow that lets data teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 198

**User Satisfaction Scores:**

- **Quality of Support:** 8.9/10 (Category avg: 8.8/10)
- **Automation:** 9.3/10 (Category avg: 8.7/10)
- **Identification:** 8.7/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 8.2/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Fivetran](https://www.g2.com/sellers/fivetran)
- **Year Founded:** 2012
- **HQ Location:** Oakland, CA
- **Twitter:** @fivetran (5,717 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/fivetran/ (1,738 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Analytics Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 57% Mid-Market, 27% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (38 reviews)
- Features (22 reviews)
- Automation (19 reviews)
- Transformation (17 reviews)
- Integrations (15 reviews)

**Cons:**

- Limited Functionality (14 reviews)
- Dependency Issues (12 reviews)
- Steep Learning Curve (10 reviews)
- Error Handling (9 reviews)
- Error Reporting (9 reviews)

  ### 6. [D&B Connect](https://www.g2.com/products/d-b-connect/reviews)
  D&B Connect (the next generation of D&B Optimizer) is an AI-driven Data Management Platform based on the D&B Cloud that provides businesses with customer data and market insights. With D&B Connect, users can collaborate on data management tasks, visualize, monitor, and benchmark data, as well as assess overall data health. Integrations with Master Data Management Platforms, Customer Data Platforms, and CRMs enable automated data updates and anomaly detection through the identity resolution engine. MAP integrations allow for the automation of cross-channel marketing tasks on social media, email, and websites.


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 131

**User Satisfaction Scores:**

- **Quality of Support:** 8.5/10 (Category avg: 8.8/10)
- **Automation:** 8.1/10 (Category avg: 8.7/10)
- **Identification:** 8.1/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 8.1/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Dun & Bradstreet](https://www.g2.com/sellers/dun-bradstreet)
- **Company Website:** https://www.dnb.com
- **HQ Location:** Short Hills, NJ
- **Twitter:** @DunBradstreet (22,529 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2385/ (5,816 employees on LinkedIn®)
- **Ownership:** NYSE: DNB

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 44% Mid-Market, 31% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (19 reviews)
- Data Accuracy (18 reviews)
- Data Quality (11 reviews)
- Easy Setup (8 reviews)
- Accuracy (7 reviews)

**Cons:**

- Limitations (9 reviews)
- Expensive (7 reviews)
- Learning Curve (7 reviews)
- Limited Functionality (6 reviews)
- Missing Features (6 reviews)

  ### 7. [DQLabs](https://www.g2.com/products/dqlabs/reviews)
  DQLabs redefines data management with Semantics and GenAI powered Modern Data Quality Platform, empowering organisations to transform raw data into reliable, actionable insights. Our automation-first, self-learning platform seamlessly integrates Data Observability, Augmented Data Quality, Data Discovery and Semantics, fostering collaborative decision-making across your data ecosystem. Turn data into action faster, easier, cost-efficiently and more reliably with DQLabs - your gateway to transformative business outcomes.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 43

**User Satisfaction Scores:**

- **Quality of Support:** 9.5/10 (Category avg: 8.8/10)
- **Automation:** 9.5/10 (Category avg: 8.7/10)
- **Identification:** 9.5/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 9.4/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [DQLabs](https://www.g2.com/sellers/dqlabs)
- **Year Founded:** 2020
- **HQ Location:** Pasadena, California
- **Twitter:** @DQLABSAI (245 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/dqlabsai/ (95 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 44% Mid-Market, 20% Enterprise


#### Pros & Cons

**Pros:**

- Data Quality (28 reviews)
- Ease of Use (24 reviews)
- Efficiency Improvement (24 reviews)
- Automation (20 reviews)
- Features (20 reviews)

**Cons:**

- Poor Documentation (7 reviews)
- Product Immaturity (2 reviews)
- Complexity (1 reviews)
- Data Management Issues (1 reviews)
- Data Quality (1 reviews)

  ### 8. [DemandTools](https://www.g2.com/products/demandtools/reviews)
  DemandTools is the secure data quality platform that ensures your data remains your most valuable asset. With DemandTools, you manage your CRM data in minutes, not months, so you always have accurate, report-ready data enabling everyone to do their job more effectively, efficiently, and profitably. By fixing common data problems, automating data quality routines, and working within your specific processes and customizations, DemandTools gives stakeholders accurate insights and reporting, improves business efficiency, and gets you clean data faster, with less effort. DemandTools has 12 modules making it the most versatile and adaptable data quality solution for CRM. Data Quality Assessment Understand how strong or weak your data is and know where to focus remediation efforts. Module: Assess Duplicate Management Detect, eliminate, and prevent duplicate records from misleading your sales and marketing teams and causing friction in your customer journey. Modules: Dedupe, Convert, DupeBlocker, Match Data Migration Management Maintain data integrity while moving data into and out of Salesforce. Modules: Import, Export, Delete, Match Standardization, mass modification, and business insights. Apply record changes en masse and standardize data to get trustworthy insights in every report. Modules: Modify, Tune, Reassign Email Verification Verify email addresses in CRM to keep communication flowing with your customers. Module: Verify Get clean data and strengthen your business with DemandTools.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 275

**User Satisfaction Scores:**

- **Quality of Support:** 8.8/10 (Category avg: 8.8/10)
- **Automation:** 8.2/10 (Category avg: 8.7/10)
- **Identification:** 9.1/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 8.8/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Validity Inc](https://www.g2.com/sellers/validity-inc)
- **Company Website:** https://www.validity.com
- **Year Founded:** 2018
- **HQ Location:** Boston, Massachusetts
- **Twitter:** @TrustValidity (1,151 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/11679353/ (344 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Salesforce Administrator
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 48% Mid-Market, 33% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (14 reviews)
- Duplicate Management (8 reviews)
- Time-saving (8 reviews)
- Efficiency (5 reviews)
- Salesforce Integration (5 reviews)

**Cons:**

- Limited Functionality (3 reviews)
- Missing Features (3 reviews)
- Learning Curve (2 reviews)
- Poor Interface Design (2 reviews)
- Slow Loading (2 reviews)

  ### 9. [Demandbase One](https://www.g2.com/products/demandbase-one/reviews)
  Demandbase is the leading, enterprise-grade account-based GTM platform for sales and marketing teams designed to make every moment and every dollar count. Since creating the category in 2013, we have been pioneering technologies to sharpen revenue teams’ ability to confidently deliver the right message to the right customers at the right time. Powered by industry-leading data, our transparent and tunable AI-enhanced model, and integrations that meet your tech stack where it is, Demandbase helps you to take meaningful action confidently and efficiently. We know that there’s no such thing as ‘one-size- fits-all’ account-based marketing and sales. That’s why we built our platform to be flexible, easily handling dynamic GTM motions, nuanced business rules, and diverse integrations that others struggle with. Demandbase One™ is your account-based GTM command center, powering your entire revenue stack. Our AI-driven engine unifies first and third-party data, streamlines cross-channel execution, and connects the tools in your stack with the same data, insights, and workflows to accelerate your revenue.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 1,888

**User Satisfaction Scores:**

- **Quality of Support:** 8.8/10 (Category avg: 8.8/10)
- **Automation:** 9.0/10 (Category avg: 8.7/10)
- **Identification:** 8.7/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 8.3/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Demandbase](https://www.g2.com/sellers/demandbase)
- **Company Website:** https://www.demandbase.com
- **Year Founded:** 2005
- **HQ Location:** San Francisco, CA
- **Twitter:** @Demandbase (21,383 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/89759/ (993 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Account Executive, Business Development Representative
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 49% Mid-Market, 32% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (225 reviews)
- Lead Generation (201 reviews)
- Insights (199 reviews)
- Features (173 reviews)
- Intent Data (170 reviews)

**Cons:**

- Learning Curve (95 reviews)
- Steep Learning Curve (77 reviews)
- Complexity (70 reviews)
- Difficult Learning (63 reviews)
- Learning Difficulty (63 reviews)

  ### 10. [Planhat](https://www.g2.com/products/planhat/reviews)
  Planhat is a customer platform that provides software and services to help organizations grow lifelong customers. Our platform powers sales, service and customer success products that scale with our customers’ needs all the way from startup to household name and beyond. Each day worldwide, over 2.6 million customers are attracted, engaged and delighted with our intuitive yet flexible system of action. The Planhat platform empowers everyone in your organization to consolidate, analyze and act on all your data, becoming more customer-centric and data-driven than ever before. From rolling out autonomous transport systems to distributing new medicines, we’re proud to help make our customers better at what they do best. Alongside our customers, we’re building at the forefront of healthcare & life sciences, finance, connected business, and more. And we need curious, daring minds to help us.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 905

**User Satisfaction Scores:**

- **Quality of Support:** 9.2/10 (Category avg: 8.8/10)
- **Automation:** 8.4/10 (Category avg: 8.7/10)
- **Identification:** 7.6/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 7.2/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Planhat](https://www.g2.com/sellers/planhat)
- **Company Website:** https://www.planhat.com
- **Year Founded:** 2015
- **HQ Location:** Stockholm, Stockholm County
- **Twitter:** @planhat (1,043 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10168756/ (227 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Customer Success Manager, Head of Customer Success
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 59% Mid-Market, 32% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (215 reviews)
- Customer Support (155 reviews)
- Customization (106 reviews)
- Automation Efficiency (103 reviews)
- Helpful (103 reviews)

**Cons:**

- Learning Curve (113 reviews)
- Complexity (78 reviews)
- Steep Learning Curve (62 reviews)
- Integration Issues (58 reviews)
- Limitations (56 reviews)

  ### 11. [Collibra](https://www.g2.com/products/collibra/reviews)
  Try Collibra for free @ Collibra.com/tour Collibra is for organizations with complex data challenges, hybrid data ecosystems—and big ambitions for data and AI. We help organizations who are trying to accelerate data and AI use cases while ensuring compliance, but are struggling with fragmented governance and visibility across the whole hybrid data ecosystem. Collibra unifies governance for data and AI across every system, data source and user—to create safe autonomy and a foundation for scaling AI and data use cases. With Collibra, you can accelerate all your data and AI use cases, safely and with well–understood data. That’s Data Confidence.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 99

**User Satisfaction Scores:**

- **Quality of Support:** 8.2/10 (Category avg: 8.8/10)
- **Automation:** 7.8/10 (Category avg: 8.7/10)
- **Identification:** 8.4/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 7.1/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Collibra](https://www.g2.com/sellers/collibra)
- **Company Website:** https://www.collibra.com
- **Year Founded:** 2008
- **HQ Location:** New York, New York
- **Twitter:** @collibra (5,736 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/288365/ (1,082 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Financial Services, Banking
  - **Company Size:** 72% Enterprise, 19% Mid-Market


#### Pros & Cons

**Pros:**

- Features (14 reviews)
- Ease of Use (13 reviews)
- Data Management (12 reviews)
- Data Governance (9 reviews)
- Integrations (9 reviews)

**Cons:**

- Limited Functionality (8 reviews)
- Complexity Issues (7 reviews)
- Complexity (6 reviews)
- Improvement Needed (6 reviews)
- Complex Setup (5 reviews)

  ### 12. [Oracle Data Quality](https://www.g2.com/products/oracle-data-quality/reviews)
  Oracle Enterprise Data Quality delivers a complete, best-of-breed approach to party and product data resulting in trustworthy master data that integrates with applications to improve business insight.


  **Average Rating:** 4.0/5.0
  **Total Reviews:** 54

**User Satisfaction Scores:**

- **Quality of Support:** 8.4/10 (Category avg: 8.8/10)
- **Automation:** 8.2/10 (Category avg: 8.7/10)
- **Identification:** 9.2/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 8.6/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Oracle](https://www.g2.com/sellers/oracle)
- **Year Founded:** 1977
- **HQ Location:** Austin, TX
- **Twitter:** @Oracle (826,383 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1028/ (199,301 employees on LinkedIn®)
- **Ownership:** NYSE:ORCL

**Reviewer Demographics:**
  - **Top Industries:** Hospital & Health Care, Information Technology and Services
  - **Company Size:** 50% Enterprise, 28% Small-Business


  ### 13. [Quest erwin Data Intelligence](https://www.g2.com/products/quest-erwin-data-intelligence/reviews)
  erwin Data Intelligence ensures trusted data and AI models are easy to find, understand, govern, score and use across your enterprise. With erwin, organizations reduce operational risk, ensure regulatory oversight, and improve trust in analytics and AI through a transparent, explainable data foundation.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 30

**User Satisfaction Scores:**

- **Quality of Support:** 8.0/10 (Category avg: 8.8/10)
- **Automation:** 10.0/10 (Category avg: 8.7/10)
- **Identification:** 10.0/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 10.0/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Quest Software](https://www.g2.com/sellers/quest-software)
- **Company Website:** https://www.quest.com
- **Year Founded:** 1987
- **HQ Location:** Austin, TX
- **Twitter:** @Quest (17,148 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2880/ (3,594 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 37% Small-Business, 33% Mid-Market


#### Pros & Cons

**Pros:**

- Compliance Management (1 reviews)
- Data Discovery (1 reviews)
- Data Lineage (1 reviews)
- Metadata Management (1 reviews)

**Cons:**

- Expensive (1 reviews)
- Outdated Design (1 reviews)
- Poor Customer Support (1 reviews)
- Poor Interface Design (1 reviews)
- User Adoption Difficulty (1 reviews)

  ### 14. [Traction Complete](https://www.g2.com/products/traction-complete/reviews)
  Traction Complete's RevOps Data Management Suite for Salesforce helps revenue operations teams cleanse, connect, and orchestrate data. Since 2019, we've been on a mission to automate complex data tasks in Salesforce, empowering organizations like Asana, Zoom, and YMCA to save time and scale faster. Say hello and learn more at www.tractioncomplete.com. COMPLETE LEADS Speed up, sell more, and keep your revenue teams in sync with the most flexible matching and routing solution built for Salesforce. COMPLETE HIERARCHIES Maximize revenue across your largest customers with the only automated account hierarchy solution for Salesforce. COMPLETE CLEAN Detect and remove duplicates with our data cleansing tool for Salesforce so you can focus on growing revenue with clean, connected data. COMPLETE INFLUENCE Close more deals by helping your sellers visualize organizational structures and identify the stakeholders with influence over their deals. COMPLETE AI Bring context to every record, flow, and decision with AI built directly into Salesforce. Normalize, validate, enrich, and activate your data automatically so RevOps teams can move faster without manual research or guesswork. COMPLETE DISCOVER Experiment with AI-powered enrichment and data logic before committing changes to Salesforce. Test, learn, and refine in a safe environment, then confidently apply what works to keep your CRM clean, complete, and governed.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 172

**User Satisfaction Scores:**

- **Quality of Support:** 9.7/10 (Category avg: 8.8/10)
- **Automation:** 9.4/10 (Category avg: 8.7/10)
- **Identification:** 9.6/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 9.4/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Traction Complete](https://www.g2.com/sellers/traction-complete)
- **Company Website:** https://tractioncomplete.com/
- **Year Founded:** 2019
- **HQ Location:** Vancouver, British Columbia
- **LinkedIn® Page:** https://www.linkedin.com/company/traction-complete/ (87 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Salesforce Administrator, Marketing Operations Manager
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 54% Mid-Market, 30% Enterprise


#### Pros & Cons

**Pros:**

- Customer Support (29 reviews)
- Ease of Use (23 reviews)
- Lead Generation (18 reviews)
- Helpful (17 reviews)
- Routing Efficiency (16 reviews)

**Cons:**

- Limitations (12 reviews)
- Learning Curve (11 reviews)
- Complexity (6 reviews)
- Steep Learning Curve (6 reviews)
- Not Intuitive (5 reviews)

  ### 15. [Atlan](https://www.g2.com/products/atlan/reviews)
  Atlan is the context layer for enterprise AI. It continuously reads your warehouses, databases, pipelines, BI tools, and business systems to reverse construct an enterprise data graph that captures assets, lineage, entities, metrics, policies, and relationships. On top of that graph, it enriches and curates machine-readable semantics — descriptions, popular joins, KPI and metric definitions, ontologies, and business rules — and organizes them into governed, versioned context repos: bounded bundles of context that reflect how your company defines key concepts and makes decisions. These context repos are then exposed through open interfaces (SQL, APIs, SDKs, OSI/MCP-style protocols) so that agents, copilots, and AI applications can call the same trusted context in real time, rather than each team hard-coding its own logic. Human-on-the-loop governance workflows for conflict resolution, deprecation, feedback, and certification keep that context trustworthy as the business, data, and models evolve.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 124

**User Satisfaction Scores:**

- **Quality of Support:** 9.2/10 (Category avg: 8.8/10)
- **Automation:** 7.7/10 (Category avg: 8.7/10)
- **Identification:** 7.6/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 6.8/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Atlan](https://www.g2.com/sellers/atlan)
- **Company Website:** https://www.atlan.com
- **Year Founded:** 2019
- **HQ Location:** New York, US
- **Twitter:** @AtlanHQ (9,709 Twitter followers)
- **LinkedIn® Page:** https://in.linkedin.com/company/atlan-hq (580 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Financial Services, Computer Software
  - **Company Size:** 53% Mid-Market, 40% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (18 reviews)
- User Interface (12 reviews)
- Features (11 reviews)
- Data Lineage (10 reviews)
- Easy Setup (10 reviews)

**Cons:**

- Learning Curve (5 reviews)
- Limited Functionality (5 reviews)
- User Interface Issues (5 reviews)
- Difficult Learning (4 reviews)
- Integration Issues (4 reviews)

  ### 16. [DataGroomr](https://www.g2.com/products/datagroomr/reviews)
  DataGroomr is a modern, AI-powered platform purpose-built to ensure exceptional data quality in Salesforce. For organizations that rely on Salesforce to drive sales, marketing, customer support, operations, and finance, clean and reliable data is not optional - it is foundational. Yet traditional data cleansing tools are often complex, brittle, and time-consuming to configure. DataGroomr changes that paradigm. Duplicate records are one of the most common and damaging data quality issues in Salesforce. They distort reporting, frustrate users, degrade customer experiences, and undermine downstream systems. DataGroomr addresses this challenge with advanced artificial intelligence that accurately identifies duplicates across Accounts, Contacts, Leads, and other objects - without requiring filters, rules, or manual tuning. Using state-of-the-art AI matching techniques, DataGroomr detects more duplicates with greater precision than traditional rule-based approaches. The platform requires no upfront configuration and continuously improves over time by learning from your data patterns and user decisions. This adaptive intelligence ensures long-term accuracy as your Salesforce org evolves. Beyond detection, DataGroomr provides powerful, flexible tools to safely merge records at scale. Teams can merge multiple records at once while maintaining full control over how fields are handled, ensuring data integrity and compliance with internal processes. DataGroomr also helps prevent data issues before they happen by deduplicating import files prior to loading them into Salesforce. Data quality goes beyond duplicates alone. DataGroomr offers built-in verification for global email, address, and phone data, helping organizations maintain trusted, enriched records across regions and use cases. A single, intuitive interface provides clear insights into data health, trends, and improvement over time—making data quality visible and actionable. Designed for ease of use, DataGroomr fits seamlessly into Salesforce workflows and is trusted by teams of all sizes. Backed by a responsive and knowledgeable support team, customers can confidently scale their data quality initiatives without added complexity. Discover why DataGroomr consistently earns 5-star reviews on the Salesforce AppExchange - and experience a smarter, simpler approach to Salesforce data quality. Start your free trial at: http://www.datagroomr.com


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 35

**User Satisfaction Scores:**

- **Quality of Support:** 9.6/10 (Category avg: 8.8/10)
- **Automation:** 9.2/10 (Category avg: 8.7/10)
- **Identification:** 9.4/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 9.1/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [DataGroomr](https://www.g2.com/sellers/datagroomr)
- **Year Founded:** 2018
- **HQ Location:** Philadelphia, PA
- **LinkedIn® Page:** https://www.linkedin.com/company/datagroomr/ (5 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Non-Profit Organization Management
  - **Company Size:** 63% Mid-Market, 20% Small-Business


#### Pros & Cons

**Pros:**

- Duplicate Management (21 reviews)
- Ease of Use (21 reviews)
- Customer Support (17 reviews)
- Intuitive (12 reviews)
- Salesforce Integration (12 reviews)

**Cons:**

- Learning Curve (6 reviews)
- Complexity (4 reviews)
- Difficult Learning Curve (3 reviews)
- Difficult Setup (3 reviews)
- Learning Difficulty (3 reviews)

  ### 17. [Insycle](https://www.g2.com/products/insycle/reviews)
  Insycle is a powerful and intuitive solution that keeps your CRM data clean, accurate, and organized. Insycle integrates with HubSpot, Salesforce, Pipedrive, and more, giving users the power to achieve and maintain pristine customer data. Insycle centralizes data management processes, including deduplication, data standardization, data enrichment, and field management — all in a no-code environment. Insycle doesn’t just stop at cleaning your data—it empowers your entire team to make better, more informed decisions. Whether you’re a marketer segmenting your audience, a sales leader refining your pipeline, or an operations professional maintaining data hygiene, Insycle provides the comprehensive tools you need to keep your CRM data in top shape. For users with both HubSpot and Salesforce, Insycle is uniquely able to ensure the sync between the platforms doesn’t break as well as identify duplicates and inconsistencies and give you the tools to fix them in a few clicks. Insycle’s most-loved features include: Deduplication: Analyze millions of records in seconds and get a detailed report of all duplicates. Use flexible matching and bulk merging to quickly eliminate duplicates. And automatically avoid creating duplicates for new records. Data Standardization: Standardize data in any field and create your own processes and templates to solve unique standardization problems. Works on any CRM data field — across contacts, companies, and deals. Data Maintenance Automation: Automate your data-management processes with workflows. Use the CRM Health Assessment tool to run reports and identify data issues. Recognized for its ease of use, robust functionality, and outstanding customer support, Insycle is the go-to solution for businesses looking to eliminate data chaos, enhance data quality, and turn their CRM into a powerful, growth-driving asset — just check out our reviews! Go to www.insycle.com to get a 14-day free trial.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 191

**User Satisfaction Scores:**

- **Quality of Support:** 9.3/10 (Category avg: 8.8/10)
- **Automation:** 9.3/10 (Category avg: 8.7/10)
- **Identification:** 9.0/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 9.0/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Insycle](https://www.g2.com/sellers/insycle)
- **Company Website:** https://www.insycle.com/
- **Year Founded:** 2016
- **HQ Location:** New York
- **Twitter:** @insycle (288 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10940435/ (11 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Marketing Manager
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 49% Mid-Market, 43% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (34 reviews)
- Data Accuracy (23 reviews)
- Duplicate Management (21 reviews)
- HubSpot Integration (19 reviews)
- Customer Support (18 reviews)

**Cons:**

- Learning Curve (16 reviews)
- Not User-Friendly (13 reviews)
- Expensive (8 reviews)
- Limitations (8 reviews)
- Not Intuitive (8 reviews)

  ### 18. [Microsoft Data Quality Services](https://www.g2.com/products/microsoft-data-quality-services/reviews)
  SQL Server Data Quality Services (DQS) is a knowledge-driven data quality product.


  **Average Rating:** 3.9/5.0
  **Total Reviews:** 47

**User Satisfaction Scores:**

- **Quality of Support:** 8.0/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft (13,090,464 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®)
- **Ownership:** MSFT

**Reviewer Demographics:**
  - **Company Size:** 53% Small-Business, 29% Mid-Market


  ### 19. [Data8 - Data Quality Solutions](https://www.g2.com/products/data8-data-quality-solutions/reviews)
  Data8, is a leading data quality management company specialising in data validation, deduplication, data cleansing, data quality, and data migration solutions. We help businesses across every sector enhance the accuracy and value of their data for better decision-making and results, ensuring data is accurate, compliant, and strategically beneficial. Our core features and solutions include: - Data Validation [Address, Bank, phone name and email address] - Data Suppression Services - Eircode, UPRN and Address Lookup - Data Deduplication and Merge - PAF Cleansing Services - Predictive Address [Autocomplete] - Data Quality Monitoring - Preference Services - Business Insights - Data Migration - Automated Data Cleansing - Data Appending and Enhancement Services What is data quality? Data quality means data must be: - Accurate: correct and true - Complete: no missing pieces - Consistent: uniform across systems - Valid: follows proper formats - Timely: current and available - Unique: no duplicates - Reliable: trustworthy Why is data quality important? rectify inaccuracies to optimize marketing efforts. Address validation services verify and standardize addresses, improving communication, reducing delivery errors, and cutting costs. These tools empower businesses to achieve compliance, boost sales, and improve marketing ROI. Why Choose Data8? Since 2005, Data8 has delivered award-winning data quality solutions that help businesses clean, enhance, and maximise their data’s value. - Royal Mail PAF updated data - Award-winning data solutions [The Queen’s Award, 2022] - We work with over 1,000 businesses worldwide - ISO27001 certified - 5-star G2 ratings - Royal Mail PAF updated data We provide versatile solutions tailored to diverse client needs. Our services support targeted marketing, compliance reporting, and more, positioning Data8 as an essential partner for organizations seeking to leverage data for growth. Contact us to explore how you can build confidence in your data.


  **Average Rating:** 5.0/5.0
  **Total Reviews:** 15

**User Satisfaction Scores:**

- **Quality of Support:** 9.8/10 (Category avg: 8.8/10)
- **Automation:** 8.5/10 (Category avg: 8.7/10)
- **Identification:** 8.6/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 8.3/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Data8](https://www.g2.com/sellers/data8)
- **Company Website:** https://www.data-8.co.uk/
- **Year Founded:** 2005
- **HQ Location:** Ellesmere Port, England, United Kingdom
- **Twitter:** @data8ltd (1,260 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/data-8-ltd/ (32 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 53% Enterprise, 33% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (8 reviews)
- Customer Support (4 reviews)
- Data Quality (4 reviews)
- Duplicate Management (3 reviews)
- Easy Setup (3 reviews)

**Cons:**

- Difficult Setup (2 reviews)
- Limited Customization (1 reviews)

  ### 20. [Melissa Data Quality Suite](https://www.g2.com/products/melissa-data-quality-suite/reviews)
  Since 1985, Melissa Data Quality Suite is the ultimate solution for contact data management, combining AI powered, gold-standard reference data to ensure your data is accurate, complete, and actionable. From data cleansing to real-time data enrichment, our solution leverages Unison to continuously learn and improve data quality by verifying and correcting contact data such as names, phone numbers, emails, and addresses. Name Verification: With intelligent recognition capabilities, Melissa identifies, genderizes, and parses over 650,000 ethnically-diverse names. This feature helps you understand and manage customer identities more effectively, ensuring your data is both accurate and inclusive. Phone Verification: Our phone verification tool checks the liveness, type, and ownership of both landline and mobile numbers. Supporting international phone validation, this tool helps you ensure that your contact numbers are active and valid, reducing communication errors and improving outreach efficiency. Email Verification: Melissa’s email verification process corrects and validates domains, syntax, and spelling, while also testing SMTP to ensure global email validation. This includes email list validation to minimize bounce rates, boost response rates, and improve deliverability for your marketing campaigns. Address Verification: Our suite provides comprehensive address verification to validate, correct, and standardize addresses. Whether you need batch processing, real-time validation at the point of entry, or single-address lookups with instant results, Melissa ensures accuracy for the U.S., Canada, and over 240 countries and territories. This leads to improved deliveries, enhanced customer service, and bulk mail discounts. Experience Flexibility at its Finest: The Data Quality Suite is available via multiplatform on-premise APIs and Web Service/Cloud APIs. This flexibility ensures scalability, security, and adaptability to fit any business size or requirement. Seamlessly integrate data verification, enrichment, and cleansing into your web applications and business processes. Why Melissa? Melissa has been a leader in data quality since 1985, setting the standard with AI-powered, gold-standard reference data that surpass the competition. Our expertise in address solutions and data management has earned us the trust of over 10,000 global customers, who rely on us to improve their business intelligence, streamline operations, and enhance their bottom line. Discover why Melissa is the go-to choice for data quality and start your free trial today at Melissa Data Quality Suite. Explore how we can help you achieve precise data management and operational excellence. Contact us for a personalized quote or explore our robust enterprise package. Additionally, take advantage of our trial version to experience the suite firsthand. Try Data Quality Suite today for free! https://www.melissa.com/lp/g2-dqsuite


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 75

**User Satisfaction Scores:**

- **Quality of Support:** 9.0/10 (Category avg: 8.8/10)
- **Automation:** 9.1/10 (Category avg: 8.7/10)
- **Identification:** 8.9/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 9.6/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Melissa](https://www.g2.com/sellers/melissa)
- **Company Website:** https://www.melissa.com
- **Year Founded:** 1985
- **HQ Location:** Rancho Santa Margarita, CA
- **Twitter:** @melissadata (2,428 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/melissa-data/ (698 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Real Estate, Marketing and Advertising
  - **Company Size:** 70% Small-Business, 19% Mid-Market


#### Pros & Cons

**Pros:**

- Accuracy (4 reviews)
- Ease of Use (4 reviews)
- Data Quality (3 reviews)
- Easy Integrations (3 reviews)
- Accuracy of Information (2 reviews)

**Cons:**

- Complexity (2 reviews)
- Limited Functionality (2 reviews)
- Accuracy Issues (1 reviews)
- Difficult Learning Curve (1 reviews)
- Expensive (1 reviews)

  ### 21. [Informatica Cloud Data Quality](https://www.g2.com/products/informatica-cloud-data-quality/reviews)
  Informatica Cloud Data Quality empowers your company to take a holistic approach to managing data quality to quickly identify, fix, and monitor data quality problems in your business applications. The solution transforms your data quality processes into a collaborative effort between business users and IT. This creates an environment that leverages data to ensure success in master data management, AI, ML, and cloud modernization initiatives.


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 19

**User Satisfaction Scores:**

- **Quality of Support:** 9.2/10 (Category avg: 8.8/10)
- **Automation:** 8.3/10 (Category avg: 8.7/10)
- **Identification:** 8.3/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 6.7/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Informatica](https://www.g2.com/sellers/informatica)
- **Company Website:** https://www.informatica.com
- **Year Founded:** 1993
- **HQ Location:** Redwood City, CA
- **Twitter:** @Informatica (99,861 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/3858/ (5,337 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 180% Enterprise, 80% Mid-Market


  ### 22. [BizProspex CRM Cleaning](https://www.g2.com/products/bizprospex-crm-cleaning/reviews)
  Sales Enablement Service with data mining, data cleaning and data appending features. Bizprospex's sales enablement service will help grow your B2B business. Our services promise to give business of all sizes the opportunity to mine and append information to sell, service, market, develop, and succeed like never before.


  **Average Rating:** 4.9/5.0
  **Total Reviews:** 14

**User Satisfaction Scores:**

- **Quality of Support:** 9.8/10 (Category avg: 8.8/10)
- **Automation:** 9.0/10 (Category avg: 8.7/10)
- **Identification:** 9.3/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 9.0/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Bizprospex](https://www.g2.com/sellers/bizprospex)
- **Year Founded:** 2013
- **HQ Location:** Burhanpur, MP
- **Twitter:** @BizProspex (562 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/biz-prospex/ (159 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 87% Small-Business, 13% Mid-Market


  ### 23. [DQE One](https://www.g2.com/products/dqe-one/reviews)
  DQE One is a real-time data quality platform that validates, standardizes, deduplicates, and enriches customer data, including email addresses, phone numbers, and postal addresses. It helps businesses maintain accurate, complete, and unified customer data across CRM systems, marketing platforms, and operational tools. DQE One solves common data quality challenges such as: - Invalid emails and poor deliverability - Incorrect postal addresses and failed deliveries - Wrong phone numbers and unreachable contacts - Duplicate records and fragmented customer data - Inconsistent data formats across systems It ensures that customer data is clean and usable from the moment it enters your systems. Key capabilities include: - Real-time validation of email, phone, and address data - Data standardization and formatting across systems - Duplicate detection and record merging to create a single customer view - Data enrichment to complete and enhance customer information - Global address validation with country-specific rules - API-first architecture for real-time processing and easy integration DQE One detects duplicate customer records and merges them to create a unified customer view. This improves CRM reliability, reporting accuracy, and overall data consistency. Typical use cases include: - CRM data cleansing and deduplication - E-commerce checkout optimization - Lead capture and contact data validation - Customer data integration across multiple systems - Data governance and data quality initiatives DQE One integrates with platforms such as Salesforce, HubSpot, and other CRM, marketing automation, and e-commerce tools. It can be deployed via API or connectors to ensure data quality across all customer touchpoints. DQE One is designed for companies that want to improve data accuracy, reduce operational inefficiencies, eliminate duplicates, and deliver better customer experiences through reliable data.


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 33

**User Satisfaction Scores:**

- **Quality of Support:** 9.6/10 (Category avg: 8.8/10)
- **Automation:** 9.0/10 (Category avg: 8.7/10)
- **Identification:** 9.2/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 8.5/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [DQE](https://www.g2.com/sellers/dqe)
- **Company Website:** https://dqe.tech/
- **Year Founded:** 2008
- **HQ Location:** Levallois-Perret, Île-de-France
- **Twitter:** @dqe_software (174 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/dqe-software/ (76 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Marketing and Advertising, Computer Software
  - **Company Size:** 42% Small-Business, 33% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (4 reviews)
- CRM Integration (2 reviews)
- Data Accuracy (2 reviews)
- Data Quality (2 reviews)
- Intuitive (2 reviews)


  ### 24. [QuerySurge](https://www.g2.com/products/querysurge/reviews)
  QuerySurge is an enterprise-grade data quality platform that leverages AI to continuously automate data validation across your entire ecosystem ‐ from data warehouses and big data lakes to BI reports and enterprise applications. With AI-powered test creation, scalable architecture, and the leading DevOps for Data CI/CD integration, QuerySurge ensures data integrity at every stage of the pipeline. Automated Data Validation Use Cases: QuerySurge provides a smart, AI-driven, data validation & ETL testing solution for your automated testing needs. - Data Warehouse / ETL Testing - DevOps for Data / Continuous Testing - Data Migration Testing - Business Intelligence (BI) Report Testing - Big Data Testing - Enterprise Application Data Testing What QuerySurge Provides: - Automation of your manual data validation and testing process - Ease-of-use, low-code/no-code features - Generative AI capabilities for test creation - Testing across 200+ data platforms - Integration into your CI/CD DataOps pipeline - Acceleration of your data analysis - Ensurance of regulatory compliance Key Features: - Data Connection Wizard provides an easy way to link to your data stores - Visual Query Wizard builds table-to-table and column-to-column tests without writing SQL - Generative AI module automatically creates transformation tests in bulk - DevOps for Data provides a RESTful API with 110+ calls and Swagger documentation and integrates into CI/CD pipelines - Create Custom Tests and modularize functions with snippets, set thresholds, stage data, check data types & duplicate rows, full text search, and asset tagging - Schedule tests to run immediately, at a predetermined date & time, or after any event from a build/release, CI/CD, DevOps, or test management solution - Multi-project support in a single instance, new Global Admin user, assign users and agents, import and export projects, and user activity log reports - Webhooks provide real-time integrations with DevOps, CI/CD, test management, and alerting tools - Ready-for-Analytics provides seamless integration with QuerySurge and your BI tool or open-source Metabase to create custom reports and dashboards and gain deeper, real-time insights into your data validation and ETL testing workflows - Data Analytics Dashboards and Data Intelligence Reports track, analyze, and communicate data quality


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 24

**User Satisfaction Scores:**

- **Quality of Support:** 8.9/10 (Category avg: 8.8/10)
- **Automation:** 9.4/10 (Category avg: 8.7/10)
- **Identification:** 9.1/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 10.0/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [QuerySurge](https://www.g2.com/sellers/querysurge)
- **Company Website:** https://www.querysurge.com
- **Year Founded:** 2012
- **HQ Location:** New York, US
- **Twitter:** @QuerySurge (6,360 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/querysurge/ (7 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 37% Enterprise, 26% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (17 reviews)
- Features (12 reviews)
- Automation (8 reviews)
- Capabilities (8 reviews)
- Easy Setup (8 reviews)

**Cons:**

- Limited Functionality (5 reviews)
- Missing Features (5 reviews)
- Inaccuracy Issues (4 reviews)
- Slow Performance (4 reviews)
- Complex Setup (3 reviews)

  ### 25. [Introhive](https://www.g2.com/products/introhive/reviews)
  Introhive is a leading Relationship Intelligence platform that empowers firms to break down data silos and gain actionable insights from their relationships to fuel collaboration and growth. With Introhive’s Relationship Intelligence, firms can identify key relationships within the firm, measure the strength of client and prospect relationships, foster cross-firm collaboration, uncover risks or opportunities by understanding the health of relationships over time, and leverage these insights for business development and client retention efforts. Trusted by industry-leading brands, Introhive’s supports over 250,000 users in 90+ countries.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 85

**User Satisfaction Scores:**

- **Quality of Support:** 9.1/10 (Category avg: 8.8/10)
- **Automation:** 8.6/10 (Category avg: 8.7/10)
- **Identification:** 8.2/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 8.4/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Introhive](https://www.g2.com/sellers/introhive)
- **Year Founded:** 2012
- **HQ Location:** Fredericton
- **Twitter:** @Introhive (9,836 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2636221/ (217 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Legal Services, Accounting
  - **Company Size:** 50% Mid-Market, 34% Enterprise


#### Pros & Cons

**Pros:**

- Customer Support (3 reviews)
- Analytics (1 reviews)
- Ease of Use (1 reviews)
- Easy Integrations (1 reviews)
- Efficiency (1 reviews)

**Cons:**

- Confusion (1 reviews)
- Difficult Learning Curve (1 reviews)
- Expensive (1 reviews)
- Learning Curve (1 reviews)



## Parent Category

[IT Infrastructure Software](https://www.g2.com/categories/it-infrastructure)



## Related Categories

- [Data Governance Tools](https://www.g2.com/categories/data-governance-tools)
- [DataOps Platforms](https://www.g2.com/categories/dataops-platforms)
- [Data Observability Software](https://www.g2.com/categories/data-observability)



---

## Buyer Guide

### What You Should Know About Data Quality Tools

### What are Data Quality Tools?

Data quality software is a set of various tools and services created to derive meaningful data for organizations. The tools condition the data to meet the specific needs of the users. Data quality is an integral part of data governance and data management processes through which all the data of the organization is governed. Data quality tools make it possible to achieve accuracy, relevancy, and consistency of data to make better decisions.

High-quality data can deliver desired outputs, whereas poor-quality data can result in disastrous insights. Organizations that are data-driven and frequently use data analytics for decision-making make data quality a prime factor in deciding its usefulness.

### What are the Common Features of Data Quality Tools?

Features of data quality tools mainly consider the dimensions or the metrics that define quality. These solutions can support some or all of the functions as mentioned below to deliver useful end results:

**Data cleansing:** It is the process of removing redundant, incorrect, and corrupt data. It is sometimes referred to as data cleaning or data scrubbing. Being one of the critical stages in data processing, most data quality tools have this feature. A few of the common data inaccuracies include incorrect entries and missing values.

**Data standardization:** It is a major step in organizing data. It involves converting data into a common format which makes it easier for users to access and analyze the data. This stage fulfills one of the parameters of data quality—consistency. Bringing the data into a single common format makes sure that data is consistent. Data standardization plays a key role in achieving accuracy which is another factor in data quality. It helps by giving users access to the latest cleansed and updated data.

**Data profiling:** Data profiling is the process of analyzing data, understanding the structure of data, and identifying the potential projects for the specified data. Data is minutely analyzed using analytical tools to detect characteristics like mean, minimum, maximum, and frequency.

**Data deduplication:** It is a process to eliminate excessive copies of data and reduce storage requirements. It is also called intelligent compression or single-instance storage or data dedupe.

**Data validation:** This feature ensures that data quality and accuracy are in place. In automated systems, there is minimal or almost no human supervision when the data is entered. This makes it essential to check that the data entered is correct. Common types of data validation include data check, code check, range check, format check, and consistency check. There also are certain data quality rules defined for data management platforms.

**Extract, transform, and load (ETL):** When organizations advance in the technology strategy, data from existing systems are transferred to the new systems. ETL forms a vital task of the data migration process. The end goal is to maintain data quality for the data that is being migrated. ETL stands third in the phases of the data quality lifecycle. Other phases are quality assessment, quality design, and monitoring. It involves extracting data from the data sources, transforming it by deduplicating it, and loading it into the target database.

**Master data management (MDM):** This feature manages quality data by organizing, centralizing, and enriching data. It includes non-transactional data like customer data and product data. MDM is important for enterprise data management.

**Data enrichment:** This feature is the process of enhancing the value and accuracy of data by integrating internal and external data with the existing information.

**Data catalog:** Data catalog hosts data and metadata to help users with their data discovery. Data quality monitoring tools have this feature to increase transparency in workflows.

**Data warehousing:** Data warehousing focuses on unifying data from various data sources. It ensures enterprise data quality by improving the accuracy of data.

**Data parsing:** Data usually is conformed to specific formats. For example address, telephone number, and email address all have data patterns. Parsing helps with such address verifications and also if the telephone numbers are conforming to the patterns. 

Other features of data quality software: [ERP Capabilities](https://www.g2.com/categories/data-quality/f/erp) and [File Capabilities](https://www.g2.com/categories/data-quality/f/file).

### What are the Benefits of Data Quality Tools?

Data is one of the most valuable resources for organizations today. Having high-quality data has the following advantages:

**Effective data implementation:** Good quality data improves the performance of teams and results in better business. It keeps all the departments of the organization on the same page and helps them work efficiently.

[**Improved customer relationships**](https://www.g2.com/categories/data-quality/f/crm) **:** Data quality plays a major role in retaining customers. It helps organizations track customer preferences and interests.

**Insightful decision-making:** The decision-makers always need up-to-date information to make better decisions. Data quality tools ensure business intelligence is attained through high-quality data. Good data quality helps in reducing the risk of bad decisions based on poor-quality data and increasing the efficiency of the decision-making process.

**Effective customer targeting:** With high-quality data at their fingertips, organizations can track the characteristics of their existing customers and create personas depending on what their customers prefer. This can further lead to forecasting the needs of the target market.

**Efficient product development:** Engineering teams in software development companies can audit their KPIs like engagement with the new product online. Auditing data points like button clicks can help engineers understand how ready their product is to be launched in the market or if there are any changes needed. 

**Data matching:** Effective data quality monitoring tools help in data matching. Data matching is the process of comparing two different data sets and matching them against each other. This process helps in identifying duplicate data within a [database](https://www.g2.com/categories/data-quality/f/database).

### Who Uses Data Quality Tools?

Data being the new fuel is driving organizations to figure out how it can be used to make business decisions. Below is a list of departments that utilize data quality management software :

**Data quality analysts:** They monitor the quality of data using data quality tools that help companies make informed decisions. They work with database developers to modify database designs as per the need. This persona primarily helps with data analysis, further improving the quality.

**Marketing teams:** Marketing managers must have high-quality data at use because good quality data helps drive efficient marketing campaigns in the future. Data quality tools help the teams filter unnecessary information and focus on the target market to gain a better understanding.

**IT teams:** Several times there are duplicate records which makes it difficult for IT teams to have data quality control in place. With the use of software, it is easier to govern the data and optimize data quality management.

### Challenges with Data Quality Tools 

Data quality changes with what is fed into the system. Sometimes there are a few of the below-mentioned difficulties faced while using data quality tools:

**Duplicated data:** Data deduplication tools are a must before passing over the data to the next steps. Since large amounts of data are generated through various disparate sources, it is often flawed, or some entries are duplicated. However, deduplication tools can identify the same data points and assign them for deduplication. 

**Lack of complete information:** Manual entries can cause incomplete information or not having information for every dataset. This could cause data quality tools to underperform.

**Heterogenous formats:** Inconsistent data formats are always a common pain point for data analysts. While working with data outsourcing services providers, it is recommended to specify preferred formats.

### How to Buy Data Quality Tools?

#### Requirements Gathering (RFI/RFP) for Data Quality Software

Depending upon the industry, there are a variety of data quality dimensions that must be kept in mind before the purchase of the software. Data management strategy is expected to address data governance requirements. Along with it, there are other requirements like data retention and archiving. An RFI or RFP from vendors helps to optimize the evaluation process. 

#### Compare Data Quality Products

**Create a long list**

To begin with, organizations should make a list of data quality software vendors providing features like data profiling, data preparation, deduplication, and other relevant features depending on the results they are looking to achieve.

**Create a short list**

On the basis of the fulfillment of primary requirements, the next step covers shortlisting the vendors by asking a few questions like:

- Do they provide automation in their software?
- How do the products/tools maintain performance and scale?
- What are their support timings and escalation procedures?

**Conduct demos**

Demos are an efficient way of verifying which vendor fits the bill. It gives the organization an in-depth understanding of the software. Organizations can also get answers to how well-stacked the vendor is. Usually, demos for data quality software would include the presentation of various tools and capabilities of the software such as data standardization feature, metadata management, and data quality management to name a few.

#### Selection of Data Quality Tools

**Choose a selection team**

The team involved in making this decision must include relevant decision makers. A chief marketing officer, who often needs clean data to nurture leads from their team, can test the tools during the demo. The next member to be kept in the loop is the sales lead. Data quality is equally important for the sales workforce as they want to focus more on revenue generation than just updating the data in the CRM. Data analysts are also involved since they are the ones who use these tools for data quality assessments. Along with it, data quality analysts are included in the team because they use the software to examine the data for quality requirements depending on different departments and share this processed data with them.

**Negotiation**

Because data quality is of utmost importance, it is advisable to choose the right tools for assessment. Tools that work in real time and that can be used easily by business users are something organizations want to have. It is advisable to look at the pricing of the software, if there are any additional costs, and also if the vendor offers any discount. Many data quality tools are available in both cloud and on-premises structures. It is better to have tools in the cloud as manual data quality monitoring for enterprise data could be difficult for one person or even a team.

**Final decision**

The decision to buy data quality software has to be taken by the teams involved throughout the buying process. Sales, marketing, and data analyst teams can benefit from buying the right data quality software.

### Data Quality Trends

**Data warehouse modernization**

Data warehouse modernization helps the current data warehouse environment work in synchronization with rapidly changing requirements. Organizations are coping with managing the expansion of data and data systems by modernizing the data warehouse. This emerging trend focuses on data automation to achieve the desired quality of data and business practices alike.

**Modern data hubs**

Data hubs are data storage architectures with a seamless flow of data that follow the hub and spoke model. Modern data hubs have features like data storage, harmonization, governance, metadata, and indexing. These features indicate that data hubs are more efficient than data consolidation.

**Data democratization**

Recently, organizations are making data available to independent business functions. This is to improvise transparency and consistency amongst all the departments in the organization. Advancements in visualizations have made data visibility easier at a technical level and as the trend progresses, it is expected to have the same effect on non-technical users, i.e., ease of access to data.

**Machine learning (ML) algorithms in data quality** 

Machine learning (ML) algorithms have become important for a company's data management strategy. Enterprise data is usually big data which makes it essential to have automation. Machine learning algorithms can make it possible to automate the process giving end results. ML algorithms help in improving data quality scores by identifying wrong data, incomplete data, duplicate data, and also help in performing functions like clustering, detecting anomalies, and association rule mining.