# Best Enterprise Data Quality Tools

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

   Products classified in the overall Data Quality category are similar in many regards and help companies of all sizes solve their business problems. However, enterprise business features, pricing, setup, and installation differ from businesses of other sizes, which is why we match buyers to the right Enterprise Business Data Quality to fit their needs. Compare product ratings based on reviews from enterprise users or connect with one of G2's buying advisors to find the right solutions within the Enterprise Business Data Quality category.

In addition to qualifying for inclusion in the Data Quality Tools category, to qualify for inclusion in the Enterprise Business Data Quality Tools category, a product must have at least 10 reviews left by a reviewer from an enterprise business.





## Category Overview

**Total Products under this Category:** 242


## Trust & Credibility Stats

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 9,000+ Authentic Reviews
- 242+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.


## Top-Rated Products (Ranked by G2 Score)
  ### 1. [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:** 498

**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, 43% 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)

  ### 2. [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:** 707

**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 (60,996 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% Small-Business, 32% Enterprise


#### 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)

  ### 3. [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 (22,552 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)

  ### 4. [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 (5,735 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)

  ### 5. [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 (1,153 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)

  ### 6. [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 (9,720 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)

  ### 7. [Acceldata](https://www.g2.com/products/acceldata/reviews)
  Acceldata is a pioneering provider of enterprise solutions in data observability and Agentic Data Management. Its technology enables organizations to monitor, manage, and improve the reliability, quality, and performance of data systems across cloud, hybrid, and on-prem environments. Building on its foundation in data observability, Acceldata developed an Agentic Data Management platform that applies AI agents to autonomously detect, analyze, and resolve issues across the data lifecycle. This approach brings together observability, governance, and optimization into a unified system, allowing data environments to self-monitor, self-heal, and adapt over time. By moving from manual, reactive operations to more intelligent, automated processes, Acceldata supports scalable, efficient, and context-aware data management across the enterprise. Core Features of Acceldata’s Agentic Data Management Platform 1. Autonomous AI Agents: Acceldata deploys over 10 specialized AI agents designed to manage core data functions such as data quality, lineage, profiling, governance, pipeline health, and cost optimization. These agents continuously scan systems, detect issues, reason about their cause, and either take direct action or escalate with human oversight. They collaborate to improve data reliability, reduce downtime, and drive informed decision-making. 2. xLake Reasoning Engine: At the core of the platform is the xLake Reasoning Engine—a high-scale, AI-aware engine built to handle exabytes of data. It executes across hybrid and multi-cloud environments, translating business rules into intelligent data actions. xLake enables context-aware processing and powers the agents’ ability to reason across telemetry, metadata, and historical trends. 3. Contextual Memory and Learning: Agents don’t operate in isolation. They remember past patterns, recall prior actions, and improve over time using contextual memory. This learning ability allows agents to adapt policies, refine thresholds, and prevent repeat incidents, making pipelines and systems progressively smarter and more resilient. 4. Natural Language Interface – The Business Notebook: Acceldata features a conversational interface called the Business Notebook. This AI-powered workspace allows business users and technical teams to interact with data in natural language. It explains agent actions, visualizes lineage, and empowers non-technical users to ask questions, make decisions, and access insights without needing SQL or scripting knowledge. 5. Real-Time Data Observability and Self-Healing: The platform goes beyond traditional monitoring by offering agentic observability. It autonomously scans data systems for anomalies, schema drift, freshness decay, and operational failures. Once detected, agents not only alert but also remediate issues in real time—ensuring continuous data reliability and pipeline health. 6. Policy-Driven Governance and Compliance: Acceldata embeds governance into the fabric of your data workflows. With policy agents, organizations can define and enforce access controls, data protection rules, audit logging, and compliance policies like GDPR, HIPAA, and BCBS 239—all without manual configuration. These policies evolve automatically using machine learning and agent feedback loops. 7. Unified Data Discovery and Classification: The Discovery engine continuously scans across cloud platforms, data lakes, and warehouses to classify, tag, and map data assets. It auto-generates lineage maps, enriches assets with context (e.g., usage, sensitivity), and supports plain-language search. This eliminates the need for separate data catalogs and makes every dataset AI-ready. 8. Agent Studio for Custom Agent Creation: With Agent Studio, organizations can build and deploy their own AI agents tailored to their business needs. Whether it’s a vertical-specific data rule, a proprietary policy, or a unique remediation workflow, Agent Studio offers the flexibility to extend the platform’s capabilities and orchestrate multi-agent workflows.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 53

**User Satisfaction Scores:**

- **Quality of Support:** 8.9/10 (Category avg: 8.8/10)
- **Automation:** 8.8/10 (Category avg: 8.7/10)
- **Identification:** 9.0/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 7.3/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Acceldata](https://www.g2.com/sellers/acceldata)
- **Company Website:** https://www.acceldata.io/
- **Year Founded:** 2018
- **HQ Location:** Campbell, CA
- **Twitter:** @acceldataio (340 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/acceldata (271 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 61% Enterprise, 22% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (18 reviews)
- Customer Support (15 reviews)
- Efficiency Improvement (13 reviews)
- Features (13 reviews)
- Monitoring (13 reviews)

**Cons:**

- UX Improvement (9 reviews)
- Complex Setup (6 reviews)
- Difficult Setup (6 reviews)
- Learning Curve (6 reviews)
- Learning Difficulty (6 reviews)

  ### 8. [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 (99,880 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


  ### 9. [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 (827,310 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


  ### 10. [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 (21,385 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:** 48% 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)

  ### 11. [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:** 199

**User Satisfaction Scores:**

- **Quality of Support:** 8.8/10 (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 (5,735 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/fivetran/ (1,738 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Analytics Engineer, Data Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 57% Mid-Market, 28% 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)

  ### 12. [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 (23,496 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)

  ### 13. [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 (13,105,844 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


  ### 14. [Alteryx Designer Cloud](https://www.g2.com/products/alteryx-alteryx-designer-cloud/reviews)
  Designer Cloud powered by Trifacta is part of the Alteryx Analytics Cloud platform. Designer Cloud democratizes data analytics across the organization with an open and interactive cloud platform for anyone who works with data to collaboratively profile, prepare, and pipeline data for analytics and machine learning. Organizations can connect to any data source, across all major cloud data platforms, and integrate Alteryx Analytics Cloud seamlessly into the existing data stack. Designer Cloud provides an interactive, visual user experience with AI/ML-based suggestions to guide users through the exploration and transformation of any dataset.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 151

**User Satisfaction Scores:**

- **Quality of Support:** 8.6/10 (Category avg: 8.8/10)
- **Automation:** 9.2/10 (Category avg: 8.7/10)
- **Identification:** 8.8/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 8.5/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Alteryx](https://www.g2.com/sellers/alteryx)
- **Year Founded:** 1997
- **HQ Location:** Irvine, CA
- **Twitter:** @alteryx (26,220 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/903031/ (2,268 employees on LinkedIn®)
- **Ownership:** Private

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Hospital & Health Care
  - **Company Size:** 35% Enterprise, 35% Small-Business


  ### 15. [Openprise](https://www.g2.com/products/openprise/reviews)
  Openprise is fueling the revolution in RevOps. Openprise automates critical RevOps processes to break down silos and align sales and marketing professionals and their technologies to deliver explosive growth. Openprise is a single, no-code platform that lets you simplify your RevTech stack, respond faster to changes in your market, and scale up operations to achieve your revenue goals. RevOps teams at industry leaders like UI Path, Freshworks, Zendesk, Zscaler, and Okta depend on Openprise to drive efficient, predictable revenue. For more information, please visit www.openprisetech.com.


  **Average Rating:** 4.9/5.0
  **Total Reviews:** 63

**User Satisfaction Scores:**

- **Quality of Support:** 9.9/10 (Category avg: 8.8/10)
- **Automation:** 9.7/10 (Category avg: 8.7/10)
- **Identification:** 9.3/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 9.8/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Openprise](https://www.g2.com/sellers/openprise)
- **Year Founded:** 2014
- **HQ Location:** San Mateo, CA
- **Twitter:** @OpenpriseTech (3,587 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/3526047/ (127 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 59% Enterprise, 34% Mid-Market


#### Pros & Cons

**Pros:**

- Booking Efficiency (1 reviews)
- Data Management (1 reviews)
- Data Quality (1 reviews)
- Flexibility (1 reviews)
- Lead Generation (1 reviews)

**Cons:**

- Poor Navigation (1 reviews)

  ### 16. [Anomalo](https://www.g2.com/products/anomalo/reviews)
  Anomalo is the AI-powered data quality platform that automatically detects, alerts, and helps resolve data issues before they impact analytics, reporting, or AI models. Built for modern enterprises, Anomalo monitors structured, semi-structured, and unstructured data without requiring teams to write rules or maintain fragile checks. Using unsupervised machine learning, advanced time-series models, and LLM-powered evaluators, Anomalo learns the normal behavior of your data and identifies anomalies such as drift, missing records, schema changes, PII exposure, contradictions, bias, and more. This deep data understanding approach goes far beyond metadata level observability. Anomalo fits into any data stack with native integrations for Snowflake, Databricks, BigQuery, Redshift, Atlan, Alation, Airflow, dbt, Jira, ServiceNow, Slack, and Microsoft Teams. It can be deployed as SaaS, hybrid, in-VPC, or as a Snowflake Native App and meets the strictest enterprise security and compliance requirements. With rich no-code tooling and AIDA— Anomalo’s Intelligent Data Analyst, anyone from data engineers to analysts and stewards can investigate issues, visualize trends, ask questions in natural language, and convert insights into new automated checks. Enterprises use Anomalo to ensure trusted data for dashboards, regulatory reporting, customer analytics, operational intelligence, and AI/ML pipelines. Fortune 500 companies choose Anomalo because it delivers unmatched data intelligence without having to make compromises on scale, security, or simplicity.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 41

**User Satisfaction Scores:**

- **Quality of Support:** 9.0/10 (Category avg: 8.8/10)
- **Automation:** 7.8/10 (Category avg: 8.7/10)
- **Identification:** 8.7/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 6.9/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Anomalo](https://www.g2.com/sellers/anomalo)
- **Company Website:** https://www.anomalo.com/
- **Year Founded:** 2018
- **HQ Location:** N/A
- **Twitter:** @anomalo_hq (551 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/anomalo (99 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Financial Services, Computer Software
  - **Company Size:** 49% Enterprise, 49% Mid-Market


  ### 17. [IBM InfoSphere Information Server](https://www.g2.com/products/ibm-infosphere-information-server/reviews)
  Better understand your data and cleanse, monitor, transform and deliver it. Build confidence in your data Delivers clean, consistent and timely information for your data warehouses or big data projects and applications. Create a flexible governance strategy Helps you adapt a data governance strategy to suit your organizational objectives, while shaping business information in unique ways to meet your needs. Modernize and consolidate your systems Enables you to consolidate applications, retire outdated databases and modernize your infrastructure, as well as automate business processes for improved cost savings. Connect business and IT Provides a unified platform that enables collaboration, which can help you bridge the gap between business and IT and align objectives.


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 22

**User Satisfaction Scores:**

- **Quality of Support:** 7.1/10 (Category avg: 8.8/10)
- **Automation:** 6.7/10 (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:** [IBM](https://www.g2.com/sellers/ibm)
- **Year Founded:** 1911
- **HQ Location:** Armonk, NY
- **Twitter:** @IBM (709,023 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®)
- **Ownership:** SWX:IBM

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Financial Services
  - **Company Size:** 96% Enterprise, 26% Mid-Market


  ### 18. [Plauti](https://www.g2.com/products/plauti/reviews)
  Plauti keeps your CRM data accurate, complete, and ready for business. Verify, deduplicate, manipulate, and assign records automatically so your teams can trust their data and act fast. Because when data is right, actions are right. And when actions are right, trust follows. - Verify: validate and format addresses, emails and phone numbers - Plauti Agentforce: power agents with data management actions - Deduplicate: find, prevent and merge duplicate records - Assign: route and assign any record instantly - Manipulate: handle data in single-action execution - Restore: Restore record changes across your data within Salesforce Whether you're improving customer experience, achieving AI readiness, improving data governance or driving operational efficiency, the solutions work together to turn scattered data into a trusted resource that fuels confident decision-making and business growth. \> 100% Native to Salesforce - No external processing, full data control. \> Enterprise security -Salesforce compliance, no third-party risks. \> No-Code customization - Adapt workflows easily, without IT reliance. \> Scalable & efficient - Automate processes and manage data at scale.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 114

**User Satisfaction Scores:**

- **Quality of Support:** 9.3/10 (Category avg: 8.8/10)
- **Automation:** 8.2/10 (Category avg: 8.7/10)
- **Identification:** 8.7/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 8.6/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Plauti](https://www.g2.com/sellers/plauti)
- **Company Website:** https://plauti.com/
- **Year Founded:** 2011
- **HQ Location:** Arnhem, Netherlands
- **LinkedIn® Page:** https://www.linkedin.com/company/plauti-b-v-/ (58 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Non-Profit Organization Management, Computer Software
  - **Company Size:** 55% Mid-Market, 35% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (30 reviews)
- Duplicate Management (29 reviews)
- Customer Support (19 reviews)
- Merging Leads (16 reviews)
- Customization (10 reviews)

**Cons:**

- Learning Curve (8 reviews)
- Complexity (7 reviews)
- Data Management Issues (6 reviews)
- Limitations (6 reviews)
- Limited Functionality (6 reviews)

  ### 19. [SAP Data Management](https://www.g2.com/products/sap-data-management/reviews)
  You cannot afford to run your business on questionable data. With SAP® Data Services software, you can access, transform, and connect data to fuel your critical business processes. Together, these enterprise-class solutions enable data integration and data quality, providing the right level of insight across your business so you can make better decisions and operate more effectively.


  **Average Rating:** 4.0/5.0
  **Total Reviews:** 31

**User Satisfaction Scores:**

- **Quality of Support:** 7.4/10 (Category avg: 8.8/10)
- **Automation:** 9.4/10 (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:** [SAP](https://www.g2.com/sellers/sap)
- **Year Founded:** 1972
- **HQ Location:** Walldorf
- **Twitter:** @SAP (297,227 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/sap/ (141,341 employees on LinkedIn®)
- **Ownership:** NYSE:SAP

**Reviewer Demographics:**
  - **Company Size:** 69% Enterprise, 22% Mid-Market


  ### 20. [Telmai](https://www.g2.com/products/telmai/reviews)
  Telmai is an AI-powered data observability platform that continuously monitors data across every stage of the pipeline—from ingestion to business applications. Designed for structured and semi-structured data, Telmai automatically detects anomalies, drifts, and data quality issues in real-time without sampling, ensuring reliable data for business intelligence, analytics, and AI workloads. Telmai's open architecture enables seamless data quality monitoring across the entire pipeline, integrating with over 250 systems, including data lakes, warehouses, streaming sources, and cloud storage. This provides deep insights into the health, accuracy, and consistency of data in complex environments. Telmai’s low-code interface empowers both business and technical teams to define custom metrics, automate remediation workflows, and ensure data is always actionable and reliable.


  **Average Rating:** 4.9/5.0
  **Total Reviews:** 22

**User Satisfaction Scores:**

- **Quality of Support:** 9.2/10 (Category avg: 8.8/10)
- **Automation:** 8.8/10 (Category avg: 8.7/10)
- **Identification:** 9.1/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 7.7/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Telmai](https://www.g2.com/sellers/telmai)
- **Year Founded:** 2020
- **HQ Location:** San Mateo , Ca
- **Twitter:** @telmai1 (98 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/telmai/ (15 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 55% Enterprise, 32% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (7 reviews)
- Automation (5 reviews)
- Anomaly Detection (4 reviews)
- Data Quality (4 reviews)
- Easy Integrations (4 reviews)

**Cons:**

- Error Handling (2 reviews)
- Learning Difficulty (2 reviews)
- Limited Functionality (2 reviews)
- UX Improvement (2 reviews)
- Alert Management (1 reviews)

  ### 21. [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 (9,860 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)

  ### 22. [WinPure Clean & Match](https://www.g2.com/products/winpure-clean-match/reviews)
  Clean & Match Enterprise is the top-rated desktop platform for data quality management – powered with AI data matching for easy & efficient data cleaning, deduplication, & transformation.This software suite is ideal for cleaning, correcting and deduplicating mailing lists, databases, spreadsheets and CRMs. Experience the world’s most advanced AI data-matching solution that enables organizations to resolve complex records within minutes. \* Detect potential duplicates based on common-sense principles \* Determine possible relationships between datasets \* Intelligently identify duplicates based on learned principles \* Perform data matching at scale in just three step No code data cleansing & standardization. Teams spend 80% of their time in manual data cleaning and standardization. WinPure’s no-code software reduces the time spent with its intuitive interface. Users can: \* Clean columns with built-in pre-set standardization options \* Split components for targeted cleaning \* Create custom abbreviation & name dictionaries\* \* Parse address data to clean & standardize at a component level Catch non-exact matches with advanced fuzzy logic. Duplicate customer records with varying information need a powerful fuzzy match algorithm like WinPure to identify exact and possible matches. Users can: \* Cross-match between and across files \* Easily merge and purge or overwrite duplicates \* Create master records of consolidated records \* Export directly into your CRM or data source WinPure™ Clean & Match will help save your business time and money. \* Increase the accuracy of virtually ANY list, spreadsheet, database, CRM, etc. \* Locally installed Windows software so no need to worry about security as all processing is done on your own systems \* Save hours of valuable time cleaning and removing duplicated records from your lists or databases using built-in sophisticated fuzzy and phonetic match algorithms. \* Affordable licences available with World Class Support & Training. \* Free Demo with Live Online Training available. Seeing is believing! Schedule a live demo today with one of our product specialist at your convenience. We can learn about your requirements, answer questions, and review ways WinPure can help you and your organization.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 73

**User Satisfaction Scores:**

- **Quality of Support:** 9.4/10 (Category avg: 8.8/10)
- **Automation:** 9.4/10 (Category avg: 8.7/10)
- **Identification:** 9.4/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 9.8/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [WinPure](https://www.g2.com/sellers/winpure)
- **Year Founded:** 2004
- **HQ Location:** Theale, Berkshire
- **Twitter:** @WinPure (2,221 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2149782/ (9 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Marketing and Advertising, Health, Wellness and Fitness
  - **Company Size:** 43% Small-Business, 35% Mid-Market


#### Pros & Cons

**Pros:**

- Customer Support (1 reviews)
- Data Quality (1 reviews)
- Duplicate Management (1 reviews)
- Ease of Use (1 reviews)
- Easy Access (1 reviews)


  ### 23. [SAS Data Management](https://www.g2.com/products/sas-data-management/reviews)
  SAS Data Management is a comprehensive solution designed to transform raw data into a valuable business asset by improving, integrating, managing, and governing data across an organization. It enables users to access data from various sources, create rules, collaborate with teams, and manage metadata, thereby preparing data for analytics and informed decision-making. Key Features and Functionality: - Data Access and Integration: Seamlessly access data from diverse sources, including legacy systems and modern platforms like Hadoop, ensuring comprehensive data integration. - Data Quality and Cleansing: Utilize embedded tools to automatically identify and rectify data quality issues, reducing errors and inconsistencies. - Data Preparation: Prepare data for analytics and reporting in a self-service environment without the need for coding or IT assistance, enhancing productivity. - Data Governance: Implement consistent policies and processes to ensure data conforms to established standards and regulatory requirements. - Personal Data Protection: Identify and monitor personal data sources to comply with privacy regulations such as GDPR. - Data Federation and Stewardship: Simplify data integration complexities with a virtual data environment that delivers a complete data picture in a user-friendly format. Primary Value and Solutions Provided: SAS Data Management addresses the critical need for organizations to manage their data effectively, turning it into a strategic asset. By providing a unified platform for data access, integration, quality, governance, and master data management, it eliminates the need for multiple, overlapping tools. This consolidation leads to improved data accuracy, streamlined operations, and enhanced decision-making capabilities. Organizations can ensure that all internal and third-party data remains clean and well-managed, facilitating compliance with regulatory standards and enabling more efficient and effective business processes.


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 96

**User Satisfaction Scores:**

- **Quality of Support:** 7.9/10 (Category avg: 8.8/10)
- **Automation:** 8.2/10 (Category avg: 8.7/10)
- **Identification:** 8.8/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 8.3/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2)
- **Year Founded:** 1976
- **HQ Location:** Cary, NC
- **Twitter:** @SASsoftware (60,996 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1491/ (18,238 employees on LinkedIn®)
- **Phone:** 1-800-727-0025

**Reviewer Demographics:**
  - **Top Industries:** Higher Education, Research
  - **Company Size:** 52% Enterprise, 26% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (12 reviews)
- Analytics (5 reviews)
- Data Cleaning (4 reviews)
- Data Quality (4 reviews)
- Data Management (3 reviews)

**Cons:**

- Expensive (7 reviews)
- Not User-Friendly (3 reviews)
- Slow Performance (3 reviews)
- Training Required (3 reviews)
- Complexity (2 reviews)

  ### 24. [Soda](https://www.g2.com/products/soda/reviews)
  Most companies struggle to operationalize data governance and quality. Business teams don’t want to manually enforce rules, and engineers get buried in pipeline issues — eroding trust in data and slowing innovation. Soda fixes this with the only end-to-end data quality platform that automates the entire workflow — from detection to resolution — with AI built for data quality. We meet users where they are: - Engineers manage everything as code in Git. - Business users create and review data contracts in a collaborative interface. - Together, they work in a shared, AI-powered workflow to define quality expectations, monitor metrics, and isolate and remediate bad data directly in their environment. By uniting teams, automating with AI, and securing trust at the source, Soda helps organizations like Disney, Nubank, and HelloFresh restore confidence in their data and decisions. Why Soda? - Best AI for Data Quality — purpose-built, faster, and more accurate, with 70% fewer false positives than traditional monitoring. - Unite Business and Engineering — collaborative data contracts that bridge governance and technical workflows. - Securely Isolate and Fix Bad Data — record-level anomaly detection and remediation inside your own environment. Soda brings width and depth to data quality — from every dataset across multiple warehouses to every individual record in a dataset. Join us in building a world where teams trust their data, decisions, and AI.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 55

**User Satisfaction Scores:**

- **Quality of Support:** 8.7/10 (Category avg: 8.8/10)
- **Automation:** 8.6/10 (Category avg: 8.7/10)
- **Identification:** 8.7/10 (Category avg: 8.9/10)
- **Preventative Cleaning:** 7.7/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Soda](https://www.g2.com/sellers/soda)
- **Year Founded:** 2018
- **HQ Location:** Brussels, BE
- **Twitter:** @sodadata (898 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/sodadata/ (125 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 44% Mid-Market, 40% Enterprise


#### Pros & Cons

**Pros:**

- Data Quality (2 reviews)
- Customer Support (1 reviews)
- Customization (1 reviews)
- Data Management (1 reviews)
- Ease of Use (1 reviews)

**Cons:**

- Limited Functionality (2 reviews)
- Access Control (1 reviews)
- Access Issues (1 reviews)
- Data Management Issues (1 reviews)
- Limited Features (1 reviews)

  ### 25. [Talend Data Fabric](https://www.g2.com/products/talend-data-fabric/reviews)
  Talend Data Fabric is a unified platform that enables you to manage all your enterprise data within a single environment. Leverage all the cloud has to offer to manage your entire data lifecycle – from connecting the broadest set of data sources and platforms to intuitive self-service data access.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 62

**User Satisfaction Scores:**

- **Quality of Support:** 7.7/10 (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:** 8.3/10 (Category avg: 8.4/10)


**Seller Details:**

- **Seller:** [Qlik](https://www.g2.com/sellers/qlik)
- **Year Founded:** 1993
- **HQ Location:** Radnor, PA
- **Twitter:** @qlik (64,285 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10162/ (4,529 employees on LinkedIn®)
- **Phone:** 1 (888) 994-9854

**Reviewer Demographics:**
  - **Company Size:** 45% Mid-Market, 28% Enterprise


#### Pros & Cons

**Pros:**

- Data Management (3 reviews)
- Data Integration (2 reviews)
- Ease of Use (2 reviews)
- Flexibility (2 reviews)
- Performance (2 reviews)

**Cons:**

- Learning Curve (4 reviews)
- Expensive (3 reviews)
- UX Improvement (3 reviews)
- Poor Documentation (2 reviews)
- Slow Performance (2 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.