Matt leads the software research and advisory for the Analytics and Data expertise at ISG Software Research, covering software that improves the utilization of information across business and IT. His focus areas of coverage include analytics, data intelligence, data operations, data platforms, and streaming and events. Matt’s specialization is in the operational and analytical use of data and use of AI where enterprises can modernize their approaches to accelerate the value realization of technology investments in support of hybrid and multi-cloud architecture. Matt has been an industry analyst for more than a decade and has pioneered the coverage of emerging data platforms including NoSQL and NewSQL databases, data lakes, and cloud-based data processing. He is a graduate of Bournemouth University.
narration area
Executive Summary
Data Observability
Maintaining trust in data remains one of the most persistent challenges in enterprise data management. Even with decades of investment in data quality initiatives, many organizations still struggle to ensure that data used for analytics and operations is accurate, reliable and accessible when needed. As enterprises accelerate automation and adopt artificial intelligence, the importance of trusted, high-quality data has never been greater. Poor-quality or inconsistent data can slow decision-making, introduce risk and undermine confidence in analytics and AI outcomes. To operate at the speed of business, enterprises must monitor not only the movement of data through pipelines but also its ongoing quality, freshness and reliability.
ISG Research defines data observability as providing the capabilities for monitoring the quality and reliability of data used for analytics and governance projects as well as the reliability and health of the overall data environment. The category builds on long-established data quality practices while introducing new methods to monitor and maintain the integrity of data pipelines. Inspired by application and infrastructure observability, data observability provides continuous visibility into data metrics, dependencies and interactions to ensure that data remains available, consistent and accurate.
The ability to detect, resolve and prevent reliability issues across large and distributed data environments has made data observability an increasingly critical component of enterprise data strategy.
Data observability emerged in response to the growing complexity of enterprise data ecosystems. Traditional data quality tools help identify and remediate issues, but they do so reactively and often focus on data already in use. By contrast, data observability platforms instrument the data environment itself, continuously collecting metrics and metadata from data warehouses, data lakes and pipelines. This instrumentation provides insight into lineage (the relationships between data sets), metadata (the descriptive attributes of data, such as format, schema and age) and logs of human or machine interactions. These metrics create a comprehensive picture of data health, enabling the proactive detection of anomalies before they affect downstream systems or business decisions.
Some data observability software extends this functionality further, applying machine learning and statistical modeling to automate anomaly detection and root cause analysis. Alerts, explanations and recommendations are generated to help data engineers and architects address issues quickly or prevent them from recurring. The ability to detect, resolve and prevent reliability issues across large and distributed data environments has made data observability an increasingly critical component of enterprise data strategy. It provides the real-time assurance needed to support analytics, governance and operational processes.
The importance of trust in data has never been greater, particularly as enterprises scale artificial intelligence. Data quality has long been a priority for business intelligence, but the stakes have increased as data now feeds automated, real-time decision systems. These systems are responsible for critical functions such as fraud detection, customer engagement and operational efficiency. As AI initiatives expand to the boardroom level, executives demand reliable data to support efficiency, innovation and growth. Data usability for AI applications is cited by more than one-half (54%) of participants in ISG’s Data and AI Programs Study as one of their biggest data challenges for 2025/6. Without trusted data, AI models can make poor or even harmful decisions that reduce confidence and slow adoption.
Assessing and maintaining the reliability of data used in analytics and AI is increasingly difficult due to the scale, diversity and velocity of modern data sources. Poor data processes can create security and privacy risks, increase storage and processing costs and erode the integrity of analytics and machine learning. Data observability software mitigates these challenges by automating the monitoring of data freshness, schema, distribution, lineage and volume. It complements traditional data quality tools by focusing on the reliability and overall health of the data environment rather than just the suitability of data for a specific task.
Assessing and maintaining the reliability of data used in analytics and AI is increasingly difficult due to the scale, diversity and velocity of modern data sources.
While both categories are interrelated, their focus differs. Data quality software evaluates whether data is accurate, complete, consistent and valid for a particular purpose. Data observability software, by contrast, monitors the operational health of data pipelines to ensure that data remains available and accurate before issues propagate. A failed data pipeline might not immediately affect data quality but could result in outdated or missing data later. Data observability detects the problem early, reducing downtime and avoiding costly remediation. Conversely, data quality tools may detect incorrect data values that pass schema validation but do not meet business rules. In practice, the two categories are complementary, often coexisting within the same enterprise.
This overlap is reflected in the vendor landscape. Some providers now offer functionality that spans both data observability and data quality. Others that historically focused on data quality have adopted the term data observability but may lack the breadth of pipeline monitoring and anomaly detection capabilities expected of mature observability platforms. Enterprises evaluating these tools should carefully assess the scope and depth of each product’s functionality to ensure it aligns with business needs. The strongest offerings integrate automated error detection, root cause analysis and remediation workflows, giving data teams the visibility and agility required to manage increasingly complex data environments.
The rise of Data Operations, or DataOps, has further driven the adoption of observability practices. DataOps applies agile and DevOps methodologies to data engineering, promoting continuous delivery and automated monitoring of data in motion. Data observability plays a foundational role in this framework, enabling teams to maintain consistent, trusted data pipelines that support both operational and analytical processes. In addition to specialized data observability software vendors, many DataOps platforms are incorporating observability capabilities to create unified environments for data development, orchestration and monitoring. This trend reflects the growing recognition that observability is essential to 
achieving end-to-end data reliability.
As data complexity and dependency increase, enterprises are focusing on proactive monitoring, automation and transparency to improve trust in data. ISG asserts that through 2027, more than two-thirds of enterprises will invest in initiatives to improve trust in data through adoption of data observability tools to address the detection, resolution and prevention of data reliability issues. Organizations seeking to strengthen their data foundations should explore how observability can be integrated into broader people, process and technology improvements. When combined with strong data governance and quality management, data observability provides a real-time, automated framework for ensuring that data remains accurate, reliable and ready for use across analytics and AI applications.
The 2025 ISG Buyers Guide™ for Data Observability evaluates software providers and products in key areas, including the detection, resolution and prevention of data reliability issues. This research evaluates the following software providers: Acceldata, Actian, Anomalo, Astronomer, Ataccama, Bigeye, Collibra, Datadog, DataKitchen, DataOps.live, Hitachi Vantara, IBM, Informatica, Monte Carlo, Precisely, Qlik, RightData, Sifflet, Snowflake, Tencent Cloud and Y42.
Buyers Guide Overview
ISG Research has conducted market research for over two decades across vertical industries, business applications, AI and IT. We have designed the ISG Buyers Guide™ to provide a balanced perspective of software providers and products that is rooted in an understanding of business and IT requirements. Utilization of our research methodology and decades of experience enables our Buyers Guide to be an effective method to assess and select software providers and products. The findings of this research provide a comprehensive approach to rating software providers and rank their ability to meet specific product and customer experience requirements.
ISG Research has designed the Buyers Guide to provide a balanced perspective of software providers and products that is rooted in an understanding of business and IT requirements.
The 2025 ISG Buyers Guide™ for Data Observability is the distillation of continuous market and product research. It is an assessment of how well software providers’ offerings address enterprises’ requirements for data observability software. The Value Index methodology is structured to support a request for information (RFI) for a request for proposal (RFP) process by incorporating all criteria needed to evaluate, select, utilize and maintain relationships with software providers. The ISG Buyers Guide evaluates customer experience and the product experience in its capability and platform.
The structure of the research reflects our understanding that the effective evaluation of software providers and products involves far more than just examining product features, potential revenue or customers generated from a provider’s marketing and sales efforts. It can ensure the best long-term relationship and value achieved from a resource and financial investment We believe it is important to take a comprehensive, research-based approach, since making the wrong choice of data observability software can raise the total cost of ownership, lower the return on investment and hamper an enterprise’s ability to reach its potential. In addition, this approach can reduce the project’s development and deployment time and eliminate the risk of relying on opinions or historical biases.
ISG Research believes that an objective review of existing and potential new software providers and products is a critical strategy for the adoption and implementation of sales data observability software. An enterprise’s review should include a thorough analysis of both what is possible and what is relevant. We urge enterprises to do a thorough job of evaluating data observability software and offer this Buyers Guide as both the results of our in-depth analysis of these providers and as an evaluation methodology.
Key Takeaways
Data observability has become essential to ensuring data reliability, quality and trust in analytics and AI initiatives. As enterprises modernize their data ecosystems, observability tools play a critical role in detecting, diagnosing and preventing data issues that can impact operations and decision-making. The ISG Buyers Guide™ for Data Observability underscores the growing importance of automation, integration and scalability in maintaining consistent data health. These platforms are helping enterprises gain visibility across complex data pipelines and ensure that insights are accurate and actionable.
Software Provider Summary
The ISG Buyers Guide™ for Data Observability evaluates software providers offering products that support data quality monitoring, anomaly detection and data reliability management. The research assessed 20 providers, with Monte Carlo ranking highest overall, followed by Pentaho and Acceldata. Providers were evaluated on Product Experience and Customer Experience criteria and this report provides the results for each provider along with strengths and areas for improvement.
Product Experience Insights
Product Experience examines how effectively providers support the full lifecycle of configuration, monitoring and resolution across data environments. Leaders in this category, Monte Carlo, Pentaho and Acceldata, performed strongly in both Capability and Platform criteria. The Capability assessment reviewed 85 function points across eight sections, while the Platform evaluation focused on adaptability, manageability, reliability and usability. Leaders demonstrated resilient, scalable architectures and seamless integration across data and analytics ecosystems.
Customer Experience Value
Customer Experience evaluates provider commitment, customer success and value realization across all stages of engagement. Informatica, Monte Carlo and Collibra achieved the highest ratings for transparency, service quality and responsiveness to customer needs. Providers that did not perform as well often lacked accessible documentation or failed to clearly demonstrate customer outcomes. These shortcomings reflected weaker communication around product value and limited evidence of customer success initiatives.
Strategic Recommendations
Enterprises should adopt data observability platforms that offer automated anomaly detection, root cause analysis and integration across diverse data sources. Prioritizing providers with proven scalability, governance alignment and ecosystem interoperability will strengthen data reliability. Decision-makers should ensure observability investments support both operational monitoring and proactive data quality improvement. A strategic approach to observability can improve decision accuracy, reduce downtime and enhance data trust across the organization.
How To Use This Buyers Guide
Evaluating Software Providers: The Process
We recommend using the Buyers Guide to assess and evaluate new or existing software providers for your enterprise. The market research can be used as an evaluation framework to assess existing approaches and software providers or establish a formal request for information from providers on products and customer experience and will shorten the cycle time when creating an RFI. The steps listed below provide a process that can facilitate best possible outcomes in the most efficient manner.
- Define the business case and goals.
Define the mission and business case for investment and the expected outcomes from your organizational and technological efforts.
- Specify the business and IT needs.
Defining the business and IT requirements helps identify what specific capabilities are required with respect to people, processes, information and technology.
- Assess the required roles and responsibilities.
Identify the individuals required for success at every level of the enterprise from executives to frontline workers and determine the needs of each.
- Outline the project’s critical path.
What needs to be done, in what order and who will do it? This outline should make clear the prior dependencies at each step of the project plan.
- Ascertain the technology approach.
Determine the business and technology approach that most closely aligns to your enterprise’s requirements.
- Establish software provider evaluation criteria.
Utilize the product experience: capability and platform with support for adaptability, manageability, reliability and usability, and the customer experience in TCO/ROI and Validation.
- Evaluate and select the software provider and products properly.
Apply a weighting the evaluation categories in the evaluation criteria to reflect your enterprise’s priorities to determine the short list of software providers and products.
- Establish the business initiative team to start the project.
Identify who will lead the project and the members of the team needed to plan and execute it with timelines, priorities and resources.
Using the ISG Buyers Guide and process provides enterprises a clear, structured approach to making smarter software and business investment decisions. It ensures alignment between strategy, people, processes and technology while reducing risk, saving time and improving outcomes. The ISG approach promotes data-driven decision-making and collaboration, helping choose the right software providers for maximum value and return on investment.
The Findings
The software providers and products evaluated in the research provide product and customer experiences, but not everything offered is equally valuable to every enterprise or is needed to operate in business processes and use cases. Moreover, the existence of too many capabilities in products may be a negative factor for an enterprise if it introduces unnecessary complexity. Nonetheless, you may decide that a more comprehensive set of capabilities in the product is important, and where they match your enterprise’s requirements.
An effective customer relationship with a software provider is vital to the success of any investment. The overall customer experience and the full lifecycle of engagement play a key role in ensuring satisfaction and long-term success. Providers with dedicated customer leadership, such as chief customer officers, tend to invest more deeply in these relationships and prioritize customer outcomes to TCO and ROI expectations. It is equally important that this commitment to customer success is clearly demonstrated throughout the provider’s website, buying process and customer journey.
Overall Scoring of Software Providers Across Categories
The research finds Monte Carlo atop the list, followed by Pentaho and Acceldata. Providers that place in the top three of a category earn the designation of Leader. Monte Carlo has done so in five categories; Acceldata and Pentaho in three; Informatica in two and Collibra, Datadog and IBM in one category.
The overall representation of the research below places the rating of the Product Experience and Customer Experience on the x and y axes, respectively, to provide a visual representation and classification of the software providers. Those providers whose Product Experience have above median weighted performance to the axis in aggregate of the two product categories place farther to the right, while the performance and weighting for the Customer Experience category determines placement on the vertical axis. In short, software providers that place closer to the upper-right on this chart performed better than those closer to the lower-left.
The research categorizes and rates software providers into one of four categories: Assurance, Exemplary, Merit or Innovative. This representation of software providers’ weighted performance in meeting the requirements in product and customer experience.
Exemplary: This rating (upper right) represents those that performed above median in Product and Customer Experience requirements. The providers rated Exemplary are: Actian, Collibra, DataOps.live, IBM, Monte Carlo and Pentaho.
Innovative: This rating (lower right) represents those that performed above median in Product Experience but not in Customer Experience. The providers rated Innovative are: Acceldata, Anomalo, Bigeye and Datadog.
Assurance: This rating (upper left) represents those that performed above median in Customer Experience but not in Product Experience. The providers rated Assurance are: DataKitchen, Informatica, Precisely and Qlik.
Merit: This rating (lower left) represents those that did not surpass the median in Customer or Product Experience. The providers rated Merit are: Ataccama, RightData, Sifflet, Snowflake and Tencent Cloud.
We advise enterprises to use this research as a supplement to their own evaluations, recognizing that ratings or rankings do not solely represent the value of a provider nor indicate universal suitability of a set of products.
Product Experience
The process of researching products to address an enterprise’s needs should be comprehensive and evaluate specific capabilities and the underlying platform to the product experience. Our evaluation of the Product Experience examines the lifecycle of onboarding, configuration, operations, usage and maintenance. Too often, software providers are not evaluated for the entirety of the product; instead, they are evaluated on market execution and vision of the future.
The research results in Product Experience are ranked at 80%, or four-fifths, using the underlying weighted performance. Importance was placed on the categories as follows: Capability (40%) and Platform (40%). Monte Carlo, Pentaho and Acceldata were designated Product Experience Leaders.
Capability of the Product
The Capability criteria is designed to assess the products and features across a broad range of data observability capabilities that support the detection, resolution and prevention of data reliability and quality concerns, as well as agile and collaborative practices.
ISG Research evaluated more than 85 different function points in eight sections to assess the full scope of data observability capabilities. It also examined the investment by the software provider. The research weights Capability at 40% of the overall rating. Monte Carlo, Pentaho and Acceldata are the Leaders in this category.
The Capability evaluation framework for data observability provides a framework for enterprises. Software providers that have more breadth and depth and support the entire set of needs fared better.
Platform of the Product
The Platform category evaluates the underlying requirements of a platform and examines how well a software product meets enterprise needs across business and IT. It measures how effectively the product can be managed and configured and integrated into enterprise environments, how efficiently it can be governed and secured, how reliably it performs and scales, and how intuitively it supports users across varied roles and skill levels. The platform category in the ISG Buyers Guide examines specific requirements for adaptability, manageability, reliability and usability.
The grading of the underlying platform focuses on a software product’s overall robustness and the flexibility of a provider’s software foundation. Adaptability measures a product’s ability to be customized and integrated across systems and data, while manageability focuses on governance, security and compliance. Reliability considers performance and scalability across environments, and usability assesses how intuitive and accessible the product is through design, use of AI and ongoing provider investment.
ISG Research evaluated 16 function points in 5 sections to assess the full scope of platforms capabilities. 
The research weights Platform at 40% of the overall rating. Datadog, Informatica, Monte Carlo and IBM are the Leaders in this category.
Platform is an essential evaluation category as it indicates the strength and resilience of a software provider’s product architecture. A well-designed platform ensures secure and compliant operations, dependable scalability and uptime, and a unified, intuitive experience for range of usage personas. It also reflects the provider’s capacity to enable deployment models while maintaining flexibility for enterprise demands.
Software providers that performed best in the Platform category were those that have support for the breadth and depth of needs across business and IT supporting adaptability, manageability, reliability and usability. Providers with lower performance were challenged in one or more of these areas or did not demonstrate a cohesive, enterprise-grade approach. The underlying platform for a software provider’s products is essential in any evaluation.
Customer Experience
The importance of a customer relationship with a software provider is essential to the actual success of the products and technology. The evaluation of the Customer Experience and the entire lifecycle an enterprise has with its software provider is critical for ensuring satisfaction in working with that provider. The ISG Buyers Guide examines a software provider’s customer commitment, viability, customer success, sales and onboarding, product roadmap and services with partners and support. The customer experience category also investigates the TCO/ROI and how well a software provider demonstrates the product’s overall value, cost and benefits, including the tools and resources to evaluate these factors.
The research results in Customer Experience are ranked at 20%, or one-fifth of the 100% index, and represent the underlying provider validation and TCO/ROI requirements as they relate to the framework of commitment and value to the software provider-customer relationship.
The software providers that evaluated the highest in the Customer Experience category are Informatica, Monte Carlo and Collibra. These category leaders best communicate commitment and dedication to customer needs.
Software providers that did not perform well in this category were unable to provide or make sufficient information readily available to demonstrate success or articulate their commitment to customer experience. The use of a software provider requires continuous investment, so a holistic evaluation must include examination of how they support their customer experience.
Appendix: Software Provider Inclusion
For inclusion in the 2025 ISG Buyers Guide™ for Data Observability, a software provider must be in good standing financially and ethically, have at least $10 million in annual or projected revenue verified using independent sources, sell products and provide support on at least two continents, and have at least 50 employees. The principal source of the relevant business unit’s revenue must be software-related, and there must have been at least one major software release in the last 12 months.
Data observability addresses one of the most significant impediments to generating value from data by providing an environment for monitoring the quality and reliability of data. Maintaining data quality and trust is a perennial data management challenge, often preventing enterprises from operating at the speed of business.
To be included in the Data Observability Buyers Guide, the product(s) must be marketed as a data observability platform or address the following functional areas, which are mapped into Buyers Guide capability criteria:
- DataOps
- Collaboration
- Acceleration
- Automation
- Ecosystem integration
- Detection of data reliability issues
- Resolution of data reliability issues
- Prevention of data reliability issues
The research is designed to be independent of the specifics of software provider packaging and pricing. To represent the real-world environment in which businesses operate, we include providers that offer suites or packages of products that may include relevant individual modules or applications. If a software provider is actively marketing, selling and developing a product for the general market and it is reflected on the provider’s website that the product is within the scope of the research, that provider is automatically evaluated for inclusion.
All software providers that offer relevant data observability products and meet the inclusion requirements were invited to participate in the evaluation process at no cost to them.
Software providers that meet our inclusion criteria but did not completely participate in our Buyers Guide were assessed solely on publicly available information. As this could have a significant impact on classification and ratings, we recommend additional scrutiny when evaluating those providers.
Products Evaluated
| Provider | Product Names | Version | Release Month/Year |
|---|---|---|---|
| Acceldata | Acceldata Data Observability Cloud | v. 4.70 | September 2025 |
| Actian | Actian Data Observability | v. Spring 2025 | June 2025 |
| Anomalo | Anomalo | v. 0.225 | August 2025 |
| Ataccama | Ataccama ONE | v. 16.2.0 | June 2025 |
| Bigeye | Bigeye | NA | August 2025 |
| Collibra | Collibra Platform | v. 2025.09 | September 2025 |
| Datadog | Metaplane by Datadog | NA | February 2025 |
| DataKitchen | DataKitchen DataOps Observability | NA | August 2025 |
| DataOps.live | DataOps.live | NA | September 2025 |
| IBM | IBM watsonx.data integration | NA | September 2025 |
| Informatica | Informatica Intelligent Data Management Cloud | NA | August 2025 |
| Monte Carlo | Monte Carlo | NA | September 2025 |
| Pentaho | Pentaho Data Quality | NA | September 2025 |
| Precisely | Precisely Data Integrity Suite | NA | September 2025 |
| Qlik | Qlik Talend Cloud | NA | September 2025 |
| RightData | DataTrust | v. 7.4 | August 2025 |
| Sifflet | Sifflet | v. v531 | September 2025 |
| Snowflake | Snowflake Platform | NA | September 2025 |
| Tencent Cloud | Tencent Cloud WeData | NA | November 2024 |
Providers of Promise
We did not include software providers that, as a result of our research and analysis, did not satisfy the criteria for inclusion in this Buyers Guide. These are listed below as “Providers of Promise.”
| Provider | Product | Annual Revenue >$10M | Operates on 2 Continents | At Least 50 Employees | Product GA |
|---|---|---|---|---|---|
| Acryl Data | DataHub | No | Yes | No | Yes |
| Ascend | Ascend | No | Yes | No | Yes |
| Avo | Avo | No | Yes | No | Yes |
| Dagster Labs | Dagster+ | No | Yes | No | Yes |
| Databricks | Databricks Data Intelligence Platform | Yes | Yes | Yes | No |
| Datafold | Datafold | No | Yes | No | Yes |
| Datorios | Datorios | No | Yes | No | Yes |
| DQLabs | DQLabs Platform | No | Yes | No | Yes |
| FirstEigen | DataBuck | No | Yes | No | Yes |
| Great Expectations | GX Cloud | Yes | Yes | No | Yes |
| Integrate.io | Integrate.io | No | Yes | Yes | Yes |
| Kensu | Kensu | No | Yes | No | Yes |
| Lightup | Lightup | Yes | Yes | No | Yes |
| Masthead | Masthead | No | Yes | No | Yes |
| Microsoft | Microsoft Purview | Yes | Yes | Yes | No |
| Mozart Data | Mozart Data | No | Yes | No | Yes |
| Pantomath | Pantomath | No | Yes | No | Yes |
| Saturam | Qualdo | No | Yes | Yes | Yes |
| Soda Data | Soda | No | Yes | No | Yes |
| Telmai | Telmai | No | Yes | No | Yes |
| Torana | iceDQ | No | Yes | No | Yes |
| Validio | Validio | No | Yes | Yes | Yes |
Appendix: Value Index Methodology
To prepare this Buyers Guide, we utilize our Value Index methodology that draws on our more than two decades of market research, which includes benchmarking and advising thousands of enterprises. Our continuous market research provides the context of the real needs of buyers, complemented by our research on software providers, knowledge of the market and subject matter expertise in this area.
The following guidelines were presented to potential participants that met our inclusion criteria:
- A software provider could submit one or more products that best meet the scope of the Buyers Guide and the inclusion criteria.
- Any products that were submitted for this Buyers Guide must be listed on the provider’s website and be generally available to enterprises.
- Software providers were requested to complete a comprehensive questionnaire covering the product and customer experience it provides.
- Verification of the product was required through documentation and/or a demonstration of the actual product.
To ensure the accuracy of the information we collect and ensure that the Buyers Guide reflects the concerns of a well-crafted RFI, we require participating software providers to provide evaluation information across all seven categories. ISG Research then validates the information, first independently through our knowledge base of software providers, product information and extensive web-based research, and then through consultation.
After validation, we grade and aggregate each software provider to determine performance in each evaluation category. Then, through weighted analytics, the ratings in the product and customer experience categories and the overall ranking are assigned. If a provider submitted more than one product for evaluation, we assessed the additional product(s) using our Capability and other evaluation categories.
We have made every effort to encompass the overall requirements that best meet an enterprise’s needs today and into the future. Even so, there may be aspects of the software provider that we did not cover but affect which products best fit your particular requirements. Therefore, while this research is complete as it stands, utilizing it in your organizational context is critical to ensure that products deliver the highest level of support for your requirements.
Executive Summary
Data Observability
Maintaining trust in data remains one of the most persistent challenges in enterprise data management. Even with decades of investment in data quality initiatives, many organizations still struggle to ensure that data used for analytics and operations is accurate, reliable and accessible when needed. As enterprises accelerate automation and adopt artificial intelligence, the importance of trusted, high-quality data has never been greater. Poor-quality or inconsistent data can slow decision-making, introduce risk and undermine confidence in analytics and AI outcomes. To operate at the speed of business, enterprises must monitor not only the movement of data through pipelines but also its ongoing quality, freshness and reliability.
ISG Research defines data observability as providing the capabilities for monitoring the quality and reliability of data used for analytics and governance projects as well as the reliability and health of the overall data environment. The category builds on long-established data quality practices while introducing new methods to monitor and maintain the integrity of data pipelines. Inspired by application and infrastructure observability, data observability provides continuous visibility into data metrics, dependencies and interactions to ensure that data remains available, consistent and accurate.
The ability to detect, resolve and prevent reliability issues across large and distributed data environments has made data observability an increasingly critical component of enterprise data strategy.
Data observability emerged in response to the growing complexity of enterprise data ecosystems. Traditional data quality tools help identify and remediate issues, but they do so reactively and often focus on data already in use. By contrast, data observability platforms instrument the data environment itself, continuously collecting metrics and metadata from data warehouses, data lakes and pipelines. This instrumentation provides insight into lineage (the relationships between data sets), metadata (the descriptive attributes of data, such as format, schema and age) and logs of human or machine interactions. These metrics create a comprehensive picture of data health, enabling the proactive detection of anomalies before they affect downstream systems or business decisions.
Some data observability software extends this functionality further, applying machine learning and statistical modeling to automate anomaly detection and root cause analysis. Alerts, explanations and recommendations are generated to help data engineers and architects address issues quickly or prevent them from recurring. The ability to detect, resolve and prevent reliability issues across large and distributed data environments has made data observability an increasingly critical component of enterprise data strategy. It provides the real-time assurance needed to support analytics, governance and operational processes.
The importance of trust in data has never been greater, particularly as enterprises scale artificial intelligence. Data quality has long been a priority for business intelligence, but the stakes have increased as data now feeds automated, real-time decision systems. These systems are responsible for critical functions such as fraud detection, customer engagement and operational efficiency. As AI initiatives expand to the boardroom level, executives demand reliable data to support efficiency, innovation and growth. Data usability for AI applications is cited by more than one-half (54%) of participants in ISG’s Data and AI Programs Study as one of their biggest data challenges for 2025/6. Without trusted data, AI models can make poor or even harmful decisions that reduce confidence and slow adoption.
Assessing and maintaining the reliability of data used in analytics and AI is increasingly difficult due to the scale, diversity and velocity of modern data sources. Poor data processes can create security and privacy risks, increase storage and processing costs and erode the integrity of analytics and machine learning. Data observability software mitigates these challenges by automating the monitoring of data freshness, schema, distribution, lineage and volume. It complements traditional data quality tools by focusing on the reliability and overall health of the data environment rather than just the suitability of data for a specific task.
Assessing and maintaining the reliability of data used in analytics and AI is increasingly difficult due to the scale, diversity and velocity of modern data sources.
While both categories are interrelated, their focus differs. Data quality software evaluates whether data is accurate, complete, consistent and valid for a particular purpose. Data observability software, by contrast, monitors the operational health of data pipelines to ensure that data remains available and accurate before issues propagate. A failed data pipeline might not immediately affect data quality but could result in outdated or missing data later. Data observability detects the problem early, reducing downtime and avoiding costly remediation. Conversely, data quality tools may detect incorrect data values that pass schema validation but do not meet business rules. In practice, the two categories are complementary, often coexisting within the same enterprise.
This overlap is reflected in the vendor landscape. Some providers now offer functionality that spans both data observability and data quality. Others that historically focused on data quality have adopted the term data observability but may lack the breadth of pipeline monitoring and anomaly detection capabilities expected of mature observability platforms. Enterprises evaluating these tools should carefully assess the scope and depth of each product’s functionality to ensure it aligns with business needs. The strongest offerings integrate automated error detection, root cause analysis and remediation workflows, giving data teams the visibility and agility required to manage increasingly complex data environments.
The rise of Data Operations, or DataOps, has further driven the adoption of observability practices. DataOps applies agile and DevOps methodologies to data engineering, promoting continuous delivery and automated monitoring of data in motion. Data observability plays a foundational role in this framework, enabling teams to maintain consistent, trusted data pipelines that support both operational and analytical processes. In addition to specialized data observability software vendors, many DataOps platforms are incorporating observability capabilities to create unified environments for data development, orchestration and monitoring. This trend reflects the growing recognition that observability is essential to achieving end-to-end data reliability.
As data complexity and dependency increase, enterprises are focusing on proactive monitoring, automation and transparency to improve trust in data. ISG asserts that through 2027, more than two-thirds of enterprises will invest in initiatives to improve trust in data through adoption of data observability tools to address the detection, resolution and prevention of data reliability issues. Organizations seeking to strengthen their data foundations should explore how observability can be integrated into broader people, process and technology improvements. When combined with strong data governance and quality management, data observability provides a real-time, automated framework for ensuring that data remains accurate, reliable and ready for use across analytics and AI applications.
The 2025 ISG Buyers Guide™ for Data Observability evaluates software providers and products in key areas, including the detection, resolution and prevention of data reliability issues. This research evaluates the following software providers: Acceldata, Actian, Anomalo, Astronomer, Ataccama, Bigeye, Collibra, Datadog, DataKitchen, DataOps.live, Hitachi Vantara, IBM, Informatica, Monte Carlo, Precisely, Qlik, RightData, Sifflet, Snowflake, Tencent Cloud and Y42.
Buyers Guide Overview
ISG Research has conducted market research for over two decades across vertical industries, business applications, AI and IT. We have designed the ISG Buyers Guide™ to provide a balanced perspective of software providers and products that is rooted in an understanding of business and IT requirements. Utilization of our research methodology and decades of experience enables our Buyers Guide to be an effective method to assess and select software providers and products. The findings of this research provide a comprehensive approach to rating software providers and rank their ability to meet specific product and customer experience requirements.
ISG Research has designed the Buyers Guide to provide a balanced perspective of software providers and products that is rooted in an understanding of business and IT requirements.
The 2025 ISG Buyers Guide™ for Data Observability is the distillation of continuous market and product research. It is an assessment of how well software providers’ offerings address enterprises’ requirements for data observability software. The Value Index methodology is structured to support a request for information (RFI) for a request for proposal (RFP) process by incorporating all criteria needed to evaluate, select, utilize and maintain relationships with software providers. The ISG Buyers Guide evaluates customer experience and the product experience in its capability and platform.
The structure of the research reflects our understanding that the effective evaluation of software providers and products involves far more than just examining product features, potential revenue or customers generated from a provider’s marketing and sales efforts. It can ensure the best long-term relationship and value achieved from a resource and financial investment We believe it is important to take a comprehensive, research-based approach, since making the wrong choice of data observability software can raise the total cost of ownership, lower the return on investment and hamper an enterprise’s ability to reach its potential. In addition, this approach can reduce the project’s development and deployment time and eliminate the risk of relying on opinions or historical biases.
ISG Research believes that an objective review of existing and potential new software providers and products is a critical strategy for the adoption and implementation of sales data observability software. An enterprise’s review should include a thorough analysis of both what is possible and what is relevant. We urge enterprises to do a thorough job of evaluating data observability software and offer this Buyers Guide as both the results of our in-depth analysis of these providers and as an evaluation methodology.
Key Takeaways
Data observability has become essential to ensuring data reliability, quality and trust in analytics and AI initiatives. As enterprises modernize their data ecosystems, observability tools play a critical role in detecting, diagnosing and preventing data issues that can impact operations and decision-making. The ISG Buyers Guide™ for Data Observability underscores the growing importance of automation, integration and scalability in maintaining consistent data health. These platforms are helping enterprises gain visibility across complex data pipelines and ensure that insights are accurate and actionable.
Software Provider Summary
The ISG Buyers Guide™ for Data Observability evaluates software providers offering products that support data quality monitoring, anomaly detection and data reliability management. The research assessed 20 providers, with Monte Carlo ranking highest overall, followed by Pentaho and Acceldata. Providers were evaluated on Product Experience and Customer Experience criteria and this report provides the results for each provider along with strengths and areas for improvement.
Product Experience Insights
Product Experience examines how effectively providers support the full lifecycle of configuration, monitoring and resolution across data environments. Leaders in this category, Monte Carlo, Pentaho and Acceldata, performed strongly in both Capability and Platform criteria. The Capability assessment reviewed 85 function points across eight sections, while the Platform evaluation focused on adaptability, manageability, reliability and usability. Leaders demonstrated resilient, scalable architectures and seamless integration across data and analytics ecosystems.
Customer Experience Value
Customer Experience evaluates provider commitment, customer success and value realization across all stages of engagement. Informatica, Monte Carlo and Collibra achieved the highest ratings for transparency, service quality and responsiveness to customer needs. Providers that did not perform as well often lacked accessible documentation or failed to clearly demonstrate customer outcomes. These shortcomings reflected weaker communication around product value and limited evidence of customer success initiatives.
Strategic Recommendations
Enterprises should adopt data observability platforms that offer automated anomaly detection, root cause analysis and integration across diverse data sources. Prioritizing providers with proven scalability, governance alignment and ecosystem interoperability will strengthen data reliability. Decision-makers should ensure observability investments support both operational monitoring and proactive data quality improvement. A strategic approach to observability can improve decision accuracy, reduce downtime and enhance data trust across the organization.
How To Use This Buyers Guide
Evaluating Software Providers: The Process
We recommend using the Buyers Guide to assess and evaluate new or existing software providers for your enterprise. The market research can be used as an evaluation framework to assess existing approaches and software providers or establish a formal request for information from providers on products and customer experience and will shorten the cycle time when creating an RFI. The steps listed below provide a process that can facilitate best possible outcomes in the most efficient manner.
- Define the business case and goals.
Define the mission and business case for investment and the expected outcomes from your organizational and technological efforts.
- Specify the business and IT needs.
Defining the business and IT requirements helps identify what specific capabilities are required with respect to people, processes, information and technology.
- Assess the required roles and responsibilities.
Identify the individuals required for success at every level of the enterprise from executives to frontline workers and determine the needs of each.
- Outline the project’s critical path.
What needs to be done, in what order and who will do it? This outline should make clear the prior dependencies at each step of the project plan.
- Ascertain the technology approach.
Determine the business and technology approach that most closely aligns to your enterprise’s requirements.
- Establish software provider evaluation criteria.
Utilize the product experience: capability and platform with support for adaptability, manageability, reliability and usability, and the customer experience in TCO/ROI and Validation.
- Evaluate and select the software provider and products properly.
Apply a weighting the evaluation categories in the evaluation criteria to reflect your enterprise’s priorities to determine the short list of software providers and products.
- Establish the business initiative team to start the project.
Identify who will lead the project and the members of the team needed to plan and execute it with timelines, priorities and resources.
Using the ISG Buyers Guide and process provides enterprises a clear, structured approach to making smarter software and business investment decisions. It ensures alignment between strategy, people, processes and technology while reducing risk, saving time and improving outcomes. The ISG approach promotes data-driven decision-making and collaboration, helping choose the right software providers for maximum value and return on investment.
The Findings
The software providers and products evaluated in the research provide product and customer experiences, but not everything offered is equally valuable to every enterprise or is needed to operate in business processes and use cases. Moreover, the existence of too many capabilities in products may be a negative factor for an enterprise if it introduces unnecessary complexity. Nonetheless, you may decide that a more comprehensive set of capabilities in the product is important, and where they match your enterprise’s requirements.
An effective customer relationship with a software provider is vital to the success of any investment. The overall customer experience and the full lifecycle of engagement play a key role in ensuring satisfaction and long-term success. Providers with dedicated customer leadership, such as chief customer officers, tend to invest more deeply in these relationships and prioritize customer outcomes to TCO and ROI expectations. It is equally important that this commitment to customer success is clearly demonstrated throughout the provider’s website, buying process and customer journey.
Overall Scoring of Software Providers Across Categories
The research finds Monte Carlo atop the list, followed by Pentaho and Acceldata. Providers that place in the top three of a category earn the designation of Leader. Monte Carlo has done so in five categories; Acceldata and Pentaho in three; Informatica in two and Collibra, Datadog and IBM in one category.
The overall representation of the research below places the rating of the Product Experience and Customer Experience on the x and y axes, respectively, to provide a visual representation and classification of the software providers. Those providers whose Product Experience have above median weighted performance to the axis in aggregate of the two product categories place farther to the right, while the performance and weighting for the Customer Experience category determines placement on the vertical axis. In short, software providers that place closer to the upper-right on this chart performed better than those closer to the lower-left.
The research categorizes and rates software providers into one of four categories: Assurance, Exemplary, Merit or Innovative. This representation of software providers’ weighted performance in meeting the requirements in product and customer experience.
Exemplary: This rating (upper right) represents those that performed above median in Product and Customer Experience requirements. The providers rated Exemplary are: Actian, Collibra, DataOps.live, IBM, Monte Carlo and Pentaho.
Innovative: This rating (lower right) represents those that performed above median in Product Experience but not in Customer Experience. The providers rated Innovative are: Acceldata, Anomalo, Bigeye and Datadog.
Assurance: This rating (upper left) represents those that performed above median in Customer Experience but not in Product Experience. The providers rated Assurance are: DataKitchen, Informatica, Precisely and Qlik.
Merit: This rating (lower left) represents those that did not surpass the median in Customer or Product Experience. The providers rated Merit are: Ataccama, RightData, Sifflet, Snowflake and Tencent Cloud.
We advise enterprises to use this research as a supplement to their own evaluations, recognizing that ratings or rankings do not solely represent the value of a provider nor indicate universal suitability of a set of products.
Product Experience
The process of researching products to address an enterprise’s needs should be comprehensive and evaluate specific capabilities and the underlying platform to the product experience. Our evaluation of the Product Experience examines the lifecycle of onboarding, configuration, operations, usage and maintenance. Too often, software providers are not evaluated for the entirety of the product; instead, they are evaluated on market execution and vision of the future.
The research results in Product Experience are ranked at 80%, or four-fifths, using the underlying weighted performance. Importance was placed on the categories as follows: Capability (40%) and Platform (40%). Monte Carlo, Pentaho and Acceldata were designated Product Experience Leaders.
Capability of the Product
The Capability criteria is designed to assess the products and features across a broad range of data observability capabilities that support the detection, resolution and prevention of data reliability and quality concerns, as well as agile and collaborative practices.
ISG Research evaluated more than 85 different function points in eight sections to assess the full scope of data observability capabilities. It also examined the investment by the software provider. The research weights Capability at 40% of the overall rating. Monte Carlo, Pentaho and Acceldata are the Leaders in this category.
The Capability evaluation framework for data observability provides a framework for enterprises. Software providers that have more breadth and depth and support the entire set of needs fared better.
Platform of the Product
The Platform category evaluates the underlying requirements of a platform and examines how well a software product meets enterprise needs across business and IT. It measures how effectively the product can be managed and configured and integrated into enterprise environments, how efficiently it can be governed and secured, how reliably it performs and scales, and how intuitively it supports users across varied roles and skill levels. The platform category in the ISG Buyers Guide examines specific requirements for adaptability, manageability, reliability and usability.
The grading of the underlying platform focuses on a software product’s overall robustness and the flexibility of a provider’s software foundation. Adaptability measures a product’s ability to be customized and integrated across systems and data, while manageability focuses on governance, security and compliance. Reliability considers performance and scalability across environments, and usability assesses how intuitive and accessible the product is through design, use of AI and ongoing provider investment.
ISG Research evaluated 16 function points in 5 sections to assess the full scope of platforms capabilities. The research weights Platform at 40% of the overall rating. Datadog, Informatica, Monte Carlo and IBM are the Leaders in this category.
Platform is an essential evaluation category as it indicates the strength and resilience of a software provider’s product architecture. A well-designed platform ensures secure and compliant operations, dependable scalability and uptime, and a unified, intuitive experience for range of usage personas. It also reflects the provider’s capacity to enable deployment models while maintaining flexibility for enterprise demands.
Software providers that performed best in the Platform category were those that have support for the breadth and depth of needs across business and IT supporting adaptability, manageability, reliability and usability. Providers with lower performance were challenged in one or more of these areas or did not demonstrate a cohesive, enterprise-grade approach. The underlying platform for a software provider’s products is essential in any evaluation.
Customer Experience
The importance of a customer relationship with a software provider is essential to the actual success of the products and technology. The evaluation of the Customer Experience and the entire lifecycle an enterprise has with its software provider is critical for ensuring satisfaction in working with that provider. The ISG Buyers Guide examines a software provider’s customer commitment, viability, customer success, sales and onboarding, product roadmap and services with partners and support. The customer experience category also investigates the TCO/ROI and how well a software provider demonstrates the product’s overall value, cost and benefits, including the tools and resources to evaluate these factors.
The research results in Customer Experience are ranked at 20%, or one-fifth of the 100% index, and represent the underlying provider validation and TCO/ROI requirements as they relate to the framework of commitment and value to the software provider-customer relationship.
The software providers that evaluated the highest in the Customer Experience category are Informatica, Monte Carlo and Collibra. These category leaders best communicate commitment and dedication to customer needs.
Software providers that did not perform well in this category were unable to provide or make sufficient information readily available to demonstrate success or articulate their commitment to customer experience. The use of a software provider requires continuous investment, so a holistic evaluation must include examination of how they support their customer experience.
Appendix: Software Provider Inclusion
For inclusion in the 2025 ISG Buyers Guide™ for Data Observability, a software provider must be in good standing financially and ethically, have at least $10 million in annual or projected revenue verified using independent sources, sell products and provide support on at least two continents, and have at least 50 employees. The principal source of the relevant business unit’s revenue must be software-related, and there must have been at least one major software release in the last 12 months.
Data observability addresses one of the most significant impediments to generating value from data by providing an environment for monitoring the quality and reliability of data. Maintaining data quality and trust is a perennial data management challenge, often preventing enterprises from operating at the speed of business.
To be included in the Data Observability Buyers Guide, the product(s) must be marketed as a data observability platform or address the following functional areas, which are mapped into Buyers Guide capability criteria:
- DataOps
- Collaboration
- Acceleration
- Automation
- Ecosystem integration
- Detection of data reliability issues
- Resolution of data reliability issues
- Prevention of data reliability issues
The research is designed to be independent of the specifics of software provider packaging and pricing. To represent the real-world environment in which businesses operate, we include providers that offer suites or packages of products that may include relevant individual modules or applications. If a software provider is actively marketing, selling and developing a product for the general market and it is reflected on the provider’s website that the product is within the scope of the research, that provider is automatically evaluated for inclusion.
All software providers that offer relevant data observability products and meet the inclusion requirements were invited to participate in the evaluation process at no cost to them.
Software providers that meet our inclusion criteria but did not completely participate in our Buyers Guide were assessed solely on publicly available information. As this could have a significant impact on classification and ratings, we recommend additional scrutiny when evaluating those providers.
Products Evaluated
| Provider | Product Names | Version | Release Month/Year |
|---|---|---|---|
| Acceldata | Acceldata Data Observability Cloud | v. 4.70 | September 2025 |
| Actian | Actian Data Observability | v. Spring 2025 | June 2025 |
| Anomalo | Anomalo | v. 0.225 | August 2025 |
| Ataccama | Ataccama ONE | v. 16.2.0 | June 2025 |
| Bigeye | Bigeye | NA | August 2025 |
| Collibra | Collibra Platform | v. 2025.09 | September 2025 |
| Datadog | Metaplane by Datadog | NA | February 2025 |
| DataKitchen | DataKitchen DataOps Observability | NA | August 2025 |
| DataOps.live | DataOps.live | NA | September 2025 |
| IBM | IBM watsonx.data integration | NA | September 2025 |
| Informatica | Informatica Intelligent Data Management Cloud | NA | August 2025 |
| Monte Carlo | Monte Carlo | NA | September 2025 |
| Pentaho | Pentaho Data Quality | NA | September 2025 |
| Precisely | Precisely Data Integrity Suite | NA | September 2025 |
| Qlik | Qlik Talend Cloud | NA | September 2025 |
| RightData | DataTrust | v. 7.4 | August 2025 |
| Sifflet | Sifflet | v. v531 | September 2025 |
| Snowflake | Snowflake Platform | NA | September 2025 |
| Tencent Cloud | Tencent Cloud WeData | NA | November 2024 |
Providers of Promise
We did not include software providers that, as a result of our research and analysis, did not satisfy the criteria for inclusion in this Buyers Guide. These are listed below as “Providers of Promise.”
| Provider | Product | Annual Revenue >$10M | Operates on 2 Continents | At Least 50 Employees | Product GA |
|---|---|---|---|---|---|
| Acryl Data | DataHub | No | Yes | No | Yes |
| Ascend | Ascend | No | Yes | No | Yes |
| Avo | Avo | No | Yes | No | Yes |
| Dagster Labs | Dagster+ | No | Yes | No | Yes |
| Databricks | Databricks Data Intelligence Platform | Yes | Yes | Yes | No |
| Datafold | Datafold | No | Yes | No | Yes |
| Datorios | Datorios | No | Yes | No | Yes |
| DQLabs | DQLabs Platform | No | Yes | No | Yes |
| FirstEigen | DataBuck | No | Yes | No | Yes |
| Great Expectations | GX Cloud | Yes | Yes | No | Yes |
| Integrate.io | Integrate.io | No | Yes | Yes | Yes |
| Kensu | Kensu | No | Yes | No | Yes |
| Lightup | Lightup | Yes | Yes | No | Yes |
| Masthead | Masthead | No | Yes | No | Yes |
| Microsoft | Microsoft Purview | Yes | Yes | Yes | No |
| Mozart Data | Mozart Data | No | Yes | No | Yes |
| Pantomath | Pantomath | No | Yes | No | Yes |
| Saturam | Qualdo | No | Yes | Yes | Yes |
| Soda Data | Soda | No | Yes | No | Yes |
| Telmai | Telmai | No | Yes | No | Yes |
| Torana | iceDQ | No | Yes | No | Yes |
| Validio | Validio | No | Yes | Yes | Yes |
Appendix: Value Index Methodology
To prepare this Buyers Guide, we utilize our Value Index methodology that draws on our more than two decades of market research, which includes benchmarking and advising thousands of enterprises. Our continuous market research provides the context of the real needs of buyers, complemented by our research on software providers, knowledge of the market and subject matter expertise in this area.
The following guidelines were presented to potential participants that met our inclusion criteria:
- A software provider could submit one or more products that best meet the scope of the Buyers Guide and the inclusion criteria.
- Any products that were submitted for this Buyers Guide must be listed on the provider’s website and be generally available to enterprises.
- Software providers were requested to complete a comprehensive questionnaire covering the product and customer experience it provides.
- Verification of the product was required through documentation and/or a demonstration of the actual product.
To ensure the accuracy of the information we collect and ensure that the Buyers Guide reflects the concerns of a well-crafted RFI, we require participating software providers to provide evaluation information across all seven categories. ISG Research then validates the information, first independently through our knowledge base of software providers, product information and extensive web-based research, and then through consultation.
After validation, we grade and aggregate each software provider to determine performance in each evaluation category. Then, through weighted analytics, the ratings in the product and customer experience categories and the overall ranking are assigned. If a provider submitted more than one product for evaluation, we assessed the additional product(s) using our Capability and other evaluation categories.
We have made every effort to encompass the overall requirements that best meet an enterprise’s needs today and into the future. Even so, there may be aspects of the software provider that we did not cover but affect which products best fit your particular requirements. Therefore, while this research is complete as it stands, utilizing it in your organizational context is critical to ensure that products deliver the highest level of support for your requirements.
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