Analyst Perspectives

Late 2024 saw the publication of the 2024 ISG Buyers Guides for DataOps, providing an assessment of 49 software providers offering products used by data engineers, data scientists, and data and AI professionals to facilitate the use of data for analytics and AI needs. The DataOps Buyers Guide research includes five reports which are focused on overall DataOps, Data Observability, Data Orchestration, Data Pipelines and Data Products. This is the first time in the industry when all software providers with tools in the portfolio of DataOps have been rated together. Below I provide an overview of each of the five reports, as well as some observations on market trends.

ISG Research’s Buyers Guides evaluate software providers in seven key categories that are weighted to reflect buyers’ needs based on our expertise and research. Five are product-experience related: Adaptability, Capability, Manageability, Reliability, and Usability. In addition, we consider two customer-experience categories: Validation, and Total Cost of Ownership/Return on Investment (TCO/ROI).

ISG Research defines data operations (DataOps) as the application of agile development, DevOps and lean manufacturing by data engineering professionals in support of data production. It encompasses the development, testing, deployment and orchestration of data integration and processing pipelines, along with improved data quality and validity via data monitoring and observability and the delivery of data products. DataOps has been part of the lexicon of the data sector for almost a decade and was initially seen as antithetical to traditional approaches to data management, which typically included batch-based, manual, and rigid tools and practices. This is no longer the case, however. Many longstanding providers of traditional data management tools have adopted DataOps capabilities and methodologies in recent years. Established incumbent providers that graded well in the DataOps Buyers guide have incorporated capabilities that make products more automated, collaborative and agile.

The overall DataOps Buyers Guide assessed 25 software providers for their capabilities to support the full range of DataOps functionality, including data pipelines, data orchestration, data observability and data products. The research found Informatica atop the list, followed by Microsoft and IBM. Providers that place in the top three of a category earn the designation of Leader. Informatica did so in five categories; Databricks and Microsoft in three; Google and SAP in two; and Alteryx, AWS, DataOps.live, IBM, Keboola and Qlik in one category.

The distinction between traditional and new approaches to data involved in DataOps is highlighted by comparing the Data Pipelines Buyers Guide with the Data Integration Buyers Guide published earlier in 2024. ISG Research defines data pipelines as the systems used to transport, process and deliver data produced by operational data platforms and applications into analytic data platforms and applications for consumption. Healthy data pipelines are necessary to ensure data is ingested, processed and loaded in the sequence required to generate BI and AI. Although data integration is performed using data pipelines, not all data pipelines perform data integration. While the Data Integration Buyers Guide focused specifically on the requirements for data integration pipelines, the Data Pipelines Buyers Guide addresses the wider requirements for data pipelines of all types, as well as higher-level data operations tasks related to pipeline testing and deployment. The Data Pipelines Buyers Guide also places greater emphasis on agile and collaborative practices as well as compatibility with the wider ecosystem of DevOps, data management, DataOps and BI and AI tools and applications. I assert that by 2026, three-quarters of enterprises will adopt data engineering processes that span data integration, transformation and preparation producing repeatable data pipelines that create more agile information architectures. 

The Data Pipelines Buyers Guide assessed 28 software providers for their capabilities to support the development, deployment and management of data pipelines. The research found Microsoft atop the list, followed by Alteryx and Databricks. Informatica earned the designation of Leader in five categories; Microsoft in four; Databricks in three; Google and SAP in two; and Alteryx, AWS, DataOps.live, Keboola and Qlik in one category.

The Data Orchestration Buyers Guide assessed 27 software providers for their capabilities to support the orchestration of data pipelines. The research found Databricks atop the list, followed by Microsoft and Alteryx. Informatica again earned the designation of Leader in five categories; Microsoft and SAP in three; AWS, Databricks and Google in two; and Alteryx, Astronomer, BMC, Keboola and Stonebranch in one category.

ISG Research defines data orchestration as providing the capabilities to automate and accelerate the flow of data to support operational and analytics initiatives and drive business value via capabilities for the monitoring and management of data pipelines and associated workflows. The orchestration of data pipelines enables enterprises to manage the increasing complexity of evolving data sources and requirements through the creation, scheduling, automation and monitoring of data workflows. Data orchestration is also integral to the development and delivery of applications driven by AI and GenAI. Specifically, data orchestration can be used to automate and accelerate the flow of data from multiple sources, including existing applications and data platforms as well as the output of large language models and vector databases. As such it is a complement to MLOps, which serves the collection of artifacts and the orchestration of processes necessary to deploy and maintain AI/ML models.

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 Data Observability Buyers Guide assessed 17 software providers for their capabilities to support monitoring of the quality and reliability of the data flowing through data pipelines. The research found Monte Carlo atop the list, followed by DQLabs and Acceldata. Informatica earned the designation of Leader in six categories, Monte Carlo in five, DQLabs in four, Acceldata and IBM in two, and Collibra and Qlik in one category.

I previously described the emergence of data observability software providers in recent years as a Cambrian explosion. While there has been some merger and acquisition activity since then, the market for products that enable the monitoring of data quality and reliability remains crowded. The Data Observability Buyers Guide may have evaluated only 17 data observability software providers, but we listed more than 25 Providers of Promise that either failed to meet the revenue threshold for consideration or offer products that are not yet generally available. Potential adopters are advised to pay close attention and assess data observability products carefully. Some data observability products offer quality resolution and remediation functionality traditionally associated with data quality software, albeit not to the same depth and breadth. Additionally, some providers previously associated with data quality have adopted the term data observability but may lack the depth and breadth of pipeline monitoring and error detection capabilities.

The metrics generated by data observability form a critical component of the development and sharing of data products, which was added to the DataOps Buyers Guide research this year. The Data Products Buyers Guide assessed 19 software providers for their capabilities to support the development and consumption of data products, which ISG Research defines as the outcome of data initiatives developed with product thinking and delivered as reusable assets that can be discovered and consumed by others on a self-service basis, along with associated data contracts and feedback options. The research found Microsoft atop the list, followed by Informatica and SAP. Informatica earned the designation of Leader in five categories; Microsoft in four; Databricks and SAP in three; and Actian, Alation, AWS, DataOps.live, Denodo, One Data and Qlik in one category.

The data product platforms category is nascent, and many software providers offer functionality that could be used to facilitate the development and consumption of data products but lack capabilities to view and manage access to data products, monitor data product usage and performance metrics, or develop and manage data contracts, which form the basis of an agreement between the data owner and the data consumer about the nature of the data product and its intended use. ISG asserts that through 2027, enterprises will increase strategic focus on data catalogs as the intersection of data production and data consumption, enabling the self-service creation and sharing of data products based on trusted and governed data sources.

As always, software products are only one aspect of delivering on the promise of DataOps. New approaches to people, processes and information are also required to deliver agile and collaborative development, testing, deployment and monitoring of data and analytics workloads and ensure the validity and quality of data. I recommend that enterprises evaluating DataOps products should be conscious of the need to also adopt processes and methodologies that support rapid innovation and experimentation, automation, collaboration, measurement and monitoring, and high data quality.

Regards,

Matt Aslett