Executive Summary

Data for the Enterprise

Data Operations (DataOps) is a methodology focused on the delivery of agile business intelligence (BI) and data science through the automation and orchestration of data integration and processing pipelines, incorporating improved data reliability and integrity via data monitoring and observability. DataOps has been part of the lexicon of the data market for almost a decade and takes inspiration from DevOps, which describes a set of tools, practices and philosophy used to support the continuous delivery of software applications in the face of constant changes.

Interest in DataOps is growing. Ventana Research asserts that by 2025, one-half of organizations will have adopted a DataOps approach to their data engineering processes, enabling them to be more flexible and agile. A variety of products, practices and processes enable DataOps, including products that support agile and continuous delivery of data analytics and continuous measurable improvement. An emphasis on agility, collaboration and automation separates DataOps from traditional approaches to data management, which were typically based on tools and practices that were batch-based, manual and rigid.

This distinction between DataOps and traditional data management tools is clearer in theory than it is in practice. There is a level of opacity as traditional data management vendors have, in recent years, incorporated capabilities that make their products more automated, collaborative and agile. There is no industry-wide consensus on the level of agility, collaboration and automation that must be provided for products be to be considered part of the DataOps category. While traditional data management vendors have also adopted the term DataOps, many have adopted a broader definition that describes DataOps as the combination of people, process and technology needed to automate the delivery of data to users in an organization and enable collaboration to facilitate data-driven decisions. This definition is broad enough that it could be interpreted to encompass all products and services that address data management and data governance, including many traditional batch-based, manual products that do not support agile and continuous delivery and continuous measurable improvement.

A narrower definition of DataOps focuses on the practical application of agile development, DevOps and lean manufacturing to the tasks and skills employed by data engineering professionals in support of data analytics development and operations. This definition emphasizes specific capabilities such as continuous delivery of analytic insight, process simplification, code generation, automation to avoid repeated errors and reduce repetitive tasks, the incorporation of stakeholder feedback and advancement, and measurable improvement in the efficient generation of insight from data. As such, the narrow definition of DataOps provides a set of criteria for agile and collaborative practices that products and services can be measured against.

Ventana Research’s perspective, based on our interaction with the vendor and user communities, aligns with the narrow definition. While traditional data management and data governance are complementary, our DataOps coverage focuses specifically on the delivery of agile BI and data science through the automation and orchestration of data integration and processing pipelines, incorporating improved data reliability and integrity via data monitoring and observability.

To be more specific, we believe that DataOps products and services provide functionality that addresses a particular set of capabilities: agile and collaborative data operations; the development, testing and deployment of data and analytics pipelines; data orchestration and data observability. These are the key criteria that we used to assess DataOps products and services as part of this Buyer’s Guide. This research is comprised of parallel evaluations of products addressing each of the three core areas of functionality: data pipelines, data orchestration and data observability. Vendors with products that address at least two of these three core areas were deemed to provide a superset of functionality to address DataOps overall. Additionally, we evaluated all products in all categories in relation to their support for agile and collaborative practices.

The development, testing and deployment of data pipelines is essential to generating intelligence from data, ensuring that data is integrated and processed in the correct sequence to generate the required intelligence. Just as a physical pipeline is used to transport water between stages in the generation of hydroelectric power, data pipelines are used to transport data between the stages involved in data processing and analytics to generate business insight. The transportation of data has traditionally been a batch process that has moved data from one environment to another. However, data-driven organizations are increasingly thinking of the steps involved in extracting, integrating, aggregating, preparing, transforming and loading data as a continual process that is orchestrated to facilitate data-driven analytics. We assert that by 2026, three-quarters of organizations will adopt data engineering processes that span data integration, transformation and preparation, producing repeatable data pipelines that create more agile information architectures.

Data orchestration provides the capabilities to automate and accelerate the flow of data from multiple sources to support analytics initiatives and drive business value. At the highest level of abstraction, data orchestration covers three key capabilities: collection (including data ingestion, preparation and cleansing); transformation (additionally including integration and enrichment); and activation (making the results available to compute engines, analytics and data science tools or operational applications). By 2026, more than one-half of organizations will adopt data orchestration technologies to automate and coordinate data workflows and increase efficiency and agility in data and analytics projects.

Meanwhile, the need to monitor the pipelines and processes in data processing and analytics environments has driven the emergence of a new category of software: data observability. Monitoring the quality and reliability of data used for analytics and governance projects is not new, but data pipeline observability utilizes machine learning (ML) to automate the monitoring of data to ensure that it is complete, valid and consistent, as well as relevant and free from duplication. Data pipeline observability also addresses monitoring not just the data stored in an individual data warehouse or data lake, but also the associated upstream and downstream data pipelines. Through 2025, data observability will continue to be a priority for the evolution of data operations products as vendors deliver more automated approaches to data engineering and improving trust in enterprise data.

In combination, data orchestration and data observability products address two of the most significant impediments to generating value from data. Participants in Ventana Research’s Analytics and Data Benchmark Research cite preparing data for analysis (69%) and reviewing data for quality and consistency issues (64%) as the two most time-consuming tasks in analyzing data.

As always, however, products are only one aspect of delivering on the promise of DataOps. New approaches to people, process and information are also required to deliver agile and collaborative development, testing and deployment of data and analytics workloads, as well as data operations. To improve the value that they are generating from their analytics and data initiatives, organizations need to investigate the potential benefits of data pipeline development, data orchestration and data observability products alongside processes and methodologies that support rapid innovation and experimentation, automation, collaboration, measurement and monitoring, and high data quality.

This research evaluates the following vendors that offer products that address at least two of the three core areas of DataOps functionality (data pipeline development, testing and deployment; data pipeline orchestration; and data pipeline observability): Alteryx, AWS, Astronomer, BMC, Databricks, DataKitchen, Google, Hitachi Vantara, IBM, Infoworks, Matillion, Prefect, Rivery, SAP, Stonebranch, StreamSets and Y42.

Buyers Guide Overview

For over two decades, Ventana Research has conducted market research in a spectrum of areas across business applications, tools and technologies. Ventana Research has designed the Buyers Guide to provide a balanced perspective of vendors and products that is rooted in an understanding of the business requirement in any organization. Utilization of our research methodology and decades of experience enables our Buyers Guide to be an effective method to assess and select technology vendors and products. The findings of this research undertaking contribute to our comprehensive approach to rating vendors in a manner that is based on the assessments completed by an organization.

This Ventana Research DataOps Buyers Guide is the distillation of over a year of market and product research efforts. It is an assessment of how well vendors’ offerings will address organizations requirements for DataOps software. The index is structured to support a request for information (RFI) that could be used in the RFP process by incorporating all criteria needed to evaluate, select, utilize and maintain relationships with technology vendors. An effective product and customer experience with a technology vendor can ensure the best long-term relationship and value achieved from a resource and financial investment.

In this Buyers Guide, Ventana Research evaluates the software 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 and Return on Investment (TCO/ROI). To assess functionality, one of the components of capability, we applied the Ventana Research Value Index methodology and blueprint, which links the personas and processes for DataOps to an organization’s requirements.

The structure of the research reflects our understanding that the effective evaluation of vendors and products involves far more than just examining product features, potential revenue or customers generated from a vendor’s marketing and sales efforts. We believe it is important to take a comprehensive research-based approach, since making the wrong choice of a DataOps technology can raise the total cost of ownership, lower the return on investment and hamper an organization’s ability to reach its potential performance. In addition, this approach can reduce the project’s development and deployment time and eliminate the risk of relying on a short list of vendors that does not represent a best fit for your organization.

To ensure the accuracy of the information we collected, we asked participating vendors to provide product and company information across the seven product and customer experience categories that, taken together, reflect the concerns of a well-crafted RFI. Ventana Research then validated the information, first independently through our database of product information and extensive web-based research, and then in consultation with the vendors. Most selected vendors also participated in a one-on-one session providing an overview and demonstration, after which we requested they provide additional documentation to support any new input.

Ventana Research believes that an objective review of vendors and products is a critical business strategy for the adoption and implementation of DataOps software and applications. An organization’s review should include a thorough analysis of both what is possible and what is relevant. We urge organizations to do a thorough job of evaluating DataOps systems and tools and offer this Buyers Guide as both the results of our in-depth analysis of these vendors and as an evaluation methodology.

How To Use This Buyers Guide

Evaluating Vendors: The Process

We recommend using the Buyers Guide to assess and evaluate new or existing technology vendors for your organization. The market research can be used as an evaluation framework to establish a formal request for information from technology vendors on their products and customer experience and will shorten the cycle time when creating a RFI. The steps listed below provide a process that can facilitate best possible outcomes.

1. Define the business case and goals.
   Define the mission and business case for investment and the expected outcomes from your organizational and technology efforts. 
2. Specify the business needs.
   Defining the business requirements helps identify what specific capabilities are required with respect to people, processes, information and technology.
3. Assess the required roles and responsibilities.
   Identify the individuals required for success at every level of the organization from executives to front line workers and determine the needs of each. 
4. 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. 
5. Ascertain the technology approach.
   Determine the business and technology approach that most closely aligns to your organization’s requirements. 
6. Establish technology vendor evaluation criteria.
   Utilize the product experience: Adaptability, Capability, Manageability, Reliability and Usability, and the customer experience in TCO/ROI and Validation. 
7. Evaluate and select the technology properly.
   Weight the categories in the technology evaluation criteria to reflect your organization’s priorities to determine the short list of vendors and products.
8. 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. 

The Findings

All of the products we evaluated are feature-rich, but not all the capabilities offered by a technology vendor are equally valuable to types of workers or support everything needed to manage products on a continuous basis. Moreover, the existence of too many capabilities may be a negative factor for an organization if it introduces unnecessary complexity. Nonetheless, you may decide that a larger number of features in the product is a plus, especially if some of them match your organization’s established practices or support an initiative that is driving the purchase of new software.

Factors beyond features and functions or vendor assessments may become a deciding factor. For example, an organization may face budget constraints such that the TCO evaluation can tip the balance to one vendor or another. This is where the Value Index methodology and the appropriate category weighting can be applied to determine the best fit of vendors and products to your specific needs.

Overall Scoring of Vendors Across Categories

The research finds IBM atop the list, followed by DataKitchen and Microsoft. Companies that place in the top three of a category earn the designation of Leader. IBM has done so in five of the seven categories; Microsoft in four; Alteryx and Databricks in three; Google in two and AWS, DataKitchen, Hitachi Vantara and SAP 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 vendors. Those vendors whose Product Experience have a higher weighted performance to the axis in aggregate of the five product categories place farther to the right, while the performance and weighting for the two Customer Experience categories determines their placement on the vertical axis. In short, vendors that place closer to the upper-right on this chart performed better than those closer to the lower-left.

The research places vendors into one of four overall categories: Assurance, Exemplary, Merit or Innovative. This representation classifies vendors overall weighted performance.

Exemplary: The categorization and placement of vendors in Exemplary (upper right) represent those that performed the best in meeting the overall Product and Customer Experience requirements. The vendors awarded Exemplary are: Alteryx, AWS, BMC, Databricks, Google, IBM, Microsoft and SAP.

Innovative: The categorization and placement of vendors in Innovative (lower right) represent those that performed the best in meeting the overall Product Experience requirements but did not achieve the highest levels of requirements in Customer Experience. The vendor awarded Innovative is: DataKitchen.

Assurance: The categorization and placement of vendors in Assurance (upper left) represent those that achieved the highest levels in the overall Customer Experience requirements but did not achieve the highest levels of Product Experience. The vendor awarded Assurance is: Matillion.

Merit: The categorization for vendors in Merit (lower left) represent those that did not exceed the median of performance in Customer or Product Experience or surpass the threshold for the other three categories. The vendors awarded Merit are: Astronomer, Hitachi Vantara, Infoworks.io, Prefect, Rivery, Stonebranch, StreamSets and Y42.

We warn that close vendor placement proximity should not be taken to imply that the packages evaluated are functionally identical or equally well suited for use by every organization or for a specific process. Although there is a high degree of commonality in how organizations handle DataOps, there are many idiosyncrasies and differences in how they do these functions that can make one vendor’s offering a better fit than another’s for a particular organization’s needs.

We advise organizations to assess and evaluate vendors based on their requirements and use this research as a reference to their own evaluation of a vendor and products.

Product Experience

The process of researching products to address an organization’s needs should be comprehensive. Our Value Index methodology examines Product Experience and how it aligns with an organization’s life cycle of onboarding, configuration, operations, usage and maintenance. Too often, vendors are not evaluated for the entirety of the products; instead, they are evaluated on market execution and vision of the future, which are flawed since they do not represent an organization’s requirements but how the vendor operates. As more vendors orient to a complete product experience, the more robust of an evaluation can be conducted.

The research based on the methodology of expertise identified the weighting of Product Experience to 80% or four-fifths of the overall rating. Importance was placed on the categories as follows: Usability (20%), Capability (20%), Reliability (15%), Adaptability (10%) and Manageability (15%). This weighting impacted the resulting overall ratings in this research. IBM, DataKitchen and Microsoft were designated Product Experience Leaders as a result of their top ranked weighted performance. While not Leaders, Alteryx, Databricks and Google were found to meet a broad range of enterprise DataOps requirements, receiving B- grades. Additionally, SAP performed well in Manageability, and Hitachi Vantara was strong in Adaptability.

Many organizations will only evaluate capabilities for those in IT or administration, but the research identified the criticality of Usability (20% weighting) across a broader set of usage personas that should participate in DataOps.

Customer Experience

The importance of a customer relationship with a vendor is essential to the actual success of the products and technology. The advancement of the Customer Experience and the entire life cycle an organization has with its vendor is critical for ensuring satisfaction in working with that vendor. Technology providers that have Chief Customer Officers area more likely to have greater investments in the customer relationship and focus more on their success. These leaders also need to take responsibility for ensuring the marketing of their commitment is made abundantly clear on website and in the buying process and customer journey.

Our Value Index methodology weights Customer Experience at 20% of the overall rating, or one-fifth, as it relates to the framework of commitment and value to the vendor-customer relationship. The two evaluation categories are Validation (10%) and TCO/ROI (10%),

Appendix: Vendor Inclusion

For inclusion in the Ventana Research DataOps Buyers Guide for 2023, a vendor must be in good standing financially and ethically, have at least $10 million in annual or projected revenue verified using independent sources, or have at least 75 employees, and sell products and provide support on at least two continents. 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 18 months. The vendor must provide a product that supports agile and collaborative data operations and is marketing themselves or products as one of the following: a DataOps tool or platform; a data orchestration tool or platform; a data observability tool or platform.  The research is designed to be independent of the specifics of vendor packaging and pricing. To represent the real-world environment in which businesses operate, we include vendors that offer suites or packages of products that may include relevant individual modules or applications. If a vendor is actively marketing, selling and developing a product for the general market and is reflected on its website that it is within the scope of the research, that vendor is automatically evaluated for inclusion. 

All vendors that offer relevant DataOps products and meet the inclusion requirements were invited to participate in the research evaluation process at no cost to them.

We categorize participation as follows:

Complete participation: The following vendors actively participated and provided completed questionnaires and demonstrations to help in our evaluation of their product: None.

Partial participation: The following vendors provided limited information to help in our evaluation: Alteryx, BMC and DataKitchen.

No participation: The following vendors provided no information or did not respond to our request: AWS, Astronomer, Databricks, Google, Hitachi Vantara, IBM, Infoworks.io, Matillion, Microsoft, Prefect, Rivery, SAP, Stonebranch, StreamSets and Y42.

Vendors 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 their classification and rating, we recommend additional scrutiny when evaluating those vendors.

Products Evaluated

Vendors of Note

There is a very large and growing number of vendors in the DataOps software segment. We did not include vendors that, as a result of our research and analysis, did not satisfy the criteria for inclusion in the Buyer’s Guide.

Most of the vendors that did not meet our inclusion criteria were excluded based on size (either revenue and/or number of employees). Inclusion criteria validation was completed to the best of our ability using information publicly available or through our research. 

Other vendors were excluded based on product suitability: either their products only addressed the orchestration or observability of data stored in a data platform rather than all upstream and downstream stages of a data pipeline, or at the time of evaluation they did not have a generally available product marketed as a tool or platform for data pipeline development, data orchestration or data observability (although some subsequently now do). Others were excluded based on having no published documentation, making it impossible to evaluate the capabilities of the product.

Additionally, only vendors with products that addressed at least two of the three core areas of DataOps functionality (data pipeline development, testing and deployment; data pipeline orchestration; and data pipeline observability) were included in the DataOps platforms evaluation.

The vendors that did not satisfy the criteria for inclusion in the Buyers Guide are listed below as “Vendors of Note.”