Executive Summary

Data Platforms

Data platforms provide an environment for organizing and managing the storage, processing, analysis, and presentation of data across an enterprise. Data platforms play a critical role in operational efficiency, supporting and enabling operational applications that are used to run the business, as well as analytic applications that are used to evaluate the business. Without data platforms, enterprises would be reliant on a combination of paper records, time-consuming manual processes, and huge libraries of physical files to record, process and store business information. The extent to which that is unthinkable highlights the level to which modern enterprises, and society as a whole, are reliant on data platforms. Data platforms are complemented by data operations platforms and tools, which are used by data professionals to apply agile development, DevOps and lean manufacturing to data production, as well as data intelligence platforms and tools, which facilitate the understanding of how, when and why data is produced and consumed across an enterprise.

Without data platforms, enterprises would be reliant on a combination of paper records, time-consuming manual processes, and huge libraries of physical files to record, process and store business information.

At the heart of any data platform is the storage and management of a collection of related data. This is typically provided by a database management system (more commonly referred to simply as a database) that provides the data persistence, data management, data processing and data query functionality that enables access to, and interaction with, the stored data. Adoption of cloud computing environments has also led to the widespread use of object stores as a data persistence layer, with query engines such as Apache Spark, Apache Presto and Trino adding the data management, data processing and data query functionality required of a data platform.

In addition to this core persistence, management, processing and query functionality, data platforms also provide additional capabilities targeted at workers in multiple roles, including database administrators, application developers, data engineers and data architects. These roles are typically part of the technology organization rather than business users or managers, but data platforms must increasingly support a range of users with differentiated responsibilities and functional requirements.

Since the 1980s, the data platforms market has been dominated by the relational data model and relational database management systems. However, non-relational data models that pre-date relational, such as the hierarchical model, remain in use today. Recent decades have also seen the proliferation of non-relational data platforms through the growth in the use of NoSQL databases using key-value, document and graph models, as well as data processing frameworks and object storage. One approach does not suit all use cases, however, and enterprises use a variety of data platforms to fulfill the spectrum of requirements for myriad applications. While most data platforms were traditionally deployed on-premises, enterprises are increasingly deploying data platforms on cloud infrastructure or consuming data platform functionality via managed cloud services. Our research shows that almost one-half of enterprises currently use cloud or software-as-a-service (SaaS) products for analytics and data, and an additional one-quarter plan to do so.

Intelligent applications, while operational in nature, rely on real-time analytic processing to deliver functionality, including contextually relevant recommendations, predictions and forecasting driven by machine learning.

When selecting a data platform, there is one fundamental consideration that comes before all others: Is the workload primarily operational or analytic? The data platforms sector has traditionally been segmented between operational data platforms deployed to support applications targeted at business users and decision-makers to run the business and analytic data platforms typically supporting applications used by data and business analysts to analyze the business. Operational data platform workloads include finance, operations and supply chain, sales, human capital management, customer experience and marketing applications. Analytic workloads include decision support, business intelligence (BI), data science, and artificial intelligence and machine learning (AI/ML).

The increasing importance of intelligent operational applications driven by AI is blurring the lines that have traditionally divided the requirements for operational and analytic data platforms, however. Consumers are increasingly engaged with data-driven services that are differentiated by personalization and contextually relevant recommendations. Additionally, worker-facing applications are following suit, targeting users based on their roles and responsibilities. The shift to more agile business processes requires ML for more responsive data platforms and applications.

The need for real-time interactivity has significant implications for the data platform functionality required to support these applications. While there have always been general-purpose databases that could be used for both analytic and operational workloads, traditional architectures have involved the extraction, transformation and loading of data from the operational data platform into an external analytic data platform. This enables the operational and analytic workloads to run concurrently without adversely impacting each other, protecting the performance of both. Over time, dedicated analytic data platforms have also evolved differentiated architectural approaches designed to improve query performance. Intelligent applications, while operational in nature, rely on real-time analytic processing to deliver functionality, including contextually relevant recommendations, predictions and forecasting driven by ML and generative AI (GenAI). While data-driven companies continue to use specialist analytic and data science platforms to train models offline, the need for real-time online predictions and recommendations requires that operational data platforms perform ML inferencing.

The popularization of GenAI has had a significant impact on the requirements for data platforms in the last 18 months, particularly in relation to support for storing and processing vector embeddings. These are multi-dimensional mathematical representations of features or attributes of raw data that are used to support GenAI-based natural language processing (NLP) and recommendation systems. Vector search can also improve accuracy and trust with GenAI via retrieval-augmented generation, which is the process of retrieving vector embeddings representing factually accurate and up-to-date information from a database and combining it with text automatically generated by a large language model (LLM). We assert that through 2027, the development of intelligent applications providing personalized experiences driven by GenAI will increase demand for data platforms capable of supporting hybrid operational and analytic processing. 

Our Data Platforms Buyers Guide is designed to provide a holistic view of a software provider’s ability to serve a combination of both operational and analytic workloads with either a single data platform product or set of data platform products. As such, the Data Platforms Buyers Guide includes the full breadth of operational and analytic functionality, considering the analytic processing capabilities of operational data platforms, and vice versa. Our assessment also considered whether the functionality in question was available from a software provider in a single offering or as a suite of products or cloud services. Software providers that primarily serve and provide only analytic or operational capabilities are represented in separate Buyers Guide research reports.

Ventana Research believes a methodical approach is essential to maximize competitiveness. To improve the performance of an enterprise’s people, process, information and technology components, it is critical to select the right software provider and product. Many enterprises need to improve in this regard. Our research analysis places fewer than 1 in 5 enterprises (18%) at the highest Innovative level of performance in their use of analytics and data. However, caution is appropriate here — technology improvements alone are not enough to improve the use of data in an enterprise. Doing so requires applying a balanced set of upgrades that include efforts to improve people skills and processes. The research finds fewer than 1 in 6 enterprises (15%) at the highest Innovative level of performance for process in relation to analytics and data, and fewer than 1 in 8 (12%) at the Innovative level of performance for people.

To be considered for inclusion in the Data Platforms Buyers Guide, a product must be marketed as a general-purpose data platform, database, database management system, data warehouse, data lake or data lakehouse. The primary use case for the product should be to support worker- and customer-facing operational applications and/or analytics workloads (such as BI or data science). The product should provide the following functional areas at a minimum: data persistence, data management, data processing and data query; database administrator functionality; developer functionality; data engineering functionality; and data architect functionality.

This Buyers Guide report evaluates the following software providers which offer products that address key elements of data platforms to support a combination of both operational and analytic workloads: Actian, Aiven, Alibaba Cloud, AWS, Cloudera, Couchbase, EDB, Google Cloud, Huawei Cloud, IBM, InterSystems, MariaDB, Microsoft, MongoDB, Neo4j, Oracle, Percona, PingCAP, Progress Software, Salesforce, SAP, SingleStore, Tencent Cloud, TigerGraph and VMware by BroadcomBuyers Guide Overview

For over two decades, Ventana Research has conducted market research in a spectrum of areas across business applications, tools and technologies. We have designed the Buyers Guide to provide a balanced perspective of software providers and products that is rooted in an understanding of the business requirements in any enterprise. 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 undertaking contribute to our comprehensive approach to rating software providers in a manner that is based on the assessments completed by an enterprise.

Ventana Research has designed the Buyers Guide to provide a balanced perspective of software providers and products that is rooted in an understanding of business requirements in any enterprise.

This Ventana Research Buyers Guide: Data Platforms is the distillation of over a year of market and product research efforts. It is an assessment of how well software providers’ offerings address enterprises’ requirements for data platforms software. The index is structured to support a request for information (RFI) that could be used in the request for proposal (RFP) process by incorporating all criteria needed to evaluate, select, utilize and maintain relationships with software providers. An effective product and customer experience with a provider 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/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 data platforms to an enterprise’s requirements.

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. We believe it is important to take a comprehensive, research-based approach, since making the wrong choice of data platforms technology can raise the total cost of ownership, lower the return on investment and hamper an enterprise’s ability to reach its full performance potential. In addition, this approach can reduce the project’s development and deployment time and eliminate the risk of relying on a short list of software providers that does not represent a best fit for your enterprise.

Ventana Research believes that an objective review of software providers and products is a critical business strategy for the adoption and implementation of data platforms software and applications. 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 platforms systems and tools and offer this Buyers Guide as both the results of our in-depth analysis of these providers and as an evaluation methodology.

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 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.

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 software provider 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 enterprise 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 enterprise’s established practices or support an initiative that is driving the purchase of new software.

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

Overall Scoring of Software Providers Across Categories

The research finds Oracle atop the list, followed by IBM and Microsoft. Companies that place in the top three of a category earn the designation of Leader. Oracle has done so in five of the seven categories; SAP in four; AWS, InterSystems and Microsoft in three; and Actian, Google Cloud, IBM and Salesforce in one.

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 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 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 places software providers into one of four overall categories: Assurance, Exemplary, Merit or Innovative. This representation classifies providers’ overall weighted performance.

Exemplary: The categorization and placement of software providers in Exemplary (upper right) represent those that performed the best in meeting the overall Product and Customer Experience requirements. The providers rated Exemplary are: Actian, AWS, Couchbase, Google Cloud, IBM, InterSystems, Microsoft, MongoDB, Oracle and SAP.

Innovative: The categorization and placement of software providers 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 providers rated Innovative are: Cloudera, Huawei Cloud and VMware by Broadcom.

Assurance: The categorization and placement of software providers 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 providers rated Assurance are: EDB, Salesforce and SingleStore.

Merit: The categorization of software providers in Merit (lower left) represents those that did not exceed the median of performance in Customer or Product Experience or surpass the threshold for the other three categories. The providers rated Merit are: Aiven, Alibaba Cloud, MariaDB, Neo4j, Percona, PingCAP, Progress Software, Tencent cloud and TigerGraph.

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

We advise enterprises to assess and evaluate software providers based on organizational requirements and use this research as a supplement to internal evaluation of a provider and products.

Product Experience

The process of researching products to address an enterprise’s needs should be comprehensive. Our Value Index methodology examines Product Experience and how it aligns with an enterprise’s life cycle 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, which are flawed since they do not represent an enterprise’s requirements but how the provider operates. As more software providers orient to a complete product experience, evaluations will be more robust.

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 (10%), Capability (25%), Reliability (15%), Adaptability (15%) and Manageability (15%). This weighting impacted the resulting overall ratings in this research. Oracle, IBM and AWS were designated Product Experience Leaders. Oracle, IBM, AWS and InterSystems were designated Product Experience Leaders. While not a Leader Microsoft was also found to meet a broad range of enterprise data platforms requirements.

Many enterprises will only evaluate capabilities for workers in IT or administration, but the research identified the criticality of Usability (10% weighting) across a broader set of usage personas that should participate in data platforms.

Customer Experience

The importance of a customer relationship with a software provider is essential to the actual success of the products and technology. The advancement of the Customer Experience and the entire life cycle an enterprise has with its software provider is critical for ensuring satisfaction in working with that provider. Technology providers that have chief customer officers are 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 this commitment is made abundantly clear on the 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 software provider-customer relationship. The two evaluation categories are Validation (10%) and TCO/ROI (10%), which are weighted to represent their importance to the overall research.

The software providers that evaluated the highest overall in the aggregated and weighted Customer Experience categories are Microsoft, SAP and Oracle. These category leaders best communicate commitment and dedication to customer needs. While not Leaders, InterSystems, AWS and IBM were also found to meet a broad range of enterprise data platforms requirements.

Many software providers we evaluated did not have sufficient information available through their website and presentations. While many have customer case studies to promote success, others lack depth in articulating their commitment to customer experience and an enterprise’s data platforms journey. As the commitment to a software provider is a continuous investment, the importance of supporting customer experience in a holistic evaluation should be included and not underestimated.

Appendix: Software Provider Inclusion

For inclusion in the Ventana Research 2024 Data Platforms Buyers Guide, a provider must be in good standing financially and ethically, sell products and provide support on at least two continents, and have at least $100 million in annual or projected revenue, or at least 50 customers. The principal source of the relevant business unit’s revenue has to be software-related and there must have been at least one major software release in the last 12 months. The product must be marketed as a data platform, database, database management system, data warehouse, data lake or data lakehouse and the primary use-case for the product should be to support worker- and customer-facing operational applications (such as financial, resource planning, human resources, customer management/experience, ecommerce, or supply chain) and/or analytics workloads (business intelligence, or data science). The provider must have a product that provides the following functional areas at a minimum, which are mapped into Buyers Guide capability criteria:     

• Core database functionality (data persistence, management, processing, and query) 
• Database administrator functionality   
• Developer functionality   
• Data engineer functionality  
• Data architect functionality 

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 platforms 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

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.”