I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well software providers’ offerings meet buyers’ requirements. The Data Platforms Ventana Research Buyers Guide is the distillation of a year of market and product research by ISG and Ventana Research.
Data platforms provide an environment for organizing and managing the storage, processing, analysis, and presentation of data across an enterprise. Data platforms play a
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.
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 Broadcom.
This research-based index evaluates the full business and information technology value of data platforms software offerings. I encourage you to learn more about our Buyers Guide and its effectiveness as a provider selection and RFI/RFP tool.
We urge organizations to do a thorough job of evaluating data platform offerings in this Buyers Guide as both the results of our in-depth analysis of these software providers and as an evaluation methodology. The Buyers Guide can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP and the definition of an RFI.
The Buyers Guide for Data Platforms in 2024 finds Oracle first on the list, followed by IBM and Microsoft.
Software providers that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader.
The Leaders in Product Experience are:
The Leaders in Customer Experience are:
The Leaders across any of the seven categories are:
To be considered for inclusion in the Analytic 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 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 are considered analytic data platforms as we define it: Actian, Aiven, Alibaba Cloud, AWS, Cloudera, Couchbase, Databricks, Dremio, EDB, Exasol, Google Cloud, Huawei Cloud, IBM, Incorta, InterSystems, KX, MariaDB, Microsoft, MongoDB, Neo4j, Oracle, OpenText, Percona, PingCAP, Progress Software, Salesforce, SAP, SingleStore, Snowflake, SQream, Starburst, Tencent Cloud, Teradata, TigerGraph and VMware by Broadcom.
The Buyers Guide for Analytic Data Platforms in 2024 finds Oracle first on the list, followed by Teradata and IBM.
Software providers that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader.
The Leaders in Product Experience are:
The Leaders in Customer Experience are:
The Leaders across any of the seven categories are:
To be considered for inclusion in the Operational Data Platforms Buyers Guide, a product must be marketed as a general-purpose data platform, database, or database management system. The primary use case for the product should be to support worker- and customer-facing operational applications. 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 are considered operational data platforms as we define it: Actian, Aerospike, Aiven, Alibaba Cloud, AWS, Cloudera, Cockroach Labs, Couchbase, DataStax, EDB, Google Cloud, Huawei Cloud, IBM, InterSystems, MariaDB, Microsoft, MongoDB, Neo4j, Oracle, Percona, PingCAP, PlanetScale, Progress Software, Redis, Salesforce, SAP, ScyllaDB, SingleStore, Tencent Cloud, TigerGraph, VMware by Broadcom and Yugabyte.
The Buyers Guide for Operational Data Platforms in 2024 finds Oracle first on the list, followed by IBM and Microsoft.
Software providers that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader.
The Leaders in Product Experience are:
The Leaders in Customer Experience are:
The Leaders across any of the seven categories are:
The overall performance chart provides a visual representation of how providers rate across product and customer experience. Software providers with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, providers that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as providers of “merit.” Software providers that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating.
Note that close provider scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every enterprise or process. Although there is a high degree of commonality in how organizations handle data platforms, there are many idiosyncrasies and differences that can make one provider’s offering a better fit than another.
Our firm has made every effort to encompass in this Buyers Guide the overall product and customer experience from our data platforms blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which software provider and products best fit an enterprise’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects.
You can find more details on our community as well as on our expertise in the research for these Buyers Guides:
Regards,
Matt Aslett