Matt Aslett's Analyst Perspectives

The data platforms market has traditionally been divided between products specifically designed to support operational or analytic workloads, with other market segments having emerged in recent years for data platforms targeted specifically at data science and machine learning (ML), as well as real-time analytics. More recently, we have seen vendor strategies evolving to provide a more consolidated approach, with data platforms designed to address a combination of analytics and data science, as well as hybrid operational and analytic processing. Snowflake, which has been hugely successful in recent years with its cloud-based analytic data platform, is a prime example. The company has expanded its purview to address data engineering and data science, as well as transactional data. Additionally, it now provides users with the ability to access and process data in on-premises environments as part of its strategy to address an increasing range of use cases.

The recent publication of our Value Index research highlights the impact of intelligent applications on the operational data platforms sector. While we continue to believe that, for most use cases, there is a clear, functional requirement for either analytic or operational data platforms, recent growth in the development of intelligent applications infused with the results of analytic processes, such as personalization and artificial intelligence (AI)-driven recommendations, has increasing influence over requirements for operational data platforms to support real-time analytic functionality. Operational data platform vendors, including MongoDB, are responding to these evolving requirements with new functionality to support the development and deployment of intelligent applications. 

Success with streaming data and events requires a more holistic approach to managing and governing data in motion and data at rest. The use of streaming data and event processing has been part of the data landscape for many decades. For much of that time, data streaming was a niche activity, however, with standalone data streaming and event-processing projects run in parallel with existing batch-processing initiatives, utilizing operational and analytic data platforms. I noted that there has been an increased focus on unified approaches that enable the holistic management and governance of data in motion alongside data at rest. One example is the recent emergence of streaming databases designed to combine the incremental processing capabilities of stream-processing engines with the SQL-based analysis and persistence capabilities of traditional databases.

Organizations require faster analytics to continuously improve business operations and stay competitive in today’s market. However, many struggle with slow analytics due to a variety of factors such as slow databases, insufficient data storage capacity, poor data quality, lack of proper data cleansing and inadequate IT infrastructure. Challenges such as data silos can also decrease operational efficiency. And as the data grows, performing complex data modelling becomes challenging for users as they spend more time managing data rather than identifying insights.

I have written about the increased demand for data-intensive operational applications infused with the results of analytic processes, such as personalization and artificial intelligence-driven recommendations. I previously described the use of hybrid data processing to enable analytics on application data within operational data platforms. As is often the case in the data platforms sector, however, there is more than one way to peel an orange. Recent years have also seen the emergence of several analytic data platforms that deliver real-time analytic processing suitable for data-intensive operational applications.

Organizations across various industries collect multiple types of data from disparate systems to answer key business questions and deliver personalized experiences for customers. The expanding volume of data increases complexity, and data management becomes a challenge if the process is manual and rules-based. There can be numerous siloed, incomplete and outdated data sources that result in inaccurate results. Organizations must also deal with concurrent errors – from customers to products to suppliers – to create a complete view of the data. Many vendors, including Tamr, have turned to artificial intelligence and machine learning to overcome the challenges associated with maintaining data quality amid the growing volume and variety of data. I assert that by 2026, more than three-quarters of organizations’ data management processes will be enhanced with artificial intelligence and machine learning to increase automation, accuracy, agility and speed.

I am happy to share insights from our latest Ventana Research Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. The 2023 Analytic Data Platforms Value Index is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect real-world criteria incorporated in a request for proposal to data platform vendors supporting the spectrum of analytic use-cases. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the Product Experience: Adaptability, Capability, Manageability, Reliability and Usability, and two related to the Customer Experience: Total Cost of Ownership/Return on Investment and Validation. This research-based index evaluates the full business and information technology value of analytic data platforms offerings. I encourage you to learn more about our Value Index and its effectiveness as a vendor selection and request for information/requestion for proposal tool.

Ventana Research recently published the 2023 Analytic Data Platforms Value Index. As organizations strive to be more data-driven, increasing reliance on data as a fundamental factor in business decision-making, the importance of the analytic data platform has never been greater. In this post, I’ll share some of my observations about how the analytic data platforms market is evolving.

Ventana Research recently announced its 2023 Market Agenda for Data, continuing the guidance we have offered for two decades to help organizations derive optimal value and improve business outcomes.

Ventana Research recently published the 2023 Operational Data Platforms Value Index. The importance of the operational data platform has never been greater as organizations strive to be more data-driven, incorporating intelligence into operational applications via personalization and recommendations for workers, partners and customers. In this post, I’ll share some of my observations on how the operational data platforms market is evolving.