Ventana Research Analyst Perspectives

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.

I recently wrote about the potential use cases for distributed SQL databases as well as techniques being employed by vendors to accelerate adoption. Distributed SQL is a term that is used by several vendors to describe operational data platform products that combine the benefits of the relational database model and native support for distributed cloud architecture, including resilience that spans multiple data centers and/or cloud regions. I noted that compatibility with existing database tools and skills was a key factor for these vendors as they lower barriers to developer adoption. A prime example is Cockroach Labs, which highlighted the importance of compatibility and developer efficiency with the recent launch of CockroachDB 22.2.

Maersk is closing its TradeLens joint venture with IBM that began in 2018. It was intended to enable port operators, customs authorities, freight forwarders and importers to track containers and their contents. The system was designed to substantially reduce the inefficiencies and delays created by paper-based systems used for customs, trade finance, tariffs and taxes as well as supply chain and logistics management. The initiative was designed to cut supply chain latency by reducing the time for cargo to clear customs from hours to seconds or minutes. Plus, providing deeper visibility and simpler access to accurate shipping data would make supply chain management easier. Our Dynamic Insights research into Sales and Operations Planning found that 38% of organizations identified an inability to access data as an important issue hampering sales and operations planning.

I have previously written about the functional evolution and emerging use cases for NoSQL databases, a category of non-relational databases that first emerged 15 or so years ago and are now well established as potential alternatives to relational databases. NoSQL is a term that is used to describe a variety of databases that fall into four primary functional categories: key-value stores, wide column stores, document-oriented databases and graph databases. Each is worthy of further exploration, which is why I am examining them over a series of analyst perspectives, starting with graph databases.

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.

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.

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 Operational 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 operational 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.

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.

I am happy to share insights from our latest Ventana Research Value Index, which assesses how well vendors’ offerings meet buyers’ requirements. The 2023 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 that support the spectrum of operational and 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.

Having recently completed the 2023 Data Platforms Value Index, I want to share some of my observations about how the market is evolving. Although this is our inaugural assessment of the market for data platforms, the sector is mature and products from many of the vendors we assess can be used to effectively support operational and analytic use cases.

The shift from on-premises server infrastructure to cloud-based and software-as-a-service (SaaS) models has had a profound impact on the data and analytics architecture of many organizations in recent years. More than one-half of participants (59%) in Ventana Research’s Analytics and Data Benchmark research are deploying data and analytics workloads in the cloud, and a further 30% plan to do so. Customer demand for cloud-based consumption models has also had a significant impact on the products and services that are available from data and analytics vendors. Data platform providers, both operational and analytic, have had to adapt to changing customer demand. The initial response — making existing products available for deployment on cloud infrastructure — only scratched the surface in terms of responding to emerging expectations. We now see the next generation of products, designed specifically to deliver innovation by taking advantage of cloud-native architecture, being brought to market both by emerging startups, and established vendors, including InterSystems.

There is always space for innovation in the data platforms sector, and new vendors continue to emerge at regular intervals with new approaches designed to serve specialist data storage and processing requirements. Factors including performance, reliability, security and scalability provide a focal point for new vendors to differentiate from established vendors, especially for the most demanding operational or analytic data platform requirements. It is never easy, however, for developers of new data platform products to gain significant market traction, given the dominance of the established relational database vendors and cloud providers. Targeting requirements that are not well-served by general purpose data platforms can help new vendors get a toe in the door of customer accounts. The challenge to gaining further market traction is for new vendors to avoid having products become pigeon-holed as only being suitable for a niche set of requirements. This is precisely the problem facing the various distributed SQL database providers.

Organizations are increasingly utilizing cloud object storage as the foundation for analytic initiatives. There are multiple advantages to this approach, not least of which is enabling organizations to keep higher volumes of data relatively inexpensively, increasing the amount of data queried in analytics initiatives. I assert that by 2024, 6 in ten organizations will use cloud-based technology as the primary analytics data platform, making it easier to adopt and scale operations as necessary.

The starting point of an era is never precise and rarely conforms to neat calendar delineations. For example, the start of the 20th century is associated with the outbreak of war in 1914. So I expect that decades from now, the consensus will hold that what became known as the 21st century began in the year 2020, with the pandemic serving as a catalyst that accelerated already existing trends and forced changes to prevailing norms and practices. This and other disruptive events that have followed are reverberating through economic and social networks and will ultimately result in some new equilibrium, but the ructions on the way there will be sharp and ever-present. Large-scale disruptions in most aspects of doing business have forced change on organizations. In this climate, the financial planning and analysis group can play a far more important role by using technology to enhance organizational agility and improve performance.

I have written recently about the similarities and differences between data mesh and data fabric. The two are potentially complementary. Data mesh is an organizational and cultural approach to data ownership, access and governance. Data fabric is a technical approach to automating data management and data governance in a distributed architecture. There are various definitions of data fabric, but key elements include a data catalog for metadata-driven data governance and self-service, agile data integration.

Ventana Research’s Data Lakes Dynamics Insights research illustrates that while data lakes are fulfilling their promise of enabling organizations to economically store and process large volumes of raw data, data lake environments continue to evolve. Data lakes were initially based primarily on Apache Hadoop deployed on-premises but are now increasingly based on cloud object storage. Adopters are also shifting from data lakes based on homegrown scripts and code to open standards and open formats, and they are beginning to embrace the structured data-processing functionality that supports data lakehouse capabilities. These trends are driving the evolution of vendor product offerings and strategies, as typified by Cloudera’s recent launch of Cloudera Data Platform (CDP) One, described as a data lakehouse software-as-a-service (SaaS) offering.

Earlier this year I described the growing use-cases for hybrid data processing. Although it is anticipated that the majority of database workloads will continue to be served by specialist data platforms targeting operational and analytic workloads respectively, there is increased demand for intelligent operational applications infused with the results of analytic processes, such as personalization and artificial intelligence-driven recommendations. There are multiple data platform approaches to delivering real-time data processing and analytics, including the use of streaming data and event processing and specialist, real-time analytic data platforms. We also see operational data platform providers, such as Aerospike, adding analytic processing capabilities to support these application requirements via hybrid operational and analytic processing.

I have written recently about increased demand for data-intensive applications infused with the results of analytic processes, such as personalization and artificial intelligence (AI)-driven recommendations. Almost one-quarter of respondents (22%) to Ventana Research’s Analytics and Data Benchmark Research are currently analyzing data in real time, with an additional 10% analyzing data every hour. There are multiple data platform approaches to delivering real-time data processing and analytics and more agile data pipelines. These include the use of streaming and event data processing, as well as the use of hybrid data processing to enable analytics to be performed on application data within operational data platforms. Another approach, favored by a group of emerging vendors such as Rockset, is to develop these data-intensive applications on a specialist, real-time analytic data platform specifically designed to meet the performance and agility requirements of data-intensive applications.

I recently explained how emerging application requirements were expanding the range of use cases for NoSQL databases, increasing adoption based on the availability of enhanced functionality. These intelligent applications require a close relationship between operational data platforms and the output of data science and machine learning projects. This ensures that machine learning and predictive analytics initiatives are not only developed and trained based on the relationships inherent in operational applications, but also that the resulting intelligence is incorporated into the operational application in real time to support capabilities such as personalization, recommendations and fraud detection. Graph databases already support operational use cases such as social media, fraud detection, customer experience management and recommendation engines. Graph database vendors such as Neo4j are increasingly focused on the role that graph databases can play in supporting data scientists, enabling them to develop, train and run algorithms and machine learning models on graph data in the graph database, rather than extracting it into a separate environment.

Streaming data has been part of the industry landscape for decades but has largely been focused on niche applications in segments with the highest real-time data processing and analytics performance requirements, such as financial services and telecommunications. As demand for real-time interactive applications becomes more pervasive, streaming data is becoming a more mainstream pursuit, aided by the proliferation of open-source streaming data and event technologies, which have lowered the cost and technical barriers to developing new applications that take advantage of data in motion. Ventana Research’s Streaming Data Dynamic Insights enables an organization to assess its relative maturity in achieving value from streaming data. I assert that by 2024, more than one-half of all organizations’ standard information architectures will include streaming data and event processing, allowing organizations to be more responsive and provide better customer experiences.

Organizations are continuously increasing the use of analytics and business intelligence to turn data into meaningful and actionable insights. Our Analytics and Data Benchmark Research shows some of the benefits of using analytics: Improved efficiency in business processes, improved communication and gaining a competitive edge in the market top the list. With a unified BI system, organizations can have a comprehensive view of all organizational data to better manage processes and identify opportunities.