2024 Market Agenda for Analytics and Data: Automating Intelligence

Ventana Research recently announced its 2024 Market Agenda for Analytics and Data, continuing the guidance we have offered for two decades to help enterprises derive optimal value and improve business outcomes. 

Enterprises of all sizes in all industries and geographies strive to improve efficiency, productivity and customer experiences through analytics and data. Doing so relies on the ability to respond faster to worker and customer requirements for more innovative, data-rich applications and personalized experiences. The need for enterprises to operate smarter requires them to collect, manage, govern and analyze data from multiple sources, including the use of discovery, machine learning, predictive analytics and visualization, as well as generative AI. I assert that by 2026, three-quarters of enterprises will realize their analytics are ineffective without GenAI capabilities to guide the workforce with personalized recommended actions necessary to improve outcomes.  

Ventana Research offers research-based guidance on analytics and data to help enterprises enhance the value of information through the smarter use of data. Going beyond earlier methods of business intelligence, dashboards and reports is essential to ensure that everyone is able to not only access data but is also empowered to act on it to optimize the business. Our Analytics and Data expertise provides a holistic perspective that addresses both the production and consumption of data across an enterprise. It includes six focus areas: Artificial Intelligence and Machine Learning, Analytics, Data Intelligence, Data Operations, Data Platforms and Streaming and Events. Our overarching Analytics and Data Benchmark Research along with our Data Governance Benchmark Research explore each of these topics, providing insights to guide decision-making, while we help enterprises assess, evaluate and select software providers through our Ventana Research Buyers Guides. 

Artificial Intelligence and Machine Learning 

AI and ML involves the development of systems and software capable of automating tasks that have previously required human intelligence. It encompasses machine learning, deep learning and generative AI to deliver capabilities including predictions, recommendations, personalization, speech and visual recognition as well as translation and summarization. To get the most out of AI and ML systems, enterprises need to involve people in business and executive roles outside of the IT department in determining use-cases and success metrics. Generative AI is key to lowering skills and expertise barriers to accessing and understanding data and is being rapidly adopted. For example, six in 10 participants in ISG’s 2023 Banking Survey are actively using or piloting the use of generative AI technology. Further insights into the AI and ML software provider landscape will be given in our 2024 Buyers Guides focused on AI Platforms, GenAI Platforms and MLOps. 

Analytics 

Analytics software is used by business analysts and decision-makers to facilitate the generation of insights from data. It encompasses business intelligence and decision intelligence software, including reports and dashboards, as well as embedded analytics and the development of intelligent applications infused with the results of analytic processes. Although natural language interfaces powered by generative AI reduce the need for technical and domain expertise to query data, it is important for enterprises to be conscious that generative AI does not reduce the need for domain expertise in interpreting results. Further insights into the Analytics software provider landscape can be found in our 2023 Buyers Guides focused on Analytics and Data, Augmented Analytics, Embedded Analytics, Collaborative Analytics and Mobile Analytics. 

Data Intelligence 

Data Intelligence is the combination of data integration, data catalog, data quality, data lineage, metadata management and master data management to facilitate the understanding of how, when and why data is produced and consumed across an organization. It also encompasses AnalyticOps, which is used to deliver agile and collaborative analytics, facilitating self-service access to data that is trusted to fulfil operational and analytics initiatives in compliance with data privacy and security policies and regulatory requirements. Ventana Research’s Data Governance Benchmark Research indicates that the more data catalog users an organization has, the greater the trust the organization has in its data and the higher the level of confidence in the organization’s ability to govern and manage data across the business. Further insights into the Data Intelligence software provider landscape will be given in our 2024 Buyers Guides focused on Data Intelligence, Data Governance, Data Quality, Master Data Management, Data Integration and Application Integration. 

Data Operations 

Data Operations (DataOps) focuses on the application of agile development, DevOps and lean manufacturing by data engineering professionals in support of data production. It encompasses the development, testing, deployment and orchestration of data integration and processing pipelines, along with improved data quality and validity via data monitoring and observability. I assert that through 2026, more than one-half of enterprises will have adopted agile and collaborative data operations practices in order to facilitate responsiveness, avoid repetitive tasks and deliver measurable data reliability improvements. Further insights into the DataOps software provider landscape can be found in our 2023 Buyers Guides focused on DataOps, Data Pipelines, Data Orchestration and Data Observability. 

Data Platforms 

Data Platforms are designed to store, manage, process and query data to support worker- and customer-facing operational applications and/or business intelligence and data science initiatives. Our data platforms focus encompasses relational and non-relational operational databases, as well as data warehouses, data lakes, data lakehouses and other analytic data platform use-cases. Data platforms are increasingly required to span a hybrid architecture of cloud and on-premises data centers as well as multiple local data processing environments and edge devices, although cloud-based environments are increasingly the center of gravity. For two-thirds (66%) of participants in Ventana Research’s Data Lakes Dynamic Insights, the primary data platform used for analytics is cloud based. Building upon the introduction presented in our 2023 Value Index, further insights into the Data Platforms software provider landscape for both operational and analytic workloads will be given in our 2024 Data Platforms Buyers Guides.  

Streaming and Events 

Streaming and Events focuses on the uninterrupted management, processing and analysis of data generated by applications, systems and devices on a continuous basis. It encompasses event brokers, messaging, event management, event-driven architecture, event processing, stream processing and streaming analytics. Streaming and events has traditionally been a niche activity with batch-based data processing being the default. That is changing as demand for real-time interactive applications grows more pervasive. I assert that by 2027, more than one-half of enterprises will adapt their data management and governance processes, taking a holistic approach to managing and governing data in motion alongside data at rest. Enterprises should ensure they do not overlook the fundamental importance of event brokers and messaging in laying the foundation for event-driven architecture and streaming analytics. 

Subscribe to our Ventana Research community to stay up to date on our 2024 research efforts. Check out the Analytics and Data expertise and focus pages for our detailed research agenda and continuously updated 90-day calendar as well as more research facts and best practices. 

Regards,

Matt Aslett