Ventana Research recently announced its 2022 Market Agenda for Data, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value and improve business outcomes.
Data volumes continue to grow while data latency requirements continue to shrink. Meanwhile, virtually every organization now finds itself managing data across a hybrid architecture involving on-premises data centers and multiple cloud providers. We assert that by 2025, more than three-quarters of enterprises will have data spread across multiple cloud providers and on-premises data centers, requiring investment in data-management products that span multiple locations. To help identify vendors and products to meet that need, we craft our Market Agenda using our firm’s expertise on business requirements and our knowledge of technology vendors and products. Through our continuous market research, we offer insights and best practices for both the lines of business and IT as well as insights across industry verticals that will help any organization reach its maximum potential.
Ventana Research offers guidance on data and information management to help expand information competencies. In our research, we explore smarter storage, computing, integration, virtualization, governance and preparation in order to uncover, and advocate for, new methods of using information effectively. Our Data expertise includes six focus areas: Analytic Data Platforms, Data Governance, Data Management, Data Operations, Operational Data Platforms and Streaming Data and Events. In 2022, in-depth vendor research on the vendors that provide products for Data Platforms will be conducted.
Analytic data platforms are designed to store, manage, process and analyze data, enabling organizations to leverage their data to operate with greater efficiency across on-premises, hybrid and multi-cloud environments. Analytic Data Platforms support applications used to analyze the business, including decision support, business intelligence (BI), data science and artificial intelligence and machine learning (AI/ML). Analytic data platforms include data warehouses and data lakes, as well as the increasing convergence of data warehouse, data lake and data streaming technologies. We assert that through 2024, data warehouse, data lake and data streaming technologies will converge to create analytic data platforms enabling organizations to collect and analyze all types of operations-generated information. We will continue to study data lakes this year in our ongoing Dynamic Insights research.
Data governance ensures that an organization’s data can be catalogued, trusted and protected. It oversees the way in which organizations control and provide access to data across the organization. Unfortunately, through 2024, more than one-half of all organizations will continue to utilize multiple data catalog technologies, a choice that leads to silos of information technology. Historically the emphasis of data governance was to prevent people from accessing information, but the emphasis has since evolved to be more on how to enable access to information for the right people. As such, good data governance can actually accelerate self-service access to, and analysis of, data. Our Data Governance Benchmark Research investigates organizations’ needs, plans and attitudes on data governance.
The data management focus area looks at the various processes and technologies that enable organizations to organize data sources and types into structured formats to maximize the value of analytic and operational use. Data management covers data integration, as well as master data management, data quality, data observability and data engineering. We assert that by 2024, 6 in ten organizations will adopt data engineering processes that span data integration, transformation and preparation, producing repeatable data pipelines that create more agile information architectures.
Data operations (DataOps) has emerged as an approach to make data integration and preparation more agile. While DataOps therefore overlaps with data management, it is specifically focused on automated data monitoring and the continuous delivery of data into operational and analytical processes. DataOps recognizes the need for enterprises to be flexible and data operations to evolve in response to changing requirements for analytics. DataOps is also a more automated and collaborative approach that involves both data consumers as well as data operators. And I assert that through 2025, awareness of DataOps will continue to increase as organizations adapt their data integration and engineering processes to the growing need for continuous and automated data ingestion, transformation and delivery. We have multiple ongoing Benchmark Research projects studying these issues, including Data Governance and Analytics and Data.
Operational data platforms are designed to store, manage and process data to support worker-, customer- and partner-facing operational applications across on-premises, hybrid and multi-cloud environments. Operational data platforms support applications used to run the business, including finance, operations and supply chain, sales, human capital management, customer experience and marketing. Operational data platforms include relational and non-relational databases (including NoSQL), as well as the increasing convergence of relational and non-relational approaches, along with hybrid operational and analytic processing to support intelligent applications infused with personalization and AI-driven recommendations. We assert that through 2024, 6 in ten organizations will re-examine their current operational database suppliers with a view to supporting more agile and intelligent operational applications and improving fault tolerance.
Today, there is more data than ever, and it is available at an increasingly fast pace. Taking advantage of this data is critical to success, and organizations need guidance on how best to manage and distribute this valuable resource.
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