In today’s rapidly evolving cybercrime landscape, cyber-risk management and threat prevention are key for effective cloud threat detection and response practices. However, recent research by TechTarget’s Enterprise Strategy Group found that the speed and scale of modern development cycles presents unique challenges for the security teams tasked with protecting their organizations’ valuable cloud assets.
Insight
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As cloud adoption increases across industries, organizations are investing in cloud detection and response (CDR) tools and technologies to protect the business-critical applications running in cloud environments. Recent research by TechTarget’s Enterprise Strategy Group revealed that organizations face myriad challenges in this endeavor, with spending plans and new feature deployments on the horizon to deal with them.
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The need to modernize cybersecurity strategies to keep pace with IT innovation is clear. Zero-trust architectures have taken the pole position as the best approach to achieve this goal. Unfortunately, the breadth of the initiative and the nuance between zero-trust strategies and the tools supporting these strategies can become lost, causing confusion. IT and security leaders need guidance and proof points from early adopters to avoid false starts and more quickly see positive results.
To learn more about these trends, download the free infographic, Trends in Zero Trust: Strategies and Practices Remain Fragmented, but Many Are Seeing Success.
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Today, it is common for organizations to deploy production and mission-critical applications across multiple public cloud service (e.g., IaaS) providers. Modern application environments are driving this trend as organizations seek to optimize performance and quickly and easily set up network connections to these cloud providers.
To learn more about these trends, download the free infographic, Enabling Modern Business With Multi-cloud Networking.
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Department heads the world over are hearing mandates to infuse data science and machine learning (ML) into their operations as quickly as possible. However, ascertaining the goals of these projects and securing the requisite budget are not simple tasks. Recent research by TechTarget’s Enterprise Strategy Group revealed that funding and confusion follow ML and data science initiatives almost everywhere they appear.
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The use of artificial intelligence (AI) and machine learning (ML) to supercharge business operations has captured the imaginations of stakeholders from every business line and department, especially with the rapid popularity of generative artificial intelligence (GenAI) tools. Along with all this excitement comes myriad concerns regarding the ethical use of such tools. TechTarget’s Enterprise Strategy Group recently surveyed data professionals to find out how they’re balancing AI use with ethical and governance standards.
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Informing business decisions with the valuable insights hidden within troves of data can only be accomplished with robust data science practices. And those data science practices cannot get off the ground without educated, motivated, and well-supported data scientists. Recent research by TechTarget’s Enterprise Strategy Group revealed several key factors can improve (or hinder) the development of these teams and professionals in their pursuit of data science goals.
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Data science has established itself as a critical factor to success in today’s competitive business climate. Recent research by TechTarget’s Enterprise Strategy Group found that organizations purchasing supporting data science technologies are looking for help with several critical stages of the data lifecycle.
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Paramount to the success of data science initiatives is enabling as many users as possible within the organization to work with the data and tools that make these projects actionable. Recent research by TechTarget’s Enterprise Strategy Group examined the experiences of data science stakeholders and where they see room for improvement in terms of team collaboration and technology support.
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Organizations across industries are making data science a strategic charter as they seek new operational efficiencies and competitive advantage. Recent research by TechTarget’s Enterprise Strategy Group revealed that agreeing on the foundational elements of data science and how to measure success are critical yet evolving elements of this consequential discipline.
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As businesses come to understand the benefits of streamlining the machine learning (ML) lifecycle to faster achieve their data-driven goals, their focus turns to optimizing ML operations (MLOps). Recent research by TechTarget’s Enterprise Strategy Group revealed vast room for improvement in terms of deploying models to production and overcoming various challenges with MLOps.
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This Complete Survey Results presentation focuses on the current state and maturity of contact center technology and how it is deployed, how contact center buying teams are structured, how the market convergence of UCaaS and CCaaS affects these dynamics, and the current prioritization of artificial intelligence within contact center strategies.
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