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.
Insight
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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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After working for 17 years in IT, I had a huge personal epiphany: I discovered cybersecurity is my passion and wanted to exclusively pursue that in my career going forward. Let me explain how I had this seminal moment.
As early as I can remember, I’ve always loved technology—especially anything involving computers. I started my professional career in the mid-1990s working in the channel for a boutique systems integration firm in Houston providing technology solutions to Fortune 2000 companies. In my first role, I was a developer building enterprise applications. These were the early days of client-server architecture, where I developed new applications that replaced older legacy systems. Daily discussions of Java, relational databases, and multithreading were commonplace.
Life was great for a few years until my company decided to spin out that part of the business. Fortunately for me, I was able to transition into a network engineering role within the company. I established net-new local area networks for clients, managed existing networks, and occasionally got the opportunity to set up a new firewall here and there. Equipped with years of hands-on technical experience under my belt, I moved into a technical pre-sales role, working closely with account executives selling various technology solutions to clients. In my final channel role after moving to the San Francisco Bay Area, I managed a team of network engineers and oversaw successful professional service delivery for the West region.
In 2013, an opportunity presented itself to join an early-stage Silicon Valley cloud security startup, which I enthusiastically jumped headfirst into—it’s a requirement to work for a startup at least once if you live in the Bay Area, right? In that role, I became educated with the security market landscape and the challenges and use cases security vendors were addressing. Product vendors solving real-world security challenges were and still are extremely interesting to me. This was my lightbulb moment! It was at that point, I realized I wanted to exclusively pursue security in some capacity going forward in my career.
After learning a lot about securing cloud resources and wearing multiple hats, I left the startup world behind to join the competitive and market intelligence team at Tenable, where I became an expert in all things vulnerability management. I supported the sales team in winning deals, advised product management on key roadmap capabilities, features, and use cases, and also identified opportunities for partnerships and acquisitions. I spent over three years at Tenable until their IPO in 2018.
From there, I moved into a market intelligence role at Synopsys, where I was responsible for tracking the broader market landscape, discovering inorganic growth opportunities. I also helped them broaden their application security portfolio in building business cases for the acquisitions of three security companies: Tinfoil Security in 2020, Code Dx in 2021, and WhiteHat Security in 2022.
The Move to ESG
Over the years, I’ve had the good fortune to get to know many well-respected industry analysts who cover the security space. I have admired and appreciated their data and reports, which were critical to me for understanding the direction of the market. So, with over 10 years of experience working for security vendors in addition to my broad technical background, it was a natural fit to transition to an industry analyst role myself.
I am excited to join TechTarget’s Enterprise Strategy Group because it provides an opportunity to leverage my existing knowledge of the security landscape while also assisting vendors at the same time. From my time working on the vendor side, I keenly understand the dynamics of the security market and the challenges that vendors face at different maturity levels, and I look forward to conducting new research.
My core coverage areas include vulnerability and risk management, with my first research projects focused on risk management coming soon—stay tuned! I’ll also be assisting with adjacent areas, including application security, cloud security, and API security, since I have deep expertise with those areas as well. I’m looking forward to working with clients and engaging with companies in the space. If you’d like to get in touch to learn about Enterprise Strategy Group research and how we may be able to help, you can reach me on LinkedIn or send an email to [email protected].