Whit Walters, Author at Gigaom

2025 Predictions: Cloud Architectures, Cost Management and Hybrid By Design

Whit Walters — Mon, 23 Dec 2024 14:32:31 +0000

In this episode of our predictions series, we consider the evolving nature of Cloud, across architecture, cost management, and, indeed, the lower levels of infrastructure. We asked our analysts Dana Hernandez, Ivan McPhee, Jon Collins, Whit Walters, and William McKnight for their thoughts.

Jon: We’re seeing a maturing of thinking around architecture, not just with cloud computing but across technology provision. Keep in mind that what we know as Cloud is still only 25% of the overall space – the other three quarters are on-premise or hosted in private data centers. It’s all got to work together as a single notional platform, or at least, the more accurate we can make this, the more efficient we can be.

Whilst the keyword may be ‘hybrid’, I expect to see a shift from hybrid environments by accident, towards hybrid by design – actively making decisions based on performance, cost, and indeed governance areas such as sovereignty. Cost management will continue to catalyze this trend, as illustrated by FinOps.

Dana: FinOps is evolving, with many companies considering on-prem or moving workloads back from the Cloud. At FinOpsX, companies were looking at blended costs of on-prem and Cloud. Oracle has now joined the big three, Microsoft, Google, and AWS, and it’ll be interesting to see who else will jump in.

Jon: Another illustration is repatriation, moving workloads away from the Cloud and back on-premise.

William: Yes, repatriation is accelerating, but Cloud providers might respond by 2025, likely through more competitive pricing and technical advancements that offer greater flexibility and security. We’re still heavily moving to the Cloud, and repatriation might take a few years to slow down.

Whit: The vendor response to repatriation has been interesting. Oracle with Oracle Cloud Infrastructure (OCI), for example, is undercutting competitors with their pricing model, but there’s skepticism—clients worry Oracle might increase costs later through licensing issues.

Jon: We’re also seeing historically pure-play Cloud providers move to an acceptance of hybrid models, even though they probably wouldn’t say that out loud. AWS’ Outposts on-premise cloud offering, for example, can now work with local storage from NetApp, and it’s likely this type of partnership will accelerate. I maintain that “Cloud” should be seen primarily as an architectural construct around dynamic provisioning and elastic scaling, and secondarily around who the provider – recognizing that hosting companies can do a better job of resilience. Organizations need to put architecture first.

Ivan: We’ll also see more cloud-native tools to manage those workloads. For instance, on the SASE/SSE side, companies like Cato Networks are seeing success because people don’t want to install physical devices across the network. We also see this trend in NDR with companies like Lumu Technologies, where security solutions are cloud-native rather than on-premises.

Cloud-native solutions like Cato Networks and Lumu Technologies have more pricing flexibility than those tied to hardware components. They will be better positioned to adjust pricing to drive adoption and growth than traditional on-premises solutions. Some vendors are exploring value-based pricing, considering factors like customer business value to get into strategic accounts. This could be an exciting shift as we move into the future.

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GigaOm Key Criteria for Evaluating Primary Storage Solutions

Whit Walters — Thu, 19 Dec 2024 18:31:05 +0000

Primary storage has evolved significantly beyond its traditional role of structured data management. Today’s solutions must address a complex array of enterprise requirements that span on-premises, cloud, and edge environments. While continuing to provide the persistent block storage that serves as the foundation for mission-critical applications and databases, modern primary storage systems are increasingly called upon to support emerging workloads like artificial intelligence (AI) and machine learning (ML).

Organizations across industries depend on primary storage to support their most critical business operations, from processing financial transactions to powering customer-facing applications. As the technology has matured, solutions now offer sophisticated data services, automation capabilities, and integration features that extend far beyond basic storage functions.

The modern storage infrastructure landscape presents several key imperatives for organizations. They must support an increasingly diverse mix of workloads while meeting stringent environmental sustainability targets. Data mobility across different environments needs to be seamless, even as organizations strengthen their defenses against ransomware and other cybersecurity threats. All of this must be accomplished while maintaining cost efficiency in the face of relentless data growth and simplifying operations through increased automation.

Business Imperative
For CxOs, primary storage represents both a critical operational requirement and a strategic investment. Modern solutions must deliver beyond traditional metrics of performance and capacity to support key business initiatives. Organizations now demand storage solutions that can accelerate digital transformation efforts while meeting environmental, social, and governance (ESG) goals through improved efficiency. These systems must enable AI/ML and analytics initiatives while ensuring business continuity and cyber resilience. Additionally, they need to optimize infrastructure costs and provide robust support for hybrid and multicloud strategies.

The stakes are high, as primary storage decisions can significantly impact an organization’s ability to innovate, compete, and operate efficiently in today’s data-driven business environment. Organizations must carefully evaluate solutions that meet their current needs and provide the flexibility and capabilities to support future growth and emerging technologies.

Sector Adoption Score
To help executives and decision-makers assess the potential impact and value of deploying a primary storage solution, this GigaOm Key Criteria report provides a structured assessment of the sector across five factors: benefit, maturity, urgency, impact, and effort. By scoring each factor based on how strongly it compels or deters adoption of a primary storage solution, we provide an overall Sector Adoption Score (Figure 1) of 4 out of 5, with 5 indicating the strongest possible recommendation to adopt. This indicates that a primary storage solution is a credible candidate for deployment and worthy of thoughtful consideration.

The factors contributing to the Sector Adoption Score for primary storage are explained in more detail in the Sector Brief section that follows.

Key Criteria for Evaluating Category Solutions

Sector Adoption Score

Deters
Adoption

Discourages
Adoption

Merits
Consideration

Encourages
Adoption

Compels
Adoption

Figure 1. Sector Adoption Score for Primary Storage

This is the fifth year that GigaOm has reported on the primary storage space in the context of our Key Criteria and Radar reports. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Key Criteria report highlights the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) for selecting an effective primary storage solution. The companion GigaOm Radar report identifies vendors and products that excel in those decision criteria. Together, these reports provide an overview of the market, identify leading primary storage offerings, and help decision-makers evaluate these solutions so they can make a more informed investment decision.

GIGAOM KEY CRITERIA AND RADAR REPORTS

The GigaOm Key Criteria report provides a detailed decision framework for IT and executive leadership assessing enterprise technologies. Each report defines relevant functional and nonfunctional aspects of solutions in a sector. The Key Criteria report informs the GigaOm Radar report, which provides a forward-looking assessment of vendor solutions in the sector.

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The evolving revolution: AI in 2025

Whit Walters — Thu, 19 Dec 2024 17:27:34 +0000

AI was 2024’s hot topic, so how is it evolving? What are we seeing in AI today, and what do we expect to see in the next 12-18 months? We asked Andrew Brust, Chester Conforte, Chris Ray, Dana Hernandez, Howard Holton, Ivan McPhee, Seth Byrnes, Whit Walters, and William McKnight to weigh in.

First off, what’s still hot? Where are AI use cases seeing success?

Chester: I see people leveraging AI beyond experimentation. People have had the opportunity to experiment, and now we’re getting to a point where true, vertical-specific use cases are being developed. I’ve been tracking healthcare closely and seeing more use-case-specific, fine-tuned models, such as the use of AI to help doctors be more present during patient conversations through auditory tools for listening and note-taking.

I believe ‘small is the new big’—that’s the key trend, such as hematology versus pathology versus pulmonology. AI in imaging technologies isn’t new, but it’s now coming to the forefront with new models used to accelerate cancer detection. It has to be backed by a healthcare professional: AI can’t be the sole source of diagnoses. A radiologist needs to validate, verify, and confirm the findings.

Dana: In my reports, I see AI leveraged effectively from an industry-specific perspective. For instance, vendors focused on finance and insurance are using AI for tasks like preventing financial crime and automating processes, often with specialized, smaller language models. These industry-specific AI models are a significant trend I see continuing into next year.

William: We’re seeing cycles reduced in areas like pipeline development and master data management, which are becoming more autonomous. An area gaining traction is data observability—2025 might be its year.

Andrew: Generative AI is working well in code generation—generating SQL queries and creating natural language interfaces for querying data. That’s been effective, though it’s a bit commoditized now.

More interesting are advancements in the data layer and architecture. For instance, Postgres has a vector database add-in, which is useful for retrieval-augmented generation (RAG) queries. I see a shift from the “wow” factor of demos to practical use, using the right models and data to reduce hallucinations and make data more accessible. Over the next two or three years, vendors will move from basic query intelligence to creating more sophisticated tools.

How are we likely to see large language models evolve?

Whit: Globally, we’ll see AI models shaped by cultural and political values. It’s less about technical developments and more about what we want our AIs to do. Consider Elon Musk’s xAI, based on Twitter/X. It’s uncensored—quite different from Google Gemini, which tends to lecture you if you ask the wrong question.

Different providers, geographies, and governments will tend to move either towards free-er speech, or will seek to control AI’s outputs. The difference is noticeable. Next year, we’ll see a rise in models without guardrails, which will provide more direct answers.

Ivan: There’s also a lot of focus on structured prompts. A slight change in phrasing, like using “detailed” versus “comprehensive,” can yield vastly different responses. Users need to learn how to use these tools effectively.

Whit: Indeed, prompt engineering is crucial. Depending on how words are embedded in the model, you can get drastically different answers. If you ask the AI to explain what it wrote and why, it forces it to think more deeply. We’ll see domain-trained prompting tools soon—agentic models that can help optimize prompts for better outcomes.

How is AI building on and advancing the use of data through analytics and business intelligence (BI)?

Andrew: Data is the foundation of AI. We’ve seen how generative AI over large amounts of unstructured data can lead to hallucinations, and projects are getting scrapped. We’re seeing a lot of disillusionment in the enterprise space, but progress is coming: we’re starting to see a marriage between AI and BI, beyond natural language querying.

Semantic models exist in BI to make data more understandable and can extend to structured data. When combined, we can use these models to generate useful chatbot-like experiences, pulling answers from structured and unstructured data sources. This approach creates business-useful outputs while reducing hallucinations through contextual enhancements. This is where AI will become more grounded, and data democratization will be more effective.

Howard: Agreed. BI has yet to work perfectly for the last decade. Those producing BI often don’t understand the business, and the business doesn’t fully grasp the data, leading to friction. However, this can’t be solved by Gen AI alone, it requires a mutual understanding between both groups. Forcing data-driven approaches without this doesn’t get organizations very far.

What other challenges are you seeing that might hinder AI’s progress?

Andrew: The euphoria over AI has diverted mindshare and budgets away from data projects, which is unfortunate. Enterprises need to see them as the same.

Whit: There’s also the AI startup bubble—too many startups, too much funding, burning through cash without generating revenue. It feels like an unsustainable situation, and we’ll see it burst a bit next year. There’s so much churn, and keeping up has become ridiculous.

Chris: Related, I am seeing vendors build solutions to “secure” GenAI / LLMs. Penetration testing as a service (PTaaS) vendors are offering LLM-focused testing, and cloud-native application protection (CNAPP) has vendors offering controls for LLMs deployed in customer cloud accounts. I don’t think buyers have even begun to understand how to effectively use LLMs in the enterprise, yet vendors are pushing new products/services to “secure” them. This is ripe for popping, although some “LLM” security products/services will pervade.

Seth: On the supply chain security side, vendors are starting to offer AI model analysis to identify models used in environments. It feels a bit advanced, but it’s starting to happen.

William: Another looming factor for 2025 is the EU Data Act, which will require AI systems to be able to shut off with the click of a button. This could have a big impact on AI’s ongoing development.

The million-dollar question: how close are we to artificial general intelligence (AGI)?

Whit: AGI remains a pipe dream. We don’t understand consciousness well enough to recreate it, and simply throwing compute power at the problem won’t make something conscious—it’ll just be a simulation.

Andrew: We can progress toward AGI, but we must stop thinking that predicting the next word is intelligence. It’s just statistical prediction—an impressive application, but not truly intelligent.

Whit: Exactly. Even when AI models “reason”, it’s not true reasoning or creativity. They’re just recombining what they’ve been trained on. It’s about how far you can push combinatorics on a given dataset.

Thanks all!

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GigaOm Key Criteria for Evaluating Scale-Out Storage Solutions

Whit Walters — Mon, 16 Dec 2024 14:08:21 +0000

The digital universe is expanding at an astonishing pace and, with it, the volume of unstructured data that organizations must manage and harness. From scientific research to media production, from healthcare records to financial transactions, unstructured data fuels innovation and drives business value. Scale-out storage has emerged as the linchpin of modern data infrastructure, providing the flexibility, scalability, and performance required to address the challenges of this data deluge.

Traditional storage architectures, with their inherent limitations in terms of capacity and performance, are ill-equipped to handle the exponential growth of unstructured data. Scale-out storage, in contrast, allows organizations to seamlessly add storage nodes as needed, expanding capacity and performance in lockstep with data growth. This ensures that data remains readily accessible and available, even as it reaches petabyte or exabyte scale.

Scale-out storage is not merely about capacity. It’s about empowering organizations to extract maximum value from their data. By leveraging advanced technologies like object storage, distributed file systems, and software-defined storage, scale-out solutions enable efficient data management, streamlined workflows, and improved collaboration.

Any organization grappling with the challenges of managing large volumes of unstructured data stands to benefit from scale-out storage. This includes industries such as media and entertainment, healthcare, life sciences, financial services, government, and education. From high-performance computing clusters to cloud-native applications, scale-out storage is a versatile solution that can be tailored to meet diverse needs.

Business Imperative
For C-level executives, scale-out storage is a strategic imperative. The ability to manage and leverage data effectively is a key competitive differentiator in today’s data-driven economy. Scale-out storage enables organizations to unlock new business opportunities, accelerate innovation, and improve operational efficiency.

The scale-out storage landscape is constantly evolving, with vendors pushing the boundaries of innovation. As organizations continue to grapple with the challenges of data growth, complexity, and security, scale-out storage will play an increasingly critical role in ensuring data accessibility, resilience, and value. This report delves into the key criteria for evaluating scale-out storage solutions, empowering IT leaders to make informed decisions that align with their organization’s unique needs and long-term data management strategies.

Sector Adoption Score
To help executives and decision-makers assess the potential impact and value of a scale-out storage solution deployment to the business, this GigaOm Key Criteria report provides a structured assessment of the sector across five factors: benefit, maturity, urgency, impact, and effort. By scoring each factor based on how strongly it compels or deters adoption of a scale-out storage solution, we provide an overall Sector Adoption Score (Figure 1) of 4.3 out of 5, with 5 indicating the strongest possible recommendation to adopt. This indicates that a scale-out storage solution is a credible candidate for deployment and worthy of thoughtful consideration.

The factors contributing to the Sector Adoption Score for scale-out storage are explained in more detail in the Sector Brief section that follows.

Key Criteria for Evaluating Scale-Out Storage Solutions

Sector Adoption Score

Deters
Adoption

Discourages
Adoption

Merits
Consideration

Encourages
Adoption

Compels
Adoption

Figure 1. Sector Adoption Score for Scale-Out Storage

This is the fifth year that GigaOm has reported on the scale-out storage space in the context of our Key Criteria and Radar reports. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Key Criteria report highlights the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) for selecting an effective scale-out storage solution. The companion GigaOm Radar report identifies vendors and products that excel in those decision criteria. Together, these reports provide an overview of the market, identify leading scale-out storage offerings, and help decision-makers evaluate these solutions so they can make a more informed investment decision.

GIGAOM KEY CRITERIA AND RADAR REPORTS

The post GigaOm Key Criteria for Evaluating Scale-Out Storage Solutions appeared first on Gigaom.

Where’s Security Going in 2025?

Whit Walters — Mon, 16 Dec 2024 11:42:12 +0000

Few areas of technology are moving as fast as security, so what gives and how is it going to evolve in 2025? We asked our analysts Andrew Brust, Andrew Green, Chester Conforte, Chris Ray, Howard Holton, Ivan McPhee, Stan Wisseman, and Whit Walters for their thoughts.

First off – is the future of cybersecurity protection agentless?

Andrew G: We are seeing the growth of eBPF, which offers more stability compared to past agent-based systems like CrowdStrike. eBPF has built-in verification mechanisms, like memory limits and timeouts, which help to prevent issues like the blue screen of death.

I’ve also seen eBPF-based alternatives that handle runtime security in the kernel without agents, with built-in verification. Note that you can do both kernel and external analysis. Some vendors, like Wiz, gather telemetry in the kernel and send it to the cloud for processing and display.

Whit: That ties back to the business model, especially after the disruption caused by the CrowdStrike outage. Many vendors are moving towards agentless solutions, and this trend is accelerating.

Howard: Analysis has to happen somewhere, even if it’s at the kernel level. If we’re analyzing kernel traffic externally, it’s not built into the kernel, which raises questions. It could be unnecessarily generating new network traffic and the trust needed for kernel access. We need to ensure companies are responsible for maintaining kernel reliability. Stewardship is key.

Chris: Agentless is popular for good reason; however, security doesn’t live in a vacuum. It was previously acceptable to have multiple independent endpoint agents, for detection and response, management, and security. This is no longer the case: all-in-one solutions, or those tightly integrated through official partnerships, have been winning the hearts and minds of security teams. One example is CrowdStrike’s Falcon, which can be licensed to perform EDR, MDR, and (combined with Veeam) recovery.

What security developments are we seeing at the edge?

Ivan: We will see more edge computing and AI: combining 5G with Internet of Things (IoT) will be a major trend next year. However, the increase in rollouts means a broader attack surface, which will drive more regulations for protection. We’re also seeing more deployments of 5G worldwide, and I expect a rapid increase in private and hybrid 5G networks.

Seth: Agreed – as a result, companies are moving toward machine-based identity management.

Stan: We’re also seeing improvements in vulnerability management for IoT, through more frequent firmware updates and the integration of encryption to prevent data exposure. Network or micro-segmentation is becoming more prevalent, especially in sectors like automotive, where adoption was relatively slow. However, given the industry’s lengthy four to five-year rollout cycles, forward-thinking measures are essential to mitigate risks effectively over the long term.

Howard: We’re finally seeing zero-trust concepts becoming feasible for average organizations. Micro-segmentation, which has been valuable but hard to implement for smaller organizations, is now more achievable due to better automation, rollout, and maintenance tools. This will improve the maturity of the zero-trust model.

Chester: I’ve noticed a trend where some established players move away from segmenting everything to focusing on the critical assets—essentially, a more risk-based approach. They’re asking simple questions like, “What are the crown jewels?” and then focusing segmentation efforts there. It’s a more pragmatic approach.

Cyber insurance is on the rise, so what are the ramifications?

Stan: While cyber insurance has become increasingly popular among executives, the escalating costs associated with breaches have put pressure on insurers and underwriters to ensure firms are protecting their assets. As a result, insurers are implementing more stringent due diligence requirements, making cyber insurance more challenging and costly.

Insurers are shifting from point-in-time questionnaires to more robust, periodic assessments. Some insurers employ third-party firms to conduct penetration tests to verify active security controls like multi-factor authentication (MFA). Although continuous testing isn’t yet required, insurers supplement their point-in-time evaluations with more frequent and rigorous checks.

Howard: The insurance industry is complex. Insurers must balance rigorous protection standards with the need to remain attractive to customers. If they’re significantly stricter than their competitors, they’ll lose business, which makes it a constant struggle between thorough protection and marketability.

I’m not sure continuous security validation is entirely a good thing. Security organizations are often not equipped to handle a constant influx of issues. Many customers are excited about continuous testing but need to adjust their operating model to accelerate how they deal with the resulting security incidents.

Finally, how ready do organizations need to be for quantum security?

Stan: While quantum computing may not be a practical reality by 2025, preparing for its impact on cybersecurity is essential now. Quantum computing will fundamentally challenge current digital asset protection best practices, and vendors are already working on how best to implement quantum-resistant algorithms.

In a post-quantum computing world, understanding the potential exposure of sensitive data is crucial. Organizations must begin assessing vulnerabilities across new and legacy systems to identify where updated controls and governance are needed. While quantum-resistant solutions are being developed, implementing them to fully protect data in a PQC environment will take time, making it essential to plan strategically and act early.

Organizations must recognize that quantum threats won’t only compromise PII data but could also erode competitive advantages and intellectual assets. To protect these sensitive assets, now is the time to start considering how to address the quantum computing challenges of tomorrow.

Andrew B: Quantum computing was on the verge of becoming a big phenomenon, gaining attention and hype. Then ChatGPT came along and drew away both attention and funding from quantum startups. Some of those startups are doing really interesting work—they remind me of the supercomputing startups in the ’80s. Quantum has a lot of potential beyond security, but it’s in a kind of suspended animation because AI has diverted so many resources.

That situation may protect us for now, but if private sector funding dries up, it leaves room for nation-state actors to advance quantum on their own.

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GigaOm Radar for Primary Storage

Whit Walters — Fri, 13 Dec 2024 16:00:22 +0000

Primary storage remains a cornerstone of enterprise IT infrastructure, providing the foundation for mission-critical applications, databases, and a growing range of workloads, including artificial intelligence and machine learning. As organizations navigate digital transformation and evolving technological landscapes, the demands on primary storage systems have expanded beyond traditional performance and capacity metrics to include broader strategic objectives.

This report examines primary storage solutions that deliver high performance, reliability, and advanced data services. These capabilities are critical for organizations of all sizes seeking to maintain competitive advantages through optimized data management and infrastructure efficiency. The primary storage market has reached a new level of maturity, with vendors offering sophisticated features such as AI-driven analytics, ransomware protection, and seamless cloud integration alongside traditional storage functionalities.

This year’s evaluation focuses on solutions that provide enterprise-grade primary storage requirements, including both traditional storage arrays and software-defined storage options. Our assessment offers an analysis of comprehensive primary storage platforms and their associated data services, emphasizing their role as the backbone for mission-critical enterprise workloads.

This is our fifth year evaluating the primary storage space in the context of our Key Criteria and Radar reports. This report builds on our previous analysis of large enterprise and mid-size solutions and considers how the market has evolved over the last year.

This GigaOm Radar report examines 21 of the top primary storage solutions and compares offerings against the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) outlined in the companion Key Criteria report. Together, these reports provide an overview of the market, identify leading primary storage offerings, and help decision-makers evaluate these solutions so they can make a more informed investment decision.

GIGAOM KEY CRITERIA AND RADAR REPORTS

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GigaOm Radar for Scale-Out Storage

Whit Walters — Mon, 09 Dec 2024 16:00:41 +0000

In today’s data-driven landscape, organizations face an unprecedented surge in volumes of unstructured data. From scientific research to financial transactions, this data explosion demands robust, flexible storage solutions. Scale-out storage has emerged as a critical technology for addressing these challenges, offering a compelling combination of scalability, performance, and adaptability.

What, Why, and Who

Scale-out storage allows organizations to expand their storage infrastructure seamlessly by adding nodes, increasing both capacity and performance as needed. This approach is crucial for several reasons:

Data growth: It accommodates the exponential growth of unstructured data without compromising accessibility or performance.
Flexibility: It supports a wide range of workloads, from high-performance computing to cloud-native applications.
Efficiency: It enables better resource utilization and simplifies management compared to traditional scale-up architectures.

This technology is particularly vital for industries dealing with massive data volumes, including healthcare, media and entertainment, financial services, and research institutions. However, its relevance extends to any organization seeking to future-proof its data infrastructure.

Business Imperative

For C-level executives, scale-out storage represents more than a technical solution; it’s a strategic asset. In an era when data is a key differentiator, the ability to store, access, and analyze vast amounts of information efficiently can significantly impact an organization’s competitiveness and innovation capacity.

Adopting scale-out storage solutions can lead to:

Improved operational efficiency through simplified management and reduced infrastructure complexity.
Enhanced agility in responding to changing data needs and business requirements.
Better support for data-intensive applications and analytics, driving innovation and insight.
Reduced total cost of ownership over time, especially for organizations with rapidly growing data volumes.

As data continues to grow in volume and importance, the choice of storage infrastructure becomes increasingly critical. Scale-out storage provides the foundation upon which organizations can not only manage their current data challenges but also leverage future opportunities in data analytics, artificial intelligence (AI), and machine learning (ML).

Given the evolving nature of data management challenges and the continuous advancements in storage technologies, organizations should regularly reassess their storage strategies to ensure they remain aligned with business objectives and technological capabilities.

This is our fifth year evaluating the scale-out storage space in the context of our Key Criteria and Radar reports. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Radar report examines 19 of the top scale-out storage solutions and compares offerings against the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) outlined in the companion Key Criteria report. Together, these reports provide an overview of the market, identify leading scale-out storage offerings, and help decision-makers evaluate these solutions so they can make a more informed investment decision.

GIGAOM KEY CRITERIA AND RADAR REPORTS

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From Products to Customers: Delivering Business Transformation At Scale

Whit Walters — Mon, 09 Dec 2024 11:27:22 +0000

Transformation is a journey, not a destination – so how to transform at scale? GigaOm Field CTOs Darrel Kent and Whit Walters explore the nuances of business and digital transformation, sharing their thoughts on scaling businesses, value-driven growth, and leadership in a rapidly evolving world.

Whit: Darrel, transformation is such a well-used word these days—digital transformation, business transformation. It’s tough enough at a project level, but for enterprises looking to grow, where should they begin?

Darrel: You’re right. Transformation has become one of those overused buzzwords, but at its core, it’s about fundamental change. What is digital transformation? What is business transformation? It’s about translating those big concepts into value-based disciplines—disciplines that drive real impact.

Whit: That sounds compelling. Can you give us an example of what that looks like in practice – how does transformation relate to company growth?

Darrel: Sure. Think of a company aiming to grow from 1 billion, to 2 billion, to 5 billion in revenue. That’s not just a numbers game; it’s a journey of transformation. To get to 1 billion, you can get there by focusing on product excellence. But you won’t get to 2 billion based on product alone – you need more. You need to rethink your approach to scaling—whether it’s through innovation, operations, or culture. Finance needs to invest strategically, sales needs to evolve, and leadership must align every decision with long-term goals.

Whit: It’s a fascinating shift. So, scaling isn’t just about selling more products?

Darrel: Exactly. Scaling requires a transformation in how you deliver value. For example, moving beyond transactional sales to consultative relationships. It’s about operational efficiency, customer experience, and innovation working together to create value at scale. I call these value-based disciplines.

Whit: Let’s break that down a bit more. You’ve mentioned product excellence, operational excellence, and customer excellence. How do these concepts build on each other?

Darrel: Great question. Product excellence is the foundation. When building a company, your product needs to solve a real problem and do it exceptionally well. That’s how you reach your first milestone—say, that 1-billion-dollar mark. But to scale beyond that, you can’t rely on product alone. This is where operational excellence comes in. It’s about streamlining your processes, reducing inefficiencies, and ensuring that every part of the organization is working in harmony.

Whit: And customer excellence? Where does that fit in?

Darrel: Customer excellence takes it to the next level beyond operational excellence. Once again, what gets you to 2 billion does not take you beyond that. You have to change again. It’s not just about creating a great product or running a smooth operation. It’s about truly understanding and anticipating your customers’ needs. Companies that master customer excellence create loyalty and advocacy. They don’t just react to customer feedback; they proactively shape the customer experience. This is where long-term growth happens, and it’s a hallmark of companies that scale successfully.

Whit: That makes so much sense. So, it’s a progression—starting with product, moving to operations, and finally centering everything around the customer?

Darrel: Exactly. Think of it as a ladder. Each step builds on the previous one. You need product excellence to get off the ground, operational excellence to scale efficiently, and customer excellence to ensure longevity and market leadership. And these aren’t isolated phases—they’re interconnected. A failure in one area can disrupt the whole system.

Whit: That’s a powerful perspective. What role does leadership play in this transformation?

Darrel: Leadership is everything. It starts with understanding that transformation isn’t optional—it’s survival. Leaders must champion change, align the organization’s culture with its strategy, and invest in the right areas. For example, what does the CFO prioritize? What technologies or processes does the COO implement? It all needs to work together.

Whit: That’s a powerful perspective. What would you say to leaders who are hesitant to embark on such a daunting journey?

Darrel: I’d tell them this: Transformation isn’t just about surviving the present; it’s about thriving in the future. It’s what Simon Sinek refers to as ‘the long game’. Companies that embrace these principles—aligning value creation with their business strategy—will not only grow but will set the pace in their industries.

Whit: Do you have any final thoughts for organizations navigating their own transformations?

Darrel: Focus on value. Whether it’s your customers, employees, or stakeholders, every transformation effort should return to delivering value. And remember, it’s a journey. You don’t have to get it perfect overnight, but you do have to start.

Whit: Thank you, Darrel. Your insights are invaluable.

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GigaOm Radar for Patch Management

Whit Walters — Tue, 19 Nov 2024 16:00:46 +0000

In today’s digitally interconnected world, patch management has transcended its role as a routine IT task. It is now a critical business imperative. Organizations face a relentless barrage of sophisticated cyber threats, and the exploitation of unpatched vulnerabilities remains a primary attack vector. Whether it’s a small business or a global enterprise, the consequences of a successful breach can be devastating, ranging from financial losses and reputational damage to operational disruptions and regulatory penalties.

Patch management, in its essence, is the systematic process of identifying, acquiring, testing, and deploying software and firmware updates to address security flaws and bugs. It’s the digital equivalent of locking your doors and windows—a fundamental practice to protect your valuable assets. However, the increasing complexity of modern IT environments, encompassing hybrid cloud architectures, diverse operating systems, and a multitude of third-party applications, has made patch management increasingly challenging.

To effectively address this challenge, organizations need a robust patch management strategy that goes beyond simply applying patches. A strategic approach must consider factors such as patch prioritization based on risk and business impact, automated patch deployment and testing to minimize disruption, and continuous monitoring to ensure patch effectiveness. Emerging trends like AI-powered threat assessment and patch orchestration are poised to transform patch management, enabling organizations to proactively identify and remediate vulnerabilities before they are exploited.

For IT executives, patch management is not just a technical issue; it’s a strategic decision with significant implications for the organization’s security posture, operational resilience, and overall business success. A well-executed patch management program can reduce the risk of cyberattacks, improve system reliability, and ensure regulatory compliance. It can also free up valuable IT resources that can be redirected towards strategic initiatives that drive innovation and growth.

This GigaOm Radar report delves into the evolving landscape of patch management, highlighting key vendors and emerging trends. It provides valuable insights to equip IT decision-makers with the information they need to select the right patch management solution for their unique business needs. By understanding the challenges, best practices, and available solutions, organizations can make informed decisions to strengthen their defenses and proactively mitigate risks in the face of ever-evolving cyber threats.

This is our third year evaluating the patch management space in the context of our Key Criteria and Radar reports. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Radar report examines 27 of the top patch management solutions and compares offerings against the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) outlined in the companion Key Criteria report. Together, these reports provide an overview of the market, identify leading patch management offerings, and help decision-makers evaluate these solutions so they can make a more informed investment decision.

GIGAOM KEY CRITERIA AND RADAR REPORTS

The post GigaOm Radar for Patch Management appeared first on Gigaom.

GigaOm Key Criteria for Evaluating Application Security Testing (AST) Solutions

Whit Walters — Tue, 08 Oct 2024 15:00:04 +0000

In an era of escalating cyber threats and increasingly complex application landscapes, robust application security testing (AST) is no longer a luxury but a necessity. AST solutions have evolved from being perceived as costly overhead to being recognized as essential tools that safeguard applications and sensitive data, ultimately protecting an organization’s reputation and bottom line.

AST is a set of methodologies and tools designed to uncover vulnerabilities, weaknesses, and misconfigurations in software applications. These solutions are crucial for preventing data breaches, unauthorized access, and malicious attacks that can disrupt operations, compromise customer trust, and lead to financial losses. AST is essential for any organization that develops or uses software applications, particularly those in industries with strict regulatory requirements or those handling sensitive data.

Business Imperative
The business case for AST is compelling. Cyberattacks are becoming increasingly sophisticated, targeting not only large enterprises but also small and medium-sized businesses. The costs associated with a data breach can be catastrophic, including legal fees, regulatory fines, loss of customers, and damage to brand reputation. Investing in AST is not just a technological decision; it’s a strategic move to mitigate risk, ensure business continuity, and maintain customer trust.

Sector Adoption Score
To help executives and decision-makers assess the potential impact and value of an AST solution deployment to the business, this GigaOm Key Criteria report provides a structured assessment of the sector across five factors: benefit, maturity, urgency, impact, and effort. By scoring each factor based on how strongly it compels or deters adoption of an AST solution, we provide an overall Sector Adoption Score (Figure 1) of 3.8 out of 5, with 5 indicating the strongest possible recommendation to adopt. This indicates that an AST solution is a credible candidate for deployment and worthy of thoughtful consideration.

The factors contributing to the Sector Adoption Score for AST are explained in more detail in the Sector Brief section that follows.

Key Criteria for Evaluating AST Solutions

Sector Adoption Score

Deters
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Figure 1. Sector Adoption Score for AST

This is the second year that GigaOm has reported on the AST space in the context of our Key Criteria and Radar reports. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Key Criteria report highlights the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) for selecting an effective AST solution. The companion GigaOm Radar report identifies vendors and products that excel in those decision criteria. Together, these reports provide an overview of the market, identify leading AST offerings, and help decision-makers evaluate these solutions so they can make a more informed investment decision.

GIGAOM KEY CRITERIA AND RADAR REPORTS

The post GigaOm Key Criteria for Evaluating Application Security Testing (AST) Solutions appeared first on Gigaom.

Whit Walters, Author at Gigaom

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DiscouragesAdoption

MeritsConsideration

EncouragesAdoption

CompelsAdoption

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CompelsAdoption

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Sector Adoption Score

DetersAdoption

DiscouragesAdoption

MeritsConsideration

EncouragesAdoption

CompelsAdoption

GIGAOM KEY CRITERIA AND RADAR REPORTS

Deters
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Discourages
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Merits
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Encourages
Adoption

Compels
Adoption

Deters
Adoption

Discourages
Adoption

Merits
Consideration

Encourages
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Deters
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