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.

The post From Products to Customers: Delivering Business Transformation At Scale appeared first on Gigaom.

In today’s digital-first world, where every interaction can influence customer satisfaction and business success, the ability to monitor and optimize internet performance is critical. Organizations are at different stages in their journey toward achieving comprehensive Internet Performance Monitoring (IPM) capabilities, and this maturity level greatly affects their capacity to deliver resilient, high-quality user experiences.

An IPM maturity model offers a strategic framework that helps organizations evaluate their observability practices and guide their progress toward more advanced, value-driven outcomes. By evaluating their existing monitoring strategies, organizations can identify where they fall on the spectrum. The ultimate goal is to move toward what we refer to as “value-based observability,” where organizations not only track and respond to performance metrics, they also anticipate and address potential issues before they affect users.

This advanced approach focuses heavily on user experience and resiliency, ensuring digital services are consistently reliable, responsive, and adaptable to changing demands. It goes beyond traditional observability by aligning technical performance with broader business objectives, driving both operational efficiency and strategic value. To reach this level of observability, most organizations need to move beyond the basics to embrace a more comprehensive approach that directly contributes to business outcomes.

As companies advance through the stages of the IPM maturity model, they shift from meeting the minimum requirements of viable observability to using it as a strategic asset that enhances user experience, builds resilience, and drives business value. This journey is essential for maintaining a competitive edge in an increasingly complex digital environment.

The post GigaOm Maturity Model: Internet Performance Monitoring (IPM) appeared first on Gigaom.

Greymatter.io

Founded in 2015, Greymatter.io specializes in managing, securing, and optimizing the performance of applications and services across hybrid, multicloud, and on-premises environments. Its platform integrates service mesh capabilities, API management, and infrastructure intelligence.

Greymatter.io leverages a decentralized architecture with distributed control planes and distributed data planes. The control planes handle critical tasks such as service discovery, configuration management, and policy enforcement for specific network segments, while the Envoy-based data plane manages traffic routing, load balancing, and security enforcement. The control plane acts as a decision point for security policies across applications, APIs, data sources, and microservices, ensuring compliance with NIST zero-trust guidelines, while the data plane enforces these policies and acts as an information point closest to the resource.

The platform offers a comprehensive service connectivity framework to secure, monitor, and optimize distributed applications across hybrid, multicloud, and sovereign cloud environments, ensuring seamless communication and observability. It includes support for both sidecar and sidecarless implementations.

Key security features include automated mutual transport layer security (mTLS) for encrypted communication and ephemeral certificate management for dynamic, short-lived certificates, and SPIFFE/SPIRE integration for secure service identity. Greymatter.io supports advanced traffic management features, including dynamic routing based on real-time traffic conditions, load balancing, and fault injection.

Greymatter.io also enhances security and operational efficiency by offering comprehensive audit trails, dynamic policy enforcement, and integration with tools like security information and event management (SIEM); security orchestration, automation, and response (SOAR); and endpoint detection and response (EDR). These enable robust incident detection and response capabilities.

The platform simplifies configuration management through CUE-based policies and playbooks, reducing configuration code by up to 90% compared to JSON, YAML, or Helm charts. This approach streamlines operations, enabling consistent policy enforcement, configuration management playbooks, and lifecycle management across all environments, from cloud systems to edge deployments. Greyymatter.io also provides predefined templates and workflows that automate routine tasks, reducing operational overhead and minimizing configuration errors.

Purchase Considerations and Use Cases

Greymatter.io is sold as an annual software subscription, with licensing based on the specific platform components used—Fabric, Sense, Data, Data Mesh-Enabled Microservices, and Standard Managed Microservices—as well as expected use cases, connections, APIs, data sources, or repositories. Support licenses are also available.

Use cases for Greymatter.io include secure service-to-service communication across multiple clouds, clusters, and hybrid environments, ensuring flexibility and scalability regardless of the underlying infrastructure. The platform supports zero-trust security enforcement and compliance, API management, real-time infrastructure intelligence, and observability through detailed telemetry data.

Table 1. Target Market and User Segment Comparison

The post GigaOm Solution Brief: Greymatter.io appeared first on Gigaom.

For technology to deliver, it must enable people to achieve their desired goals. This means determining how to define value in a way that assures business fit at both a strategic level and in operational and organizational processes.

Let’s get to it.

Defining Value for Strategic Business Fit

Should we develop and implement the solutions we conceive of in our heads? That is as much a value question as an economic one. How can we weigh, compare, and contrast cost to value to proffer a decision? Should we consider cultural and societal impact? Sometimes, value is measured in terms of what happens if you don’t do something, rather than if you do.

At the core, a business is trying to do some fundamental things. If it’s publicly traded, executives are trying to drive shareholder value—that is, make money or save money, or save time (to make or save money). From this standpoint, being profitable is an ongoing concern.

My preferred definition of a business is exactly that—an ongoing concern. To succeed in business, you must successfully define and execute a strategy. Using business school principles, we can define a value-based strategy based on three pillars: operational efficiency, customer intimacy, and product/performance superiority.

• Operational efficiency is about saving, reducing, optimizing, and modernizing (waste, costs, processes, infrastructure, etc.). It’s an expense you’re trying to reduce or a process you’re trying to improve or optimize.
• Customer intimacy is about investing to gain more customers, drive more revenue, and make more profit. That’s an investment in doing business in the manner and location your customers wish to do business with you.
• Product/performance superiority is an investment in staying ahead of your competitors, attracting more business, or developing new business to generate more revenue and profit.

Digital transformation is nothing more than those three things: business strategy leveraging digital technology and delivery as a primary lever. Tying a solution to one (or more) of these three pillars provides the relevance required to convince decision-makers of fit for purpose within their business strategy.

For providers, trade-offs and leapfrogs can be translated into sales value. For instance, I can create a technology or system that will provide 100% data availability, or unfettered performance, or unlimited capacity. But what must I trade off to provide that, and what leapfrog technology must I use—or convince you of its value—to get you to accept it?

At a customer executive conference over 20 years ago, we asked the audience to consider what they could accomplish if we, as an industry, could provide them seemingly infinite data storage capacity, network bandwidth, and compute resources. Today, we are on the brink of achieving that aspiration. In many ways, we are already providing it.

Even so, why would a business buy? Sellers must relate, or translate, how a technology solution enables enterprises to accomplish their strategic business goals. It must fit the strategy—or strategies—they are trying to implement.

If technology providers want to align with value-based strategy, they need to ask three questions:

1. How and where does my solution impact the three pillars?
2. Does that align with the customer’s strategic direction?
3. How can I best translate my resources and capabilities to reflect that?

For vendors, that is how you create marketing value statements and how you tie your technology to business at the strategic level.

Defining Value For Operations Fit

Even with a technology solution that fits their business strategy, you have to convince decision-makers and budget holders to buy and implement the solution. To do this, you must address their specific, persona-based decision criteria that are inevitably based on people and operational models.

There are, broadly, three buying personas that need to be satisfied: the executive buyer, the architect buyer, and the engineer buyer. Each has unique buying decision criteria you must address by translating your solution to meet those terms. Their perspectives are going to be influenced by how they view the impact to the operating model. In my experience, here is how all that shakes out.

• For the executive, you must address aspects related to awareness, urgency, and trust.
• For the architect, you must address aspects related to technical fit, scalability, and security.
• For the engineer, you must address aspects related to use and function, performance, and support.

Vendors must address each persona’s unique buying decision criteria to convince them to allocate the scarce resource known as budget. You are fighting for that resource and must convince both decision-maker and budget-holder of the operational value of your solution.

Combining Value For Best Fit

Your marketing and sales organizations must be able, and enabled, to translate and bridge the technical aspects of your solution to an organization’s business aspects and buying criteria. And as a seller, you won’t be the only one attempting to do this. You will have competition.

Ultimately, as a provider, the goal is to map your own value-based business strategy onto your buyer’s business strategy and values, through value statements that reflect their needs and provide resources and capabilities to enable them to achieve their desired business outcomes.

You need to know what target you’re aiming at and how you are responding, at a business level, to hit that target. Technology marketing teams are always striving to develop a value statement, but they’re typically thinking in terms of speeds and feeds or technical capability and differentiators. This approach will have limited appeal and “legs” beyond the specific buying persona or decision-maker interested in these aspects.

As a buyer, challenge your providers and sellers to meet your organization’s needs, strategies, and values on your terms. Analyze, review, and judge them on that basis. Why buy, otherwise?

Next Steps

At GigaOm, we work with clients to develop, enable, and activate the value propositions and statements unique to them based on the research we produce, matching users and providers for best technical and operational fit to needs and requirements.

How do you feel about your organization’s resources and capabilities to do this? Would you like some help?

If so, contact us to get started.

The post Machines as Amplifiers: Constructing Value Statements appeared first on Gigaom.

I’ve learned to see it as the art of designing IT solutions that amplify human productivity, capability, and creativity. For these aspirations to be realized however, these solutions need to be reframed and translated into business value for acquisition and implementation. 

It’s a tricky proposition in this hypercompetitive world, which we’re seeing unfold in front of our eyes due to the current buzz around AI and Large Language Models (LLMs). The ‘arrival’ of AI onto the scene is really the delivery of the promise and aspirations of six decades of iterative effort.

However, its success – defined in terms of business value – is not a given. To understand this, let me first take you back to a technical article I came across early on in my career. “All machines are amplifiers,” it stated in a simple and direct manner. That statement was an epiphany for me. I’d considered amplifiers as just a unit in a stereo system stack or what you plugged your guitar into. 

Mind blown.

As I have pondered this realization across my career, I have come to consider IT as a collection of machines offering similar amplification, albeit on a much broader scale and with greater reach.

IT amplifies human productivity, capability, and creativity. It allows us to do things we could never do before and do them better and faster. It helps us solve complex problems and create new opportunities – for business and humanity.

To augment or to replace – THAT was the question

However, amplification is not an end in itself. In the 1960s, two government-funded research labs on opposite sides of the University of Berkeley Stanford campus pursued fundamentally different philosophies. One believed that powerful computing machines could substantially increase the power of the human mind. The other wanted to create a simulated human intelligence. 

These efforts are documented in John Markoff’s book, “What The Dormouse Said – How the Sixties Counterculture Shaped the Personal Computer Industry”.

One group worked to augment the human mind, the other to replace it. Whilst these two purposes, or models, are still relevant to computing today, augmenting the human mind proved to be the easier of the two to deliver – with a series of miniaturization steps culminating in the general consumer availability of the personal computer (PC) in the 1980s. PCs freed humans to be individually productive and creative, and changed how education and business were done around the globe. Humanity rocketed forward and has not looked back since.

Artificial Intelligence (AI) is now becoming commercially viable and available at our fingertips to replace the human mind. It is maturing rapidly, being implemented at breakneck speeds in multiple domains, and will revolutionize how computing is designed and deployed in every aspect from this point forward. While it came to fruition later than its 1960s sibling, its impact will be no less revolutionary with, perhaps, an end-state of intelligence that can operate itself.

Meanwhile, automation on the augmentation front has also rapidly advanced, enabling higher productivity and efficiencies for humans. It’s still a human world, but our cycles continue to be freed up for whatever purpose we can imagine or aspire to, be they business or personal endeavors.

Systems engineering – finding a path between trade-offs

From a high-level fundamental compute standpoint, that’s all there really is – augment or replace. Both models must be the starting point of any system we design. To deliver on the goal, we turn to systems engineering and design at a more detailed, complex, and nuanced level. 

The primary task has always been simple in concept – to move bits (bytes) of data into the processor registers where it can be operated upon. That is, get data as close to the processor as possible and keep it there for as long as practical. 

In practice this can be a surprisingly difficult and expensive proposition with a plethora of trade-offs. There are always trade-offs in IT. You can’t have it all.  Even if it were technically feasible and attainable you couldn’t afford it or certainly would not want to in almost every case. 

To accommodate this dilemma, at the lower levels of the stack, we’ve created a chain of different levels of various data storage and communications designed to feed our processors in as efficient and effective a manner as practical, enabling them to do the ‘work’ we request of them. 

For me, then, designing and engineering for purpose and fit is, in essence, simple. Firstly, am I solving for augmentation or replacement? Secondly, where’s the data, and how can I get it where it needs to be processed, governed, managed, and curated effectively? 

And one does not simply store, retrieve, manage, protect, move, or curate data. That stuff explodes in volume, variety, and velocity, as we are wont to say in this industry. These quantities are growing exponentially. Nor can we prune or curate it effectively, if at all, even if we wanted to. 

Applying principles to the business value of AI

All of which brings us back to the AI’s arrival on the scene. The potential for AI is huge, as we are seeing. From the systems engineer’s perspective however, AI requires a complete data set to enable the expected richness and depth of the response. If the dataset is incomplete, ipso facto, so is the response – and, thus, it could be viewed as bordering on useless in many instances. In addition AI algorithms can be exhaustive (and processor-intensive) or take advantage of trade-offs. 

This opens up a target-rich environment of problems for clever computer scientists and systems engineers to solve, and therein lies the possibilities, trade-offs, and associated costs that drive all decisions to be made and problems to be solved at every level of the architecture – user, application, algorithm, data, or infrastructure and communications.

AI has certainly ‘arrived’, although for the systems engineer, it’s more a continuation of a theme, or evolution, than something completely new. As the PC in the 1980s was the inflection point for the revolution of the augmentation case, so too is AI in the 2020s for the replacement case. 

It then follows, how are we to effectively leverage AI? We will need the right resources and capabilities in place (people, skills, tools, tech, money, et al) and the ability within the business to use the outputs it generates. It resolves to business maturity, operational models and transformational strategies.

Right now I see three things as lacking. From the provider perspective, AI platforms (and related data management) are still limited which means a substantial amount of DIY to get value out of them. I’m not talking about ChatGPT in itself, but, for example, how it integrates with other systems and data sets. Do you have the knowledge you need to bring AI into your architecture?

Second, operational models are not geared up to do AI with ease. AI doesn’t work out of the box beyond off-the-shelf models, however powerful they are. Data scientists, model engineers, data engineers, programmers, and operations staff all need to be in place and skilled up. Have you reviewed your resourcing and maturity levels?

Finally, and most importantly, is your organization geared up to benefit from AI? Suppose you learn a fantastic insight about your customers (such as the example of vegetarians being more likely to arrive at their flights on time), or you find out when and how your machinery will fail. Are you able to react accordingly as a business?

If the answer to any of these questions is lacking, then you can see an immediate source of inertia that will undermine business value or prevent it altogether. 

In thinking about AI, perhaps don’t think about AI… think about your organization’s ability to change and unlock AI’s value to your business.

The post What’s the Business Value of AI? A Systems Engineer’s Take appeared first on Gigaom.