Amazon Executive Insights

Articles from the Amazon AWS Executive Insights Blog

Digital Transformation: Hype or a Strategic Necessity?

While the term digital transformation has become slippery and overused, it nevertheless remains a priority of many business and technology executives. In this blog post, Dr. Saša Baškarada, a senior transformation architect at Amazon Web Services (AWS) Professional Services, explains the drivers of digital transformation as well as its three interdependent stages. The resulting conceptual model may facilitate the construction of more effective transformation strategies. ―Mark Guest post by Dr. Saša Baškarada, Senior Transformation Architect, AWS Professional Services The only constant in life is change. ―Heraclitus Like agility, digital transformation is one of those overused terms that mean different things to different people and are at risk of becoming meaningless. That is rather unfortunate, since, like agility, digital transformation is a fundamentally important concept that is critical for continuing success in today’s dynamic business environments. For that reason, please allow me to indulge in a little conceptual clarification. The need for digital transformation is driven by increasing competitive pressures due to the accelerated emergence and proliferation of new digital technologies. While digital technologies may help you create new value, reduce risk, and optimize operations, they also increase the threat of new competitors (by lowering the barriers to entry) and substitute products or services. At the same time, technology-savvy customers are changing their expectations and behaviors. No industry is seemingly immune, as evidenced by the disruption to the hotel industry caused by Airbnb and the disruption to the financial services industry caused by various buy-now, pay-later platforms, just

7 Pitfalls to Avoid When Building a CCOE

  Creating a Cloud Center of Excellence (CCOE) is often a great way to jumpstart a company’s migration to the cloud and to guide the migration with an eye on best practices and long-term suitability. The company sets up a team of experts on cloud best practices and uses their skills and their passion for the cloud to lend urgency to the rest of the company’s cloud transformation. At AWS, we’ve identified a few ways that organizations can go wrong when setting up a CCOE. In this blog post, Néstor Gándara and Eric Lin, Senior Partner Solutions Architects at AWS, show how to avoid those pitfalls and achieve the full potential of a CCOE. ―Mark Guest post by Néstor Gándara and Eric Lin, AWS Sr. Partner Solutions Architects  Companies are moving to the cloud to take advantage of benefits such as increased scalability, high availability, cost efficiency, agility, and innovation. But their existing organizational structures and methodologies often hold companies back from realizing many of these benefits. Many companies build a cloud center of excellence (CCOE) team to evangelize, drive their cloud transformations, and rethink all their processes in the cloud. But if the CCOE is not implemented correctly, companies may not fully realize its benefits. Here are seven common mistakes to avoid when building a CCOE. 1. Lack of Executive and Key Stakeholder Engagement A common mistake is not seeking support from your executive leaders to help drive the cloud transformation and not aligning the company’s many

Thinking Strategy? Add Dimensions!

My peers and I carry the ominous title of Enterprise Strategist at AWS. While perhaps we aren’t entirely immune to title inflation, one could reasonably assume that we know a thing or two about enterprises and strategy in particular. After all, we are former CIOs, CTOs, Chief Architects, and IT leaders and have executed major cloud migrations and organizational transformations ourselves. What we don’t do, though, is hand our customers a ready-made cloud strategy. A strategy can only be successful if it accounts for an organization’s existing strengths as well as the constraints that it operates under. A strategy, therefore, isn’t something you can copy and paste from a template or another customer (or even from Amazon). What our team does instead is help customers articulate their strategy. What questions should they ask? What are the critical decisions they need to make? Where can they start? When should they stick to their strategy and when should they adjust it to new circumstances? Within the scope and complexity of modern IT organizations, these are critical questions to ask. But they aren’t easy to answer, so we are happy to assist. From those conversations as well as our own experiences, we have learned many aspects that help make a strategy successful. We have also discovered several common pitfalls. I want to share one pitfall that’s particularly easy to drop into. And that is a strategy that’s lacking important dimensions. Being a fan of visual thinking and metaphors, I present a visual metaphor to

CISO Insight: Every AWS Service Is a Security Service

Amazon Web Services customers have many services to contemplate, and perhaps integrate into their cloud footprint, irrespective of where they are in their cloud journey. The relentless pace of innovation continues to be one of the main attractions for customers with AWS as their cloud provider; knowing that new services and features are always coming, customers feel confident that many (if not all) of their organization’s needs can be fulfilled by one or more AWS products. Customer CISOs (or their respective teams) may want to take the time to ensure that they are well versed with all AWS services because there may be a security, risk, or compliance objective that can be met, even if a service doesn’t fall into the “Security, Identity, and Compliance” category. As one might imagine, when I meet with customer CISOs, the topic quickly turns to AWS’s security services. “How many are there?” “How does a particular AWS security service help me meet a certain security, risk, or compliance objective?” and “How do I integrate an AWS security service into my existing portfolio of security tools?” are just a sampling of common questions. In these conversations, I encourage CISOs to think broader and focus on security outcomes rather than security “check the box” exercises, like making sure you have a firewall. Those conversations get very interesting when I propose that with some open-mindedness and creativity, most AWS services (perhaps not Amazon Lumberyard, but maybe I’m not creative enough) can be used to achieve security, risk,

Engineering for DEI: Tapping IT Creativity and Technique to Address Diversity, Equity, and Inclusion

By Wesley Story and Mark Schwartz In previous posts we’ve talked about the increasing enterprise focus on environmental, social, and corporate governance (ESG), or stakeholder capitalism. One of the most important components of ESG is diversity, equity, and inclusion, or DEI. In this blog post we won’t spend time on why it’s important—we’ve covered that in previous posts. Instead, we’d like to talk about how to address it. In particular, we’d like to raise the question: what if we were all to take an engineering approach to solving DEI? In other words, can we apply the problem-solving skills and creativity that exist in IT organizations to the challenges of DEI? Reframing DEI We’ve all been talking about diversity and inclusion for a long time now. And the fact that we’re still talking about it is pretty good evidence that what we’ve done to address it isn’t working, or at least isn’t working sufficiently. We technologists have often come across this situation: a problem whose obvious solution turns out to be ineffective. It’s not a good analogy on all points, but think for a moment about how we replaced monolithic develop-then-test approaches with more iterative ones, fine-tuning and enhancing minimum viable products and identifying and fixing defects. If your company has been unsuccessful in meeting its DEI objectives, consider that a defect. As with any defect, you have to use your problem-solving skills to fix it, then re-deploy. Create an acceptance test for meeting your diversity goals. Keep working until the

Two-Pizza Teams Are Just the Start, Part 2: Accountability and Empowerment Are Key to High-Performing Agile Organizations

In my previous blog post, I shared with you why the legacy approach of project and skill-based teams stands in the way of organizations becoming high-performing agile organizations that are able to innovate quickly, bring new ideas to market faster, and deploy advanced technology solutions in less time. So what is the answer? Accountability and empowerment are a must for these high-performing agile teams. Accountable employees manage their workload according to team objectives, proactively seek help when they need it, take responsibility when they make mistakes, and do so in an environment where they are empowered and given the authority to do something. It is the direct opposite of micro-management. In my experience, and now having worked with numerous enterprises to advance their digital transformation, I have found four key characteristics that enable enterprises to become high-performing agile organizations. Single-Threaded Leaders A single-threaded leader is a leader who is 100% dedicated and accountable to a specific product, such as your mobile application, customer account, or the search capability in your e-commerce store. The single-threaded leader is responsible for turning strategy into real results, and they are empowered to do so. The best way to undercut a strategic initiative is to make it someone’s part-time job. Yet this seems to be the preferred way of working. The CIO declares the initiative to be critical, but no one is empowered to make it happen end to end. Everyone expects someone else to do it. This is where the single-threaded leader steps in.

Two-Pizza Teams Are Just the Start, Part 1: Accountability and Empowerment Are Key to High-Performing Agile Organizations

Go Faster, Innovate More Go Faster. Innovate more. It sounds like a simple directive. Almost every executive I talk to wants to bring new ideas to market faster, accelerate innovation, and deploy advanced technology solutions in less time. Oh, and they need to do all that while building secure, reliable, compliant, and resilient solutions. But how? Our legacy approach to organizing and empowering teams does not lend itself to doing this easily. True agility is not something you can order or purchase, and although the cloud certainly helps make these things possible, it alone will not achieve the benefits enterprises want. What will? Accountability and empowerment are must-haves for high-performing agile teams. To truly become a high-performing agile organization, you must look at your organization structure differently and be willing to change your mindset and behavior. At Amazon Web Services (AWS), this starts with the concept of two-pizza teams: teams that are small enough to be fed by no more than two pizzas each, typically composed of five to 10 people. But just focusing on team size is not enough. The key to going faster, being more innovative, and utilizing new advanced technologies is to establish teams that have clear lines of accountability for delivering business value and empower them to do so. In this post, I’ll discuss how our traditional organizational model holds us back from achieving our dreams of agility. In my follow-up post, I’ll propose practical strategies for baking accountability and empowerment into an organization’s structure.

Strategy for Efficient Cloud Cost Management

One of the most common questions I get from customers is how to effectively manage their cloud costs. Amazon Web Services (AWS) offers many programs to help customers evaluate and optimize their spending. The Cloud Economics team is fantastic at providing a detailed, holistic analysis of your environment and can identify areas of optimization and improvement. If you have not taken advantage of them, I strongly suggest you do. But what do you do the day after they leave? What do you do day to day? In my experience as a CIO, I’ve found that effective management of cloud costs is more about the mechanisms that you use to manage these costs on a regular basis rather than any one-time event. In this post, I’ll share with you six ways to build capability and processes to help manage your cloud costs. Create Transparency It is somewhat obvious, and you probably have heard it before, you can’t govern what you can’t measure. You might be surprised how many organizations don’t have an effective process for creating transparency and understanding their costs. I occasionally encounter customers who are worried about their cloud costs, only to find most of the time these customers are taking a very reactionary posture to the cloud costs. They are often only looking at their expenses when they get their bill at the close of every month. This is akin to driving your car by looking out the rearview mirror. In order to create transparency, you have

The Search for COVID Answers Begins with the Right Data (Part 6)

The COVID-19 pandemic has raised questions never faced before. Businesses getting the best answers were already prepared to use AI, data, the cloud, and agile practices. By Bret Greenstein & Tom Godden     Originally posted at https://digitally.cognizant.com/the-search-for-covid-answers-begins-with-the-right-data-codex6529/.   When the COVID-19 pandemic struck, businesses had more questions than answers: Which policies would stem the spread? What’s the best way to distribute vaccines? How can we safely reopen? While many questions are still being debated, it’s clear what distinguishes leaders from laggards: preparedness with data, artificial intelligence (AI), the cloud, and agile practices. The maturation and readiness of these four components have enabled organizations to ask—and answer—questions they couldn’t have fathomed a short time ago. How can we rapidly screen antibodies to determine which vaccine has the highest efficacy? How can cell phone data help us understand COVID-19 spread? And how do we do all this faster than has ever been accomplished before? For those who had already prepared their data, migrated to cloud, and honed their agile skills, adapting to the changed environment was less of a pivot in their strategy than an acceleration of it. What might have been a multiyear endeavor to rethink data architectures and make sure data was available for scalable AI/machine learning (ML) analytics suddenly became a highly focused project that promised near-term outcomes, in phases that delivered high business value. For businesses that weren’t similarly prepared, migrating to the cloud or beginning an AI pilot were suddenly no longer abstract “someday”

Activating ML in the Enterprise: An Interview with Michelle Lee, VP of Amazon Machine Learning Solutions Labs

In the previous blog post I explored with Michelle K. Lee some of the societal impacts of artificial intelligence (AI) and machine learning (ML). In this post I dive into the patterns Michelle has seen organisations implement to take advantage of the promises of ML. ―Phil Some surveys show a gap between an understanding of the value of AI/ML as a transformative tool and a practical understanding of what that means and how it can be prioritised. How effectively have enterprises taken advantage of the promise of these tools? In reality, most of us are using AI- and ML-based systems multiple times every day. For instance, when you deposit a check by taking a photo of it, a machine learning model developed by your bank recognizes what’s written on the check. This prevalence of ML in our everyday lives is evidence that enterprises are taking advantage of the promise of this innovative technology—but it doesn’t mean that there isn’t more work to be done to bridge the gap between understanding its value and making it a business priority and asset. The Amazon Machine Learning Solutions Lab has helped countless customers adopt ML, and the most successful engagements have the following characteristics: an ambitious, top-down vision for applying ML across an entire organization, and a mandate that ML be considered a business priority. Without these, it’s difficult to move beyond understanding that ML is powerful and transformative to benefitting from it. Where are the more significant gaps in application? What

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