What do CIOs and CTOs deliver for the company? If you said “technology,” that’s just the beginning. According to their research, McKinsey found that 85% of CIOs and CTOs interviewed in the spring of 2019 said they were essential for at least two of the three most common CEO priorities—revenue acceleration, improved agility and time to market, and cost reduction.

IT modernization – including migrating to the cloud – is key to business growth and agility. Yet, according to a recent McKinsey study, 80% of CIOs report that regardless of their level of cloud migration, they still haven’t reached their projected agility and business benefits. Sometimes, this is because of issues like training and skills gaps in the IT workforce. Surprisingly often though, the barrier to reaching the goals is based on trade-offs that CIOs themselves feel they must make to strike a balance between the perfect and the possible.

But what if you could have it all without the trade-offs? As Will Grannis, Managing Director of the CTO Office at Google, and Arul Elumalai, Partner at McKinsey & Company discussed in our recent digital conference, many of the compromises CIOs make can be avoided with new technology, modern architectures and by encouraging a transformation mindset across the business. In interviews, CIOs explained how they’ve leveraged the best of the cloud without compromising on security, agility, and flexibility. Here’s how these leaders avoid three of the top perceived trade-offs—both with technology and by transforming their operating model.

Trade-off #1: Developer agility vs. control and governance

Moving to the cloud offers new opportunities for speed, but 69% of organizations indicate that stringent security guidelines and code review processes can slow developers significantly. One CISO of a multinational company mentioned that cloud development was so fast that they had to institute manual checks on their developers’ code. So much for agility. 

To overcome this trade-off and maintain both speed and security, some respondents found success in DevOps, hiring security-experienced talent and introducing automation for security and quality. Building in security into the CI/CD pipeline and increasing automation don’t just eliminate the tradeoff, they result in higher quality and faster innovation.

At Google Cloud, we’ve also observed that customers with strong DevOps practices have increased speed-to-market and product/service quality. From our own journey, we’ve learned seven critical lessons essential to adopting a DevOps model, ranging from taking up small projects and embracing open source to building an overall DevOps culture.

Trade-off #2: Single-vendor benefits vs. freedom from lock-in

CIOs perceive benefits to using the fewest number of clouds, specifically avoiding introducing multiple systems that require their teams to develop and maintain multiple skillsets. Unfortunately, 83% of the CIOs interviewed said that while they would prefer fewer clouds, the potential financial and technical lock-in drives them to multiple providers. 

Successful CIOs said that they can avoid lock-in pitfalls not just with contractual guardrails and executive and board education, but with evolving hybrid cloud technologies that provide additional choices. Hybrid cloud platforms based on containers can further mitigate the risk of using a single cloud vendor. The key to successful hybrid architectures is the infrastructure abstraction and portability that containers create for them, enabling disparate environments to work together. 

This notion has been at the heart of our strategy at Google Cloud with Anthos, which provides an abstraction layer and an application modernization platform for hybrid and multi-cloud environments. Enterprises can use Anthos to modernize how they develop, secure, and operate hybrid-cloud environments and enable consistency across cloud environments.

Trade-off #3: Best-of-breed tools vs. standardization and familiarity

Optimizing tool chains for different environments can improve productivity, but many CIOs believe that this means reduced functionality and tools. While 77% of CIOs said they had to standardize to the lowest common denominator, some have found a better solution. Rather than giving up the languages, libraries, and frameworks that their teams prefer, effective leaders said that they found success by investing in training programs to upscale talent and adopting new open and vendor-agnostic solutions. Architectures that are based on open-source components have been the keys that helped remove this tradeoff, and eliminate the notion of a lowest common denominator. 

This is why we have built Anthos on open-source components like Kubernetes, Istio and Knative. Anthos gives your business the choice you need. With the ability to create code that works in most environments using the tools, languages, and systems you prefer, you can do more without major changes to how you work.

Regardless of your current cloud adoption level, check out “Unlock business acceleration in a hybrid cloud world” to discover more about McKinsey’s findings, including how CIOs drive agility, methods to make trade-offs unnecessary, and how to prepare your team for the cloud. Then, stay tuned for subsequent posts that  take a closer look at how hybrid solutions and strategies can help CIOs drive a transformation mindset across the business—without compromising on security, agility, and flexibility.

Kubernetes made a splash when it brought containerized app management to the world a few years back. Now, many of us are using it in production to deploy and manage apps at scale. Along the way, we’ve gathered tips and best practices on using Kubernetes and Google Kubernetes Engine (GKE) to your best advantage. Here are some of the most popular posts on our site about deploying and using Kubernetes. 

1. Use Kubernetes Namespaces for easier resource management. Simple tasks get more complicated as you build services on Kubernetes. Using Namespaces, a sort of virtual cluster, can help with organization, security, and performance. This post shares tips on which Namespaces to use (and not to use), how to set them up, view them, and create resources within a Namespace. You’ll also see how to manage Namespaces easily and let them communicate.
2. Use readiness and liveness probes for health checks. Managing large, distributed systems can be complicated, especially when something goes wrong. Kubernetes health checks are an easy way to make sure app instances are working. Creating custom health checks lets you tailor them to your environment. This blog post walks you through how and when to use readiness and liveness probes.
3. Keep control of your deployment with requests and limits. There’s a lot to love about the scalability of Kubernetes. However, you do still have to keep an eye on resources to make sure containers have enough to actually run. It’s easy for teams to spin up more replicas than they need or make a configuration change that affects CPU and memory. Learn more in this post about using requests and limits to stay firmly in charge of your Kubernetes resources.  
4. Discover services running outside the cluster. There are probably services living outside your Kubernetes cluster that you’ll want to access regularly. And there are a few different ways to connect to these services, like external service endpoints or ConfigMaps. Those have some downsides, though, so in this blog post you’ll learn how best to use the built-in service discovery mechanisms for external services, just like you do for internal services.
5. Decide whether to run databases on Kubernetes. Speaking of external services: there are a lot of considerations when you’re thinking about running databases on Kubernetes. It can make life easier to use the same tools for databases and apps, and get the same benefits of repeatability and rapid spin-up. This post explains which databases are best run on Kubernetes, and how to get started when you decide to deploy.
6. Understand Kubernetes termination practices. All good things have to come to an end, even Kubernetes containers. The key to Kubernetes terminations, though, is that your application can handle them gracefully. This post walks through the steps of Kubernetes terminations and what you need to know to avoid any excessive downtime.

For even more on using GKE, check out our latest Containers and Kubernetes blog posts.

With stated goals of increasing competition, innovation, and financial inclusion, bank regulators across the world are mandating that banks open their systems, enabling consumers to share their financial data with third parties.

One touted benefit of this sharing is that it may create digital banking ecosystems that offer consumers more services than ever, provide banking information and capabilities in more useful and convenient contexts, expand the market reach of ecosystem participants, and boost financial participation among the unbanked and underbanked.

These benefits involve requiring that banks produce application programming interfaces (APIs) to make data and functionality easy to share with partners in a standardized way and to give consumers control over the services with which their data is shared. Because APIs enable developers to leverage and reuse software for new services and digital experiences, including by combining APIs from multiple providers, tech pundits and commentators often refer to the “API economy” — that is, to digital ecosystems in which companies symbiotically share and combine their software to create richer offerings, to complement their proprietary strengths with offerings from other organizations, to share innovation across enterprises, and to expand into new sectors.

Rather than being able to access and act on financial data only through specific channels, for example, consumers in an Open Banking world would theoretically be able to use their money across a constantly-expanding array of apps and digital experiences. Likewise, rather than being confined to one bank’s specific digital services, consumers would be able to opt into a range of services, such as better loan matching or debt reduction advice, to help them do more with their money. Banks, meanwhile, would not have to create all aspects of digital experiences themselves but could rely on external partners, which they can add at unprecedented scale via APIs, to shoulder some of the burden of attracting and creating value for customers.

The envisioned disruption is sweeping, but key questions for bank leaders and bank investors remain, notably the extent to which the Open Banking movement will exert significant, durable impacts on market dynamics — and the extent to which Open Banking compliance constitutes an effective market entry into digital ecosystems and the celebrated benefits of the API economy.

Put simply, is complying with Open Banking regulations adequate to advance a bank’s digital strategy?

Building compliant APIs vs. entering the API economy

Individual regulators are fundamentally constrained in their ability to give banks detailed instructions on how to behave in digital ecosystems. Mandatory regulations can be originally conceived with only one or two business models in mind, not the many thousands of business models that could be partially or fully enabled by the API economy. Detailed rules and specifications may threaten the functionality of services, as regulatory interventions that give highly specific guidance may not be system- and business model-agnostic.

In terms of policy, the willingness of regulators to force banks to adopt APIs is highly significant, but because these regulatory interventions do not offer a meaningful and durable market entry roadmap for banks to follow, the regulations may be an initial catalyst to prompt financial market evolution rather than a natural and enduring end-state for business activity.

Aside from the constraints at the level of the individual bank regulator, there is limited consistency among Open Banking regulations across the globe. Europe’s PSD2 was the regulatory intervention that kicked off this global wave, but there are small but significant variations in the ripple of regulatory actions around the world.

In Australia, Open Banking is part of the Consumer Data Right, an initiative to give customers the right to access their data in a machine-readable form — a right the country does not confine to banking. In Singapore, the Monetary Authority of Singapore is pushing for a lightweight regulatory framework regime. Japan’s Amended Banking Act introduced a registration system for third party providers. Korea’s Financial Services Commission has launched a Fintech Open Platform. Mexico’s recent law to regulate financial technology institutions lays groundwork for an Open Banking regime. The Central Bank of Brazil is aiming to implement the relevant regulatory reforms by the end of 2019. For the largest, internationally diversified banks, these regional differences further complicate any effort to regard mere compliance with these interventions as an effective and coherent market entry strategy.

Despite these complexities and ambiguities, I’ve observed in my work consulting with financial institutions that some banks nevertheless treat Open Banking compliance projects as a means of market entry into the API economy. This mindset could be a costly mistake and may leave these banks at a significant competitive disadvantage as we continue to accelerate into the era of digital ecosystems. In addition to compliance, banks should view a commercial entry into a foreign country as a useful reference for entering the API economy.

Entering the API economy is like entering a foreign market

Banks executing a commercial entry into a foreign country will encounter cultural differences, whether in the form of language, ethnicity, religion, social networks, values or norms. When these cultural differences are large, market entry into a foreign market is more difficult.

The practical implications of administering new business activity in a foreign country also impact the level of difficulty. The physical distance between the home country and the foreign country has to be managed. Border hardness, time zones, and climate also impact the administrative challenges posed by the foreign country. Additionally, the market landscape is foreign. New countries can significantly differ in their natural, financial, and human resources. Staff sent to build up a new division in a new country will have to cope with different levels of market Infrastructure, information, and knowledge. Finally, the economics of entering a foreign country can be heavily influenced by historical and political factors such as a shared colonial history, common memberships of trading blocs, and shared currency zones.

These patterns of differences and similarities can likewise be observed when a bank tries to enter the API economy.

Like entry into a new territory, entry into the API economy may pose sharp cultural and operational differences for banks to grapple with. In this world of APIs and software ecosystems, an API provider’s commercial aim is to make third-parties the dominant force for innovation — that is, to securely share data and services with external contributors who build new connected experiences that generate value for the provider, much as ridesharing companies have generated value by leveraging APIs such as Google Maps. Enterprises focused on ecosystem development market their APIs as products for developers so that new innovation can emerge organically, without necessarily being pre-planned. Investment decisions in the API economy are driven by a desire to help preferred ecosystems to evolve fastest. All of this may be a very sharp change in culture for many banks that historically have sought to be the dominant innovator shaping customer experiences.

The established approach to innovation in banks is highly deliberate, aiming to match bank financial products to specific customer needs and to beat the financial product offerings of peer banks. Compared to modern digital ecosystems, these legacy banks’ resources for, scope of, and receptivity to innovation may be considerably constrained. Banks that actively treat the culture of the API economy as very foreign to their traditional corporate culture have a far greater chance of acknowledging these challenges and making a successful market entry into the API economy.

Many banks may also face challenges transitioning to ecosystem administration models. In traditional banking models, the C-suite commands and controls the bank staff that distributes products. Typically, very few external business partners are involved, and those that are have generally been deliberately, if not laboriously, selected. In contrast, the API economy requires the orchestration of very large numbers of third parties that add value to a bank’s innovation and distribution capabilities. Literally thousands of partners may access a bank’s APIs to build services atop banking data, extend a bank’s functionality or insert the bank’s APIs into new business contexts. In many ways, the whole point is that partnerships don’t have to occur in slow-moving, methodically planned formal partnerships but rather can be achieved at Internet scale while preserving control over and visibility into customer data. This shift in strategy is no small departure for many banks — so again, thinking of the endeavor as entry into a foreign market, rather than something that can be jumpstarted via regulations, is wise.

Crucially, the market landscape in the API economy is also very different from what legacy financial institutions are used to. Traditionally, banks have segmented the market into very large market segments (e.g. consumer banking, corporate banking, private banking etc.). In sharp contrast, the API economy sees partners working together to serve market micro-segments that would not be reachable and profitable by each individual enterprise working in isolation. Simply put, addressing all customer needs, from mainstream use cases to niche applications, is beyond the capabilities of any single enterprise and can generally only be achieved through ecosystems and partnerships. Because partners working together in the API economy seek to serve the customer through the customer’s preferred digital interface (which may be different than bank’s preferred digital interface), ecosystem partnerships represent a massive shift in marketing management, with major implications for a bank’s business architecture, technical architecture, governance, and risk management.

Additionally, in the API economy, pricing decisions are generally driven by a desire for ecosystem growth. Partners work together to extend the customization and value that customers experience when consuming services. By design, the intellectual property in an ecosystem becomes dispersed. Banks have traditionally sought to protect the exclusivity of their intellectual property, and they have often sought economic returns via cost-plus pricing and minimal customization of financial products. Just as with other aspects of the API economy, these ecosystem dynamics and economic relationships typically fall outside a bank’s status quo operations and strategies — more akin to entering a foreign market than to simply satisfying regulatory requirements.

The C-suite should be involved

To approach entry into the API economy as they would approach market entry into a foreign country, banks should consider focusing on a small number of products or segments. They should seek complementary partners who can help them adapt their skills to the API economy and extend the reach of their core differentiating strengths. Just like entry into a foreign market requires combining elements of the home country business model with new local elements, banks will have to transform to increase the effectiveness of their ecosystem participation.

Because an Open Banking compliance project involves regulators specifying the scope and schedule for mandatory APIs, the C-suite may feel relegated to the role of budget provider, which may tempt executives to delegate the sponsorship and steering of the project. Such delegation is not an option for a foreign market entry, where all decisions on scope, cost and timetable are strategic and entirely in the hands of the bank itself — and the C-suite should not consider it an option for entry into the API economy. Bank leaders need to be involved, as the organization likely will not be able to make the necessary strategic and operational transformations if the vision is not defined and driven from the top.

How many times have you closed an application when you encounter the “spinning wheel of death” (a melodramatic way of saying an application that is taking too long to load)? In today’s digital economy where many organizations rely on applications as a primary source of revenue, that spinning wheel of death (or poor application performance) can mean lost users or revenue. And just about every modern application relies on APIs as the nervous system across distributed systems, third-party services and microservice architectures. While meeting the need for rapid release cycles and frequent API updates, it is also imperative for IT teams to ensure that your APIs are meeting SLOs, performance requirements, and proactively mitigating issues.

Why are synthetic monitoring tools not enough?

When thousands or even millions of users are making multiple requests to your APIs, just relying on synthetic monitoring tools (that rely on sampling or limited API availability information) is not enough for precision diagnoses or useful forensics. At the same time, monitoring every single aspect just increases your overhead and mean time to diagnosis. Apropos, API monitoring has become absolutely critical — and a fusion of art and science — for operations teams to make sure all APIs are running and performing as intended. If you are worried about monitoring blind spots or overheads, let us look at 3 key practices you can follow to stop dreading your sev1 alerts.

#1 Prioritize alerts for critical events requiring immediate investigation

• Ask any engineer on-call for a critical service and they will tell you about the overheads created by incorrectly prioritized alerts. For example let us imagine a distributed application with 20 APIs. Even if you set up basic alert monitors across latency, error, and traffic for these APIs, you end up monitoring and maintaining 60 alert definitions – which is a lot. To balance avoiding monitoring blindspots and alert fatigue at the same time, operations teams must develop a clear understanding of all events and prioritize configuring alerts for events supporting critical traffic.

• Consider quality over quantity while defining new alert conditions where each new condition is urgent, actionable, and actively or imminently user-visible. Every alert condition created should also contain intelligence that requires active engagement from a user as opposed to a mere robotic response. Apigee’s API monitoring allows creation of alert conditions based on metrics or logs while providing actionable information (Ex: status code, rate etc.,) and playbooks for diagnosi
• In today’s multilayered systems, one team’s symptom (“what’s broken?”) is another downstream system’s cause (“Why?”). Even if some events are not suitable for actionable alerts, a failure needs to create an informational broadcast to a downstream system to mitigate the impact of the upstream dependency. In such cases, investing in automating alerts, grouping multiple incidents in notification channels, and incident tracking. For example, Apigee lets you integrate and group your alert notifications in channels like Slack, Pagerduty, webhooks etc.,
• Modern production systems are ever-evolving where an alert that’s currently rare might become frequent and automatable. Analogous to ticket backlog grooming, alert policies need to be reviewed periodically to make sure new conditions are identified and existing alerts are refined with new thresholds, prioritization and correlation. Controls like Advanced API Ops leverage AI and ML to detect anomalous traffic differentiated from random fluctuations to help define accurate alert definitions

Check out some examples of alerts here

#2 Isolate problem areas quickly with dashboards

Google’s Site Reliability Engineering book presents the case for efficient diagnosis by building dashboards that answer basic questions about every service, normally including some form of the four golden signals — latency, traffic, errors and saturation. But at the same time, capturing just these golden metrics at different levels of granularity can quickly add up. Like all software systems, monitoring can become an endless pit of complexity, complicated to change and a burden to maintain. In the same book, the most effective direction to create a well-functioning standalone system is to collect and aggregate basic metrics, paired with alerting and dashboards.

If you are running a large scale API program with a dedicated team to monitor your APIs, you can leverage the out-of-the-box monitoring dashboards in your API Management solution (like Apigee’s API monitoring) to gather real-time insights into your API performance, availability, latency, and errors. In other cases, you can use solutions like Cloud Monitoring that can provide visibility across your full application stack where individual metrics, events, and metadata can be visualized in a rich query language for rapid analysis. Leveraging a single system for your application stack provides observability in context and can reduce your time spent navigating between systems (Apigee customers can use Cloud Monitoring by default or integrate with other systems using Cloud Monitoring API)

Even after you collect and aggregate the metrics, it is important to have impactful data visualizations to quickly understand the issue and identify correlations during diagnosis. In data visualizations as well, focusing on too many dashboards creates a steep learning curve and increases mean time for every diagnosis. For example, Apigee API Monitoring provides the following visualizations as a standard to balance simplicity and efficiency:

• Timeline view to diagnose traffic (in 1 min intervals), error rates (4xx and 5xx across traffic) and latencies (50th, 90th, 95th and 99th percentiles)
• Pivot tables of metrics and attributes for all API traffic, to help compare activity across different metrics
• Treemaps of recent API traffic by proxy to get a snapshot of incidents, error rates and latencies

#3 Incorporate distributed tracing into your end to end observability strategy

Modern application development accelerated the adoption of technologies and practices like cloud, containers, APIs, microservice architectures, DevOps, SRE etc. While this increases release velocity, it also introduces complexity and more points of failure in an application stack. For example, a slow response to a customer request spans across multiple micro services owned (and monitored) by various teams who might not observe any individual performance issues. Without an end-to-end contextual view of a request, it is nearly impossible to isolate the point of high latency.

In such cases, distributed tracing is the best way for DevOps, Operations and SREs to get answers to questions such as service health, root cause of defects, or performance bottlenecks in a distributed system. Organizations should invest in instrumenting their distributed applications using open source standards such as OpenCensus and Zipkin. Using tools like Cloud Trace with a broad platform, language and environment support can help easily ingest data from any source — open instrumentation or proprietary agents.

While distributed tracing helps in narrowing the issue to a given service, in some cases you might need further context to pinpoint the root cause. For example: Even if you have isolated the source of a performance issue to an API proxy, it is still a tedious process to identify the right bottleneck among multiple policies being executed. Tools like Apigee Debug enable you to zoom into an API proxy flow and probe the details of each step to see internal details like policy executions, performance issues, and routing etc.,

As soon as a request starts to span across a handful of microservices, tools like distributed tracing and Debug will become crucial elements of your monitoring strategy. When every service in a distributed system emits a trace, the amount of data can quickly become overwhelming leading to the classic “needle in the haystack” problem. In such scenarios, it becomes vital to ask the right questions and choose between head-based sampling (randomly selecting which traces will be sampled for analysis) and tail-based sampling (observe all trace information and sample the traces with unusual latency or errors) based on application complexity

Implement effective API monitoring in Apigee

Apigee’s API monitoring (based on metrics exposed by the internals of the system) capabilities work with your existing monitoring infrastructure to help reduce mean time to diagnosis and increase application resiliency. Specifically, operations teams can leverage

• Monitoring dashboards to gain in-depth insights into API availability and performance metrics.
• Debug to precisely diagnose with deeper insights into an API proxy flow without toggling multiple tools.
• Alerts and notifications to gather contextual Insights, facilitate customization, and grouping with first class integrations to various tools (like Slack, PagerDuty, email and support for webhooks).
• Best of Google technologies such as Data Flow, Pub/Sub, Stackdriver, Bigtable and BigQuery to handle massive volumes and complex metrics at scale

Using Apigee’s API monitoring will help you maintain high application resiliency with comprehensive controls to reduce mean time to diagnosis and resolution. Get started with Apigee today or explore Apigee’s API monitoring for free here. Check out our documentation for additional information on API monitoring.

It has been seven years since Marc Andreesen’s famous article “Why Software is Eating the World” was published in the Wall Street Journal, and given the slow pace of change in many companies, some executives still may not be taking the threat of being “eaten” seriously enough. 

Software, and platform business models based on software, have the potential to deliver powerful economic forces into virtually any company or industry. Every company has valuable assets—such as data, expertise or access to certain services or user bases—and most of these assets can be delivered via software. Once an asset is expressed as software in a modern way—that is, as an application programming interface (API)—it can be combined with other software to create new applications and digital experiences.

Benefits of this approach, just to name a few, include near-zero marginal cost to scale up APIs for new users or use cases; global reach for both partners using APIs and end users consuming the digital experiences those APIs power; and network effects triggered as more partners use a given company’s digital assets and spread its services into new markets and use cases.

Disruption by software-powered business models

In the last two decades we’ve seen individual companies and entire industries upended by these kinds of software-powered business models. Examples abound: Amazon and the retail industry, Netflix and movie rentals, Uber and ride hailing, Airbnb and hotels, etc. We’ve reached the point that these companies’ names have become verbs synonymous with being “eaten” by software (e.g., “Amazoned” or “Netflixed”).

The most famous examples of digital disruption involve digital natives, of course, but legacy businesses are leveraging software to evolve too. Brazilian retailer Magazine Luiza—a company I’ve worked with through my employer, Google Cloud’s Apigee team—has enjoyed enormous revenue growth and seen its stock soar, for example, as it has built out its digital platform capabilities and transitioned from a primarily brick-and-mortar model to an omnichannel one. The point is, whether a company has been in business five decades, five years or five months, software remains ravenous and is always looking for new companies and industries to “eat.”

Change in the face of serious threats

Facing this threat, how should corporate leadership respond? There are some excellent examples of CEOs who have galvanized their companies and led them through the massive, gut-wrenching change required to pivot in the face of a serious threat. A few of the biggest examples include: 

• In 1995, it became apparent to Microsoft co-founder and then-CEO Bill Gates that the internet was “the most important single development to come along since the IBM PC,” and, if not embraced in haste, a threat to many of Microsoft’s businesses. In May of that year he published the “The Internet Tidal Wave” memo and focused all of Microsoft on adopting and building for the internet. Almost 20 years later, current Microsoft CEO Satya Nadella similarly made the bold decision to redirect the company for a cloud-first world. 

• Facebook went public at $38 per share in May of 2012 but within months, stocks could be had for a little over half that. The concern? Facebook was a desktop-optimized website without a polished mobile presence, and by 2012, consumer attention had begun to accelerate towards mobile at a much higher rate than many initially predicted.  Facebook CEO Mark Zuckerberg reacted by not only proclaiming Facebook a mobile-first company, but also backing up that proclamation with action.  

• Turning to another company I’ve worked with via Apigee, T-Mobile launched its highly visible “Uncarrier” campaign—which offers streamlined, customer-friendly plans and services—while also investing in and executing a new IT vision dedicated to ongoing digital transformation. T-Mobile execs have credited the technology effort, spearheaded by CEO John Legere, with helping the company to introduce new services and better service customers. T-Mobile’s market cap has more than doubled since Legere took over in 2012.  

Keeping pace with changing customer needs

In the face of an existential threat, strong executive leadership is required to pivot the company to safety, as these examples attest. Digital transformationisn’t about deploying new technologies just to make an existing approach more efficient or to add a few new apps or features to the status quo; digital transformation is about keeping pace with changing customer needs by leveraging software platforms to continuously evolve how the business operates. This can be akin to turning an enormous ship—and a ship can’t turn very well without her captain, first mate, and other leaders showing the way.  

Research supports this. A recent Deloitte survey, for example, found that over “80 percent of respondents from digitally maturing organizations say their leaders have sufficient knowledge and ability to lead the company’s digital strategy,” compared to only “22 percent of early-stage business respondents [who] have the same belief.” Similarly, Gartner research finds that CEOs are seeking a “deeper understanding of digital business” as they shift their focus from growth in general to how technology helps them attain it.  

More recently, the onslaught of software devouring the world has been further accelerated by machine learning making everything smarter, voice interfaces changing how people interact with devices, and more. To keep pace, corporate leaders need to galvanize their companies to build and deploy software faster, make systems and data easily accessible inside and outside their companies, and improve digital experiences through not only machine learning but also constant data-driven iteration. 

Seven years after Andreesen’s editorial, the pace of digital disruption is still increasing, and so is the need for strong leadership to pivot fully into digital. Over half of the Fortune 500 has been acquired, merged or declared bankruptcy since 2000—and the companies that survive in coming years won’t be those whose leaders treat technology as an IT concern rather than a core part of the business.