Month: April 2023

Why Digital Transforma­tions Fail – the Monolith Syndrome

Previously published on Silicon Valley Software Group Insights in March 2023.

A number of our engagements come from clients who experience a similar pattern of symptoms: release velocity is trending down, critical bugs pop up with each release, yet hiring more developers does not seem to improve anything. In parallel, the digital imperative, which has gained momentum over the past couple of years, whether imposed by the pandemic, or simply overall evolution, keeps building the pressure: consumers require a flawless digital experience. When the technology team does not deliver, the consequences for the business are painful: customers are disappointed, competition edges ahead and, even more heartbreaking, our clients are unable to capture the demand that their marketing has generated.

The goal of this post is to inform both CEOs and CTOs on how to diagnose what we term the “Monolith Syndrome”. As with any condition, early diagnosis vastly improves the chances of success. It is thus critical for CEOs and CTOs to know how to recognize this pattern, and take the necessary early actions. Further, it often falls on the CEO to identify the situation, because the CTO is usually consumed in trying to just keep up.

Symptoms

The symptoms of what we term the “Monolith Syndrome” look like this:

  • The application’s response time keeps degrading;
  • Outages are becoming more frequent;
  • As outages occur, new features requests do not get delivered. Customer complaints rise;
  • Re-prioritization of the product roadmap occurs before the main features of the previous roadmap are delivered (because they took too long);
  • Distrust between the executive and the technology teams grows.

Like any challenge, each company faces its own flavor of the “Monolith Syndrome”, yet to the experienced eye, the pattern is easily recognizable. More fundamentally, it is absolutely normal: it occurs when a company has grown into a new stage of maturity – where a new way of running the business, including the technology, is now necessary. Like most living organisms, when looking on a short time horizon, companies grow incrementally. However, when taking a step back, discrete stages become evident. On the technical front, transitioning between maturity stages call for what is called a “Digital Transformation”.

The Monolith Syndrome encapsulates scenarios of pain when the technology team cannot keep up with the needs of the business through “business as usual”.

There are multiple scenarios that require a digital transformation, the Monolith Syndrome is one of them. We will explore the others in subsequent posts.

Causes

From a technical perspective, the root causes of the “Monolith Syndrome” are often a combination of:

  • The architecture of the current codebase was developed more than five years ago, and has changed little since;
  • The code is built on a single codebase and uses a single database – hence the term “monolith”;
  • Development expediency has been the priority which has led to: poorly organized code, little documentation, few tests, and even fewer automated tools for QA, release and operational management;Critical areas of functionality are implemented in “dark code”: code that was written by developers who are no longer employed by the company, and which current developers are scared to touch, because the code is difficult to understand and there is no documentation.

The Monolith Syndrome encapsulates scenarios of pain when the technology team cannot keep up with the needs of the business through “business as usual”. We described the symptoms above in technical terms. Yet, the underlying cause is that the company has grown into a different maturity level – where “what got you here” no longer works.

To be clear, a monolithic codebase is usually the right way to go in the early stages of a company: there are a handful of developers, a manageable number of lines of code, and few features that are quick to test manually. Yet, at some point in the company’s growth, the nimbleness and expediency become a detriment rather than an asset. For example, it becomes cumbersome to develop, let alone release, when twenty-plus developers are writing code in a monolith: different developers’ new code interact with each other in a way that creates unforeseen bugs.

The underlying cause of the Monolith Syndrome is that the company has grown into a different maturity level, but not the technology team.

As a company battles through the Monolith Syndrome, the CEO and CTO have a heart-to-heart: the CEO asks “what do you need to develop new features faster?” – to which the CTO invariably answers “I need more engineers”, and then proceeds to build a “better monolith”, i.e continue to work on the same codebase with the same processes and tools. Yet with poor architecture, software organization, and documentation, the extra developers only create more confusion and barely accelerate development velocity. The root cause of this lack of progress is that the business side has gone through a change of paradigm, but not the technology team.

Again, this is why it is the CEO, who understands the business context, who needs to recognize the pattern.

The goal of the transformation is not to update to the latest and greatest technologies, but rather to identify the technologies most appropriate for the foreseeable needs of the business.

The Proper Mindset

In order for the transformation to be successful, everyone needs to have the proper mindset:

  • Recognize that this effort is the “price of success”. Understand that current architecture, code, tools, etc. were not a mistake – no one deserves blame. On the contrary, they were optimal for the previous stage of maturity. Now that the business has grown, and evolved, technology also has to transform to a more mature architecture.
  • The goal of the transformation is not to update to the latest and greatest technologies, but rather to identify the technologies most appropriate for the foreseeable needs of the business.
  • The transformation will require a set of skills that is typically not present in-house. Rare are the CTOs who have successfully led digital transformations. Hence, it is usually wise to enlist the help of technical leaders who do have this experience.

SVSG’s Framework

SVSG follows the following framework:

  • Re-align the technology to the business: understand the main stakeholder journeys (customer and employee), which have likely evolved since the current architecture was designed.
  • Design the architecture – and data models – before coding, based on the new stakeholder experiences, as well as needs for scale, resilience, security, etc.
  • Incorporate the full business context such as scale, security, resiliency, etc.
  • Design an incremental migration path from the current state to the desired state. For example, start by breaking up the monolith by creating one additional microservice, validating its design before moving one to a second microservice.
  • Evangelize that the transformation goes beyond architecture and code. The whole development process, from end to end, must align with the company’s new stage of growth.

Final Thoughts

Digital transformations are rare events in the life of a company. Technology leaders are usually selected and trained to design and build technology incrementally. Unless you have gone through it before, detecting that your company might be experiencing the Monolith Syndrome is an unusual, and difficult, challenge for both CTOs and CEOs; but when the symptoms arise, it’s important to act swiftly if the business is to keep up with its growth.

Technical Due Diligence For Companies On The Cusp Of High Growth

Published on Forbes Technology Council 12/27/2022

You are ecstatic: You just executed a term sheet with a startup, which, thanks to your large investment, will grow two to three times each year for the foreseeable future (i.e., two years). Now begins the hard work of ensuring that the CTO delivers the technology and features laid out on the product roadmap. Yet, sustaining high growth, defined (arbitrarily here) as growing revenues at more than 100% per year for at least two years, requires a different playbook than a more mundane growth rate. For example, bigger hardware may accommodate the first doubling of traffic, but the second or third will likely require substantially different software and data architectures, which need to be planned long in advance.

While it is not an investor’s job to identify or address these challenges, the return on investment will ultimately depend on how well and how timely the portfolio company manages them. This article provides pointers on what investors should know and look out for during technical due diligence, as well as post-investment.

The Difference Between High Growth And Regular Growth

In general, growing at a high rate raises four types of challenges.

• Tough Technical Challenges

Handling twice the traffic, with twice the amount of data stored, leads to a different category of problems, technically, compared to handling 10% more traffic. In addition, when you decide to build a new architecture because your traffic is doubling every year, you actually need to design for 10 times the traffic so that you do not go through the same exercise again each year.

• Incremental Changes No Longer Effective

Changes need to be performed in discrete steps. As illustrated above, as traffic surges, incremental measures (e.g., bigger hardware) will keep the business going for a while, but a new architecture needs to be analyzed, designed, implemented and deployed rapidly. Because this work is complex, it needs to start early—well before the real pain starts. Furthermore, the transition to the new architecture often presents a more complex challenge than the new architecture itself.

• The Need For Everything To Change At Once

Along with technical changes in the architecture and the tech stack comes the need to deliver more features faster. This, in turn, requires more engineers as well as a new team organization, along with new tools and new processes.

• Changing Nonfunctional Requirements (NFR)

As the company grows and acquires bigger customers, securing data, meeting regulatory compliance, protecting privacy, preventing downtime and ensuring business continuity take heightened importance. While security might not appear critical for a company managing $10 million worth of transactions, it becomes critical when $100 million flows through the platform. Growing companies often miss this because a slow evolution over time eventually adds up to a category-changing situation.

Where Technical Due Diligence Should Focus

The first step when reviewing a company prior to investment is to identify and quantify impediments to growth. For example, is the amount of technical debt such that even a minor increase in traffic or features will create serious risks of downtime? Do the CTO and the technical leadership have the talent and experience for the design and implementation of the next-generation architecture? Does the CTO have the business acumen, in addition to the technical expertise, to align technical operations with the evolving business?

Next, the plans for growth need to be examined. Are they aggressive enough in scope as well as technology to meet the anticipated growth? How well developed are the plans: Are they conceptual, or do detailed designs exist along with development plans? How robust is the new architecture design? Without detailed plans, the product roadmap is aspirational rather than achievable.

In our investigations, we often see parallel roadmaps for the product, technology and NFR, each assuming access to the same resources. This is a recipe for disaster; fuzzy resource plans lead to fuzzy budgets, misalignment with the CEO and confusion about the allocation of the newly invested funds. The worst case scenario is to find out six months after a deal has closed that the engineering budget needs a 25% increase to deliver the product roadmap because the resources to upgrade the architecture, scalability or security were double counted.

Recruiting and new employee onboarding are often overlooked activities, but when they’re performed poorly, they are a huge, yet hidden, drain on productivity. Because high growth often entails increasing the size of the team quickly, engineers must spend time interviewing prospects. When the recruiting process is poor, candidates do not meet standards, and desirable prospects accept offers from other companies.

As a consequence, engineers end up spending a lot more time in interviews, and building the team takes longer than it should, thus delaying the product roadmap. In addition, frustration builds because time spent in interviews is rarely factored in project scoping, causing delays in projects. Investing time upfront in building efficient recruiting and onboarding processes will be recovered many times over.

Companies rarely have everything figured out. The purpose of the review is not to give a “beauty contest” score but rather to determine whether critical changes need to take place before the company is ready to fully “step on the accelerator,” as well as how much these changes will cost and how long they will take. Getting technical debt to an acceptable level, hiring a new CTO, building a baseline of automated regression tests—all these projects can easily take one or two quarters and commensurately affect the growth rate and revenue.

Conclusion

High growth differs materially from traditional growth by the breadth and speed of the changes that are needed, thus requiring a different playbook. Investors need to know whether a company is ready from day one, whether it will require time to pay down technical debt and whether its growth plans are ready for execution. A lack of readiness can easily consume two quarters, which is a long time in the startup world. It may determine whether the company will dominate its market or get edged out by a faster competitor.