Category: technical due diligence

Digital Transforma­tions – Preparing Your Data for AI

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

Digital transformation and data

In an earlier post, “Why Digital Transforma­tions Fail – Future Proofing”, we advocate that digital transformations must “design for capabilities for which both a strong business case and well defined requirements exist”. We recommend “future proofing enough – but not too much”. Yet, in this post, we present what seems to be a contrarian view: to invest in data in a way that, at first sight, might be guilty of future-proofing.

A digital transformation is a critical event intended, among other goals, to position the company for a new stage of growth for the next 2-5 years. Typically, at the time when a company reaches a maturity level where scaling has become a strategic priority, the value of its data becomes meaningful. An intuitive explanation is that data has reached the critical mass where insights that go beyond intuition can be harvested. As a corollary, data needs to be properly architected in order to yield these insights. Furthermore, proper data collection and curation is a foundational prerequisite for building an Artificial Intelligence (AI) sub-system into the product.

Why think about data during a digital transformation?

Digital transformation empowers a company’s growth to a new stage of maturity – and new business practices. During this transformation, data generated by the product also evolves in three major directions.

The rearchitecture of data

First, data needs to be rearchitected along with the code. Often, data is the primary dimension, more important than code, that drives the architecture.

  • Localizing data to each microservice is a core design goal of any re-architecture project.
  • Optimizing performance of data access is often a key driver to increase scale. While code can be scaled horizontally almost ad infinitum, it is much more difficult to do so with data.
  • With growth, data has to meet more onerous security and compliance requirements – for example data locality to meet GDPR.

Future-proofing your data

When a company is small, the amount of data it holds is small. Insights can be derived by combing over a spreadsheet. As the company grows, and the data it holds becomes larger and more varied, business intelligence and data science tools can discover insights that intuition alone could not have imagined. Consequently, ensuring that data is consistent across the product, as well as with internal company data, will save a huge amount of time in the future. This is where “future-proofing” comes in. Even if there is no immediate plan to harvest product data, it is important to:

  • Have consistent data formats and meaning across the product, accompanied by data dictionaries.
  • Promote data sharing along with access and discoverability across all functions of the company, while maintaining proper security, so that each department can experiment with the data in order to gain more insights on its own operations.

Opportunity for increased revenue

Finally, data can be used to increase revenues – which we cover in the next section.

Examples of how data increases revenue

The power of data lies in the diversity of ways it can be applied. Rather than attempt to provide an exhaustive list of applications, this section is meant to provide examples that stimulate the imagination.

Improve decision making

Data can be universally used to improve decision making. 

The simplest approach is to combine data gathered in the product with data from internal operations. For example, track which marketing campaigns are the most effective, predict demand and churn.

In addition, by instrumenting the product, product managers can track which features are used, or not, particularly to confirm that a newly introduced feature is seen and used by end users. Similarly product managers can track usage patterns to identify areas of the product that are confusing, or follow patterns that lend themselves to simplification. Finally, tracking usage patterns should confirm how users perceive the value of the product, and thus lead to pricing optimization.

Leveraging user analytics, growth marketers can directly drive revenue growth by using data generated from individuals’ interactions with the product to prompt them to purchase additional features relevant to their usage. For some companies, this is the primary driver of revenue growth.

Generate new sources of revenue

The examples below show various means to increase revenue, either by increasing engagement and the perceived value of the product (and thus increasing retention and the ability to raise prices), by increasing usage by better understanding users’ needs, or by monetizing the data directly. 

Trend analysis and recommendation systems increase product and services unit sales by suggesting additional purchases based on purchase history, product similarity or purchases of users with similar profiles. While seasons or news are well understood influencers of purchase decisions, other trends can only be discovered through the application of machine learning. 

AI-based language analysis allows a company to ‘read the minds of its users’ by analyzing all text-based and voice-based exchanges from users, as well as prospects, across all communication channels, internal or external to the company such as phone, email, chat, and social media. Companies can thus discover friction with existing features, as well as unmet needs.

Analysis of data aggregated across all of the company’s customers may reveal trends that are not visible at a smaller level, or local trends may be generalized – simply because the aggregated data pool is bigger and broader. As for all complex endeavors, a progressive approach, with measurable success milestones, is recommended. For example, a capability-driven progression could be:

1. Descriptive analytics:  Document ‘what happened?’ (e.g. ‘Alert, a server crashed’, ‘N customers bought item X today’.) Nowadays, this capability is expected from any non-demo software.

2. Diagnostic analytics: Explain ‘why did it happen?’ (e.g. ‘what specific service/line of code caused the server to crash?’, ’What drove this customer to purchase item X?’) This is expected from mature software. It is important information to improve the product on both technical and business fronts.

3. Predictive analytics: Predict what will happen. Provide insights into the future. (e.g. ‘this service requires data to be cached’, ‘people who bought this product also bought this other product’.) Thanks to the insights derived from predictive analytics, companies can drive additional revenues and optimize costs. This technology is now widely available.

4. Prescriptive analytics: Forecast ‘how can we make it happen?’ (e.g. ‘automatically increase the compute capacity for a service based on intelligence gathered from data’, ‘automatically order more supplies, or buy more advertising based on algorithms and data’). Decisions are made faster, without a human in the loop, based on the data collected. Billion-dollar companies do this. For smaller companies, gaining and applying this expertise is a clear opportunity to differentiate themselves, and get the associated lift in revenues. 

5. AI-driven operations: Discover unknown unknowns. (e.g. improve predictive analytics even further by applying AI algorithms to the company’s data, or leveraging generative AI, which trains its algorithms on vast amounts of data publicly available.) AI-driven operations is leading edge technology, which requires an internal team of experts as well as sustained investment over time to fine tune the technology to the company’s use cases. At the time of this writing, generative AI is an emerging technology, whose applications are yet to be fully discovered.Finally,  provided the company obtains users’ consent, the company can sell its user-generated data.

Preparing for AI

In SVSG’s experience, it is dangerous to attempt to skip steps in the progression presented in the previous section for the simple reason that analytics always produce a result, but do not tell you whether the result is correct, or optimum. It is easy to make a prediction, it is much harder to make a good prediction.

Capture relevant data

A critical first step is to capture all the company’s relevant data, in a clean way, as described earlier in the section “Why think about data during a digital transformation?”  The importance of clean data with correct meaning cannot be overstated. Incorrect data will lead to incorrect decisions (to state the often-overlooked obvious). The commonly accepted rule is that 80% of the cost of AI projects is spent in data preparation. Hence, the earlier tools and processes are put in place to curate data, the lower the cost.

Progressing through the first four levels of data-skills demonstrates the company’s skill at collecting and analyzing data correctly, and thus its readiness for AI.

Grow your AI talent

The second step is to acquire AI talent. AI is a different engineering field from software development. The best software engineer without AI education will not deliver quality AI capabilities. To be clear, both skills are needed, yet AI algorithm development is more akin to science. Once the AI team has figured out the algorithms (and the data required) to generate new revenue, then the software team jumps in to productize it.

In practice this means that time and resources for experimentation need to be budgeted for the AI team to research algorithms, tune them, and optimize them to the company’s use cases, and demonstrate business value. Naturally, as for any research project, success is not guaranteed. 

AI research and development

Finally, investment in AI, both research and data operations, must be maintained. Unlike software, which can be left alone once it works, AI requires constant optimization as data and the people who generate it change. In addition, processes must be set up to ensure avoiding known side effects such as drift, and bias.

Data is a product

The examples above are not exhaustive by far, yet they illustrate the value of data. While the harvesting of data in a data warehouse, or data mesh, may lag the digital transformation effort, it is critical that during this digital transformation, data be properly architected primarily because the cost and time to do so after the fact is so much greater.

In practice, data must be treated as a product with its own product manager(s) and development team(s).The data team’s role is to:

  • Nurture data to ensure accuracy, completeness, as well as correctness.
  • Ensure quality so that data has well defined and has consistent meaning and  format across the product and internal systems.
  • Provide access and tools to harvest the data across the whole organization, while maintaining security. Insights come from unplanned places and people.
  • Drive the company’s capabilities along the skill-progression outlined earlier. Exploring the potential use of AI requires enlisting qualified AI engineers, as well as patience in terms of time and budget for the research to demonstrate customer-value and business case.

Final thoughts

With the emergence of generative AI, ‘don’t forget data’ seems like a timid recommendation, yet for most companies, it is the necessary, difficult, first step in a pivot to a world that has become data-first.

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.

Lessons Learned From 50 Technical Due Diligence Reviews For Acquirers

Previously published on Forbes on August 12, 2022

Management teams seem to forget a critical rule when acquiring another company: The original product road maps of both acquiring and acquired companies must be delayed by at least one quarter. The reason is simple: Resources from both acquiring and acquired teams need to dedicate this time to merging the technology stacks, tools and processes of the two companies.

In a prior article, I covered the technical review needed prior to an investment. An acquisition requires additional work, which I’ll cover here.

The benefits of buying a company are easy to get excited about: New market segment, new customers to which to upsell the current product, new technology, etc. Yet, the effort and time needed to realize these benefits are often overlooked. Whether because of time pressures or over-exuberance, the acquiring management team often glosses over the intricacies of integration, oversimplifying the work needed, which results in a vastly underestimated budget, human resources and time.

In the worst case, the impact goes beyond delaying the benefits of the acquisition—because existing resources must be reallocated to the integration of the acquired company, the acquiring company’s original product road map itself is delayed, resulting in lower revenues. By engaging in thorough technical due diligence (tech DD) the acquiring management team can avoid these pitfalls.

Tech DD will force answers to tough questions on the future operation of the combined entities:

• Will the two products run side-by-side (simpler initially but likely costlier to operate), or will they merge into a single platform (challenging initial integration efforts and generating multiple long-term benefits)?

• What is the long-term technology stack—and how much effort will it take to get there? Even with similar technology stacks, framework versions have to be aligned, along with templates, design patterns, log aggregation, performance monitoring, etc. Tool stacks must be evaluated: code repository, CI/CD toolchain, identity framework, test automation, application monitoring and alerting, security, etc. There are often dozens of such evaluations to make.

• For each tool or framework that differs between the two companies, an analysis of “merge” versus “siloed” must be made comparing the upfront costs of merging versus the long-term savings. The absence of automated tests often increases the effort and risk of merging, whether it entails refactoring code or changing tools.

• On the other hand, keeping siloed not only duplicates costs but reduces knowledge sharing and increases the overall complexity in releasing features, as well as managing a more fractured team.

• On the operations side, migrating data centers is no easy task. The more a product leverages the services offered by a cloud provider, the more complex the migration is, whether it is for databases, container orchestration or management consoles.

• Unifying data is another challenge: Something as apparently simple as standardizing the attributes and representation of core entities in the system (e.g., a user) demands lengthy detailed analysis and code refactoring.

• Who will execute the technical integration? At least initially, the most valuable members of both teams are needed to make the critical evaluations. As a corollary, what projects will be neglected, and which new features will be delayed? How does this impact customers and projected revenues?

• Alternatively, outside contractors can be brought on to handle the temporary surge of work caused by the integration. In practice, because of the overhead of onboarding contractors, this approach works best if working with an existing partner—or one that the company intends to work with for the long term.

• How quickly, and through what processes, must the acquired company rise to the security and compliance requirements to those of the acquiring (larger) company?

• Were expectations properly managed? In the euphoria of the deal, double-dipping often happens. The sales team expects that the two companies’ road maps will be delivered unaltered, while the financial team expects cost savings from the two companies’ synergies. In addition, the integration budget is often severely underestimated.

As an illustration, imagine a company running on AWS with a tech stack based on Node.js and RDS/PostgreSQL acquiring a company running on Azure with a .NET tech stack. What is the cost/benefit of running the two products “as is” on separate software infrastructure, versus migrating to AWS and/or Node.js? An alternative might be to acquire a competitor of the target company that runs natively on an AWS/Node tech stack, if one exists, even if its business position is not as strong. A simpler integration will accelerate the time-to-market for the combined company, making up for the initial comparative disadvantage.

In short, the amount paid to transfer ownership of the acquired company may only be a fraction of the total cost of the acquisition. Other costs stem from additional resources, financial and human, needed for the integration and from revenue offsets from delays due to integration.

At a minimum, tech DD for an acquisition will present a more realistic view of the total cost of acquisition. While tech DD will only outline the myriad “merge” versus “siloed” technical decisions that will eventually need to be made, this will force a critical examination of the integration road map, along with refined estimates of the effort and time required. With this information, the management team can de-risk the decision to acquire, build post-deal milestones and accelerate the time-to-market of the combined products.

Seven Critical Technical Due Diligence Questions For Technology Investors

Previously published by Forbes on June 20, 2022

In the excitement of having signed a term sheet, investors may be tempted to consider technical due diligence (tech DD) as a formality to assuage their colleagues and limited partners. Tech DD, however, should be considered more than a defensive tool to avoid embarrassment and the loss of the money invested.

Tech DD, when performed correctly, can limit risk and ultimately increase an investment’s return by laying out the technology milestones critical to the success of the business. With proper tech DD, investors gain agency, and thus peace of mind, in shepherding a company’s growth.

While situations such as Theranos or WeWork are extreme, my organization has encountered “unexpected” situations in the course of tech DD projects, such as:

• A company running tens of thousands of users on the Ruby-on-Rails code that it demoed for its seed round.

• A company where the code had yet to be written for a large proportion of the advertised functionality.

• A founder/CTO who had reached his/her limit of expertise and was unlikely to be the right person to lead the company in its next stage of growth.

• A company with large amounts of legacy code running core functionality without any of the engineers who wrote the code still working for the company.

Being alerted to the scenarios above, along with the estimates of the time and effort required to put the company on a solid footing for scaling, allowed the investors to rebase the financial projections with more realistic time frames.

Seven Crucial Questions For Tech DD

None of the scenarios are intrinsically deal killers, yet they likely warrant action from investors pre- or post-investment. These, and countless other scenarios like them, can often be missed if tech DD is treated as a “check-the-box” exercise. In order to limit the risk of investments, as well as provide visibility on deliverables over the next couple of years, the following questions have proven to be particularly important:

1. How reliable is the delivery schedule of the product road map? Delays in the product road map are indicators of delayed revenues since delayed features make it harder to attract new customers. In addition, the efficiency of product and engineering in managing the product road map and the associated release schedule is critical to the overall development velocity of the company.

2. Will the technology handle the user growth over the next couple of years (taking into account the technology upgrades on the road map)? Has the technology team properly scoped the complexity, time and effort for the refactoring or re-architecting needed to reach the projected scale?

3. Are non-customer-facing aspects of technology aligned with the maturity, size and market of the company? Companies in high-growth mode can easily lose track of the product’s security, resiliency and business continuity. Similarly, it is difficult to ensure that tools and processes for QA, CI/CD, operations are upgraded in line with growth.

4. Does the tech team have a plan to maintain its velocity while scaling? This question should go beyond the software architecture and addresses how and when organization, tools, processes and metrics will adapt in engineering and operations.

5. Does a new CTO need to be hired (or other technical leaders)? Is the technology leadership team ready for the next phase? How well have they mapped out the next big set of projects?

6. Are all the technology projects in the budget? Do they have the proper funding, staffing and time estimates?

7. Does the company have uniquely differentiated intellectual property? Intellectual property is rarely about patents. Rather, investors want to know whether the company has built a “defensible competitive moat” through market research, unique use of available technologies, proprietary technology or algorithms (e.g., for data science or machine learning).

How Investors Can Leverage Tech DD Findings

The benefits to investors who embrace the tech DD process outlined above materialize in the form of one evaluation and two numbers.

• The ultimate evaluation is that of risk. Has the riskiness of the investment increased dramatically? It’s crucial to understand whether the investor will need to be more involved than planned in monitoring how well the company executes or possibly spend time supporting the management team.

• The first set of numbers is the quarterly revenue projections, and whether they need to be adjusted based on the information received during the review. A delay in features, or scalability, will likely delay revenues and thus ultimately the value of the company. In the worst case, the company could lose out to a more nimble competitor.

• The second number is the amount to be invested in the company. Does this number need to be adjusted to account for delayed revenues, increased costs from a larger than planned technology team or unanticipated development?

An important additional benefit of this effort occurs when investors review the tech DD findings with the company’s management team and align expectations. This reduces the likelihood of unpleasant surprises post-investment.

In terms of deliverables, investors should expect an overall assessment of the technology and the technical team’s ability to deliver the features, customer-facing and not, that underlie the product road map and thus the revenue projections.

Whether this assessment matches their own will determine whether their risk projection for the deal needs to be adjusted. In addition, investors should receive a quarter-by-quarter list of technology deliverables that are critical to the success of the company. With this information, investors improve the odds of the company meeting its plan by taking actions early, in collaboration with the company, to set it up on a path to success.

Lessons Learned From 50 Technical Due Diligence Reviews, Part 2

Previously published on Forbes Technology Council – April 26, 2022

In a prior post, Lessons Learned From 50 Technical Due Diligence Reviews, we offered information and advice to founders, CEOs and CTOs on what to expect from, and how to approach, technical due diligence review (tech DD). In this post, we cover how founders, CEOs and CTOs can prepare for tech DD.

In our prior post, we emphasized that tech DD is forward-looking. Investors want to confirm that as a management team, you will master the future opportunities and challenges on both business and technical fronts. Furthermore, an injection of capital usually comes around an inflection point in the growth of the company. For example, when the primary focus shifts from developing the product to increasing revenues, or when adding a major product line extension to conquer new markets. This period is conducive to a strategic reflection on how the company will win in this new phase, under the Marshall Goldsmith adage “What got you here won’t get you there.” After gaining a baseline for where the technology is today, the focus of a tech DD review will, by and large, be very similar to the questions addressed in a strategic review.

Below, we share the most common questions that we ask during tech DD in the hope that they help you prepare your strategic review as well as the tech DD itself.

We always start our reviews with the business context because the role of the technology team is to deliver the products that will enable the business to reach its goals. The product road map for the upcoming 24 months is, for the purposes of tech DD, the materialization of the business objectives. In this context, the product road map must include noncustomer-facing features such as performance, scale, security, business resilience and continuity.

The focus of the tech DD is to determine how well prepared the technology team is to deliver the product road map as promised and the associated revenues or other business metrics.

At the risk of simplifying, tech DD will ask the same questions, listed below, for all areas related to technology (see list further down), which we will later illustrate with examples:

• Is what you are doing today working?

• What are your plans for fixing what’s not working?

• Will the next 24 months create a discontinuity compared to the past year?

• If so, do you have a plan? If not, do you have a plan for a plan?

• Are there areas where you need to acquire competence (learn, experiment, hire, buy)?

The standard areas we investigate, and to which we apply the questions above, are:

• Software architecture and data architecture.

• Technical stack: frameworks for back end and front end, data stores, APIs.

• Performance and scale.

• Security, compliance, data privacy.

• Testing.

• Operational management: deployment, management, alerting, performance.

• SDLC process and toolchains: code analyzers, test automation, SCCS, CI/CD.

• Team: talent, organization.

In practice, this translates to questions like:

• Is the product road map aspirational (wishful thinking) or actionable (backed up by an engineering plan)?

• How much technical debt is there? What is the plan to tackle it, if need be?

• Will major components of the code require re-architecting or major refactoring?

• Are the data models consistent with the main use cases as well as future ones?

• How do your security, data privacy and risk profiles look, both now and once you have scaled 10x?

• Will you require new certifications for compliance?

• What critical hires do you need to make? By when?

• What would be the impact, financial and technical, of a two-day outage of your cloud hosting provider availability zone?

• Have the major technology initiatives (re-architecture, technical debt reduction, security upgrade) all been approved by the business team and budgeted (i.e., have time, resources and money been allocated)? Is the product road map based on budgeted resources?

Investors in high-growth companies, by and large, have a strong stomach and anticipate that at least one major software project will be needed every year or two. From what we can observe, investors generally have the strongest negative reactions to misalignments. For example, an aspirational rather than actionable road map (which implies that both budget and revenue plans are aspirational), or leadership that does not acknowledge that architecture, code quality, processes or security have been outgrown by the success of the company (which implies either lack of competence or lack of teamwork in the business and technical leadership).

In summary, the best scenario is when the business and technical leaders have performed a strategic review prior to fundraising. Preparing for the technical due diligence review should not be about cramming to figure out the answers to anticipated questions; rather, it should be about visualizing how the business, and its technology, will grow over the next two years and identifying the new categories of challenges that growth, and success, will bring.