2021 Investment Focus: Digital Transformation (Act II), Data Agility, and Industry Transformation (Act II)

TL;DR: Integrate, Collaborate, and Automate

At the beginning of 2020, I was fresh off of celebrating the birth of my first child, and it was an exciting time. I had some extra time to think through my investment focus for 2020 and was able to put pen to paper. Then, 2020 happened (eek) and the whole world was flipped on its head. Surprisingly (rather, luckily), many of my focus areas within B2B software investing ended up seeing tailwinds in 2020 — AI, cloud infrastructure, data science, developer tools, security— and my appetite has only increased in these areas heading into 2021. I fully recognize that I am operating within industries that have been largely spared by the pandemic, and I am extremely grateful (again, lucky) for this.

Mapping things out on paper helps me synthesize and organize my learnings and thoughts after many conversations with founders, execs at portfolio companies, customers, and other investors. And publishing these thoughts puts some pressure on myself to really think it through. I met hundreds of companies in 2020 that fell within the focus areas I mapped out at the beginning of the year, and I invested in a few of them. But unsurprisingly, I also invested in a couple of companies that were completely outside of the box. As an early stage investor, I reserve the right to be opportunistic! In fact, I fully embrace the serendipity of meeting an entrepreneur with unique experiences who looks at the world completely differently from me (or anyone else for that matter), and these are often the times that I fall the hardest for their vision of the future.

So, I am “running it back” this year in hopes that colleagues, co-investors, customers, and founders will find it helpful to know where my head is at in 2021. This year, I am double-clicking on three over-arching themes: Digital Transformation (Act II), Data Agility, and Industry Transformation (Act II). The TL;DR? Integrate, collaborate and automate.

First Principles

  1. Invest in extraordinary founders with a unique insight and clear vision for how they will define a massive category
  2. Understand why the market timing is right to build a $1B+ business by creating a new category, or disrupting an existing one
  3. Have a thesis for how the company can build long-term defensibility (network effects, data moats, IP, distribution advantage, etc.)
  4. Identify a credible path to a business model that will become highly profitable over time
  5. Take early stage risk: Welcome GTM, product/technology, and competition risk, NOT founder or market timing risk

Theme #1: Digital Transformation (Act II)

Act II is about productive changes that organizations can now make with key information digitized and accessible. Companies will invest in integrated design and developer tools to continuously improve digital experiences for their employees and customers. They will invest in process automation technologies to automate laborious and repeated processes within the organization to improve efficiency and output. In response to a wider surface area and therefore new risk exposures in result of digital transformation, companies will double down on the security and reliability of their crown jewels. Below is a map of how I view the key opportunities within this theme.

Sub-themes:

  • Design & Developer Tools: It is too difficult for the majority of companies to offer a first class UI / UX for their web and mobile applications, which is why we still see stale websites / broken applications from even the most important companies in the world. Organizations are prioritizing improvement in their digital experiences across the board, but top design and engineering talent is scarce. There is real opportunity and enterprise budget available for innovative design & developer tools that improve the productivity of teams developing rich user experiences. Tools that improve design <> developer collaboration, reduces repetitive workflows for developers, and abstracts / automates DevOps tasks are very interesting to enterprises right now. These tools enable greater efficiency in pushing updates and launching new applications to keep pace with the rate of innovation required for companies to stay competitive.
  • Process Automation: With on-premise or paper-based processes now transformed into digital-native processes, companies have improved operational efficiency dramatically. Moving into Act II, companies are now tapping into the power of process automation to speed up or automate laborious, repetitive tasks that are bogging down teams from focusing on higher-impact work. This new class of process automation is enabled by technologies such as RPA, computer vision, machine learning, and low-code/no-code interfaces. There are meaningful automation opportunities to provide value to key roles that suffer from heavy data entry work or repetitive tasks such as Customer Service, Finance, HR, Marketing, Ops, and Sales. I see opportunities for industry-specific process automation tools as well which I’ll discuss more in the Industry Transformation section below. In order to effectively understand existing processes within the organization and identify automation opportunities, companies are also adopting process mining / mapping tools, which is still an underinvested area with a lot of enterprise pull.
  • Security & Reliability: As more sensitive information (PII) is digitized and stored in the cloud and hybrid environments, security is an even higher priority than it has already been. And with the lifeblood of the company all flowing through digital interfaces, reliability of software applications becomes increasingly mission critical. More budget will be allocated towards DevSecOps and SRE tools so that developers can bake in better security and reliability standards into the applications they build pre-production. Telemetry data acquisition and observability tools will see greater investment for ground truth information on the reliability of applications in production. Protecting against online fraud risk, identity spoofing, and phishing attacks will also continue to be prioritized. Lastly, there will be increased investment into data privacy and data security due to increased scrutiny and regulation over how user information is being stored and accessed by enterprises.

Theme #2: Data Agility

In result of these developments, a key focus for me in 2021 is on companies that enable greater data agility. In infrastructure, I am paying close attention to the emerging area of DataOps, or technologies that make it easier for engineers to ingest, move, synch, manage, and monitor data. While the cloud players have invested a great deal into data science tooling, there is still plenty of startup opportunities to make the data scientists life easier by improving collaboration, centralizing their assets, automating infrastructure / ops tasks, and abstracting complex models to enable the masses to access AI capabilities. Lastly, there is an appetite for better tools for the growing segment of data-aware business analysts to run more advanced analytics that are integrated into their business systems and datasets.

Sub-themes:

  • DataOps: Even post-digital transformation, data in most enterprises is still extremely difficult to access, move around, or synch across services. There are different data formats, schemas, naming conventions, etc. and many different SaaS apps, microservices, databases, and clouds that the data is sitting in. Tools such as Fivetran have helped with sending data in and out of the data warehouse, but there is room for more innovation in this area to give developers superpowers in managing the flow of data across enterprise systems. Given the critical nature of the datasets to serving successful applications (internal and external), data health, governance, and reliability is of growing importance. There is a growing need for data management and monitoring tools in modern data architecture to continuously assess and manage key datasets.
  • Data Science Toolchain: AWS, Google, and many open source projects have made great strides with providing tools to data scientists to be productive. The trouble is that these are typically point solutions catered towards the individual. With large enterprises hiring more data scientists and launching AI initiatives, there is a need for enterprise-grade data science tools that bring better collaboration for larger teams, centralize key assets such as models and datasets in one place, automate operational tasks required to train / deploy / monitor AI models, and abstract complexity to democratize advanced AI models (i.e. AutoML).
  • Data-Aware Business Analysts: This is really an emerging persona that I believe is still under-resourced. There are droves of business analysts that are not engineers but technical enough to run basic SQL queries or excel analysis that would benefit from access to more advanced analytics capabilities. Low-code / no-code tools such as Airtable have emerged successfully addressing some of these needs, but I believe there are more opportunities for function-specific tools (Customer Service, Finance, HR, Marketing, Ops, Sales) and industry-specific tools that make business analysts look like superheroes. Integrated analytics tools with embedded connectors to various datasets and services across the enterprise will enable data-aware business analysts to run sophisticated analyses while reducing the massive queue of requests to data engineers and data scientists.

Theme #3: Industry Transformation (Act II)

I believe we are now entering Act II of Industry Transformation. More digitized processes means there is more data available for companies in these industries to analyze and there are labor-intensive processes to automate so people can focus on higher-impact work. What was seen as frontier technology a few years ago (AI, robotics, etc.) and merely in the pilot stage, is now being deployed into production. The more sophisticated companies in each of these industries are beginning to invest in technologies that have matured — AI applications, additive manufacturing, collaboration software, process automation, and robotics. SaaS services, cloud infrastructure, APIs, and no-code applications have made it easier than ever to trial and launch meaningful technology innovations at even the slowest-moving companies on a short implementation cycle. I am increasingly excited about bringing some of the most impactful technologies that are in heavy use by digital-native companies to some of the oldest (and largest) industries in the world.

Sub-themes:

  • Finance & Insurance: There is a spectrum of where large financial institutions are in their digital transformation process. There was already a significant push to investing into moving applications into the cloud, DevOps, and better frontend experiences for customers to process tasks digitally. The pandemic / remote work only increased the urgency to make these infrastructure and organizational transitions in 2020 and that will continue in 2021. The next step is further investment in process automation, low-code/no-code tools for non-technical users in business lines, and AI applications to capitalize on their most prized datasets as a competitive advantage. Insurance companies may be more behind in its digital transformation, but there is appetite to invest in process automation tools to bring more efficiency into laborious tasks such as claims processing, and AI applications to improve the accuracy of their underwriting models.
  • Healthcare: In the healthcare arena, there have been regulatory, privacy, and security hurdles in the way of fully realizing digital transformation. Much of healthcare is still stuck in Act I of digital transformation, and I expect we see more investment into tools that enable greater data privacy and security to unlock more technology use cases in collaboration and analytics. Healthcare providers are already investing in collaboration tools for greater efficiency internally and with their patients + partners. Providers, payers, and pharmaceutical companies with proper data privacy, security, and governance controls in place will invest further into analytics applications powered by computer vision, ML, and NLP to automate laborious data processing, aid in clinical research, and improve the quality of care.
  • Manufacturing: Manufacturing is another one of the largest industries in the world that has been largely shielded from startup disruption despite promising technologies available. Technologies such as 3D printing / additive manufacturing and agile robotics are maturing to the point that they can be cost effectively deployed for massive efficiency gains on the factory floor, and in the more extreme case reduce reliance on cross-border manufacturing altogether, cutting out significant supply chain costs.
  • Supply Chain: The growth in e-commerce and cross-border transactions have put immense pressure on the supply chain to get its act together and improve operational efficiency. This is a tough nut to crack, as there are multiple stakeholders involved in product shipment, multimodal transportation, ports and regulatory bodies, and delivering a product to the end destination. All while aiming to provide speed, transparency, and security to the many stakeholders along the way. There is appetite for more supply chain visibility which can be achieved by more workflow / process automation software to improve digitization of key information and better collaboration tools.

Closing Thoughts

Dad, VC @ Crosslink Capital, surfer, skier, drummer. In that order.

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