2022 Investment Themes: Data Assembly Line, Rise of “x”Ops, and Old Economy Transformation

*Logos represent Crosslink portfolio companies

As we turn from 2021 into 2022, there is a general feeling that I think we all share — the 2020s got off to a rough start for our world! So far… a global pandemic, disruptions to our children’s educations, mental health issues, fires / hurricanes / tornadoes, widening economic inequality, supply chain shortages, and now… inflation. Most of these are challenges that we as people will continue to grapple with as we head into 2022.

With that being said, one of my favorite things about us humans is that we are incredibly resilient, especially when united together under a common goal. We have seen courageous frontline workers put themselves in harm’s way to keep others safe and keep our economy humming. We have seen healthcare systems survive under immense stress and pharmaceutical companies rise to the occasion to deliver on new vaccines and treatments to combat a raging virus in unthinkably short order. We have seen people learn to work from home, cutting out costly commutes and bringing families closer together. And we have seen the technology industry not just survive, but thrive by delivering us products that enable effective remote work, virtual healthcare, food to our door within an hour, and almost anything else imaginable to our door within a couple days. While much of this was already in motion pre-2020, the pandemic forced a collective human response that is accelerating the inevitable transformation of entire industries to become digital-native and remote-first. It’s truly amazing to think about how much has changed in just two years, and the many more opportunities on the horizon.

I am inherently an optimist. As an early-stage venture capitalist, I have to be or I’d be out of a job. One of my favorite things about this job is being able to identify, partner with, and support great entrepreneurs who aim to solve our world’s most pressing challenges. I love working with bold founders who hold a firm belief that they can defy the odds and take an idea, a small team, and a *relatively* small amount of capital to build the next category-defining businesses.

As I have done for the past couple of years, I am synthesizing where I plan to focus my time in terms of investment themes in 2022. This year, I am particularly excited about the Data Assembly Line, the Rise of “x”Ops, and Old Economy Transformation.

First Principles

Each year, I like to take a renewed look at what I consider are my first principles in identifying truly special early-stage investment opportunities.

  1. Invest in extraordinary founders with a unique insight and clear vision for how they will define a massive category
  2. Identify why the market timing is right to build this business
  3. Form a thesis on how the business can build defensibility over time
  4. Chart a credible path to a business model that can become highly profitable
  5. Take early-stage risk: Welcome GTM, product/technology, and competition risk, NOT founder or market timing risk

Theme #1: Data Assembly Line

With digital transformation, every company is now a software company. And every software company is really a data company at its core. In 2022, data is a company’s most important asset. How you store that asset (DBs, DWs, integrations), maintain that asset (DataOps, MLOps), protect that asset (data security, privacy), and derive value from that asset (AI, analytics, automation) is mission critical. I think of this lifecycle as the “data assembly line” and today, the assembly line is still very disjointed. It turns out that throwing everything into a cloud data warehouse (Snowflake, BigQuery) doesn’t solve all of your problems. In fact, it can create more problems as yet another destination for your data. As a result, the current data stack still requires massive teams of talented engineers, security professionals and data scientists to store, maintain, protect, and derive value from underlying datasets. Because most companies in the world do not have access to premier engineering, security, and data science talent, they need better products and abstractions to enable an effective data assembly line within their organizations. Nirvana would be a continuously replenishing system of high quality, recent, contextual and scrubbed data served directly to the application or user in real-time. We’re far from that in reality.

I see great opportunities for startups up and down the modern data stack. At the infrastructure level, I believe there needs to be better abstraction and automation to reduce the burden on data engineers. Data engineers are commonly dealing with an endless queue of requests coming from developers, data scientists, and data analysts to access, prepare, and maintain high quality and compliant data for their projects and applications. There needs to be better integration, visualization, security, and control capabilities that reduce repetitive and laborious tasks required to deliver clean, compliant data repeatedly to the end user / application.

The data science toolchain today is very powerful thanks to services offered by the cloud platforms, but it still requires highly specialized experts for companies to build and maintain functioning AI / ML models. I believe there are opportunities for companies that improve data scientist collaboration (such as Crosslink investment in Vectice) to get builders out of their silos and more connected with the rest of the organization. There needs to be better model abstraction to offer building blocks for developers to create sophisticated AI / ML applications without requiring a PhD in the field. And enterprises are going to need more complete MLOps tools to help them train, deploy, and monitor their models at scale.

The last leg of the data assembly line is where the data analysts and business users interact with the data to create tangible business value. This is the largest pool of end users, and the least technical of the bunch. While there has been innovation and success already with horizontal low-code / no-code and robotic process automation (RPA) tools, there is still a wide open set of opportunities for startups to build functional and vertical-specific applications that enable the growing population of what I call data-aware business analysts.

Theme #2: Rise of “x”Ops

This is an area in the enterprise I’ve been watching emerge over the years that really deserves its own post. As cloud computing and SaaS tools have become mainstream, enterprises have long been moving away from centralized IT to a more decentralized format of deploying and managing technology at the team, or even user-level. In addition, there has been a surge in adoption of “best in class” tools to serve individual tasks or processes within a function (sales, marketing, customer success, engineering, finance, etc.). As a result of the movement towards decentralized IT and a proliferation of SaaS / cloud systems across functions in the company, there has been an emergence of new Operations roles within each individual function of the company — i.e. SalesOps, Marketing Ops, Success Ops (collectively RevOps), DevOps, FinOps, etc. These roles are tasked with difficult and important duties that typically include (i) building core systems of record, (ii) maintaining data quality and integrity of those systems, and (iii) improving interoperability of the systems / functions they support with the rest of the company.

So what? These “x”Ops roles need better tools and systems to do their jobs more effectively and deliver maximum value to the functions they support. We’ve already seen rapid growth in what is now a full landscape of great software companies serving the DevOps role, including GitHub, GitLab, and Armory (*Crosslink investment), but this is just the beginning. With growing pressure on organizations to ship more resilient, feature-rich software at a high velocity, we’ll continue to see more automation and embedded security policies infused into what is now DevSecOps. We’ll see more automation and deeper integrations across the RevOps toolchain to improve the interoperability between Sales, Marketing, and Customer Success, delivering better sales performance and customer service. We’ll see an emerging FinOps category deliver more automation and higher quality datasets to the finance and accounting departments that are still stuck in patterns of manual workflows within brittle spreadsheets prone to errors and wasted effort. I believe there will be a full landscape of compelling companies addressing these distinct needs of each of the “x”Ops functions that are of growing importance to companies. I’ve made two investments in the past year that serve pressing needs of the RevOps / SalesOps / FinOps functions (Syncari and Forma.ai), and I’m on the hunt for startups innovating in each of these critical functions.

Theme #3: Old Economy Transformation

It’s no longer a “nice to have” for the old economy industries to digitize their operations. It’s now a means of survival. Many traditional companies in older industries that have been slower to adopt modern technology had a major wakeup call during the pandemic. It’s no longer just the early adopters that are leaning into digital transformation, it is every company that wants to maintain its place in the Fortune 1000.

Supply chain pressures and labor shortages exposed major needs for manufacturing companies to digitize and automate core processes to be more nimble, efficient, and resilient. Strains on food supply, logistics & transportation challenges, and (again) labor shortages have exposed drawbacks to traditional methods of agriculture and the food supply chain. Given the physical nature of these industries and company operations, their problems are often not solved through standardized, horizontal software solutions. The most interesting companies innovating in these markets often require a combination of hardware and software to produce a full-stack solution or combine an integrated set of technologies. While challenging to deliver on solutions in these market spaces, I do believe that many more multi-billion dollar companies will emerge that combine data / AI applications, robotics, process automation, and collaboration software to build the future of food & agriculture, manufacturing, and the global supply chain. There are clear opportunities to improve transparency and coordination, automate laborious processes, and improve the quality and consistency of end products. At Crosslink, we have been investing in this area extensively, with portfolio companies such as Axle, Descartes Labs, GoExpedi, Iron Ox, and OPT Industries. These are markets that measure in the trillions instead of billions, so there is no shortage of interesting startup opportunities that remain to be capitalized on moving forward.

In the financial services, insurance, and healthcare industries, the pandemic also was an accelerant of existing trends towards digitization and automation. As frontends and backends become increasingly digitized, there is more data to be analyzed and there are more processes to be automated. Given the regulated nature of these markets, data security and privacy is a constant challenge to be addressed, particularly as datasets, SaaS applications, and cloud infrastructure services grow exponentially. Crosslink has made several investments in companies solving key challenges in these markets as well, including Arturo, Brace, Inscribe, Near Space Labs, and Overjet, and the opportunities are vast.

Closing Thoughts

As we enter 2022, we have a lot to reflect on and learn from the 2020s thus far. This is true for our personal lives and also for companies large and small seeking to survive and thrive as we move forward. Let’s take to heart the lessons we’ve learned from a couple of challenging years and use them to our advantage to make us more resilient companies, better friends, more present family members, and more mindful individuals. Cheers!

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

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Phil Boyer

Phil Boyer

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

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