Home Venture Capital Tech stock sell-off signals tough times for data vendors

Tech stock sell-off signals tough times for data vendors

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The tech stock sell-off that has erased millions in value from startups and massive companies alike is expected to reverberate across the entire sector.

The three major indexes — the Dow Jones, Nasdaq composite and S&P 500 — are down by more than 10% since the start of 2022, with the tech-heavy Nasdaq down by more than 25% as of May 26.

Within the tech sector, cloud computing giants Amazon, Google and Microsoft are all down this year, with Amazon down 37% with its online retail business hit particularly hard.

Among publicly traded data, AI, augmented intelligence and analytics vendors, Snowflake, which in September 2020 set a record for the largest initial public stock offering by a tech vendor, is down more than 60%, year to date. Others, including analytics and business intelligence vendors Domo and MicroStrategy and AI hardware/software giant Nvidia, are all down significantly as well.

It’s not just stock prices that are down. Initial public offerings by tech vendors have stalled.

[The tech sell-off] affects everybody’s ability to raise capital funding. We’re already seeing less funding going into the tech ecosystem, quarter over quarter.
Vanessa LarcoPartner, New Enterprise Associates

Analytics and BI specialist Qlik filed an IPO months ago, but has yet to move forward. However, MariaDB on Feb. 1 said it would go public in the second half of 2022 by way of a merger with a special-purpose acquisition company and pegged the combined valuation at $672 million. The company said in a statement that it sees no changes to the plan it set forth in February.

The venture capital funding rounds that exploded in the data, AI, analytics and other tech sectors nearly every month since the middle of the pandemic — with investments consistently topping $100 million and at times reaching $1 billion — have slowed dramatically, though haven’t stopped completely.

“[The tech sell-off] affects everybody’s ability to raise capital funding,” said Vanessa Larco, a partner at venture capital investment firm New Enterprise Associates. “We’re already seeing less funding going into the tech ecosystem, quarter over quarter.”

Layoffs

Another recent result of the Russia-Ukraine war and supply chain-hobbled economy has been tech layoffs not seen since the coronavirus pandemic first wave hit in March 2020.

AI and automation vendor DataRobot, which grew explosively since it was founded in 2012 and came to challenge tech giants in the automated machine learning (AutoML) market, laid off about 70 employees earlier this month. The Boston-based vendor conducted a round of layoffs in March 2020, but rebounded in 2021 with a $300 million series G funding round in August of that year, bringing its market valuation to $6.3 billion. DataRobot said in a statement that the decision to reduce its overall workforce by 7% was “difficult but necessary.”

“Like any healthy business, we’re always evaluating how we optimize operations based on customer needs and market conditions, particularly in a market as complex and unpredictable as we’re all facing globally,” the vendor said.

Two other notable tech vendors cut employees as the market nose-dived over the past three months.

Hyperscience, a software automation vendor based in New York City, laid off 100 staff members — 25% of its workforce — in early March, while Rasa, a conversational AI specialist based in Berlin and San Francisco, laid off 59 people — 40% of its workforce — in late March, according to the layoff-tracking site Layoffs.fyi.

Neither company responded to requests for comment.

On May 23, Israel-based AutoML vendor BeyondMinds notified its 65 employees that the company is shutting down. The vendor was founded in 2018 and had raised $16 million in funding.

Sell-off affects entire tech sector

The effect of the tech sell-off on publicly traded companies is straightforward. With their stocks down, they’re worth less, and when they’re worth less, they can’t easily use their company value as leverage to make investments in new technologies — either through acquisitions or R&D — or marketing campaigns that attract new customers and fuel cash flow, earnings and growth.

The downturn also affects privately held companies, whether they’re established vendors such as AI and data lakehouse vendor Databricks — which raised $1 billion in February 2021 — that have gone through numerous rounds of venture capital funding, or startups such as open source graph database vendor ArangoDB that have completed only a handful of funding rounds. Their valuations are tied to the valuations of publicly traded companies, so their worth is sliding as well. It’s now more difficult for them to raise the venture capital funding to support growth.

Chart showing tech vendor funding slowdown as stock market struggles
Venture capital funding for data vendors slows dramatically amid down market.

The likelihood of new AI startups attracting seed capital in the current economic climate is slim, especially in newly crowded niches such as MLOps, according to Mike Gualtieri, an AI analyst at Forrester Research.

“It’s going to be nearly impossible,” he said. “Now I think companies are more likely to acquire someone than to see a new startup funded.”

Meanwhile, those tech companies that are raising funding are being forced to sell shares to investors at a lower price — known as a down round — compared with previous funding rounds, according to Ray Wang, founder and analyst at Constellation Research. That could lead to cash flow problems for vendors that have exhausted the capital they raised in previous rounds.

“If they don’t have enough money, or runway, for the next two or three years, they are in massive trouble,” Wang said. “All the AI and analytics startups that don’t have any source of additional capital or are not net positive are in trouble.”

While the stock market decline is affecting nearly all data vendors in some way, its effect depends on the stage of the vendor’s lifecycle, according to observers.

The length and severity of the tech downturn also matters, with a continued decline and possible recession potentially leading to more layoffs and a wave of consolidation. A quick recovery would have far less severe ramifications.

The challenge for startups

Startups often attract their initial funding by differentiating themselves from existing vendors.

For example, Databricks, which was founded in 2013 and has raised $3.5 billion over 10 funding rounds, introduced the concept of the data lakehouse, a combination of data warehouses and data lakes that made it easier for users to access and work with their data.

ThoughtSpot, founded in 2012, has raised $663.7 million in 12 funding rounds and was among the first analytics vendors to make natural language processing a foundational part of its platform. Its leaders have flirted with the idea of an IPO, but it’s unclear if favorable conditions for going public will develop anytime soon.

And more recently, automated analytics vendor Sisu, founded in 2018, raised $128.7 million in three rounds and differentiated itself by automatically detecting anomalies in data and why those anomalies occurred.

In a down market, a new idea might not be enough.

In addition to offering innovative capabilities, startups need to show financial stability to attract additional investments, and that’s not simple when a company is just getting started. They need to spend efficiently, and their margin — the ratio of revenue to expense — needs to be high.

“Startups always need to have very deep and differentiated tech to compete with the big tech,” said Omri Kohl, co-founder and CEO of Pyramid Analytics, which recently raised $120 million. “Companies in early … rounds are still defining product market fit and need to further develop their core offerings. This puts a lot of pressure on their growth KPIs and, in this market, could shrink investors’ appetite to invest.”

However, being a startup amid the tech sell-off also carries a benefit, according to New Enterprise Associates’ Larco.

Unlike more mature companies that have hundreds of employees and incur high costs to maintain operations, startups can be more nimble, she said. With a small number of employees and a small marketing budget, they can slow operations more easily than a larger company and make $10 million last a lot longer than many bigger vendors can make $100 million last.

“When you’re in seed [funding] or [series] A, you’re more flexible and nimble and can respond to things quicker, but when you’re in series D or E, you’re kind of on a track. And there’s a lot of momentum on that track, and it’s a lot harder to be as flexible and nimble,” Larco said, adding that startups that recently raised capital can make it last.

Tech giants like AWS, Google and Microsoft, despite the decline in their stock prices, are sitting on large cash reserves and are somewhat insulated from market fluctuations. So are mature analytics vendors such as Qlik, SAS and Tibco that have established sales pipelines and strong annual recurring revenue.

Those companies are in position to raid startups for the talent that makes startups viable.

“There’s going to be a war for talent — that AI/data engineering talent, anyone that’s really good with algorithms, any math talent,” Wang said. “That’s where the risk is.”

Maturing vendors

While startups have the advantage of flexibility, more mature data vendors that need more capital to maintain operations and fuel growth are in danger of running out of money. Especially companies that don’t have steady sales pipelines to accurately forecast revenue and didn’t raise capital shortly before the tech sell-off.

With hundreds of employees on the payroll and thousands of customers hungry for new capabilities, they can’t just slow spending without it affecting something else. They can’t simply make $100 million last three years instead of one, according to Larco.

It could mean significantly more layoffs and a substantial slowdown in product development.

“Now, that [next move] is off the table,” Larco said. “That $100 million has to, all of a sudden, last two or three years, and there’s no way to do that other than big cuts to teams. That’s why you’re seeing tech layoffs across the board. It’s late-stage companies laying off employees so they can make their runway last just a little longer.”

Companies that can accurately forecast stable annual recurring revenue, however, will be more able to raise funding at reasonable valuations than those without stable ARR, according to Wang.

Like Pyramid, another company forecasting stability is TigerGraph, a graph data and analytics vendor. The company raised $105 million in February 2021 and has raised a total of $171.7 million through its series C round. And while TigerGraph doesn’t publicize its finances, in August 2021 its founder and CEO Yu Xu said the vendor’s goal that year was to triple revenue and reach more than $100 million in ARR within three years.

“I think we’re in a relatively strong position,” said Victor Lee, TigerGraph’s vice president of machine learning and AI. “As long as we can communicate that message and we’re offering the customer a fair deal, we see ourselves in a pretty good position.”

The outlook

With IPOs at a standstill and venture capital funding more difficult to come by, data vendors are in a difficult position.

Many of the most successful vendors are positioned to withstand market fluctuations — even those such as analytics vendor Qlik that are forced to delay IPOs and go without the expected influx of capital that comes with going public.

A widening gap between well-established companies with stable recurring revenue and those in dire need of additional capital could bring a new consolidation wave, according to Pyramid’s Kohl. “Some companies will not survive and others may be acquired,” he said.

Wang also predicts consolidation. “There is going to be a wave of mergers and acquisitions, because if you’re full flush with cash, you can pick up someone else really cheap at the moment if they’re out of runway,” he said.

Data, analytics and AI vendors, however, might be better positioned than some other vendors in the tech sector to withstand a lengthy downturn.

Data is exploding, and so is the need for data-informed decision-making. Analytics is no longer a luxury, according to observers. Sparked by the onset of the COVID-19 pandemic, the use of data to help make critical decisions has accelerated over the past two-plus years. With companies investing in their data infrastructures, even amid a decline in the stock market and a possible recession, there’s the potential for data vendors to grow.

“When buyers are forced to scale back their spending, it’s a question of which things do they maintain and which things do they cut back on,” TigerGraph’s Lee said. “Data services tend to be a little bit more stable.”

Some tech vendors — particularly those that sell into recently hard-hit economic sectors such as retail, finance and aerospace — are struggling more than others, depending on their customer base, noted Julia Valentine, COO and CTO at AlphaMille, a technology advisory firm.

“If you are a database company, or you are an alternative data company, and Gap is buying from you, you’re not going to sell somebody expensive services if they’re barely making it in the retail market,” Valentine said.

A technology like AI that promises to increase operational efficiency and lower costs for enterprises continues to be a solid investment for many companies, Forrester’s Gualtieri said.

“In a down economy, it’s actually going to benefit organizations to automate more things using AI,” he said. “I don’t see people backing off AI investments.”

Still, financial fundamentals are far more critical amid the tech sell-off than they were just a handful of months ago, according to Larco.

Data management, AI and analytics vendors do perhaps have a built-in advantage over others in the broad tech sector. But with access to capital getting more difficult, if they are to survive an economic downturn, it’s crucial that they are fiscally sound.

“There’s a lot of categorically interesting things that make data possibly more resilient than other categories,” Larco said, “but they have to have good margins, they have to have efficiency in how they burn, and they have to be solving real problems for their customers and showing real evidence of that.”

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