Data Startups: Why the Eye-Popping Funding Rounds?

    In 2016, dbt Labs got its start as an analytics consulting company, helping startups implement the modern data stack. But over time, it would add a hosted service to make it easier for deployment. Then there was the release of an integrated development environment (IDE), which was focused on the enterprise.

    dbt Labs’s evolving strategy paid off in a big way. In February, the company announced a $222 million Series D round (the total raised since inception is $413.4 million). Some of the investors included Altimeter, Andreeson Horowitz and Sequoia. There were also strategic investors like Databricks and Snowflake – two of the hottest Big Data companies out there.

    The interesting thing is dbt Labs wasn’t even actively trying to raise money when investors came calling.

    “The process happened fairly quickly,” said Tristan Handy, co-founder and CEO of dbt Labs.  “Our existing investors had expressed strong interest to invest and new investors also reached out along the way. We weren’t actively looking but wanted to plan ahead for the next few years to give us room to aggressively invest in our business as needed. It’s always better to raise when we don’t need to.”

    Of course, the funding success of dbt Labs is not an outlier. Data startups have seen a surge in interest. According to PitchBook, the venture funding in the space tripled from $2.5 billion in 2020 to $7.5 billion in 2021. As for this year, the investments have come to about $2.4 billion so far.

    So what are the industry drivers? And might the good times continue? Let’s take a look.

    Also read: Top 7 Data Management Trends to Watch in 2022

    The Data Explosion

    Data growth continues at a rapid clip. IDC projects that the rate will be 23% per year, reaching a staggering 175 zettabytes by 2025 (a zettabyte is a trillion gigabytes). Yet there is something interesting about this analysis. Only about 2% is saved or retained. In other words, much of the available data goes wasted. But therein lies the opportunity for data startups.

    “We need new and innovative ways to store large data sets as efficiently as possible,” said Michael O’Malley, SVP of Strategy, SenecaGlobal.  “We need ways to quickly analyze, compare and retrieve data records in these datasets so that AI and machine learning can offer more real-time or near real-time insights.”

    The Breakthrough Data Company

    History shows that data can be an incredibly valuable business. Just look at Oracle. The company was able to leverage a technology it did not invent – that is, the relational database – into a massive business. Despite all the innovation since the company was founded in the mid-1970s, Oracle remains a dominant player.

    “When considering both the strategic value of data and its broad appeal, growth in data companies tend to grow exponentially when they find true product-market fit,” said Will Lin, Managing Director, Forgepoint Capital. “As data becomes a competitive advantage for organizations wanting to better understand everything, VCs tend to look for 10x improvements in this space, either individually or collectively between ease of use, better results, and cheaper to store. These are the hallmarks of standout data startups and what drives larger funding rounds.”

    But of course, there are some elements that are not necessarily about the technology. There needs to be a rock-solid business sales and marketing organization. Again, Oracle showed the importance of this.

    “VCs invest in people,” said O’Malley.  “Visionary leaders have the ability to form and grow teams and drive focus to get things done. Good leaders also understand what their people excel at and find outside experts to complement their team’s strong suits.”

    Also read: AI Suffers from Bias—But It Doesn’t Have To

    The Bear Market

    After a 10+ year bull market for tech stocks, the category has come under tremendous pressure lately. The Federal Reserve’s tightening of monetary policy is having an impact. Since late December, the NASDAQ-100 Technology Sector Index has plunged from 9764 to 7064 or 27%.

    Some of the high-flier data companies have suffered significant losses. For example, Snowflake’s shares have gone from a high of $405 to $166.

    This does not necessarily mean that funding will dry up. The fact is that there is still large amounts of venture capital sloshing around. In one hopeful sign, Pyramid Analytics today announced a $120 million Series E funding round – an oversubscribed round exceeding the company’s target by $20 million. For startups offering genuine solutions to data challenges, investor demand will continue to be there.

    But valuations will inevitably get more tempered and it could get tougher for some companies to attract interest from investors.

    “The most important component to consider has less to do with valuation and more to do with budgets,” said Lin. “How do customers store, transform and analyze more data and with more techniques if their budgets slow or reduce?”

    Read next: Top Artificial Intelligence (AI) Software 2022

    Tom Taulli
    Tom Taulli
    Tom Taulli is the author of Artificial Intelligence Basics: A Non-Technical Introduction, The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems and Modern Mainframe Development: COBOL, Databases, and Next-Generation Approaches (will be published in February). He also teaches online courses for Pluralsight.

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