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At the IMPACT Summit yesterday, I shared our Top 10 Trends for Data in 2024.

1. LLMs Transform the Stack : Large language models transform data in many ways. First, they have driven an increased demand for data and are causing a complete architecture inside companies. Second, they change the way that we manipulate data. Analysts will use automated data analysis, and it will be an expected tool in every product : notebooks, BI, databases, etc.

If you’re curious about the evolution of the LLM stack or the requirements to build a product with LLMs, please see Theory’s series on the topic here called From Model to Machine.

On the heels of yesterday’s post, DataDog announced earnings . The company exceeded its plan for the quarter & raised the plan for next quarter.

DataDog leads the cloud monitoring & security space. They will generate about $2b in revenue this year. That they see strength in their customer behavior is a good sign for Startupland broadly.

Here are some takeaways from their call yesterday :

The cost-cutting activities that dominated the start of this year are plateauing, which should drive better predictability in bookings.

Six quarters ago, profitability became the most important factor to public software valuations.

14 public software companies have reported earnings in this quarter so far. These companies’ earnings surprise has been prodigious.

Earnings surprise is the difference between how much the market expected in earnings compared to actuals. Positive surprise means a better result than expected.

The average earnings surprise so far is 48%. Contrast that figure to 1% revenue surprise for the same companies.

How much should quotas increase next year?

With the end of the fiscal year approaching for many software companies, management teams are running the numbers for different scenarios.

This year has been tumultuous : most startups reduced their bookings, and grinded through the first six months suffering through pipeline shocks - the result of CFO-budget pressure.

For the last 3 quarters, startups have been executing on reduced targets, attempting to squeeze more juice from their Meyers.

The Federal Trade Commission’s focus on technology has transfigured the M&A market for startups.

The Google anti-trust lawsuit & Amazon anti-trust cases may stifle those companies’ ability to acquire startups for significant sums. Facebook has faced a similar suit for illegal monopolization, which resulted perhaps indirectly in the divestment of two of its more recent acquisitions. Microsoft’s most recent acquisition of Blizzard nearly didn’t succeed & the FTC continues to pursue means to reverse it or win concessions.

In 2021, Amazon announced they had reduced prices on Amazon Web Services 107 times since launch.

This drop in prices has grown AWS into a $90b revenue business in 17 years. As storage & compute became less expensive, the economic viability of new use cases became increasingly apparent & developers built software on the cloud.

The same cost-reduction phenomenon is occurring with blockchains, though it’s not nearly as well publicized.

Yesterday, both Google and Microsoft announced their earnings for their cloud businesses. I’ve been tracking the growth rates of these companies and product lines for the last 18 months to develop a broad gauge of enterprise buying patterns after the downturn.

In the chart, it’s immediately evident that Mongo has seen dramatic increase in growth rate last quarter. We await the earnings from everyone aside from Google and Microsoft in the next few weeks.

On November 8th, I’ll share my 10 Top Trends in Data & AI at the IMPACT Summit.

Last year, I covered 9 topics:

1. Cloud data warehouses will process 75% of workloads by 2024.
2. Data workloads segment into in-memory, cloud data warehouse, & cloud data lakes.
3. Metrics layers unify the data stack.
4. LLMs change the roles of data engineers.
5. WASM becomes an essential component of web applications.
6. Notebooks win 20% of Excel users.
7. SaaS applications use the cloud data warehouse as a reading & writing database.
8. Data observability becomes a must-have.
9. The decade of data continues.

How many of those turned out to be true? I’ll be scoring them!

The patois of data teams has become a dialect of modern engineering teams because the commonalities in the stack.

This convergence signals how far data teams have evolved into core engineering teams. Machine learning’s demand for data has accelerated this movement because AI needs data to function.

Data teams receive tickets from their internal customers & develop data products that serve both internal & external users, much like a classic product management & engineering team.

On Thursday, November 2nd at 9:30 AM Pacific, Office Hours welcomes Raj Sarkar to talk about Unleashing Outbound Marketing Fury.

Raj started his career as a marketing manager at Cisco focused on the SMB market. He then jooined Google to become the lead product marketing manager on Google Cloud Platform (GCP). Next, he headed marketing at Atlassian for Trello, Confluence, Jira for Business Teams & other products. Last a short stint at Palo Alto Networks before becoming CMO at 1Password.