There’s plenty of financial analysis of the DoorDash S-1 out there. But no analysis so far has focused on how exactly DoorDash went from last to first in a super competitive market — and the answer includes “by using data”.
DoorDash interlocked its operating model with their technology platform, making execution driven by data the key drivers that propelled DoorDash from 17% to 50% marketshare between January 2018 and October 2020.
With Twilio’s acquisition of Segment, it’s a good time to publish my deal memo for RudderStack, a Customer Data Platform for Developers.
I wrote the deal memo for RudderStack’s $5M Series Seed in May 2020, and partnered up with Founder and CEO Soumyadeb Mitra on his journey to improve the lives of developers working with data.
For venture-backed companies, the clock to the cash-out date is always ticking. Growth requires funding, and for more funding, venture investors expect a company to hit certain growth milestones.
The outcome is almost binary.
I experienced that first-hand, when we had to raise our Series A for intermix.io in early 2020. …
For early-stage enterprise software start-ups, there is a disconnect between venture investors’ expectations for revenue, and the lead and sales velocity the founding team can deliver.
The venture market for Series A and beyond has shifted — investors expect multi-million ARRs and rapid growth. They’ve extrapolated backwards from successful IPOs and are looking at the metrics the likes of Slack, Twilio and other “unicorns” had when they were a seed stage company.
But the typical seed stage start-up doesn’t have the pipeline, processes and sales skills to sell fast enough to hit that growth.
We hear about viral adoption, product-led growth and network effects. But reality is that most start-ups acquire their first 20–30 paying customer in the trenches. What may look like a repeatable, predictable sales process is rather founder-led selling with hand-to-hand combat for each new account. …
In this post, I’m sharing a note that I sent to our angel investors late January, as we had started the process to sell the company, explaining the reason for the exit, and the events leading up to that.
Of course, Covid-19 played a key role in the decision to sell. In early February, venture funding dried up within a matter of days, and we were in the middle of a product overhaul, with cash in the bank until August 2020. Hope that Covid would go away and things would go back to normal wasn’t a strategy. …
Data is valuable resource powering up analytics, predictive models and decision making. For a company to make data-driven decisions, it first must go through building its data infrastructure. And a data warehouse plays a central role in such an infrastructure.
Data warehouses are data storage and processing systems that aggregate data from different sources into a single place. With data in one place, you can combine and query it across the different sources. That includes data coming from users interacting through web and mobile, background system logs, or third party data.
Amazon Redshift is a cloud warehouse solution by Amazon Web Services (AWS). Since AWS first introduced Redshift in 2012, it got everyone’s attention for its amazing performance and low price point. In the following years, there were massive improvements from operational and data pipeline perspective. Today, it’s the market leader for cloud warehouses. …
When customers start using intermix.io for the first time, they can see the set-up and configuration of their Amazon Redshift cluster in the context of their queries and workflows. At that point, customers experience one common reaction:
“Knowing what we know now, how would we set up our Redshift cluster had we do it all over again?”
A frequent situation is that a cluster was set up as an experiment and then that set-up grew over time. Little initial thought went into figuring out how to set up the data architecture. …
One of the major propositions of Amazon Redshift is simplicity. It only takes minutes to spin up a cluster. The time-to-first-report, i.e. the time it takes to go from creating a cluster to seeing the results of their first query, can be less than 15 minutes. That’s true even for petabyte-scale workloads.
Because it’s so easy to set-up a cluster, it can also be easy to overlook a few housekeeping items when it comes to the set-up. That can cause problems with scaling workloads down the road. A general complain we often hear is “slow queries”, or “slow dashboards”.
A key configuration to use is the Amazon Redshift Workload Management (WLM). Without using WLM, each query gets equal priority. The result is that some workloads may end up using excessive cluster resources and block business-critical processes. …
Amazon’s Simple Storage Service (S3) has been around since 2006. Enterprises have been pumping their data into this data lake at a furious rate. Within 10 years of its birth, S3 stored over 2 trillion objects, each up to 5 terabytes in size. These companies know their data is valuable and worth preserving. But much of this data lies inert, in “cold” data lakes, unavailable for analysis, as so-called “dark data.”
So what lies below the surface of data lakes? The first thing for organizations to do is to find out what dark data they have accumulated. Then then need to analyze it in search of valuable insights. …
The past two weeks have seen a lot of talk about the implosion of early-stage seed funding. The numbers may be somewhat deceiving, since founders and funds don’t always report their rounds (and with convertible / SAFE notes don’t have to).
But the trend is clear — the fundraising environment for seed stage start-ups has changed.
That means if you’re entrepreneur, your fundraising strategy needs to change.
And that includes raising money from an an AngelList Syndicate. AngelList Syndicates are the single best financial innovation for start-ups in the past five years.