In 2016, I was winding down what was basically the 12th technology startup I had been involved with over the past decade. In that startup I was serving as head of data science and analytics.
The team was great, and I loved the work, but it came in fits and spurts. In most cases, I felt like it didn’t make sense for the business to have me on staff full time working on a very narrow problem where there was a lot of downtime between setting things up as we waited for data to accumulate. I felt like I could do the same work for less money for that company, plus take on a lot of additional work from other companies, and they would all benefit from the shared knowledge gained. I was hungry to learn more, and grow in this particular area.
Another important point to mention is that I had made a habit of doing what many in the SF Bay Area and Silicon Valley tech scene did: you worked as a “consultant” for multiple seed stage startups until one of them raised enough money to afford to bring you on full time, so you were really auditioning into full time roles. Given my new ambition, I didn’t want to do that anymore, to consult myself into a job. I wanted to explore being a freelancer and consultant, intentionally.
That summer of 2016 after I left my last full time W-2 job, I called what felt like hundreds of people to a) ask questions about how to set up a business and b) ask who they knew that I should also know as I networked my way to more clients. That summer was very slow, business wise. It made my wife anxious. We created an agreement that if I didn’t manage to replace my income with new clients by the end of the summer I would spend the last 30 days of my savings to hustle and get myself a full time job, any job, and give up on this entrepreneurial dream.
It turns out that all of those phone calls I was doing was priming the pump and creating momentum that paid off in a big way by the time the summer ended. I found myself with a lot more work than I could handle on my own, so in September I filed incorporation papers to create Acorn Analytics Inc, and it became official in October 2016.
There’s another pivotal moment in the history of Acorn that happened in early 2019 if not before. I read books written by Jack Stack and Bo Burlingham that left an impression on me. That impression was that if I had it in me to build a business that provided safe and satisfying employment, that it was my obligation as an entrepreneur to do that. So, I’ve been working ever since then to make that a reality, to make a business that struck an appropriate balance between taking care of its financial viability and also taking care of its people. My definition of success is to create a business that is organized enough that it could be sold tomorrow, but is also built in a robust enough manner to last forever. To do this, I’m working to build a business that is not overly reliant on any one person, organization, customer, or vendor, employee, or supplier. We still have work to do to make this vision a reality, but it’s a labor of love. As a former professional musician, there were days when trying to come up with something new to say about love in a song was a struggle, but I find no shortage of ways to express creativity in this organization, and I couldn’t ask for better "band mates" than those who are along with me for the ride here at Acorn Analytics.
– Mike Zawitkowski, founder (written December 2023)
The following attributes are what set apart Acorn from other data analytics and engineering service providers:
One of the most important aspects of how we run things at Acorn is the values that our teammates have. There are so many amazing human beings that don’t share these values and that is OK, it just means they are not the right fit for us here at Acorn. These values are not aspirational, nor are they something we try to teach people who come to work with us. In everything that we do, whether it is working with our clients or hanging out socially, our team lives and breathes these values. Because these are so important and they govern who we choose to work with, we routinely make changes to them as we learn better ways to communicate what is in our hearts and minds.
Where does the name come from?
Prior to starting Acorn Analytics, I had attempted to start a little side hustle of a business called Acorn Depot that sold a variety of products on Amazon and eBay. It ended up not working out and it was a humbling experience. I didn’t want to lose that humility, and it turns out that the domain name for Acorn Analytics was available so I jumped on the opportunity. I also liked the fact that on lists of companies sorted alphabetically, the name of the company would feature prominently at the top of the list.
How did you transition from the music industry to what you do now? How is music related to data science?
When I moved to the SF Bay Area in 2006 after graduating from Berklee College of Music, I found myself working more and more in high tech startups and less and less in the music industry companies that were collapsing at that time. High tech startups would ask me if I could work with “big data.”
I would ask, “How many hard drives worth of data are we talking?”
They would answer, “Uh, just one? It’s a spreadsheet on my laptop.”
I would explain why coming from the music industry this is not big data and is easily handled using techniques learned in the recording studio. For example, let’s say we have a bass guitarist, a drum kit, a guitar, and a singer. The drumkit has a bass drum, three toms, two symbols, and we’ll also put up two mics above the drumset and one for room sound. That’s 12 tracks of audio each at 44.1 kilohertz per second at 16 bit resolution. Let’s say we grab the raw audio, then are doing some on-the-fly mixing and adding effects, so let’s double the tracks. To be safe, we should have enough hard drive space for at least 15 GB of audio, and we need two backups in addition to the original. So we have three 20 GB harddrives we are using for the session. You may end up with 100 takes of a single song that you have to review and stitch back together. Often you are rapidly doing the math and mixing immediately after a take in order to have the band listen to something before they make it back into the booth so they can listen to their work.
Oh, and keep in mind that you aren’t doing this when you are fresh at your desk at 9 am, but probably at 2 am when there was downtime at the studio that was affordable for you and the band, and the musicians are tired, intoxicated, or both.
The point is that when I started being asked to do analytics on telecommunications data the datasets were much, much smaller and more manageable. The formulas for the math and the visualization in my mind of ones and zeroes moving through a series of processing stages is also not that different. The rules for good data hygiene are also mostly the same.
I’ve also noticed that a great majority of my classmates that moved on to careers more stable than the music industry often took on similar roles in data science and software engineering. So it seems that the skillset is greatly transferable.
I’m not saying that anyone can go from being an audio engineer to being a data scientist, and I did take advantage of the massive amounts of open courseware and mentorship I received from others more experienced in the field, but it was not as challenging of a transition as one would believe.
Are you hiring right now?
We are always looking for talented individuals to add to our team. In order to be eligible you must: