AiThority Interview with Rob Woollen CEO and Co-Founder at Sigma Computing
Tell us about your journey into Data Analytics and Business Intelligence.
Prior to founding Sigma, I had always built platforms that developers could use in pretty generic ways. At BEA, I built JAVA applications servers, which are essentially platforms to build applications on. Then I went to Salesforce. I was excited to build a platform in the Cloud where applications could be built on top of it fairly quickly and without all kinds of messy provisioning. The applications could pretty much just be built, and they were ready to go.
If you look at things like spreadsheets, databases, and SQL, Excel is really the world’s ultimate application development platform. While I realize no one sits down and thinks, “today I am going to build an application in Excel,” but, in effect, your financial model is a little application that you built in a spreadsheet. This is, in part, why we decided to use the spreadsheet as inspiration for Sigma’s interface. Excel is extraordinarily flexible, and I like things that can be used in lots of different ways, especially unexpected ways. I love finding out that our customers are using our product in ways that we never even envisioned.
What made you launch a Data Analytics and Business Intelligence platform?
I was at Salesforce for nearly seven years and saw pretty early on in my tenure there just how much Cloud Computing was taking over the world. It gave me a pretty clear vision of the tremendous technology transformation that would come to pass and continues to this day. Salesforce, and the countless other SaaS applications that were cropping up daily, made it so easy for people to sign up, that soon companies had data everywhere. It was this wide distribution of data across applications that intrigued me. Trying to figure out how to combine all this data and analyze it together that planted the seed for what would become Sigma.
When it was time for me to move on from Salesforce, I was invited to be an Entrepreneur-in-Residence at Sutter Hill Ventures, which is where I met Sigma’s CTO and Co-founder, Jason Frantz. Together, he and I saw the rise of AWS and the creation of Cloud data warehouses like Snowflake. We quickly realized that a programmer’s power had grown exponentially in just a few short years because projects that were once considered monumental, you could essentially do in a single afternoon with the Cloud.
I kept coming back to my desire to bring all of a company’s data together to be looked at holistically. It made me think about when I was at Salesforce and was doing financial planning. It was still a heavily Excel-based task despite all the data being in the Cloud. The people doing the analysis were experts in their domain, but they didn’t have access to all the technological advancements that developers had been granted with the Cloud.
At the time, only developers or people that knew how to write SQL could really analyze the data, but they weren’t experts in other domains. They didn’t have the experience or knowledge about Marketing or Sales to really know which data was useful or which questions would lead to game-changing insights. Jason and I wanted to connect those two worlds so everyone could benefit from all these advancements. We needed to find a way to close the gap between the data and the experts that needed it to make insight-driven decisions.
What are the core tenets of Sigma Computing’s platform?
Our goal is to enable anyone to securely and safely ask any questions of their data. Anyone that has curiosity, anyone that has an analytic-minded role, or anyone that has a data-driven decision to make should have the freedom to explore their data.
At the same time, we want to give companies extensive security, governance, and control. Data teams need to be able to ensure that everything within the Cloud data warehouse, and by extension within Sigma, is correct and create a single source of truth of data for the entire company.
Essentially, we want to balance openness with security, providing controlled data exploration.
Considering the rise of data-driven decisions, why should the enterprise look at Data Analytics and Business Intelligence solutions?
Data Analytics and Business Intelligence (A&BI) is no longer a nice to have – but a must, if you want to compete. It is not only an issue of being able to make data-driven decisions either. There is a time factor that has entered the equation. Companies need to be able to make decisions much more quickly and they need to make decisions based on data from across their company. Just about everything today is time sensitive because the world is moving at such a rapid, always-on pace.
The rise of Agile – on product teams and across the enterprise – has made real-time analysis and mid-project pivoting a daily occurrence. Leaders that are tasked with strategy development no longer have the luxury of waiting two weeks for the data team to fulfill their ticket request before they can move forward or make a change. Furthermore, the way companies work now is so much more unified than even five years ago. Each department must work together with every employee rowing in the same direction to achieve common objectives. Decisions can no longer be made in a vacuum with data from a single source.
Take Marketing, for example. We are entering the Post Digital Era in which both B2C and B2B customers expect personalized products, services, and experiences on-demand. Marketing departments use countless tools from CRMs to Email Automation and PR performance to deliver the integrated campaigns that are now the standard. Everything in a campaign is connected, regardless of the channel, and Marketing teams need to be able to view and analyze the data that those various tools produce holistically. At the end of the day, your Marketing program is only as good as your understanding of the big picture.
What are the key benefits of Data Analytics and Business Intelligence? How can your users benefit from using Sigma’s platform?
The single most important benefit of A&BI is the ability to make data-driven decisions. Traditional BI tools require that you know SQL in order to query data, which means that business leaders and domain experts are dependent upon the data team to ask questions of the data on their behalf. This can lead to costly delays and missed insights, among other cons.
Sigma on the other hand simultaneously allows the data team to enforce critical data governance while providing a single source of truth of data for the company that business leaders and domain experts can then explore themselves via a familiar spreadsheet-like interface. Sigma’s magic is that no one needs to know SQL in order to build the equivalent of any SQL query.
Tell us about your most outstanding project or campaign at Sigma?
We spent a large portion of this year building out Sigma’s modeling capabilities, the data catalog, and data curation functionality. It was an incredibly large-scale project with a lot of uncertainty initially as we figured out what we should actually do. Others in the A&BI space have largely relied on proprietary coding, but we didn’t want to do that because it limits who can model to those who have a technical proprietary skillset and we are committed to empowering anyone to explore data.
I am proud of this project because it’s the most significant product change that we have undertaken since we founded Sigma. Large architectural changes to the product like this aren’t that big of a deal when you’re a party of three, but we have a much larger team now, which made this project that much more complex. It was extremely gratifying to start the project and complete it within a respectable time frame.
How do Sigma’s solutions help to process Big data?
Traditionally, people have thought of processing data in very rigid terms. Take ETL, for example. ETL stipulates that you extract the data, transform the data, and then, ultimately, load the data. It feels like there are these seamless stages where you are going to do one thing, complete it, and then start the next step.
However, if you look at how someone uses a spreadsheet, it is so much more of a fluid journey. You may start with one thing in mind, but you will likely take a few twists and turns as you move from one discovery to the next. It is a very iterative experience.
Sigma takes this same approach to analytics. There is no rigid, specific path you must take or process you have to follow. That’s largely because you’re able to thoroughly explore the data yourself rather than ask someone to answer a specific question for you and then cringe and apologize profusely as you send them your follow up questions.
What does your ‘Ideal Customer’ look like? Which new geographies are you currently targeting?
Our ideal customer is just about any company using a Cloud data warehouse because that’s what Sigma is built on. As I mentioned, the end-users of our product are anyone with an analytic-minded role, anyone in an operations role, whether that is Sales, Marketing, or just regular Operations. We also have users on the finance and security teams. This is really anyone at a company today can benefit from having access to data and the ability to explore it.
We believe A&BI should be a collaboration between the data team and the domain experts, especially as knowledge workers are getting started. And data teams love Sigma because a query or series of queries that could have taken them hours to write in SQL before is now just a matter of a few clicks. They can still see the SQL code too because Sigma automatically writes it in the background.
What are the biggest challenges and opportunities for your users? How does Sigma help?
Prior to getting Sigma, our customers were stuck waiting for someone else to build a report for them or were working from dashboards that likely hadn’t been updated in months or even years, so they weren’t able to answer any new questions. With Sigma, they can answer any question, any time. They can safely follow the rules of the road, but they are also the driver in their data journey.
Providing everyone at the company with the ability to safely and security explore data is the only way to scale A&BI. No company can afford to hire all the analysts they will need to support the demand for answers that has only just begun, nor can you mandate that everyone at the company learns SQL, so they can run their own queries in a traditional BI tool.
Today, the biggest challenge for our customers is ensuring that they are getting everything they can out of their data and putting insights into action. A&BI is still a fairly new and we are all just beginning to understand its incredible potential.
Where do you see Data Analytics and other smart technologies heading beyond 2025?
Migrating to the Cloud has gone from “if” to “when” and I expect the Cloud to be ubiquitous by 2025. I honestly don’t understand why anything is done on-prem today. We are also seeing storage trending toward being all but free and compute is becoming limitless, which will generate a new breed of applications.
Just think, Sigma is an application that came out of the ability to store data in a Cloud warehouse. Sigma couldn’t have existed just 10 years ago when on-prem warehouses were the norm. I can only begin to imagine the plethora of applications that will leverage these fundamental changes in technology that we are seeing today.
What start-ups and labs are you keenly following?
I’m not sure what this says about me – and perhaps I am a little biased – but I really admire companies, like Airtable and Smartsheet, that have taken the humble spreadsheet and turned it into something new and exciting.
I am also excited about other companies in the data space, like Fivetran and Segment. Data is everywhere, but it’s only useful if you can get it into once place and you are able to look at it holistically. Fivetran can pull data from just about every source at your company and funnel it into your Cloud data warehouse. Segment is interesting because they are not only focused on bringing several sources of data together, but also democratizing that process.
What technologies within computing are you interested in?
I am a big fan of a programming language called Rust. It is the first systems programming language to give the longstanding champions C and C++ a run for their money. Rust is like C++, but it prioritizes safety, particularly memory safety, without sacrificing performance in any way. It has been the favorite in the programming world for a few years now and was even on a whiteboard in an episode of Silicon Valley.
This is probably going to date me a bit, but I am also still pretty in awe of my iPhone, and smartphones in general. It blows me away that I carry around this small computer with me everywhere I go. I use it constantly – so much more than a traditional computer or laptop. It makes me wonder how much longer those things will last and how the function of a computer will evolve, as our phones and other devices continue to get smarter and faster.
What’s your smartest work-related shortcut or productivity hack?
This isn’t much of a secret shortcut or hack, but during my commute to work every morning, I write down my to-do list for the day. I don’t always complete my list each day and I will roll things over from one day to the next. I have learned that if the same things get rolled over a few times, that I am never going to do them, so I just delete them from the list. If I haven’t done something or responded to something in a few days and no one pings me for it, then it really wasn’t that important. Time isn’t a renewable resource and this technique helps me prioritize what really matters.
Tag the one person in the industry whose answers to these questions you would love to read:
Frank Slootman, CEO at Snowflake.
Thank you, Rob! That was fun and hope to see you back on AiThority soon.
[This interview was first published in Jan 2020]
Rob has over 20 years of experience building distributed and cloud systems. He spent 6 years at Salesforce.com serving as the CTO for the Salesforce Platform and Work.com and Sr Vice President, Platform Product Management. Rob holds a Bachelor of Science degree in Computer Science from Princeton University.