How to Organize Your Data Team: Lessons from Crunchbase
Meet the Guest
Jess is a Senior Analyst at Crunchbase, a LinkedIn Learning instructor, and has over 90,000 LinkedIn followers she talks to daily about SQL, data careers, and remote work. If you want to connect with Jess click here to download a free resume template and subscribe to her newsletter for more tips and data analyst information.
Watch the Episode
Organizing the team around key business objectives is critical. Define the most important problems the company is trying to solve, and make sure you’re doing a good job prioritizing analytics activities that truly support those goals.
Weekly sprint meetings can be an effective way for an Analytics team to align on priorities and make sure everyone is working on the right things.
It’s easy to fall into the trap of presenting all of the data you have. But streamlining your presentations and dashboards to just KPIs and actionable metrics can help your stakeholders focus on what really matters.
Read the Transcript
Hi everyone. Welcome to Mavens of Data, the show where we bring you analytics industry experts to share their tips and best practices to help you use data more effectively and build strong analytics teams. I'm John Pauler, one of the instructors at Maven Analytics, and we've got an amazing guest for you today in Jess Ramos.
You might know Jess from LinkedIn. She's built over 90,000 followers into her audience. She's also got a course on LinkedIn Learning that's already out and doing great. And she's got another one on the way teaching SQL, close to my heart. She shares a bunch of tips that anyone who's looking to build an analytics team can really benefit from. There's some great stuff here that you can learn and apply to your own organization.
Hope you'll tune in and enjoy the show. Let's get into it. Jess, thank you so much for doing this with us. It's great to have you here.
Thanks for having me. I'm happy we finally found a time to connect.
Yeah, it’s really great to connect face to face. After feeling like I've known you for a while through your posts and through messaging. So, thanks again. I'm sure a lot of the audience will know you from social media. On LinkedIn, you've built a following of over 90,000 people. You're a LinkedIn instructor, too. You've got a successful course out and you've got another course on SQL coming up very soon. So really impressive stuff and I definitely want to get into that. But first I think we should start talking about your 9 to 5 and what your experience has been like as an analyst and a manager in an analytics department. I think that's probably a great place to start. And then we can get to the crazy rocket ship social media presence towards the end. Does that sound good?
Yeah, that sounds great. I'm looking forward to it.
So, you're at Crunchbase now. Can you talk a little bit about your role, what business problems you work on and maybe what your typical tool stack is on a day-to-day basis?
Yeah, so I just passed my six-month mark, which is crazy. I can't believe I've been there six months. Time flies when you're working in a startup and working on a bunch of different things at once. So, most of my work is in SQL and we use Snowflake. I love the cloud platform. I really built my SQL foundation on SQL Server Management Studio.
So coming to the cloud platform, I'm like, Holy crap, this is amazing. There are so many cool features. It's awesome. We also do our dashboarding in Sisense, I'll be honest, I like Powered BI the best for data visualization, but I think Sisense is good because users are able to refresh certain visuals one at a time instead of a whole data model, like in Powered BI.
So, that's really our main tech stack. Most of my work is done in SQL, so I'm working on optimizing queries and dashboards, building different views, and tackling all these different business problems. So, one of the main projects that I'm working on is our trial onboarding experience. We have three senior data analysts on our team and each one is assigned to a project group and mine is all about improving our trial experience.
So, when users come on and start a trial, they need to have some sort of onboarding or tutorial to understand how to use our product. And right now, we're experimenting with our onboarding. And that has taken up a good majority of my time because I'm working with all these different stakeholders and really just trying to keep the ship moving in the right direction for that project.
So, we actually have an AB test running right now, which is super cool. I'm hoping we can wrap it up in a few weeks and have results that have just been like the big project I've worked on throughout all of 2023 from the very beginning of planning it, designing the metrics, our kill conditions, and our front-end events.
And finally, I'll be able to interpret the results in a few weeks. So it’s been a really cool project.
Yeah, that's really fun. Early in my career, one of the things I focused on in a major way was experimentation. And it's such a great place that analysts can add value to a business. So, I want to dig into that one. But let's come back to that experimentation as a follow-up question.
But I'd love to hear what the data culture is like at Crunchbase. What does the team look like and how do you guys think about trainings. You're relatively new in the role. What was it like coming in there? Did you get a lot of support? Is there a standard infrastructure to help people get on board? I'd love to understand how they think about that for the business.
Yeah, absolutely. Crunchbase is a company of 250 people, so it's kind of in that perfect middle spot, in my opinion. It's not a huge company where you just get lost. You don't know who to reach out to for X, Y, Z, but then it's also not a super small company that doesn't have the documentation and the infrastructure and experience to really support you.
It's been an amazing place for me to really learn and grow. Our analytics team has, I think, seven people on the centralized analytics team and then two people in the marketing analytics team. So total, we have about nine individual contributors doing analytics, and then the centralized team, we all report to one manager. So, this has been the biggest analytics team I've been on, which is awesome because I'm kind of right in the middle.
There are a lot of people I can learn from all around me, and I feel like I'm growing so much as an individual contributor. The way that we approach analytics at Crunchbase.
"We take tickets from stakeholders, we plan our two-week sprints and everybody has a delegated amount of tickets for each sprint. It's a very organized team and we aren’t just controlled by stakeholders. We really vet all of our tickets, we ask, is this something we should be working on? If so, why? How are we going to approach it? So, it's a true example where the analytics team is driving the company. "
Whereas I’ve been in situations where it felt like the stakeholders were really controlling the analytics team.
Yeah, a little more reactive.
Yeah. So, in that sense, it's been an amazing place for me to really grow and set the standard for me. Like, wow, this is a very organized, awesome team. And as far as onboarding, we just have tons of documentation. Everything our team and company work on is documented. If you open up a project, there's a link to this, a link to this, and definitions here.
So, onboarding was pretty easy in a way because there was just so much information and it was very well thought out.
Cool. That sounds so good. I love a few things about that. The team size sounds really ideal where if you're one or two analysts in a company, you're not going to get as much cross-pollination learning going on like ten different people. They've got different skill sets. You're going to learn different things from different people, and you can just grow really fast.
So that's fantastic. It sounds like that's how you're feeling there too, which is awesome. And then the other thing that I love is treating analytics like a precious resource and being really smart about prioritizing and running those sprints every couple of weeks. In every company, you see that with engineering resources that have always been treated as the scarce resource, not all the time do you get that level of thoughtfulness with analytics talent.
Sometimes analysts can just be getting wagged from every different direction. And I really like that really thoughtful approach. That sounds awesome. It sounds like Crunchbase has some really good things going on. That's fantastic that you get to be in that environment.
Absolutely. I love Crunchbase. I'm always plugging it on podcasts, on LinkedIn. It's just a great place to work and I'm genuinely super happy here.
That’s awesome. Very cool. So, I want to ask you about experimentation a little bit more. Talk about how you think about experimentation and how Crunchbase thinks about running experiments and maybe why do you think this function just running experiments on a business tends to always sit within a data team.
To me it's an interesting one because I have my perspective on the answer, but it doesn't inherently have to be within a data team. But I've almost always seen experimentation run by the analytics department. So, I guess a bunch of questions I threw at you at once, but I'd love to hear what you think.
Okay, so the first question is how Crunchbase approaches experiments? So, it was a really cool time when I walked into Crunchbase because I mean, the economy's just been really iffy the last 6 to 8 months. I think a lot of companies are really working on their strategy right now and really focusing on money, making more money, losing less money.
So, when I came into Crunchbase, we were working on all these different initiatives. We have these three main big projects going on and the company, it almost feels like a democracy in a way. Everyone's really just working together and collaborating on all these different ideas. Everyone's voice really feels heard, and we decided on the three main projects we wanted to work on at the beginning of 2023, and one of those was the onboarding experiment, which is the project that I'm on.
So, from the very beginning, we just had a big document outlining all the questions we wanted to answer and different metric ideas. We all collaborated asynchronously through Google Docs and then we would come together for meetings and discuss certain topics. It was really cool being able to design this experiment and work as a team.
I was working with designers, PM, engineers, all these different people. So, when it came to actually launch the experiment, that was something that was new for me in a way, because this is the first beginning-to-end AB test I'd gotten to do. I've gotten to hear about all these other AB tests and learn about them, but this is the first one that I've actually gotten to do in the real world.
So, it was a really awesome growth opportunity for me, and we definitely faced a lot of challenges with our experiment. We had some technical issues and just so many bumps in the road, but it's been really cool to see it working successfully online on our experiment platform and I'm excited to be able to interpret the results and make a final decision.
And depending on how it ends up, we're going to iterate and launch V2. We're either going to roll it out to 100% if we don't think it's risky based on the first experiment or we're going to the second experiment if the results are kind of unclear from the first one.
Very cool. That’s awesome. One of the things I love about getting to do experimentation as a data person is you don't usually get asked to run experiments on non-important parts of the business. Your new customer onboarding flow is so critical to a business like Crunchbase and you're right in the thick of that process right now in figuring out how to get it better and working with stakeholders and being really smart about like what are all the things that we care about influencing.
And it's probably one important metric. But then there's other things you're like, we don't want to mess these up. So, it can be pretty complicated, and I found that stuff so fun to work on. And probably the biggest thing that I love about it is it's like true experimental design.
You can say we did this as a team and we're going to get this much more of whether it's revenue or new customer sign ups or whatever the thing is you're trying to influence, you can say, black and white. You can quantify how successful it was. And I think that's a super fun thing to get to work on as a data person. So very, very cool experience.
Something I've talked about a lot with our PM is that onboarding is a really difficult problem to tackle because it can go so many different ways. Sometimes some users want an onboarding, some users find it annoying. It's really hard to find that balance between showing the value of the product and giving the user some guardrails and directions and annoying them and getting in their way.
We have a lot of other experiments going on right now. We have Google Pay and Apple Pay launching. Of course, that experiment is doing well. It’s adding another payment method. Everyone loves integrated payment methods, but onboarding is just such a difficult problem to solve.
Yeah, it's hard.
It's definitely been a really difficult project for my first AB test from beginning to end, but I've learned so much. So, I'm really excited to be on this team and work with the people I'm getting to work with.
Yeah, that's awesome. I think one thing that's being highlighted for me, just hearing you talk about this is I think a lot of people hear analyst and they're thinking about database, reporting. And I think what you're showing here is you're really thinking about the customer, what do they want? And, yes, obviously you're going to translate that into metrics.
But what's really important is a great experience for the customer that gets them to want to use the product and understand how to use the product. And that's really important for us as data people to think about the customer, to think about the business. It's not just data extraction. Once you're off of that first role where people are pretty much telling you, Go get me this number where the real value gets added is doing exactly the things that you're talking about.
So that's fantastic. Really, really cool stuff. So let's talk a little bit about reporting. I know you're working a lot with dashboards and putting reporting together. And now you've seen that process across a couple of different organizations. What are some things you've seen different organizations struggle with and what do you think are some keys to getting reporting done really well at an organization?
And you don't have to name specific organizations to call anybody out, but just curious to hear pain points and what's been really successful.
Yeah, it's always funny when I talk about my past experiences because really, I've only worked at three companies, so it's really not that hard to figure out who I'm talking about. But no, I'm thankful for all the places I've worked. I've learned really unique, awesome things at each place. So, the first place I worked at as an analyst was like the catalyst of my career in a way.
And the analytics there was very reactive focused. It was a lot more focused on reporting and dashboards, and there's a fire that needs to be put out today. The sales team needs X, Y, Z right now, so there was always urgent requests. And I did get to do some of the insight stuff like tracking users through a payment funnel and the more product analytics stuff there.
But a lot of it was more reporting and dashboards focus. And I think a pitfall we fell into on our team is that we just made a lot of dashboards, but we didn't necessarily always make dashboards that were actionable, so I was definitely learning this more towards the end of my time there. But in the beginning, you're a brand-new data analyst, you get access to Tableau or Power BI and you're like, Oh, look at all these cool charts I can make. I have this USA map and pie charts and tree maps.
"So, it's really easy to fall into just making all the visuals you can. And in theory, you think more options are better, and more data is better, but something I really learned there is that it really hurts the people who aren't as data literate, it’s distracting.
They don't know what's important. They don't know what to look at. They don't know how to make decisions off of the data. I think one thing I really learned there was it's better to streamline things and make sure that the data is actionable and the KPIs and metrics are all pointed to a business question somewhere."
Yeah, that's awesome. And I'd say that's a very common problem where people sometimes think more reporting is better. But at the same time, like what you're saying is it can be overwhelming. There's probably a small number of questions that you should really be digging into that are going to cause a major impact. And then if you crowd those with a whole bunch of other things that don't really matter or they're not associated with a lever that the business can pull to improve, they're really detracting from your best stuff.
So, I think that like that focus is a really smart insight. I love that. I think that's something probably every organization could still get better at. It can be hard to really nail that, but one of the things you can really try to do is try to get in the business owners shoes, think like okay who's the P&L owner on this, what are the things that would actually cause them to take action and what are the actions you could expect them to do based on certain data and go after those types of data points?
And if you can focus there, I found that to be a really effective framework to thinking about like, okay, I can give them all the data in the world, but these other things, they wouldn't really take action on them. It's not necessarily going to do much. But then these other things, if I could show that there was a pocket of efficiency or a certain type of customer was really valuable, they might really jump at that and want to do more of those things that are really winning for the business.
So, I love your advice on focus there is really good. Now we'll come to the part where we ask a bunch of questions of everybody. We call them the Maven Multipliers. So, they're these four questions that everybody gets. Trying to get some advice from you for analytics leaders at different companies. So first off, what is an example of something that you've seen analytics talent do to make a major impact on a business?
I would probably say my time at Crunchbase has been the best example for that if I can continue to plug Crunchbase, but it's just been really cool seeing all of these analysts working on all these different projects and most analysts are assigned to a project in a way. So, we all are the expert in that project and it's just really cool being able to see each analyst really take ownership and really understand the data.
They know all the quirks and the nuances about that specific topic and it's cool seeing all these projects going forward and making an impact on the company. I mean, I don't have numbers to share for my project yet because I'm still running my experiment, but it's really cool seeing all these experiments wrap up and they're either really awesome, like, look at how much money we just made the company, or maybe we need to iterate and rework this design or try the experiment again.
"So, it's really cool how all of our decisions and product advancements are all made off of experiments and analytics. Nobody at the company is pushing something out just because they think it's a good idea or they really want to see this happen. "
And I think that's especially true for onboarding. It would be really easy for us to just roll it out and say, Oh, it's not going to impact the product too much.
Let's just roll it out and see what happens. But really, like you said before, with trials, we want to convert these users into paid users. So, it's kind of risky to mess with our trial users because we don't want to give them a bad experience and lose a lot of revenue long term. So, it's cool to see a company really thinking through the risk factors and thinking about the impacts on the user and actually running experiments to think out decisions.
Yeah, that's so awesome. I talk about this as the working on the playing field versus just working on the scoreboard and a lot of times people think analytics and they think like, let's track new user trials over time or trial conversion rate to paid and things like that. And what I love about what I'm hearing from you is Crunchbase and the analytics team is not just focused on tracking the metric but trying to move the metric higher.
And I think that's so important. It sounds like you guys are really doing a good job of that. So that's awesome. So next question in the Maven Multipliers, if you could give just one piece of advice to analytics leadership about hiring, training or mentoring young talent, what do you think your best piece of advice would be?
I would say give people a chance. I think a lot of times when it comes to technical roles, people are looking for the most exciting projects or experience, but they're not really hiring that underlying person. They're just looking for a certain amount of technical skills. I think there's a lot of people wanting to break into the field right now, and they might not have five years of SQL experience or these really cool projects, but maybe they have a lot of passion and really want to learn them.
And when I first started in analytics, it was definitely a different job market, but I was in my grad program, so I'd only taken a few weeks of SQL classes by the time they hired me, I truly knew nothing. I've never felt more imposter syndrome in my life, but they chose me out of all the other people that applied because they said they could tell that I knew how to think.
I knew how to problem solve, and I was really excited about it. And I definitely did my networking on LinkedIn, messaged the hiring manager so they could just tell I really wanted the job, and I was excited about it. And I think a lot of employers don't necessarily hire for that. I think they just look for, you know, this person built a deep learning model that will be able to do reporting for my company so, I just would encourage people to hire for those soft skills and the passion, because that's what's last longer, especially if they're willing to learn quickly.
Yeah, that's awesome. You're reminding me of a specific person that we were interviewing as part of this campus recruiting program. I was at a large company and basically there's a very formalized process. We have all these students come in that are seniors. We go off separately and grill them in different areas, and then we all debate who gets a job offer.
And there was a guy who he did very poorly on the technical, which was SQL and statistics, but he did he did pretty good on the problem solving case that was kind of tool agnostic. It was more about how you think and there was a really heated debate and I, I loved the guy and I thought he just had a ton of heart and he's one of the guys that I had seen more enthusiasm and excitement from than kind of any of the other candidates I've seen before.
And there was another guy on the other side of the argument that said, his technical is pretty bad, you know. And we said, well, look at the problem solving. He is really solid there. It's his thinking, we can teach technical. We teach technical all day. And I was really happy, I didn't give up and we ended up we did hire him, and he stuck around for many years, and he ended up being really good.
He just sort of had that Rudy work ethic. You could tell he was he was going to make himself good and he was just kind of starting at a little lower place from some of the other candidates. And he ended up being great. I don't think he ever knows that that conversation, that debate happened. So, I'm definitely not mentioning him here. But I loved him as a person, too, once he started working with us. So, I'm actually pretty proud that we got to have him in there. So that's I think really good advice. You know, it's not just about the technical skills, look for the aptitude, look for the heart.
It can go a really long way. That's really awesome. All right, next question from the Maven Multipliers. What would be your number one pitfall that analytics organizations or companies fall into when it comes to data or using their data?
I kind of have two things coming to mind. I think one of them is just a lack of education and documentation because it's really hard to do analytics when you don't know what anything means, how it was made, where it came from and then also from the stakeholders’ point of view. If there's not good documentation and education on their side, they might not know what they're looking at.
It really just leaves a big gap in data literacy on their side as well. I've seen companies with basically no documentation, and I've seen a company with a lot of documentation. And I definitely noticed the difference there that people are able to help themselves and self-serve more. And then I think another pitfall is, I guess kind of a little contradicting to my first one, but I think another pitfall is when people have too much access to data.
Because I think it's good when stakeholders can self-serve those easy questions. Maybe there's a standard report or standard definition for certain terms. Everyone's on the same page with it. But when there are those more complex analytics questions, it's kind of scary for people to have access to all this data if they don't necessarily know what it means or how certain fields were built.
So, I think from an analytics point of view, you do want to gatekeep a little bit of the data because you want to have the person who has that data expertise and you know, the database knowledge. You want to have that person involved in the process as well.
Yeah, that's great. Those are really two awesome pitfalls to look out for. So, I think that's fantastic. How about kind of the flip side of that? If you were starting a new analytics organization or charged with building the data culture in a company, what would be one or two critical factors to success in making a company become a truly data driven organization?
I think just contrasting my experience now with Crunchbase versus when I was managing a small team, I fully admit my manager is a much better manager than I was, and I'm allowed to say that. But she's just very organized. Not that I'm not organized. I just mean the way she runs the team is a lot more organized.
When I was managing a small team, it was in a really small startup. Everything was kind of messy, pivoting really quickly. We had 100 requests from 100 different directions, but I really love how my manager just keeps the team super organized and documented. All of our tickets have a purpose. They point back to some business question. We collaborate as a team to really groom our tickets and figure out what needs to be done for them.
So, it just feels very purposeful, and it keeps us from being driven by stakeholders too much so that way we’re focused on the real business issues versus all these little ad hoc requests all the time.
Yeah, it sounds like having good leadership and organization is a real benefit that Crunchbase has there. And it seems like the organization within the analytics department is fantastic and you guys are running in the right directions and working on important business problems and being smart about prioritization. I love it, sounds great. And don't beat yourself up too much over not having that same level as a manager.
I think the next time you're a manager, you'll have seen somebody as a prototype that you've loved working with and that'll be you next time, right? And that's all part of learning the ropes and stuff. So that's fantastic. I know a lot of people are really curious, you've built this amazing LinkedIn following. I think over 90,000 followers in a very short amount of time. You've been asked to be a LinkedIn instructor. You've got a course that's live, that's doing really well already. You've got another one coming out very soon on SQL, close to my heart. Talk us through that journey. What has that been like? How did you get started? Anything you want to share about that, I think people would love to hear it.
First of all, it's just been absolutely crazy. It’s so weird. If I look back to who I was a year ago, I'm a completely different person now. I could never even imagine that I would be in this spot that I am, which is just so crazy. Looking at all the things I've done and gone through in the past year is just mind blowing.
I didn't really know content creation was a thing on LinkedIn. I just posted one day about remote work, and I laugh because I posted it on a Sunday night from my phone in bed. I thought nothing of it. I just told this story, went to sleep, woke up with hundreds of likes, and I was like, That's pretty weird.
I never really use LinkedIn that much. It's kind of weird that I have a few hundred likes. Went into a call that afternoon, came out, had over a thousand because The LinkedIn commented on my post. From there that post went viral and I got like 5000 followers from that one post. And people were like, I can't wait to see more of your content.
And people were telling me their life stories, very personal things like, Remote work is important to me because my mom has cancer, and I can take care of her. I have like chills right now. But it was just so cool. I connected with so many people and I really gained like my first 5000 followers.
From there I told my coworkers, I don't think I'm going to post again. I was just a one hit wonder.
I just went viral once, that was my 5 minutes of fame. And then I just kept posting and building my niche. I was kind of clunking around at first trying to figure out what I was really going to post about.
Of course, I landed on SQL, I have my SQL shirt on today, I love SQL, love being able to share my passion for analytics with others, especially women in STEM. I love being able to provide that representation and get more women excited and show them the analytics is the cool, sexy job to be in for young women.
So, from there I really just kept posting, never looked back and I don't know, I just feel really lucky that I've been able to grow this audience. And now being a LinkedIn Learning instructor, being able to share my passion and teach other people things that I'm excited about, it's been really awesome.
It's really cool and I think I've seen it at least most of the way. I think I caught you pretty early on and what stood out to me is you seemed very authentic, and you do a ton of things that when I see them, I see this is real genuine value to someone who's trying to learn.
And so, you're teaching people in your posts, your giving people a picture of what the life of an analyst looks like. And I think a lot of times what you do a really good job of is inspiring people and encouraging people. And I think that's one of the things that's really special about what you've been able to do with your online presence.
So, it's probably impossible to quantify how many people you've helped, but I bet the number is just nuts. So, thank you from the whole data community thank you for doing that for everybody. That's what we're all about. And you're a perfect prototype of just giving a ton out to help people. So really, really awesome. And you should be super proud of it.
I really appreciate that. I always try to be authentic. I never want to be that person who’s like I know everything. I'm so smart. I don't ask questions. I'm an expert. Follow me. No.
That person doesn't know everything. Nobody does.
I would be more concerned if someone said they did know everything in this field. I try to be authentic. Sometimes I hope I don't sound too cynical, but I do try to also be really realistic. The market is trash right now. It's not easy to get your first job in analytics, but if you're passionate about it, it’s worth it to me. I try to find that balance of encouragement but also realistic and not just posting fluffy, feel-good stuff.
That’s great. And you're crushing it. Jess, you've been amazing here. I've got one final question. If people wanted to get in touch with you or learn more about you, what's the best way to connect with you or to see you in action?
Well, you can definitely find me on LinkedIn. Send me a message so you stand out. Mention you saw me here. Send me a personal DM, I will respond if you mention something personal about this podcast or something about my content. I'm also on Instagram and TikTok as @JessRamosData. It's been kind of a slow roll there but follow me there if you want to see a more intimate view of a day in my life.
Also on the LinkedIn Learning platform, I have my first course Preparing to Get a Job in Data Analytics that is out and up now and right now I'm working on an intermediate SQL course that is going to have a hands-on, In-browser practice experience. So, I’m working really hard on that to make it really realistic and be very business question oriented.
Perfect. That's so great. We’ll get all this stuff up in show notes so people can find easy links. Jess, thank you so much for being here today. This was awesome.
Thank you. Appreciate it.