236: The Top 1% of Data Analysts Stopped Doing Grunt Work. Here's How

The top 1% of data professionals have stopped doing the boring grunt work that has caused massive burnout for decades, and instead they're starting to implement systems that look a little bit like this. In today's episode, I'll break down what these new modern systems are, why they work, and how you can start building your very own. But first, this episode is brought to you by Lookout from RudderStack, but more on them later. You see, the problem right now is that so many data analytics teams are so heavily understaffed, and a lot of the data analysts have days that kind of sound a little bit like this Oh man, another Monday. So much fun. But I guess I do have some pretty exciting projects to work on that are gonna have a big impact at the company and probably help me get that big promotion to senior data analyst. All right, here we go Oh, let me just check this Slack message real quick. Oh no, it's from Marcus in marketing again Hey Avery, hope you're having an amazing day. My friend, I need your help. I think in our email marketing campaigns, we're waiting way too long after purchases to send our next offer. But I think I need some data to see if I'm right. Can you just pretty please pull that data really quick for me? Shouldn't take long. Thanks so much in advance. Marcus Oh my gosh, Marcus, that's the fricking third time this month you've asked me for data. This isn't even my job, but whatever, I guess. I'll be your grunt sequel monkey one more time. I can probably just bang this out pretty quick anyways So sure, I'll do it But you see, that's the problem. These ad hoc requests, they never take thirty minutes. They drag on to hours, days, weeks. You pull the initial data, and then Marcus from marketing is like, "Oh, that's not quite what I meant." You as this data grunt, you're just writing sequels, queries, you're pulling reports, all just to help a team member out. But before you know it, you're dozens of iterations in, and you finally have a dashboard that answers Marcus from marketing's question, plus a CSV with the new target audience, but you give it to him, and he loses the link, and then he forgets about it, and then he never even uses it. All that time and effort and analysis absolutely wasted. And even worse, when your annual performance reviews come around, your boss asks, "Avery, you didn't get any of these high-impact projects done I asked you to do." And you'll have to answer again, "Well, I was really busy doing ad hoc analysis for people like Marcus from marketing." And you go another year without a raise. Sad. Does that sound familiar? I know I experienced something pretty similar when I worked as a data analyst at ExxonMobil and other places in my career. But in twenty twenty-seven and onward, I think we'll have less of this problem due to some really cool tools like Lookout from RudderStack. These cool companies are building data platforms that bridge the gap between the data and the business context, the people who are actually making the decisions, without overtaxing the data team. RudderStack specifically focuses on helping data teams coordinate with their marketing teams to avoid unnecessary dumb things like ad hoc project work. And this allows the data teams to focus on what's called higher order work. You know, these are the bigger initiatives that actually get you raises, like updating a prediction model to be more accurate or building out the company's AI plan and infrastructure or whatever is really important for you to do. And you have more time to do these more important projects while you're still staying in control and giving the marketing team accurate and timely data, as well as it has great infrastructure that can take action on the data. The marketing team, well, they no longer have to bug you, and they can actually just get their data faster and easier on their own and hopefully make more money for the company. That's the goal, right? So let me show you how Marcus from marketing could have saved Avery, the data analyst, his pay raise and solved this request on his own using Lookout So this is Marcus, and he's the senior marketing manager at Thistle, a UK homeware and lifestyle brand that mostly sells candles and bath supplies. He's in charge of the company's SMS marketing and email marketing campaigns. And recently, the data team at Thistle helped him set up Rudder Lookout for Marcus and the rest of the marketing team. And this is an agentic marketing activation platform on all high integrity data from the data warehouse that literally gives answers that you can actually trust. You see, Marcus has only been in the role for a few months, and recently he's noticed that although marketing is going fairly well and they're bringing in a decent amount of money, the marketing systems that he inherited aren't really quite optimized. Right now, any lead deemed as inactive isn't actually reached out to again until ninety days after they were last active, which basically means the last time they bought something or clicked on a link or responded to a message. And since the candles and bath products are pretty high quality, most customers are restocking in the few days or weeks. Marcus has a hunch that Thistle could actually convert more customers again if they sent the, "Hey, come back, here's a discount" campaign at, like, day ten or day thirty instead of day ninety. But that's just the problem. It's just a hunch. Marcus doesn't actually have access to the data to support this, and even if he did have access, Marcus is a marketer. He doesn't have the time or ability to explore and analyze data. Plus, his customer engagement platform, CEP for short, basically the platform that sends the emails and the SMSs, doesn't have any analysis ability, and it struggles to update customer profiles dynamically. But the company has the data. It's just locked away in some Snowflake databases, So this is where usually Marcus would submit another ticket to the data team to pull the data for him. And he low-key kind of hates it because the data team never gets it right the first time, and they always take forever, and they always get mad at him. But he thinks that this data could save the company hundreds of thousands of dollars So instead of making another ticket for the data team, he now can just ask Lookout in the chat interface Hey, Lookout. Our win-back flow doesn't fire until someone's been silent for 90 days, but I think most of our customers would buy way more often than that. Do you agree? How much revenue are we losing to customers who's effectively churned, but they haven't tripped any of our churn flows yet? Lookout then searched through all the company data context, found the right data tables in the data warehouse, wrote the correct SQL queries, and gave him a written response. And it turns out Marcus may be right. These 90 days could be costing the company hundreds of thousands of dollars. Lookout then suggested several next steps, one of which was actually building a dashboard to see this analysis. And after seeing the visual graphs and the tables, he could actually confirm everything with his own eyes, and then he talked to the rest of the marketing team using the share feature. Marcus and the marketing team actually made the decision to change this value from a fixed value to a dynamic value for each customer. Instead of the overly simple fixed ninety-day number, it will become a custom dynamic value for each customer based on their previous purchase cadence. Lookout actually calculated all these values for each customer based on their historical billing period automatically, but it does a lot more than just calculate these values. As a next step, it actually suggested creating an audience for these who are already past three times their own new personal cadence metric. We can see that ends up being around two thousand customers that are actually already ready to buy again. And if that audience looks good with the marketing team, they can go ahead and click activate and send that data back to their own email and SMS marketing platforms and trigger the win-back workflow already today. And wow, did you hear that? I think that's a one hundred dollar candle purchase from the campaign already. We're already making money from this. This is The win-back flow will continue to bring in tens of thousands of dollars every single week, ending up in millions gained for Thistle over the next year or so. And both Marcus and Avery got big bonuses because of their work on this project. Marcus got to solve his marketing team problem without requesting the data team for any help. And Avery, the analyst, was able to work on those higher impact projects and save the company additional millions in his own way, and everyone lived happily ever after. This is just an example of how I think the top data teams will work moving forward. And Lookout is more than just a marketing analytics platform, it's a marketing action-taking platform. It helps bridge the gap between the data team and the marketing team and how they work together this type of synergy and data enablement of non-technical teams will open up so many doors. It's so exciting. So if you're a data professional, this AI tool, it's not here to take your job. It's here to help you do yours better. And if you're a career pivoter, tools like Lookout could help you break more into data faster and easier because you can just take your current domain and start analyzing data in a better method. And more importantly, you can take action with that data. Tools like Lookout are literally the future. So if you are in marketing, if you work in the marketing industry, be sure to tell your team about Lookout, and you can actually get started today at Rudderstack.com, or you can use the link in the description down below to get started. The future of data analytics is bright, you guys, and it's only getting brighter with these awesome tools