Best Practices

Tipping points: How individual decisions make big impacts in data careers & data teams | Census

Nicole Mitich
Nicole Mitich November 22, 2022

Nicole Mitich is the content marketing manager @ Census. She's carried a love for reading and writing since childhood, but her particular focus is on streamlining technical communication through writing. She loves seeing (and helping) technical folks share their wisdom. San Diego, California, United States

When you’re working in data for a rapidly growing company, the number of hats you wear can change daily. 🎩 Just ask Emily Hawkins, data platform lead at alcohol delivery service Drizly

When Emily joined Drizly in 2019, the data team included four people – two BI analysts, a data scientist, and a data engineer. But as we know, all of our careers (and the collective careers of our data teams) are the sum of critical decisions made at the right or wrong time. With those crucial decisions and just three short years, that scrappy little team grew sevenfold. 🤯

We explored this rapid growth further in a keynote session titled, Tipping points: How individual decisions make big impacts in data careers & data teams during this year’s Summer Community Days

🎧 To make things even more interesting, this keynote doubled as a live episode of The Sequel Show, exploring:

  • How Emily has navigated tipping points in her career (and how they’ve helped her scale up)
  • How she’s navigating the growing pains of a successful data team
  • How her role within her team has changed since Drizly acquisition by Uber
  • How data process and data sharing have shifted as the team has grown
  • Lessons learned in making decisions that will impact your career, your company, and your customers

Scaling fast (but with control)

Specializations help data folks narrow their focus and avoid being overwhelmed when the company is scaling fast. As the business grew, Emily shifted from general business intelligence to product analytics, then to data infrastructure and engineering, which led to her current role.

“When there were only two of us working in BI, we helped with everything – marketing, product, operations. We had our hands in all the different things,” Emily explained. “As we grew, we were able to specialize.”

The first departments to require their own data specialists are the ones relying most heavily on business intelligence. At Drizly, that meant marketing (which drove the company’s skyrocketing growth) and strategic partnerships (which deliver insights to the alcohol brands partnering with Drizly to sell their products). 📈

For a company to use its BI effectively, data people can help users make good decisions faster. According to the Experian 2021 Data Experience Report, the more we can get relevant insights into the hands of business users, the quicker (and better) a company can adapt to ever-changing business needs.

Helping people use the data

That might sound easy, but there’s a balancing act involved in getting more people to use data while maintaining its integrity. Companies take a lot of paths to getting employees comfortable with their data, from Uber’s open-source query engine to Airbnb’s Data University.

At Drizly, most users view data insights through Looker. Realizing that most roles in the company don’t have an analytics background, the data team created training materials to help people understand the information they consume. 📚

Some pieces of training are held live, while others are recorded for reference later down the road. 🛣️ They also include hands-on, over-the-shoulder training regarding how to read the company’s most important dashboards.

“Everyone should know how to read the OKR dashboard. They should all know our metrics and how we’re tracking against them,” Emily said. “As the data team, we want to encourage curiosity. We want people to be excited about finding data. So we put out learning resources as much as we can.”

Building trust in the data (and the data team)

Teaching people to understand data has another benefit (besides allowing them to act on it quickly and effectively): It builds trust in the data and in the people managing it. 🤝 This actually goes hand in hand with taking quick action – the more trust business users have in the data team, the faster and more confidently they can make data-driven decisions.

“I remember early on when we wanted to move from Redshift to Snowflake, our director brought it to the CEO and CFO,” Emily recalled. “He had this whole presentation ready about why we needed to make the change, and they just asked, ‘Do you think this is what we need to do? Yes? OK, go do it.’ We’re very lucky to have had that level of trust from the beginning.”

Trust comes from relationships, and we can’t build those from ivory data towers. 🗼 Emily said her most effective tactic for educating her colleagues and gaining their trust is clear communication. After all, the data team’s job is not just to deliver impassive numbers; it’s to deliver insights and recommended actions.

“You have to be able to clearly say, ‘This is how we can implement this and see a result,’” Emily said. “You can also find success by becoming an expert in technology and finding ways to optimize it, but you still have to explain what you did and what you improved. You can shorten a model’s time from an hour to less than a minute, but if you don’t tell anybody, nothing’s going to happen for you.”

Emily shared more insights about scaling fast and about the impact of individual decisions on a data career with Census co-founder and CEO Boris Jabes during her keynote. Watch the full discussion and more Summer Community Days talks here. 👈

✨ Then head over to the Operational Analytics Club to share your view and join the conversation.