The Expected (and Unexpected) Rewards of Building a Data Culture at Guild Education
At Guild Education, our goal is to put frontline workers back to school, debt-free, through employer-funded tuition assistance. Within the platform, our student coaches personalize the education experience by helping workers map out their program and providing them with ongoing support from start to finish. This process ends up gathering a LOT of data — data from our partners, data from schools, data from our website, and on and on. The trick is, this data is only as good as the insights and utility that are derived from it.
The term “data culture” refers to a company culture that empowers stakeholders at whatever level to explore data to learn and inform their decisions. In order for this to take place, not only does data and reporting need to be easily accessible, but end users need to be informed and educated about best practices and availability of certain data. As we began our journey of creating a data culture here at Guild, our coaches were at the forefront, helping us build a culture where everyone can feel empowered to do impactful work with data.
Kicking things off
To begin building a data culture at Guild, the team started by implementing Looker as our data platform and by hiring a data analyst and two business intelligence (BI) specialists, including me.
When the three of us joined Guild Education, we were faced with thousands of spreadsheets, no single source of truth, and a culture that frequently leaned on gut-level decision making as a result. Our mission as a data team was to roll out Looker to the company by transitioning a few departments at a time until the whole organization was plugged into our new way of approaching data analytics.
Fast forward to today, the successes we’ve experienced solidify that we are well on our way to becoming a fully data-driven company. Roughly 50–60% of the company logs into Looker on a weekly basis — which is truly fantastic considering we’ve more than quadrupled the number of employees from 150 to 700 during the last two years since adopting Looker. In addition, data-centric roles at Guild Education have grown considerably, with about 30 people now working under the umbrella of analytics, BI, research, and operational enablement.
Transforming our Student Success program with Looker
One of the programs that’s completely transformed with the help of data and Looker — which we are especially proud of — is our Student Success program.
In the Student Success program, coaches work directly with students in the program and provide ongoing support throughout their entire educational journeys.
When the program first launched, coaches were able to remember their students by name off the top of their head; but as enrollments increased, even spreadsheets were not able to accurately keep track of caseloads. We needed a singular source of truth that could be updated in almost real time.
As a solution, we created what we call “Student Rosters,” which leverage Looker dashboards and serve as operational tools to manage a coach’s student outreach. When we rolled out the Student Rosters to the first group of coaches, they were thrilled about how streamlined the process of reaching out to students was with the new tool. As we continued the rollout, however, things started to get complicated.
Growing pains
The Student Success team was growing at a wild rate — climbing from about 20 to 90 employees to date. To keep up with growth, coaching teams started developing subteams, many of whom were using data from different sources and with different outreach strategies, making consistency and accuracy a major issue. Plus, because programs and benefits changed over time, the roster dashboard had to be modified frequently.
I was the main point of contact for all the coaches. Before long, my days were increasingly spent fielding questions, training users, and making adjustments to the dashboard. I had to figure how to get my time back under control.
From users to data-empowered task forces
Recognizing these complications with the Student Rosters as a bottleneck for making progress in other areas at Guild, my team and I brainstormed ways to empower more users with data in the midst of our rapid growth. This led to the creation of our ambassador groups — first the Student Success BI task force, and 6 months later, the Education Coach BI task force.
For the creation of the first group, we partnered with Student Success managers to find one coach for each sub-team in the department. The managers quickly bought into the goal of developing a task force given they were well aware of the need for data experts throughout the department.
Every two weeks, I’d spend an hour with the group of coaches, getting updates about their world, training them on the Looker platform (and analytics more generally), and discussing all of our projects in the works.
Through establishing the BI task force, the coaches and I were able to get a finger on the pulse of what was happening across the organization.
A data-driven evolution
These ambassadors became the go-to leaders for fielding user questions, training coaching teams, and for creating updates and documentation for ongoing Looker work. For people like me on the BI and analytics team, the program opened our eyes to the unique problems each group faced and helped us stay up to date on the growth of the coaching program.
Unexpectedly, the Student Success task force also became an engine for new BI work. They blew my highest expectations out of the water and started owning virtually the entire process: building requirements, prototyping, testing, and deploying new dashboards and panels — with hardly any help from the BI team. They became so self-sufficient that they ended up fully rebuilding all four student rosters and significantly improved their outreach strategy in the process.
Accelerating opportunities for professional development
The task forces accomplished our original goal of creating a team of experts beautifully, but that wasn’t all. They also had a tremendous impact on the professional development of the people involved.
A great example of this is the career evolution of one of our Student Success coaches. From the moment Alex joined the task force, she led the charge on the ideation and creation of new dashboards and metrics and acted as a facilitator for the other members of the team. She proved herself to be tremendously hardworking and gifted in her work with both people and data. Six months after she joined the first BI taskforce, we officially hired her onto the BI team. Ultimately, I handed over leadership of the team to her, leaving the group of coaches to be led by a former coach!
Today, five out of 13 members from the first two iterations of the Student Success task force now hold new and more data-intensive jobs at Guild, with many of the remaining coaches also in new positions following promotions. And each of the eight coaches from the first iteration of the Education Coach task force ended their six-month involvement in the program with new jobs within the company: two were promoted to team leads, two were promoted to managers, and the remaining four were hired into other teams.
Considering all of these evolutions, both expected and unexpected, the main learning is that if your company is on its way to becoming truly data driven, providing opportunities for people to build their Looker chops will only make them more valuable as an employee. It’s a huge advantage to be able to pull reports and make data-driven decisions, and having that knowledge gives everyone increased job mobility.
Tips for building a data culture
As we continue to build upon the practices that create our data culture here at Guild, I’ve had the chance to reflect on why this initiative has been so successful. Here are some of my top tips for other organizations looking to kickstart their own data culture:
Make data seem approachable and fun (because it is!)
In general, most people are apprehensive about data. Many associate data with messy spreadsheets, math, and/or computer programming. It’s important to teach early on that analytics is more about the questions and thought process than it is about the actual work with data. Can you ask questions and brainstorm different approaches to answering those questions? That’s analytics!
One thing that helps a lot when teaching folks about analytics is not to take yourself too seriously. The goal isn’t to be the expert on anything; the goal is to encourage others to explore the platform and for them to become the expert. The less approachable you seem as the facilitator, the less approachable learning Looker will seem. If you talk openly about your previous data blunders or about the imposter syndrome many of us have experienced, others might start thinking, “Hey, I have been feeling like this wasn’t for me, but this guy seems to know his stuff and he has felt that same way before. Maybe I can learn this.”
Form small groups and use the right collaboration tools
When it comes to creating groups for learning, I find it easier and more productive to hold meetings with no more than 10 coaches and 1–3 analytics team members for support. At Guild, we have bi-weekly meetings in the calendar and also use a Slack channel to keep the communication flowing.
Follow a standard structure with each task force
- Provide an overview: At the first meeting of a new task force group, I do a rundown on the purpose of the group and provide an overview of the world of data and BI, linking this back to the business. We then spend the second half hour of that meeting talking about the different types of data the team can find on Looker at the dashboard level, and then do a quick introduction to Explore mode.
- Take a deeper dive: The second meeting is focused on Explore mode, so that everyone can understand how to experiment with ad-hoc queries or perform one-off calculations. In this meeting, coaches learn about the differences between dimensions and measures, how to save Looks, and more. At the end of the class, I ask them to build a specific panel, and we then review the panel as a group. I have found that it takes about 3–5 meetings (plus some project work between meetings) to really get the whole team comfortable using Explore mode.
- Incorporate training into every meeting: After the first two meetings, we generally spend about 20 to 40 minutes on training and hands-on exercises with Looker. I find that consistent training up front reaps big dividends down the line. Eventually, we wind down to only 10 to 15 minutes of training and 45 to 50 minutes discussing the issues specific to each group.
- Exchange ideas and set goals: At the beginning of every meeting, we share everything we’ve accomplished during the previous two weeks, and, at the end of every meeting, we decide what we want to work on over the next two weeks. Think of it as an informal sprint planning session.
Bring in new data enthusiasts
Every four to six months, we rotate out some members of the group to get some fresh faces. We want to build a diverse group of power users, so we like to spread the love and bring new folks into the program so that we end up with as many Looker experts as possible.
As folks are trained and then move to other teams, the number of people with in-depth Looker knowledge grows. Before you know it, you’ll have experts across your whole company.
In other words, you’ll have a data culture.
