What does “self-serve” in analytics mean to you?
In my view, it’s imperative for domain owners to “own” their own data. As in, they need to know how to access the basic performance metrics that relate to their domain and be able to perform simple analysis to understand how to make decisions that they encounter on a day-to-day basis. Of course, you can’t expect a marketer to run a regression analysis, but they should be able to find basic data about the performance of their campaigns (how many transactions did my email drive?) and be able to dig at least one layer deeper (how much was driven by different pre-defined customer groups?).
Ideally, business users would be able to construct their own basic reports and dashboards, but in reality, there will likely always be a few “power users” across functional teams who can assist their teammates - with final review from the data team. And, of course, there will be times where an analyst is needed to set up new reporting, which the end user can then access whenever they need it.
All of this depends on having an analytics team to make sure the data that’s surfaced to end users is as clear and easy to use as possible, is accurate and reliable, and is maintained as the business changes. The priority should be automation and iterative improvement, so self-service for the basics can be maintained into the future.
How self-service is analytics currently at your company?
Not very self-service at all. We have an unfortunate combination of data access issues combined with not a lot of data literacy.
We’ve been stuck in the era of Google docs that aggregate data from Looker reports and compare to targets that are housed in Excel. We have Looker built out, but it’s overwhelming for end users, so only a handful of people actually use it. This is partially from poor set up and maintenance within Looker and partially because of the poor schema design of our analytical data warehouse in combination with compounding pressure from a changing business. We’ve recently launched Metabase, but our operational databases are too complex for a non-coder or non-analyst to navigate, so we’re stuck waiting for our new data models to be built before we can really open it up.
Another side-effect of being stuck in the era of excel is that there have been only a few people who have built and maintained reporting over the years, as there are only a few who know how to work in Excel. This has meant that the company as a whole has developed a reliance on these few people to get all of their data and reporting from. Lots of “where did this data come from?” “why don’t these numbers match?” “why are we up/down compared to target/LY” that those few people have been answering because they’re the only ones who knew how.
How self-service do you want analytics at your company to be?
Very - for the simple stuff. Retail is not rocket science. Every person at the company should be able to build a basic report if they need to. Key people/teams (product, marketing, social, planning, merchandising, operations, tech) should be able to do surface-level analysis to inform future testing/improvement.
An example - marketing should be able to analyze creative performance reporting and assess the success of their campaigns (potentially in partnership with planning). A marketing analyst/data scientist can help with things like building an attribution model and identifying new triggers for marketing automation (things a marketer can’t do on their own).
The value analytics can bring is in creating data models that are easy to understand and use, maintaining view-level platform(s) for end business user use, doing analysis that requires a bit more than slicing the data via an existing attribute, and helping to connect the dots across the entire org (facilitating collaboration and communication through the use of data). An analytics team can’t do that if they’re bogged down doing simple analysis that a marketer (for example) could be doing (and I would argue should be doing).
What are the barriers you’ve encountered to creating an empowered, self-service culture?
Once someone has developed a dependency on someone else to do something for them, it’s really hard to break that habit, especially if you are pushing them to learn a new skill set. You just have to keep advocating for data, sending them the pre-made reports that you’ve created, and showing them how to do things instead of sending them the end result (if it’s simple enough). Then they’ll build confidence, you’ll have built a collaborative relationship with them, and the trust will be there to build on going forward. Delegation is also important - if you’re doing something that isn’t driving value (copying numbers into a google doc), then try to find the relevant domain owner and give them the link to the Looker report or find a way to automate it away so no one is spending their time doing that.
Another challenge has been understaffing. Due to the difficulties with our existing ETL (among other reasons), our data team has slowly dwindled to two (soon to be one) analysts and a lone data engineer. There’s only so much you can do with a data team that represents 1% of your global company and supports three markets, while you’re trying to simultaneously prop up your dying ETL and build a new one. We’ve been hard at work building out the team roadmap, laying the foundations of understanding the value of data and good infrastructure, and advocating for additional resources.
Beyond that, for us, it’s really just been about the difficult data models. But that’s in the process of being fixed thanks to dbt + Fishtown and increasing support from leadership to shift focus toward the project.
How important is data documentation in creating and maintaining a self-service culture?
It’s very important at Birchbox, as our operational databases have nuances. We’re trying to find a balance though, as not everyone likes to use documentation. Keep the documentation within the data models rich for more in-depth users and future team members, but keep it light and simple for end business users.
Is there anything you’ve found to be particularly useful in creating a self-service culture?
Persistence. Advocacy. Friendliness. Compassion. Trust. Integrity. Transparency.
Tell people about the work that your team is doing and how it will benefit them. Listen to people when they’re telling you about the questions they have or the problems they’re trying to solve. Be empathetic toward them. Admit when mistakes are made and be clear about how you’re correcting them. The people relationships are as important as the data itself in getting people to buy in. Constant reminders that you exist and are delivering value alone is powerful.