Encouraging data curiosity and user adoption at eMoney Advisor

  • 28 March 2022
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This content, written by Alicia Minella, was initially posted in Looker Blog on Jul 27, 2020. The content is subject to limited support.

At eMoney, we know our people and processes are just as critical, if not more, as the tools we use. Data is no exception.

When we first set out to create a data and analytics program, our goal was to shift the organization from a siloed, product-focused mindset to a united, client-first one. Before we got started, we knew we could not transform our day-to-day operations or our solutions if our data was not joined, governed, and accessible. The first step we took was to choose the right tools which would help us to create a single source of truth that would allow all of eMoney to speak a common language.

Selecting our tech stack

To get started, we thought about our tool selection criteria:

  • Cloud-based solution
  • Fast ramp time
  • Low headcount investment
  • Governance at all levels
  • Emphasis on security

We chose Etleap as our transformation layer. They create the connections to our various systems and we control what objects and data points are piped into AWS Redshift, our choice for a data warehouse. For our BI layer, we chose Looker. Our focus was on the many ways to share data from Looker and the ability to leverage , Looker’s git-versioned modeling layer, to help us create a single source of truth.

Each part of the tech stack was up and running in 6 weeks. However, that is really where our story starts. Even with these great tools in place, we quickly found that user adoption was going to be more challenging than building the tech stack and we needed to shift our time and energy to focus on gaining traction with our users.

Early attempts and misses

To gain traction with our users, we tried everything that the experts recommend. But, for a while it really felt like nothing was working.

First, we had a cross-functional team do a kick-off at the Looker office. We talked through tons of relevant use cases. However, we scheduled this trip before we had implemented Looker, so no one could immediately use and explore their own data.

Next, we put a group together to be part of our initial user group and data literacy kickoff. This group was interested in Looker and asking data-driven questions, but it was a little too broad, and the trust in the data wasn’t established yet, so people stuck to their old ways of reporting. We also tried holding Office Hours, but these were poorly attended because we were so early in the adoption journey and people preferred one-on-one time over group learning. We tried hosting a roadshow where we presented to each department about who we were and how we could help them, followed up by a newsletter outlining what data was available. Still, no success.

I knew we had so much value to provide to the organization and I didn’t understand why people weren’t jumping out of their seats to work with us.

Lessons learned

It turns out that every time we thought we failed, we were getting some small, but compounding, wins.

Each of the above attempts was an opportunity for Looker, and the team, to be socialized around the organization. Any time we put ourselves in front of a group, we were advertising ourselves as a team and building important relationships. As we did this, Looker started to become a brand name as well, and something people would specifically ask to use for their reporting. We were also able to use the time to really learn, understand and validate the data, build out explores for core analytics, and anticipate the next questions that would be asked.

Not only were we able to leverage these unexpected wins to move adoption forward, but as the organization scaled, we started to identify the key partnerships that would help us win.

We found people that would help us drive the program forward, instead of just pulling them along for the ride. These new partners became Looker power users. They were excited about data, brought a vision for their analytic agendas, and were ready to share their findings and knowledge with others. We used many hours of one-on-ones to solidify these partnerships, build trust, do tool training as needed, and further build out use cases for data success throughout the company.

Data adoption today: putting those lessons into action

Today, our data and analytics success is driven by a hub and spoke model, where the hub is us, the data & analytics team, and the spokes are the power users.

The hub

  • Tiffany: Senior Business Data Governance Analyst. She created a data governance program from scratch that has complete leadership buy-in and is yielding amazing results. Her work is critical to every success we have with Looker

  • Jess: Data Engineer. She works on all things Looker and is able to translate data vision into analytic reality, which has been key to our success

  • Me: Manager, Data & Analytics. I navigate the team through the needs of the org, unite cross-functional teams under one analytic agenda and turn stakeholder vision into results.

The spokes

They are not only power users, but real data drivers. They sit at all levels and in all departments across our organization. They bring a vision for their analytic agendas, build looks and dashboards, teach their teams how to access data, and allow our team of three to support the entire organization.

The framework

The people are only one part of the equation. You need a solid framework to have continued success.

Start with the vision of your data program — a very small sliver of the vision — and work closely with your spokes as you build and they validate the results and share feedback, you’re not only building out the program, but you’re building trust.

Next is promotion. Once your data driver feels comfortable with the output, work with them on the best way to share the data. Consumption comes naturally, once people understand what exists and where to find it, they are able to self-serve.

Last is exploration. You’ll know user adoption has been a success when people not only make decisions with data, but start asking new questions as well. From there you repeat the cycle, keeping the hub and spokes tightly integrated every step of the way.

If you’re interested in learning more, check out this webinar at eMoney.

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