Frontify builds a self-service, data-driven culture with ThoughtSpot

Frontify is a fast-growing SaaS platform that enables branding teams to better manage their assets and collaborate. The Swiss-based company is recognizing the value in data for success and is in the process of building an organization-wide self-service, data-driven culture, through the use of ThoughtSpot. 

ThoughtSpot describes itself as the ‘Google for numbers’, whereby it provides users with advanced search capabilities, as well as suggested search prompts, so that teams can get insights into their untapped data. Its CEO has said that the company’s ambition is to ‘kill of BI dashboards’ and to create a dynamic data environment for businesses that are struggling to get the valuable insights that they need. 

It seems that this vision is resonating with Frontify, which has shifted away from using Tableau BI dashboards, and is driving adoption throughout its organization with ThoughtSpot. Speaking with Sibel Atasoy Wuersch, Head of Data at Frontify, she said: 

Our data team’s vision is actually to enable every single person at Frontify to make an impact on our success with data. So, it's not only the data team's responsibility to analyze data, to uncover new insights from data, but we want to enable everyone to be able to make decisions every day with data. And a lot of what we have done is to make sure that they have the capabilities to do that.

We want to create these powerful data capabilities and that means that we need to build a holistic data ecosystem for Frontify. That data ecosystem should enable data quality, should enable data access - meaning everybody should have access to the data, to be able to do this analysis - and we need to have a single source of truth. We need to make sure that everyone is data fluent. 

When Wuersch joined Frontify the central data team consisted of just three people, including herself. The team was facing an increasing number of questions and had various needs from the business, which it was not able to meet. Part of this was down to Frontify’s legacy setup, which Wuersch said was not built for scale. 

Historically Frontify had relied on data ingestion pipelines into a MySQL database. Wuersch added: 

It was not really even a data warehouse. We were a start-up at the time and we were doing things really, really fast. The team did basically what they thought was best - first you ingest that data, you put it into a database, and on top of it you bring in a BI tool for people to analyze that data. 

But what we often suffered, and the situation I found when I came in, is that the data was not reliable. It was not consistent. Every day we would get questions about: Hey, why is this data missing? Or, someone saying: that doesn't look right. And we were fixing those pipelines constantly, so things are breaking. 

We were really stuck in that endless set of questions that were coming from the business. Tableau adoption was really low and we were the ones that were building everything for the business - so we were the bottleneck. 

Building maturity

Wuersch explained that when she joined Frontify, the data maturity of the organization was in the very early stages. There was a push for all the data sources to be connected to a database, to simply be there, but they were not being analyzed, leveraged, and weren’t readily available. The legacy set-up was not working for the business, in terms of enabling self-service and using data for decision making. 

As such, Frontify began using ThoughtSpot last year, in a bid to change the data culture of the business. Wuersch said: 

We needed to make changes and we needed to do that in a structured way. So the first thing that we said is that we needed to do is modernize our infrastructure to make sure that these latency issues, these data consistency issues, are not there anymore. 

Firstly modernizing our data infrastructure and then bringing in a self-service analytics platform - one that not only the data team or technical people like us can use, but non-technical people can also leverage confidently.

And the last part, which is something that I'm really, really passionate about, is about investing heavily in building the technological transformation at the company. Making sure that we invest heavily in efforts to build a strong data culture through adoption, through power users, through acceptance, through empowerment, through inclusiveness. 

ThoughtSpot was part of that modern data stack, it was the best of breed self-service tool out there. 

Central to the decision to use ThoughtSpot was Wuersch’s belief that it is a platform that can enable building a strong data culture across a company. The aim is to stop people feeling scared about using data and reaching more users, so that value can be created from everyone in the company. She added: 

We're not there yet, but we went from maybe a handful of people who were on Tableau on a weekly basis, to 85 or 90 users on ThoughtSpot, which is about 40% of the business. 

A learning experience

Frontify is building this data-driven culture through a process of experimentation, learning, listening and adapting. Wuersch said that when ThoughtSpot first launched, the data team thought that it had everything figured out in terms of how it was going to onboard users to the tool and how data sources would be introduced. However, they quickly learnt that humans are complex and a willingness to adapt was needed. Wuersch said: 

What we do is we experiment with something, and if we see success with it, we keep it. But if we don't, we drop it. Or we ask for feedback and we change it. 

So everyone that wants to use ThoughtSpot has to go through onboarding. And it's purely to teach them the skill, it's purely to learn how to use it, to go and explore. How do you search? How do you select a data source? How do you filter? Really, really basic essentials. We don't want to do that with them, we want to do that on their own, at their own pace, in their own time.

On top of that, once people go through that first onboarding, we have data source deep dives. Learning the tool itself is one thing, but learning what data is there, how you can work with that data, what kinds of questions you can ask that data, what are the different kinds of insights that you extract from it, is another thing. 

We do that through the data deep dive sessions that we offer. We also have power users, which are people that are either coming to us, or you can see them on Slack channels, and we say ‘we're gonna bring you into these power user sessions’. 

Some people are really very passionate, also very tech savvy, they have an analytical mindset and they can really go deeper, they can go more advanced. Some people simply want you to be in the room, to take them through creating a formula, maybe even applying a simple filter. 

Some people are afraid they're gonna break something, so it’s about reassuring them that everything is okay. So we really do a tonne of things to make sure that we reach people, we are there for them. 

Frontify’s heaviest users of the ThoughtSpot platform are in finance and revenue operations, but the data team is seeing a wide variety of use cases emerge. For instance, the tool is helping management better understand company performance, when it comes to certain KPIs. Wuersch said: 

We do have budget values that we have set at the beginning of the year, so understanding where things are going well, being able to give the management the ability to see that data, to trust it, to rely on it, that has been very crucial for us. 

Frontify has also seen a lot of success with the tool in analyzing subscription revenue and the behaviour of customers, in terms of churn, revenue retention rates, etc. Wuersch added: 

It allows us to be able to display insights and have the business drill down to different customers, different situations, being able to look at that data. 

What we did as the data team is we said ‘we think the product data is so valuable’, so we're going to internally push for that data to be built and become available, even if it was not requested by the business. 

Having built that product data, that actually comes from our own product databases, made us ready when the business started asking questions about monthly active users. We said: oh, we have the data, so we can start building that and surfacing it to you. So it's been very powerful.