Yieldify's Data-Driven Culture Is Central To Its Success

Interview with Adam Dathi, Senior Business Intelligence Consultant at Yieldify


In recent years, a data-driven culture within startups has become synonymous with success. Championed early on by only the most data literate or strategically savvy, the use of data in companies large and small is now spreading to every team and individual. Used to evidence decision-making, generate insights or test hypotheses, giving all employees access to data is proving to be a significant competitive advantage. Yieldify, a British-based startup backed by the likes of GV and Softbank, is testament to the philosophy’s success. Its phenomenal growth in the past two years has been in no small part down to its use of data, and its democratization of data to every nook and cranny of the business. Data is core to both Yieldify’s operations and its product. Its software, used by some of the biggest e-commerce vendors globally, uses browsing history and onsite behaviour to increase conversion rates. 

We spoke with Adam Dathi, a Senior Business Intelligence Consultant at the startup, to shed some light on the successes Yieldify has had in democratizing data around the company.

1. How important is it to democratize access to data?

It’s extremely important. Democratizing data means giving all stakeholders access to the data in the company and nurturing a culture that understands how, when, and why to use it. Yieldify’s key values are openness and transparency; we need to show our employees the key metrics that illustrate how the business is performing and where there is potential for improvement. To achieve this, it is imperative to build accessible data: a single source of truth that is democratized and understandable. For example, say you’ve got a team that needs to increase revenues by 5%, each member needs to contribute to reaching this target. By democratizing the data, every person is able to see their individual impact and can drill into the underlying problems themselves to better understand what they need to do. There are other less obvious advantages. All data has latent value that may not have been realized, with insights and uses waiting to be discovered. In the ‘old world’, a company may have been reliant on a BI team (or equivalent) to act as gatekeepers. This naturally leads to a bottleneck in terms of accessibility and value-generation. Through the use of Looker (our BI tool), there are now a lot more eyes interrogating the data, more minds attempting to use it in new ways. Insights and innovation are now delivered much faster than before and by a far wider-array of teams.

2. What challenges have you faced in your attempts to build a more data-driven culture?

There were three main problems that we encountered: accessibility of data, the understandability of data and ‘inertia’. Let’s start with accessibility. When I joined the company, we had far less data available and what was available was laborious to get. The root cause was that the data was ‘siloed’, which basically means that we had a lot of disparate data sources that didn’t connect. In order to solve this, our engineers built a data warehouse (using Amazon Redshift), where we now keep all the data together. The BI team then worked with our Operations team to develop a number of processes that enforced the mapping of these databases at the point of creation. This ensured that the data was accessible for processing and analysis. Next, we had an issue with the understandability of the data. The data collected in the BI Warehouse were raw and difficult to use. We circumvented this through the use of custom tables created in Looker. These tables were summarized, enriched and the fields renamed before being presented to the user in comprehensible language. This created useful, understandable data that no longer required an engineering or data background to grasp. Finally, we had inertia. This is a common problem whenever one attempts to implement change to a business. We had the reports and dashboards, we knew that they had value, but we needed to break everyone out of their routine and ensure that they actually began to use it every day. To achieve this we firstly convinced the managers of the value of what we were doing and made data the focus of meetings and performance reviews. Once people understand that they’re being judged by certain metrics by their managers, they become far more engaged in monitoring and understanding them for themselves. We also selected and worked closely with ‘champions’ from within the teams. Champions were our advocates, consultants, and BI/Looker mentors. They would encourage usage from the ground up, advise us on what reports and data points were useful and ran the training sessions. It’s taken a while to break through this inertia, but we’re finally at the point where data is a necessity for our business to function, rather than a nicety.

3. How much ground do you think you lost as a result of siloed data?

Operationally, we’re much stronger than before, but it’s hard to say exactly simply because we don’t have the data from before to compare it too. By having the data in one place, the main two advancements have been our ability to automate and provide visibility. Automation has cut down the number of hours it takes us to conduct analyses and reporting across teams. Thanks to Looker, we are now a lot more efficient, which has enabled us to spend more time on client meetings, campaign creation and less time on pulling data. Visibility around how teams are performing towards goals was previously extremely limited, but it’s now easy to see whether people are meeting targets, their levels of performance, and the best practices that are driving improvements.

4. How has Looker helped you?

Before the whole process started, there was data everywhere. We had to provide the right people with access when they needed it. In order to do this, it was key to get it all into one interface. Looker acts as that interface, providing all the teams with access to everything they need, whenever they need it, and in a manner that is curated and understandable. The ability to use LookML (Looker’s language that structures and presents our SQL queries) to define our own tables and iterate quickly has by-passed our engineering bottleneck. Now the engineers can focus on creating products for our customers whilst BI worry about internal data. At the moment, Looker is the go-to tool for our Services, Technical Solutions and Finance teams. They use it for performance reporting, investigation or analysis. We have started the process of on-boarding our Sales team and are rolling-out a suite of dashboards that present a clear overview of individuals’ and teams’ pipelines and revenue generation against targets. Our goal is to get every team on-boarded by the end-of- the-year and using data in every business decision.

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