As a native video advertising company with 29 offices across 19 countries and 520 employees, data is an integral part of our daily operations. Since we’re a global company at the operational and client level, we needed a way to track crucial KPIs across our sprawling business.
At Teads, we heavily rely on Chartio, a Business Intelligence solution that helps us gather operational insights and track our metrics.
Data Infrastructure at Teads
Before implementing Chartio, we didn’t have a straightforward data process. Often, our data sources were not consistent. Previously, we were running different platforms at the same time in addition to our numerous data lakes, data warehouses and data sources. Additionally, we had a MySQL database supplemented with an Infobright layer on top of it.
So, when we purchased Chartio four years ago, and saw first-hand that our data wasn’t able to render certain dashboards that we built out because the data was inaccurate or inconsistent—we knew it was time to focus on data maintenance.
We decided to rebuild our data infrastructure by focusing on creating data marts from our internal reporting engine that taps into a handful of our critical databases:
- Stats clusters in Cassandra
- MySQL database
These data marts are designed and built by our Analytics team, with each data mart serving a specific business use case. Each data mart then gets built in our MySQL/Infobright data warehouse and into Chartio for analysis. We are moving towards Google BigQuery solution to handle more use cases.
Chartio: Allowing for Data Transparency
With the Analytics team architecting each data mart, the data that is pushed into Chartio is flat, ultimately simplifying the process of creating dashboards for the entire company because the data doesn’t need any additional complex joins or layers in the query to produce dashboards.
Today, we have 50 dashboards in Chartio that are divided into two sections:
- Innovation (Product and Technology)
- Operations (Sales activity, Finance, etc.)
Again, each dashboard is connected to a business use case and data mart. So a Finance KPI dashboard connects to a Finance specific data mart. Further, within each dashboard, we’ve created dashboard sections, so everyone can see what’s happening for any sector of the company.
As a fast-growing company, we also have “Draft Mode” dashboards as well, so everyone knows what dashboards are upcoming. Our process in creating these dashboards is quite systematic: there’s a dashboard owner and a validator of the data/dashboard.
Once a dashboard is ready and validated, it is made public to everyone in our Chartio account because we want everyone to have context and understand the overall objective and benefit of each dashboard.
Even though only a few people are creating dashboards, we’re moving towards being a data-driven company. We want people to view and validate the dashboards and be in the mindset of a data analyst, and Chartio has helped us achieve that goal.
Today, Chartio helps us understand three key things about our business:
- What’s going on
- Why is this happening
- How can you improve the situation based on the why
One of our Chartio power users is our Finance team. The Finance dashboard that we’ve created tracks KPIs such as daily revenue and margins across our global market. Additionally, we’ve broken down the dashboard based on our different markets such as APAC and the US market.
Managing Directors look at these dashboards every morning, often scheduling reports to be sent directly to their mobile phones.
In addition to our Finance dashboards, our Publisher Account Management team also relies heavily on their Chartio dashboards to track KPIs. Each of our Publisher Account Managers have an account portfolio of 20 Tier 1 publishers, and so they have a dashboard that tracks:
- Client performance rates (fill rates, scroll rates, quality of integration, …)
- Contractual commitments fulfilment
- Platform features adoption
At first, we were hesitant to create so many dashboards because it can get complicated quickly. However, Chartio has helped us clean up our data infrastructure and allowed us to become more transparent about data, ultimately allowing us to gather more insights about our business.