Livesport’s mission is to provide fans worldwide with all the important sports moments in the shortest time possible. With more than 60 websites and mobile applications headed by the world-famous FlashScore.com, Livesport brings the fastest sports information and scores from more than 35 sports to 100 million people worldwide.
Keeping data organized in a fast-growing company
Since their founding fifteen years ago, Livesport has managed to grow its user base to 100 million. The media company has been gradually expanding their coverage of live sports results and statistics; they now provide sports fans with information on over 35 major sports in over 60 countries. In order to understand current and future customer behavior, as well as improve their marketing and products, they needed to gather information from all these different channels. However, managing this rapidly expanding pool of data became a bottleneck.
With this growth, the product and marketing teams at Livesport developed a need for insights into user behavior—mostly from inside the app—to make data-driven decisions around product changes. But Livesport’s data pipeline was too complex for business users to use easily, and their small data team, grown from 2 people to 8, was saddled with the significant task of fulfilling all the internal requests.
Zdeněk Hejnák, a Senior BI Analyst and Engineer at Livesport, gave an example of some requirements he’s seen before. He says the marketing team would create a campaign to acquire new users in the UK and is targeting people who are looking for darts championship results. They expect people who came to see dart results will continue using the app to look at other sports, especially the very popular football (also known as soccer, in the US).
“So we need to analyze the connection between the campaign for Google ads, the spending for Google ads, and how many people we acquire. We want to see new people, how much do they use the product over the next 2, 4, 8 weeks? Which sports do they consume? We have data about how people interact with our application, which features they're using, which sports, which pages or screens. This is our major goal - to see the path of how the user interacts with our application.”
Zdeněk went on to explain that all these different questions and types of data makes this a complex and time-consuming analytics problem to solve. This was only one of many similar requests the data management received from internal teams, and they were expected to deliver clear enough data to provide these results.
At the same time, the data management team needed to constantly update the data models for an increasing number of data sources. Even though they spent hours every week manually documenting relationships between their data tables in BigQuery and their Tableau reports, the effects changing tables would have on downstream dashboards were not apparent until it was too late. Tableau dashboards would be broken, and they would then need to spend extra time and resources fixing mistakes.
Zdeněk and his team quickly realized that in order to use data effectively in their organization and scale their business, Livesport would need help automating parts of their data pipeline, and looked solve this with data discovery.
We evaluated more than 7 different data cataloging tools, and Select Star fit the best for what we needed. Select Star helped us to immediately see the connection between BigQuery tables and Tableau data sources.
Senior BI Analyst & Engineer
Select Star automatically exposes metadata and field-level lineage between BigQuery and Tableau
To combat the issue of critical errors made during data model updates and to better serve their internal teams, Livesport began looking for a solution to automate metadata collection. Livesport evaluated seven companies and found they fell on two sides of a spectrum.
On one end, some companies had very focused data discovery solutions, but the cost was too high to justify. On the other end, some solutions had too many extra features that wouldn’t serve their needs in the long run and didn’t feel like the best fit. After several vendor demos and POCs, Livesport determined that Select Star had the best data discovery solutions at the right price point.
"We evaluated more than 7 different data cataloging tools, and Select Star fit the best for what we needed. Select Star helped us to immediately see the connection between BigQuery tables and Tableau data sources." - Zdeněk Hejnák
Set-up quickly and easily
Select Star worked closely with Livesport providing hands-on support as needed. Select Star’s one-click integrations meant that loading metadata from BigQuery and Tableau was simple and easy. Livesport was able to see their data lineage model immediately, making it much easier for them to see the parts of their data that were duplicated or outdated. With this new visibility into their data, they could immediately get to work designing their new data models that fits the business needs.
We have finally set up our data mart and built our data governance strategy, because of Select Star.
Reduce support requests
Having access to Select Star means Livesport’s product and marketing teams don’t need to go to Zdeněk’s team for answers anymore. They have improved visibility into the data models and data lineage, so they can explore the data on their own and find the answers they need, saving time on both sides.
Avoid making breaking changes to data models
Select Star shows which upstream tables affect which dashboards, and which tables have dashboards relying on their data. Now that Livesport does not have to spend time documenting relationships between source tables and downstream dashboards, they can perform maintenance and make changes to these tables knowing exactly what the effect will be, rather than spending time tracking down the cause after the damage is done.
18 hours every week saved on data management
Automatic metadata collection and visibility into how their datasets connect have given Livesport more flexibility in working with their data. With this visibility, they have fewer data requests from other teams, but more company-wide access to data.
Livesport’s data team no longer needs to spend time documenting relationships between tables and dashboards, or tracking down different people to understand who created which data, how it was created, or who is using it. Select Star surfaces that information for them automatically.
Once Livesport has all their business metrics represented in Select Star and has opened up the app to their entire company, they imagine they’ll save even more time. By providing their business end users with a self-service platform, they can access and understand their own data instead of sending requests to the data team. When Select Star is fully available to all these users, Livesport’s data team will be saving up to 18 hours a week, and hundreds of hours a year in data management resources.
To provide the best user experience on their app to sports fans around the world, Livesport will work with Select Star to keep making new discoveries and to grow and evolve their data management system. Select Star gives them a simple way to organize, govern, and intelligently use their data.