In today’s world of complex data environments, having the right tools in place to empower teams to make data driven decisions is of the utmost importance. For a company such as ShopBack, which prides itself on a strong culture of self-service analytics, it is crucial that teams can trust that the data they are analyzing is organized, accessible and reliable. This becomes an even more complex task when you bring into play ShopBack’s operations throughout Asia and Europe, as well as varied teams that are all producing and analyzing data.
ShopBack is a pioneering digital platform that revolutionizes the way consumers shop online. Offering a comprehensive suite of products and services, ShopBack provides users with access to an extensive network of retailers, allowing them to earn cashback rewards on their purchases. With a mission to empower consumers to make smarter shopping decisions, ShopBack leverages innovative technology to create seamless and rewarding experiences for its users. Founded with a vision to transform the e-commerce landscape, ShopBack continues to lead the industry by providing value-added solutions and fostering a vibrant community of savvy shoppers.
Some issues that ShopBack was experiencing that led them to pursue a Data Observability platform revolved around the time the data team spent on data quality and accuracy issues.
Before implementing a Data Observability tool, teams were checking with the data team in order to verify accuracy of the data they were using to make key decisions. This amount of time spent verifying data accuracy took time away from important projects for the data team. Other issues around data accuracy that ShopBack encountered were missing data, low volume data and duplicates within data.
As well as spending time verifying data accuracy, the data team at ShopBack needed to put efforts towards finding the root cause of any data issues. The time spent identifying the root cause of the data issue were additional to the time spent fixing the problem.
By implementing Sifflet into their data stack, ShopBack was able to see benefits that improved the cycle time of data decisions, as well as the level of trust various teams had in the data they were utilizing for important decisions. Another benefit ShopBack realized was the streamlining of the data team’s work. ShopBack was able to expand adoption of Sifflet to various teams throughout the organization due to ease of use. ShopBack utilizes data quality as code, which is Sifflet’s method to programmatically create monitors at-scale using YAML files, as well as pre-built templates that help streamline work for users without experience with SQL.
With the benefit of knowing that Sifflet has validated the accuracy and quality of the data, stakeholders can make important data-driven decisions without the step of validating data with the data team. This leads to quicker decision-making processes, while also trusting that the data is empowering you to make the best decisions.
Now that ShopBack’s data team can utilize Sifflet to pinpoint the cause of data accuracy and quality issues, the data team has the opportunity to take a more proactive approach to data issues, rather than previously needing to be reactive to data issues that came up. This has allowed ShopBack to save many data team hours spent on identifying the cause of issues that accumulate throughout the year.
With an organization such as ShopBack, it is important to realize the key benefits of Data Observability in order to match data literacy and data quality with the scale at which the incoming data is growing. ShopBack is on a strong trajectory for growth in Asia and Europe, and the need for accurate and reliable data will continue to grow with the company. ShopBack will be utilizing Sifflet to continue to make the best business decisions in a quick manner, despite the increase in data volume that comes with rapid growth.
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